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Study Objectives: To evaluate wearable devices and machine learning for detecting sleep apnea in patients with stroke at an acute inpatient rehabilitation facility (IRF)., Methods: A total of 76 individuals with stroke wore a standard home sleep apnea test (ApneaLink Air), a multimodal, wireless wearable sensor system (ANNE), and a research-grade actigraphy device (ActiWatch) for at least 1 night during their first week after IRF admission as part of a larger clinical trial. Logistic regression algorithms were trained to detect sleep apnea using biometric features obtained from the ANNE sensors and ground truth apnea rating from the ApneaLink Air. Multiple algorithms were evaluated using different sensor combinations and different apnea detection criteria based on the apnea-hypopnea index (AHI ≥ 5, AHI ≥ 15)., Results: Seventy-one (96%) participants wore the ANNE sensors for multiple nights. In contrast, only 48 participants (63%) could be successfully assessed for obstructive sleep apnea by ApneaLink; 28 (37%) refused testing. The best-performing model utilized photoplethysmography (PPG) and finger-temperature features to detect moderate-severe sleep apnea (AHI ≥ 15), with 88% sensitivity and a positive likelihood ratio (LR+) of 44.00. This model was tested on additional nights of ANNE data achieving 71% sensitivity (10.14 LR+) when considering each night independently and 86% accuracy when averaging multi-night predictions., Conclusions: This research demonstrates the feasibility of accurately detecting moderate-severe sleep apnea early in the stroke recovery process using wearable sensors and machine learning techniques. These findings can inform future efforts to improve early detection for post-stroke sleep disorders, thereby enhancing patient recovery and long-term outcomes., Clinical Trial: SIESTA (Sleep of Inpatients: Empower Staff to Act) for Acute Stroke Rehabilitation, https://clinicaltrials.gov/study/NCT04254484?term=SIESTA&checkSpell=false&rank=1, NCT04254484., (© The Author(s) 2024. Published by Oxford University Press on behalf of Sleep Research Society. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Living-skin detection is an important step for imaging photoplethysmography and biometric anti-spoofing. In this paper, we propose a new approach that exploits spatio-temporal characteristics of structured light patterns projected on the skin surface for living-skin detection. We observed that due to the interactions between laser photons and tissues inside a multi-layer skin structure, the frequency-domain sharpness feature of laser spots on skin and non-skin surfaces exhibits clear difference. Additionally, the subtle physiological motion of living-skin causes laser interference, leading to brightness fluctuations of laser spots projected on the skin surface. Based on these two observations, we designed a new living-skin detection algorithm to distinguish skin from non-skin using spatio-temporal features of structured laser spots. Experiments in the dark chamber and Neonatal Intensive Care Unit (NICU) demonstrated that the proposed setup and method performed well, achieving a precision of 85.32%, recall of 83.87%, and F1-score of 83.03% averaged over these two scenes. Compared to the approach that only leverages the property of multilayer skin structure, the hybrid approach obtains an averaged improvement of 8.18% in precision, 3.93% in recall, and 8.64% in F1-score. These results validate the efficacy of using frequency domain sharpness and brightness fluctuations to augment the features of living-skin tissues irradiated by structured light, providing a solid basis for structured light based physiological imaging.

This paper proposes a smartphone-based method for measuring Blood Pressure (BP) using the oscillometric method. For oscillometry, it is necessary to measure (1) the pressure applied to the artery and (2) the local blood volume change. This is accomplished by performing an oscillometric measurement at the finger's digital artery, whereby a user presses down on the phone's camera with steadily increasing force. The camera is used to capture the blood volume change using photoplethysmography. We devised a novel method for measuring the force applied of the finger without the use of specialized smartphone hardware with a technique called Vibrometric Force Estimation (VFE). The fundamental concept of VFE relies on a phenomenon where a vibrating object is dampened when an external force is applied on to it. This phenomenon can be recreated using the phone's own vibration motor and measured using the phone's Inertial Measurement Unit (IMU). A cross device reliability study with three smartphones of different manufacturers, shape, and prices results in similar force estimation performance across all smartphone models. In an N = 24 proof of concept study of the BP measurement, the smartphone technique achieves a mean absolute error of 9.21 mmHg and 7.77 mmHg of systolic and diastolic BP, respectively, compared to an FDA approved BP cuff. The vision for this technology is not necessarily to replace existing BP monitoring solutions, but rather to introduce a downloadable smartphone software application that could serve as a low-barrier hypertension screening measurement fit for widespread adoption., (© 2024. The Author(s).)

Background: To expand veterans' access to health care, the Veterans Affairs (VA) Office of Connected Care explored a novel software feature called "Vitals" on its VA Video Connect telehealth platform. Vitals uses contactless, video-based, remote photoplethysmography (rPPG) through the infrared camera on veterans' smartphones (and other devices) to automatically scan their faces to provide real-time vital statistics on screen to both the provider and patient., Objective: This study aimed to assess VA clinical provider and veteran patient attitudes regarding the usability of Vitals., Methods: We conducted a mixed methods evaluation of Vitals among VA providers and patients, collecting data in July and August 2023 at the VA Boston Healthcare System and VA San Diego Healthcare System. We conducted analyses in October 2023. In-person usability testing sessions consisted of a think-aloud procedure while using the software, a semistructured interview, and a 26-item web-based survey., Results: Usability test sessions with 20 VA providers and 13 patients demonstrated that both groups found Vitals "useful" and "easy to use," and they rated its usability highly (86 and 82 points, respectively, on a 100-point scale). Regarding acceptability or willingness/intent to use, providers and patients generally expressed confidence and trust in Vitals readings, with high ratings of 90 and 85 points, respectively. Providers and patients rated Vitals highly for its feasibility and appropriateness for context (90 and 90 points, respectively). Finally, providers noted that Vitals' flexibility makes it appropriate and advantageous for implementation in a wide range of clinical contexts, particularly in specialty care. Providers believed that most clinical teams would readily integrate Vitals into their routine workflow because it saves time; delivers accurate, consistently collected vitals; and may reduce reporting errors. Providers and veterans suggested training and support materials that could improve Vitals adoption and implementation., Conclusions: While remote collection of vital readings has been described in the literature, this is one of the first accounts of testing a contactless vital signs measurement tool among providers and patients. If ongoing initiatives demonstrate accuracy in its readings, Vitals could enhance telemedicine by providing accurate and automatic reporting and recording of vitals; sending patients' vital readings (pending provider approval) directly to their electronic medical record; saving provider and patient time; and potentially reducing necessity of some home-based biometric devices. Understanding usability issues before US Food and Drug Administration approval of Vitals and its implementation could contribute to a seamless introduction of Vitals to VA providers and patients., (©Lynn Garvin, Eric Richardson, Leonie Heyworth, D Keith McInnes. Originally published in JMIR Formative Research (https://formative.jmir.org), 23.10.2024.)

Background: Trigeminal neurostimulation of the dorsal anterior mucosal surface of the tongue has been proposed to treat a variety of pathologies and to promote neuro-muscular coordination and rehabilitation. Dental ULFTENS can also be considered a form of trigeminal neurostimulation applied to the skin surface bilaterally at the level of the tragus. It has been used for years in dentistry for practical and diagnostic purposes. Previous work has combined the two stimulation techniques showing an efficacy in improving HRV in healthy young women of dental ULFTENS applied to the mucosal surface of the tongue. This work sought to assess whether there is a difference in HRV in relation to the site of application of dental ULFTENS (tragus vs. tongue). If effective in reducing the activity of arousal circuits, this tongue-level stimulation technique could have new clinical applications., Material and Method: A new intraoral device allowed electrical stimulation of the dorsal anterior mucosa of the tongue in 80 healthy young women divided into two groups: TUD group (ULFTENS stimulation on the mucosa of the tongue) and Tragus group (stimulation with ULFTENS bilaterally in the area of the tragus). The effects on HRV were monitored by photoplethysmographic wave (PPG). The HRV parameters studied were RMSSD, HF, LF, LF/HF., Results: Only the TUD group showed a significant change in selected HRV parameters that was maintained even in the epoch after the end of electrical stimulation. This effect can be considered as a vagal activation and an increased of HRV parameter. The Tragus group did not show significant change in the direction of increased HRV but showed an opposite trend. There were no undesirable or annoying effects of stimulation., Conclusion: Stimulation of the dorsal anterior (trigeminal) mucosal surface of the tongue with ULFTENS applied with an intraoral device was shown to be able to increase HRV while the same stimulation on tragus area, according to traditional dental ULFTENS procedure, did not show the same effects., Clinical Implications: This stimulation technique could be an aid in the diagnosis and treatment of disorders characterized by autonomic disequilibrium such as, in the dental field, TMDs., Trial Registration: "Effects of Trigeminal Neurostimulation on Heart Rate Variability: Comparing Tragus and Tongue Stimulation". ID number: NCT06549205. Date of first registration: August 1st 2024. https://clinicaltrials.gov/study/NCT06549205?id=%09NCT06549205&rank=1 ., (© 2024. The Author(s).)

Background and Objectives: Sepsis, a leading global health challenge, accounts for around 20% of deaths worldwide. The complexity of sepsis, especially the difference between bacterial and viral etiologies, requires an effective assessment of microcirculation during resuscitation. This study aimed to evaluate the impact of infusion therapy on microcirculation in patients with sepsis, focusing on bacterial- and COVID-19-associated sepsis using remote photoplethysmography (rPPG) and the automated capillary refill time (aCRT). Materials and Methods: This single-center prospective study was conducted in the ICU of Pauls Stradins Clinical University Hospital, including 20 patients with sepsis/septic shock. The patients were selected based on hemodynamic instability and divided into COVID-19 and Bacterial Septic Shock groups. Fluid responsiveness was assessed using the Passive Leg Raising Test (PLRT). Systemic hemodynamics and microcirculation were monitored through MAP CRT, rPPG, and serum lactate levels. Statistical analyses compared responses within and between the groups across different stages of the protocol. Results: The Bacterial group exhibited higher initial serum lactate levels and more pronounced microcirculatory dysfunction than the COVID-19 group. rPPG was more sensitive in detecting perfusion changes, showing significant differences between the groups. The automated CRT demonstrated greater sensitivity compared to the manual CRT, revealing significant differences during PLRT stages between bacterial- and COVID-19-associated sepsis. Both groups had a transient hemodynamic response to PLRT, with subsequent stabilization upon fluid infusion. Conclusions: When managing patients with sepsis in intensive care, monitoring microcirculation is of paramount importance in infusion therapy. Our study highlights the potential of rPPG and aCRT as tools for this purpose. These techniques can be used in conjunction with routine parameters, such as lactate levels and systemic hemodynamic parameters, to provide a comprehensive assessment of a patient's condition.

Objective. Continuous monitoring of blood pressure (BP) is crucial for daily healthcare. Although invasive methods provide accurate continuous BP measurements, they are not suitable for routine use. Photoplethysmography (PPG), a non-invasive technique that detects changes in blood volume within the microcirculation using light, shows promise for BP measurement. The primary goal of this study is to develop a novel cuffless method based on PPG for accurately estimating continuous BP. Approach. We introduce BP-Diff, an end-to-end method for cuffless continuous BP waveform estimation utilizing a conditional diffusion probability model combined with a U-Net architecture. This approach takes advantage of the stochastic properties of diffusion models and the strong feature representation capabilities of U-Net. It integrates the continuous BP waveform as the initial status and uses the PPG signal and its derivatives as conditions to guide the training and sampling process. Main results. BP-Diff was evaluated using both uncalibrated and calibrated schemes. The results indicate that, when uncalibrated, BP-Diff can accurately track BP dynamics, including peak and valley positions, as well as timing. After calibration, BP-Diff achieved highly accurate BP estimations. The mean absolute error of the estimated BP waveforms, along with the systolic BP, diastolic BP, and mean arterial pressure from the calibrated BP-Diff model, were 2.99 mmHg, 2.6 mmHg, 1.4 mmHg, and 1.44 mmHg, respectively. Consistency tests, including Bland-Altman analysis and Pearson correlation, confirmed its high reliability compared to reference BP. BP-Diff meets the American Association for Medical Instrumentation standards and has achieved a Grade A from the British Hypertension Society. Significance. This study utilizes PPG signals to develop a novel cuffless continuous BP measurement method, demonstrating superiority over existing approaches. The method is suitable for integration into wearable devices, providing a practical solution for continuous BP monitoring in everyday healthcare., (© 2024 Institute of Physics and Engineering in Medicine. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)

With the acceleration of global population aging, the elderly have an increasing demand for home care and nursing institutions, and the significance of health prevention and management of the elderly has become increasingly prominent. In this context, we propose a biometric recognition method for multi-modal biomedical signals. This article focuses on three key signals that can be picked up by wearable devices: ECG, PPG, and breath (RESP). The RESP signal is introduced into the existing two-mode signal identification for multi-mode identification. Firstly, the features of the signal in the time-frequency domain are extracted. To represent deep features in a low-dimensional feature space and expedite authentication tasks, PCA and LDA are employed for dimensionality reduction. MCCA is used for feature fusion, and SVM is used for identification. The accuracy and performance of the system were evaluated using both public data sets and self-collected data sets, with an accuracy of more than 99.5%. The experimental data fully show that this method significantly improves the accuracy of identity recognition. In the future, combined with the signal monitoring function of wearable devices, it can quickly identify individual elderly people with abnormal conditions, provide safer and more efficient medical services for the elderly, and relieve the pressure on medical resources.

Inversion of the photoplethysmographic (PPG) signal is a rarely reported case. This signal anomaly can have implications for PPG-based cardiovascular assessments. The conditions for PPG signal inversion in the vicinity of the dorsalis pedis (DPA) artery of the foot were investigated. Wireless multi-wavelength PPG sensing with skin-probe contact pressure and local skin temperature were studied at different sensor positions, and the occurrence of inversion (OOI) was investigated. Twelve healthy adult volunteers were studied over four LED wavelengths at three levels of contact pressure for 11 probe positions. A novel algorithm quantified the proportion of inverted samples with respect to the abovementioned variables. Our algorithm classifying inverted vs. non-inverted pulses achieved 98.3% accuracy. Ten of the participants had at least one inverted signal identified. The impact of interindividual variation on inversion prevalence was large, but different LEDs, relative position to the DPA and sensor contact pressure also affected OOI. Skin surface and room temperatures showed no impact on OOI. Lateral measurements showed 39.6% more inversion at maximum compared to minimum contact pressure. Mechanical capillary bed variations and arterial reflections during venous engorgement are considered viable explanations for our observations. These findings motivate an expanded study of the occurrence of PPG signal inversion.

Cuffless blood pressure (BP) measurements have long been anticipated, and the PPG (Photoplethysmography)-only method is the most promising one since already embedded in many wearable devices. To further meet the clinical accuracy requirements, PPG-only BP predictions with personalized modeling for overcoming personal deviations have been widely studied, but all required tens to hundreds of minutes of personal PPG measurements for training. Moreover, their accurate test periods without calibration practice were not reported. In this work, we collected records of PPG data from our recruited subjects in real-life scenarios instead of relying on the openly available MIMIC dataset obtained from intensive care unit (ICU) patients. Since our objective is commercial application and a substantial reduction in training data, we tailored our model training to closely mimic real-world usage. To achieve this, we developed a training approach that only requires 9-minutes of personal PPG signal recordings and mixed with other PPG data from our recruited 364 subjects. The modeling is conducted with two-channel paired inputs to the convolutional neural network (CNN)-based model, which we called Mixed Deduction Learning (MDL). The test results of 88 samples from 15 subjects, under testing period up to 30-plus days without extra calibration, revealed that MDL meets most of the standards of AAMI, BHS, and IEEE 1708-2014 (for static test only) for BP measurement devices, which indicates MDL's long-term stability and consistency. Furthermore, we found that the model with two-channel inputs presents a trend of improving performance as the pool of mixed training data increased, while the conventional one-channel input revealed degraded performance. The outperformance of MDL is attributed to many significant features remained in the first CNN layer even when mixing personal 9-minutes data with the other 364 subjects. Consequently, PPG-only with MDL introduces a new avenue for overcoming challenges in training due to personal physiological variations. Given our consideration of real-life usage, this technology can be seamlessly translated to commercial applications., (© 2024. The Author(s).)

Background: Photoplethysmography (PPG) is a technology routinely used in clinical practice to assess blood oxygenation (SpO 2 ) and pulse rate (PR). Skin pigmentation may influence accuracy, leading to health outcomes disparities., Objective: This systematic review and meta-analysis primarily aimed to evaluate the accuracy of PPG-derived SpO 2 and PR by skin pigmentation. Secondarily, we aimed to evaluate statistical biases and the clinical relevance of PPG-derived SpO 2 and PR according to skin pigmentation., Methods: We identified 23 pulse oximetry studies (n=59,684; 197,353 paired SpO 2 -arterial blood observations) and 4 wearable PR studies (n=176; 140,771 paired PPG-electrocardiography observations). We evaluated accuracy according to skin pigmentation group by comparing SpO 2 accuracy root-mean-square values to the regulatory threshold of 3% and PR 95% limits of agreement values to +5 or -5 beats per minute (bpm), according to the standards of the American National Standards Institute, Association for the Advancement of Medical Instrumentation, and the International Electrotechnical Commission. We evaluated biases and clinical relevance using mean bias and 95% CI., Results: For SpO 2 , accuracy root-mean-square values were 3.96%, 4.71%, and 4.15%, and pooled mean biases were 0.70% (95% CI 0.17%-1.22%), 0.27% (95% CI -0.64% to 1.19%), and 1.27% (95% CI 0.58%-1.95%) for light, medium, and dark pigmentation, respectively. For PR, 95% limits of agreement values were from -16.02 to 13.54, from -18.62 to 16.84, and from -33.69 to 32.54, and pooled mean biases were -1.24 (95% CI -5.31 to 2.83) bpm, -0.89 (95% CI -3.70 to 1.93) bpm, and -0.57 (95% CI -9.44 to 8.29) bpm for light, medium, and dark pigmentation, respectively., Conclusions: SpO 2 and PR measurements may be inaccurate across all skin pigmentation groups, breaching U.S. Food and Drug Administration guidance and industry standard thresholds. Pulse oximeters significantly overestimate SpO 2 for both light and dark skin pigmentation, but this overestimation may not be clinically relevant. PRs obtained from wearables exhibit no statistically or clinically significant bias based on skin pigmentation., (©Sanidhya Singh, Miles Romney Bennett, Chen Chen, Sooyoon Shin, Hamid Ghanbari, Benjamin W Nelson. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 10.10.2024.)

Out-of-hospital cardiac arrest (OHCA) is a global health problem affecting approximately 4.4 million individuals yearly. OHCA has a poor survival rate, specifically when unwitnessed (accounting for up to 75% of cases). Rapid recognition can significantly improve OHCA survival, and consumer wearables with continuous cardiopulmonary monitoring capabilities hold potential to "witness" cardiac arrest and activate emergency services. In this study, we used an arterial occlusion model to simulate cardiac arrest and investigated the ability of infrared photoplethysmogram (PPG) sensors, often utilized in consumer wearable devices, to differentiate normal cardiac pulsation, pulseless cardiac (i.e., resembling a cardiac arrest), and non-physiologic (i.e., off-body) states. Across the classification models trained and evaluated on three anatomical locations, higher classification performances were observed on the finger (macro average F1-score of 0.964 on the fingertip and 0.954 on the finger base) compared to the wrist (macro average F1-score of 0.837). The wrist-based classification model, which was trained and evaluated using all PPG measurements, including both high- and low-quality recordings, achieved a macro average precision and recall of 0.922 and 0.800, respectively. This wrist-based model, which represents the most common form factor in consumer wearables, could only capture about 43.8% of pulseless events. However, models trained and tested exclusively on high-quality recordings achieved higher classification outcomes (macro average F1-score of 0.975 on the fingertip, 0.973 on the finger base, and 0.934 on the wrist). The fingertip model had the highest performance to differentiate arterial occlusion pulselessness from normal cardiac pulsation and off-body measurements with macro average precision and recall of 0.978 and 0.972, respectively. This model was able to identify 93.7% of pulseless states (i.e., resembling a cardiac arrest event), with a 0.4% false positive rate. All classification models relied on a combination of time-, power spectral density (PSD)-, and frequency-domain features to differentiate normal cardiac pulsation, pulseless cardiac, and off-body PPG recordings. However, our best model represented an idealized detection condition, relying on ensuring high-quality PPG data for training and evaluation of machine learning algorithms. While 90.7% of our PPG recordings from the fingertip were considered of high quality, only 53.2% of the measurements from the wrist passed the quality criteria. Our findings have implications for adapting consumer wearables to provide OHCA detection, involving advancements in hardware and software to ensure high-quality measurements in real-world settings, as well as development of wearables with form factors that enable high-quality PPG data acquisition more consistently. Given these improvements, we demonstrate that OHCA detection can feasibly be made available to anyone using PPG-based consumer wearables., (© 2024. The Author(s).)

Aims: Reduction of atrial fibrillation (AF) burden is the preferred outcome measure over categorical AF rhythm recurrence after AF ablation. In this sub-analysis of the TeleCheck-AF project, we tested the feasibility of smartphone app-based approximation of time spent with AF and/or symptoms., Methods and Results: Patients scheduled for at least one teleconsultation during the 12-month follow-up after AF ablation were instructed to use a smartphone photoplethysmography-based application for simultaneous symptom and rhythm monitoring three times daily for 1 week. Proxies of time spent with AF and/or symptoms (% recordings, load, and % days), temporal aggregation of AF and/or symptoms (density), and symptom-rhythm correlation (SRC) were assessed. In total, 484 patients (60% male, 62 ± 9.9 years) were included. Adherence, motivation, and patient satisfaction were high. %AF recordings, AF load, and %AF days (rs = 0.88-0.95) and %symptom recordings, symptom load, and %symptom days (rs = 0.95-0.98) showed positive correlations. The SRC correlated negatively with time spent with symptoms (rs = -0.65-0.90) and with time spent with AF (rs = -0.31-0.34). In patients with paroxysmal AF before ablation and AF during the monitoring period, 87% (n = 39/44) had a low-density score <50% ('paroxysmal AF pattern') while 5% (n = 2/44) had a high-density score >90% ('persistent AF pattern'). Corresponding numbers for patients with persistent AF before ablation were 48% (n = 11/23) and 43% (n = 10/23), respectively., Conclusion: On-demand, app-based simultaneous rhythm and symptom assessment provides objective proxies of time spent with AF and/or symptoms and SRC, which may assist in assessing AF and symptom outcomes after AF ablation., Competing Interests: Conflict of interest: E.S., A.N.L.H., M.G., K.B., A.So., C.H.F., R.M.J.v.d.V., D.V., D.Sc., A.Su., D.St., R.P., M.H., P.L., D.G., H.G., N.A.H.A.P., J.M.H., M.N., M.M., and D.L. declared no conflict of interests. D.D. received modest lecture honorary, travel grants, and/or a fellowship grant from Abbott, AstraZeneca, Biotronik, Boehringer Ingelheim, Boston Scientific, Bristol Myers Squibb, CVRx, Medtronic, Microport, Pfizer, Sanofi, and Zoll. H.A.K.H. received modest lecture honorary and/or a fellowship grant from AstraZeneca, Boston Scientific, and Zoll., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology.)

(1) Background: Wearable sensors support healthcare professionals in clinical decision-making by measuring vital parameters such as heart rate (HR), respiration rate (RR), and blood oxygenation saturation (SpO 2 ). This study assessed the validity and reliability of two types of wearable sensors, based on electrocardiogram or photoplethysmography, compared with continuous monitoring of patients recovering from trauma surgery at the postanesthesia care unit. (2) Methods: In a prospective observational study, HR, RR, SpO 2 , and temperature of patients were simultaneously recorded with the VitalPatch and Radius PPG and compared with reference monitoring. Outcome measures were formulated as correlation coefficient for validity and mean difference with 95% limits of agreement for reliability for four random data pairs and 30-min pairs per vital sign per patient. (3) Results: Included were 60 patients. Correlation coefficients for VitalPatch were 0.57 to 0.85 for HR and 0.08 to 0.16 for RR, and for Radius PPG, correlation coefficients were 0.60 to 0.83 for HR, 0.20 to 0.12 for RR, and 0.57 to 0.61 for SpO 2 . Both sensors presented mean differences within the cutoff values of acceptable difference. (4) Conclusions: Moderate to strong correlations for HR and SpO 2 were demonstrated. Although mean differences were within acceptable cutoff values for all vital signs, only limits of agreement for HR measured by electrocardiography were considered clinically acceptable.

Background: Although ankle-brachial index (ABI) and photoplethysmography (PPG) have also shown adequate sensitivity in detecting peripheral arterial disease, their diagnostic performance is less reliable in asymptomatic cases or those with high atherosclerotic cardiovascular risks., Methods and Results: We evaluated 130 participants using ABI, PPG, and duplex ultrasonography, diagnosing 65 with peripheral arterial disease. From the PPG, we derived 2 parameters: PPG amplitude ratio of the lower-to-upper extremities (PPG ratio ) and the PPG amplitude of the lower extremity (PPG amp ). Sensitivity, specificity, accuracy, and the area under receiver operating characteristic (ROC) curve were calculated for PPG parameters and ABI, and their combination of both methods. Univariate and multivariate logistic regression assessed the prognostic potential of these parameters. ROC analysis revealed optimal cutoff values in diagnosing peripheral arterial disease were 0.417 for PPG ratio and "58" for PPG amp . Both PPG ratio and PPG amp demonstrated significantly higher sensitivities, 78.4% and 75.7%, respectively, compared with 55.9% for ABI <0.9 ( P <0.05). The areas under the ROC curves of combination models, including model 1 (ABI <0.9 and PPG ratio ), model 2 (ABI <0.9 and PPG amp ), and model 3 (ABI <0.9, PPG ratio , and PPG amp ), exhibited improved performance with areas under the ROC curves of 0.922, 0.922, and 0.931 (all P <0.01) compared with ABI alone (area under the ROC curve, 0.822). Additionally, the PPG parameters, both alone and combined with ABI, were associated with major adverse cardiac events and all-cause mortality after adjusting for other relevant factors., Conclusions: On the basis of duplex ultrasonography, combining ABI and PPG markedly improves peripheral arterial disease diagnosis in high-risk individuals compared with either method alone and provides crucial insights into major adverse cardiac events and all-cause mortality risks.

Background: Knowledge feature (KF) with clear physiological significance of photoplethysmography are widely used in predicting blood pressure. However, KF primarily focus on local information of photoplethysmography, which may struggle to capture the overall characteristics., Methods: Firstly, functional data analysis (FDA) was introduced to extract two types of data feature (DF). Furthermore, data-knowledge co-driven feature (DKCF) was proposed by combining FDA and constraints of KF. Finally, random forest, ada boost, gradient boosting, support vector machine and deep neural network were adopted, to compare the abilities of KF, DFs and DKCF in predicting blood pressure with two datasets (A published dataset and a self-collected dataset)., Results: Under the premise of extracting only 9 features, the average mean absolute errors (MAE) of systolic blood pressure (SBP) and diastolic blood pressure (DBP) obtained by DKCF are both the smallest in dataset 1. In dataset 2, DKCF acquires the smallest MAE in predicting SBP and obtains the second smallest MAE in predicting DBP., Conclusions: The results demonstrate that low-dimensional DKCF of photoplethysmography is closely correlated with blood pressure, which may serve as an important indicator for health assessment., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Remote photoplethysmography (rPPG) is a low-cost technique to measure physiological parameters such as heart rate by analyzing videos of a person. There has been growing attention to this technique due to the increased possibilities and demand for running psychological experiments on online platforms. Technological advancements in commercially available cameras and video processing algorithms have led to significant progress in this field. However, despite these advancements, past research indicates that suboptimal video recording conditions can severely compromise the accuracy of rPPG. In this study, we aimed to develop an open-source rPPG methodology and test its performance on videos collected via an online platform, without control of the hardware of the participants and the contextual variables, such as illumination, distance, and motion. Across two experiments, we compared the results of the rPPG extraction methodology to a validated dataset used for rPPG testing. Furthermore, we then collected 231 online video recordings and compared the results of the rPPG extraction to finger pulse oximeter data acquired with a validated mobile heart rate application. Results indicated that the rPPG algorithm was highly accurate, showing a significant degree of convergence with both datasets thus providing an improved tool for recording and analyzing heart rate in online experiments., (© 2024. The Author(s).)

Utilizing injectable devices for monitoring animal health offers several advantages over traditional wearable devices, including improved signal-to-noise ratio (SNR) and enhanced immunity to motion artifacts. We present a wireless application-specific integrated circuit (ASIC) for injectable devices. The ASIC has multiple physiological sensing modalities including body temperature monitoring, electrocardiography (ECG), and photoplethysmography (PPG). The ASIC fabricated using the CMOS 180 nm process is sized to fit into an injectable microchip implant. The ASIC features a low-power design, drawing an average DC power of 155.3 µW, enabling the ASIC to be wirelessly powered through an inductive link. To capture the ECG signal, we designed the ECG analog frontend (AFE) with 0.3 Hz low cut-off frequency and 45-79 dB adjustable midband gain. To measure PPG, we employ an energy-efficient and safe switched-capacitor-based (SC) light emitting diode (LED) driver to illuminate an LED with milliampere-level current pulses. A SC integrator-based AFE converts the current of photodiode with a programmable transimpedance gain. A resistor-based Wheatstone Bridge (WhB) temperature sensor followed by an instrumentation amplifier (IA) provides 27-47 °C sensing range with 0.02 °C inaccuracy. Recorded physiological signals are sequentially sampled and quantized by a 10-bit analog-to-digital converter (ADC) with the successive approximation register (SAR) architecture. The SAR ADC features an energy-efficient switching scheme and achieves a 57.5 dB signal-to-noise-and-distortion ratio (SNDR) within 1 kHz bandwidth. Then, a back data telemetry transmits the baseband data via a backscatter scheme with intermediate-frequency assistance. The ASIC's overall functionality and performance has been evaluated through an in vivo experiment.

Despite the increasing popularity of ambulatory assessment, the reliability and validity of psychophysiological signals from wearable devices is unproven in daily life settings. We evaluated the reliability and validity of physiological signals (electrocardiogram, ECG; photoplethysmography, PPG; electrodermal activity, EDA) collected from two wearable devices (Movisens EcgMove4 and Empatica E4) in the lab (N = 67) and daily life (N = 20) among adults aged 18-64 with Mindware as the laboratory gold standard. Results revealed that both wearable devices' valid data rates in daily life were lower than in the laboratory (Movisens ECG 82.94 vs. 93.10%, Empatica PPG 8.79 vs. 26.14%, and Empatica EDA 41.16 vs. 42.67%, respectively). The poor valid data rates of Empatica PPG signals in the laboratory could be partially attributed to participants' hand movements (r = - .27, p = .03). In laboratory settings, heart rate (HR) derived from both wearable devices exhibited higher concurrent validity than heart rate variability (HRV) metrics (ICCs 0.98-1.00 vs. 0.75-0.97). The number of skin conductance responses (SCRs) derived from Empatica showed higher concurrent validity than skin conductance level (SCL, ICCs 0.38 vs. 0.09). Movisens EcgMove4 provided more reliable and valid HRV measurements than Empatica E4 in both laboratory (split-half reliability: 0.95-0.99 vs. 0.85-0.98; concurrent validity: 0.95-1.00 vs. 0.75-0.98; valid data rate: 93.10 vs. 26.14%) and ambulatory settings (split-half reliability: 0.99-1.00 vs. 0.89-0.98; valid data rate: 82.94 vs. 8.79%). Although the reliability and validity of wearable devices are improving, findings suggest researchers should select devices that yield consistently robust and valid data for their measures of interest., (© 2024. The Psychonomic Society, Inc.)

Emotion is a complex physiological phenomenon, and a single modality may be insufficient for accurately determining human emotional states. This paper proposes an end-to-end multimodal emotion recognition method based on facial expressions and non-contact physiological signals. Facial expression features and remote photoplethysmography (rPPG) signals are extracted from facial video data, and a transformer-based cross-modal attention mechanism (TCMA) is used to learn the correlation between the two modalities. The results show that the accuracy of emotion recognition can be slightly improved by combining facial expressions with accurate rPPG signals. The performance is further improved with the use of TCMA, for which the binary classification accuracy of valence and arousal is 91.11% and 90.00%, respectively. Additionally, when experiments are conducted using the whole dataset, an increased accuracy of 7.31% and 4.23% for the binary classification of valence and arousal, and an improved accuracy of 5.36% for the four classifications of valence-arousal are achieved when TCMA is used in modal fusion, compared to using only facial expression modality, which fully demonstrates the effectiveness and robustness of TCMA. This method makes it possible to realize multimodal emotion recognition of facial expressions and contactless physiological signals in reality.

change of blood glucose (BG) level stimulates the autonomic nervous system leading to variation in both human's electrocardiogram (ECG) and photoplethysmogram (PPG). In this article, we aimed to construct a novel multimodal framework based on ECG and PPG signal fusion to establish a universal BG monitoring model. This is proposed as a spatiotemporal decision fusion strategy that uses weight-based Choquet integral for BG monitoring. Specifically, the multimodal framework performs three-level fusion. First, ECG and PPG signals are collected and coupled into different pools. Second, the temporal statistical features and spatial morphological features in the ECG and PPG signals are extracted through numerical analysis and residual networks, respectively. Furthermore, the suitable temporal statistical features are determined with three feature selection techniques, and the spatial morphological features are compressed by deep neural networks (DNNs). Lastly, weight-based Choquet integral multimodel fusion is integrated for coupling different BG monitoring algorithms based on the temporal statistical features and spatial morphological features. To verify the feasibility of the model, a total of 103 days of ECG and PPG signals encompassing 21 participants were collected in this article. The BG levels of participants ranged between 2.2 and 21.8 mmol/L. The results obtained show that the proposed model has excellent BG monitoring performance with a root-mean-square error (RMSE) of 1.49 mmol/L, mean absolute relative difference (MARD) of 13.42%, and Zone A + B of 99.49% in tenfold cross-validation. Therefore, we conclude that the proposed fusion approach for BG monitoring has potentials in practical applications of diabetes management.

Current blood pressure (BP) estimation methods have not achieved an accurate and adaptable approach for ambulatory diagnosis and monitoring applications of populations at risk of cardiovascular disease, generally due to a limited sample size. This paper introduces an algorithm for BP estimation solely reliant on photoplethysmography (PPG) signals and demographic features. It automatically obtains signal features and employs the Markov Blanket (MB) feature selection to discern informative and transmissible features, achieving a robust space adaptable to the population shift. This approach was validated with the Aurora-BP database, compromising ambulatory wearable cuffless BP measurements for over 500 individuals. After evaluating several machine-learning regression methods, Gradient Boosting emerged as the most effective. According to the MB feature selection, temporal, frequency, and demographic features ranked highest in importance, while statistical ones were deemed non-significant. A comparative assessment of a generic model (trained on unclassified BP data) and specialized models (tailored to each distinct BP population), demonstrated a consistent superiority of our proposed MB feature space with a mean absolute error of [Formula: see text] for systolic BP and [Formula: see text] for diastolic BP on the whole dataset. Moreover, we present a first comparison of in-clinic vs. ambulatory models, with performance significantly lower for the latter with a drop of [Formula: see text] in systolic ( ) and [Formula: see text] for diastolic ( ) estimation errors. This work contributes to the resilient understanding of BP estimation algorithms from PPG signals, providing causal features in the signal and quantifying the disparities between ambulatory and in-clinic measurements.

Pulse rate (PR) and respiratory rate (RR) are two of the most important vital signs. Monitoring them would benefit from easy-to-use technologies. Hence, wearable devices would, in principle, be ideal candidates for such systems. The neck, although highly susceptible to artifacts, presents an attractive location for a diverse pool of physiological biomarkers monitoring purposes such as airflow sensing in a non-obstructive manner. This paper presents a methodology for PR and RR estimation using photoplethysmography (PPG) and accelerometry (Acc) sensors placed on the neck. Neck PPG and Acc signals were recorded from 22 healthy participants for RR estimation, where the resting subjects performed guided breathing following a visual metronome. Neck PPG signals were obtained from 16 healthy participants who breathed through an altitude generator machine in order to acquire a wider range of PR readings while at rest. The proposed methodology was able to provide rate estimates via a combination of recursive FFT-based dominance scoring coupled with an exponentially weighted moving average (EWMA)-driven aggregation scheme. The recursion aimed at bypassing sudden intra-window amplitude deviations caused by momentary artifacts, while the EWMA-based aggregation was utilized for handling inter-window artifact-induced deviations. To further improve estimation stability and confidence, estimates were calculated in the form of rate bands taking into account the relevant clinically acceptable error margins, and results when considering rate values and rate bands are presented and discussed. The framework was able to achieve an overall pulse rate value accuracy of 93.67 ± 7.64 % within the clinically acceptable ± 5 BPM with reference to the gold-standard reference devices while providing an overall respiratory rate value accuracy within the clinically appropriate ± 3 BrPM of 94.94 ± 3.56 % with reference to the guiding visual metronome, and 88.4 ± 7.63 % with respect to the gold-standard reference device. The proposed methodology achieves acceptable PR and RR estimation capabilities, even when signals are acquired from an unusual location such as the neck. This work introduces novel ideas that can lead to the development of medical device outputs for PR and RR monitoring, especially capitalizing on the advantages of the neck as a multi-modal physiological monitoring location., (© 2024. The Author(s).)

Wearable monitors continue to play a critical role in scientific assessments of physical activity. Recently, research-grade monitors have begun providing raw data from photoplethysmography (PPG) alongside standard raw data from inertial sensors (accelerometers and gyroscopes). Raw PPG enables granular and transparent estimation of cardiovascular parameters such as heart rate, thus presenting a valuable alternative to standard PPG methodologies (most of which rely on consumer-grade monitors that provide only coarse output from proprietary algorithms). The implications for physical activity assessment are tremendous, since it is now feasible to monitor granular and concurrent trends in both movement and cardiovascular physiology using a single noninvasive device. However, new users must also be aware of challenges and limitations that accompany the use of raw PPG data. This viewpoint paper therefore orients new users to the opportunities and challenges of raw PPG data by presenting its mechanics, pitfalls, and availability, as well as its parallels and synergies with inertial sensors. This includes discussion of specific applications to the prediction of energy expenditure, activity type, and 24-hour movement behaviors, with an emphasis on areas in which raw PPG data may help resolve known issues with inertial sensing (eg, measurement during cycling activities). We also discuss how the impact of raw PPG data can be maximized through the use of open-source tools when developing and disseminating new methods, similar to current standards for raw accelerometer and gyroscope data. Collectively, our comments show the strong potential of raw PPG data to enhance the use of research-grade wearable activity monitors in science over the coming years., (©Paul R Hibbing, Maryam Misal Khan. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 27.09.2024.)

There are two widely used methods to measure the cardiac cycle and obtain heart rate measurements: the electrocardiogram (ECG) and the photoplethysmogram (PPG). The sensors used in these methods have gained great popularity in wearable devices, which have extended cardiac monitoring beyond the hospital environment. However, the continuous monitoring of ECG signals via mobile devices is challenging, as it requires users to keep their fingers pressed on the device during data collection, making it unfeasible in the long term. On the other hand, the PPG does not contain this limitation. However, the medical knowledge to diagnose these anomalies from this sign is limited by the need for familiarity, since the ECG is studied and used in the literature as the gold standard. To minimize this problem, this work proposes a method, PPG2ECG, that uses the correlation between the domains of PPG and ECG signals to infer from the PPG signal the waveform of the ECG signal. PPG2ECG consists of mapping between domains by applying a set of convolution filters, learning to transform a PPG input signal into an ECG output signal using a U-net inception neural network architecture. We assessed our proposed method using two evaluation strategies based on personalized and generalized models and achieved mean error values of 0.015 and 0.026, respectively. Our method overcomes the limitations of previous approaches by providing an accurate and feasible method for continuous monitoring of ECG signals through PPG signals. The short distances between the infer-red ECG and the original ECG demonstrate the feasibility and potential of our method to assist in the early identification of heart diseases.

Background: To detect preload responsiveness in patients ventilated with a tidal volume (Vt) at 6 mL/kg of predicted body weight (PBW), the Vt-challenge consists in increasing Vt from 6 to 8 mL/kg PBW and measuring the increase in pulse pressure variation (PPV). However, this requires an arterial catheter. The perfusion index (PI), which reflects the amplitude of the photoplethysmographic signal, may reflect stroke volume and its respiratory variation (pleth variability index, PVI) may estimate PPV. We assessed whether Vt-challenge-induced changes in PI or PVI could be as reliable as changes in PPV for detecting preload responsiveness defined by a PLR-induced increase in cardiac index (CI) ≥ 10%., Methods: In critically ill patients ventilated with Vt = 6 mL/kg PBW and no spontaneous breathing, haemodynamic (PICCO 2 system) and photoplethysmographic (Masimo-SET technique, sensor placed on the finger or the forehead) data were recorded during a Vt-challenge and a PLR test., Results: Among 63 screened patients, 21 (33%) were excluded because of an unstable PI signal and/or atrial fibrillation and 42 were included. During the Vt-challenge in the 16 preload responders, CI decreased by 4.8 ± 2.8% (percent change), PPV increased by 4.4 ± 1.9% (absolute change), PI finger decreased by 14.5 ± 10.7% (percent change), PVI finger increased by 1.9 ± 2.6% (absolute change), PI forehead decreased by 18.7 ± 10.9 (percent change) and PVI forehead increased by 1.0 ± 2.5 (absolute change). All these changes were larger than in preload non-responders. The area under the ROC curve (AUROC) for detecting preload responsiveness was 0.97 ± 0.02 for the Vt-challenge-induced changes in CI (percent change), 0.95 ± 0.04 for the Vt-challenge-induced changes in PPV (absolute change), 0.98 ± 0.02 for Vt-challenge-induced changes in PI forehead (percent change) and 0.85 ± 0.05 for Vt-challenge-induced changes in PI finger (percent change) (p = 0.04 vs. PI forehead ). The AUROC for the Vt-challenge-induced changes in PVI forehead and PVI finger was significantly larger than 0.50, but smaller than the AUROC for the Vt-challenge-induced changes in PPV., Conclusions: In patients under mechanical ventilation with no spontaneous breathing and/or atrial fibrillation, changes in PI detected during Vt-challenge reliably detected preload responsiveness. The reliability was better when PI was measured on the forehead than on the fingertip. Changes in PVI during the Vt-challenge also detected preload responsiveness, but with lower accuracy., (© 2024. The Author(s).)

Objective. We investigated fluctuations of the photoplethysmography (PPG) waveform in patients undergoing surgery. There is an association between the morphologic variation extracted from arterial blood pressure (ABP) signals and short-term surgical outcomes. The underlying physiology could be the numerous regulatory mechanisms on the cardiovascular system. We hypothesized that similar information might exist in PPG waveform. However, due to the principles of light absorption, the noninvasive PPG signals are more susceptible to artifacts and necessitate meticulous signal processing. Approach. Employing the unsupervised manifold learning algorithm, dynamic diffusion map, we quantified multivariate waveform morphological variations from the PPG continuous waveform signal. Additionally, we developed several data analysis techniques to mitigate PPG signal artifacts to enhance performance and subsequently validated them using real-life clinical database. Main results. Our findings show similar associations between PPG waveform during surgery and short-term surgical outcomes, consistent with the observations from ABP waveform analysis. Significance. The variation of morphology information in the PPG waveform signal in major surgery provides clinical meanings, which may offer new opportunity of PPG waveform in a wider range of biomedical applications, due to its non-invasive nature., (© 2024 Institute of Physics and Engineering in Medicine. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)

Monitoring heart rate (HR) through photoplethysmography (PPG) signals is a challenging task due to the complexities involved, even during routine daily activities. These signals can indeed be heavily contaminated by significant motion artifacts resulting from the subjects' movements, which can lead to inaccurate heart rate estimations. In this paper, our objective is to present an innovative necklace sensor that employs low-computational-cost algorithms for heart rate estimation in individuals performing non-abrupt movements, specifically drivers. Our solution facilitates the acquisition of signals with limited motion artifacts and provides acceptable heart rate estimations at a low computational cost. More specifically, we propose a wearable sensor necklace for assessing a driver's well-being by providing information about the driver's physiological condition and potential stress indicators through HR data. This innovative necklace enables real-time HR monitoring within a sleek and ergonomic design, facilitating seamless and continuous data gathering while driving. Prioritizing user comfort, the necklace's design ensures ease of wear, allowing for extended use without disrupting driving activities. The collected physiological data can be transmitted wirelessly to a mobile application for instant analysis and visualization. To evaluate the sensor's performance, two algorithms for estimating the HR from PPG signals are implemented in a microcontroller: a modified version of the mountaineer's algorithm and a sliding discrete Fourier transform. The goal of these algorithms is to detect meaningful peaks corresponding to each heartbeat by using signal processing techniques to remove noise and motion artifacts. The developed design is validated through experiments conducted in a simulated driving environment in our lab, during which drivers wore the sensor necklace. These experiments demonstrate the reliability of the wearable sensor necklace in capturing dynamic changes in HR levels associated with driving-induced stress. The algorithms integrated into the sensor are optimized for low computational cost and effectively remove motion artifacts that occur when users move their heads.

Sleep staging is a crucial tool for diagnosing and monitoring sleep disorders, but the standard clinical approach using polysomnography (PSG) in a sleep lab is time-consuming, expensive, uncomfortable, and limited to a single night. Advancements in sensor technology have enabled home sleep monitoring, but existing devices still lack sufficient accuracy to inform clinical decisions. To address this challenge, we propose a deep learning architecture that combines a convolutional neural network and bidirectional long short-term memory to accurately classify sleep stages. By supplementing photoplethysmography (PPG) signals with respiratory sensor inputs, we demonstrated significant improvements in prediction accuracy and Cohen's kappa (k) for 2- (92.7 %; k = 0.768), 3- (80.2 %; k = 0.714), 4- (76.8 %, k = 0.550), and 5-stage (76.7 %, k = 0.616) sleep classification using raw data. This relatively translatable approach, with a less intensive AI model and leveraging only a few, inexpensive sensors, shows promise in accurately staging sleep. This has potential for diagnosing and managing sleep disorders in a more accessible and practical manner, possibly even at home., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Michelle Khine reports a relationship with Vena-Vitals that includes: equity or stocks., (Copyright © 2024. Published by Elsevier Ltd.)

Study Objectives: This study investigated the impact of automatic positive airway pressure (APAP) therapy on vascular behavior and its potential to lower cardiovascular risk in patients with obstructive sleep apnea (OSA), as well as differentiating APAP therapy heterogeneity., Methods: All participants were diagnosed with OSA by portable monitoring, and pulse wave parameters and cardiac risk composite parameter index were obtained by photoplethysmography before and after APAP. Clustering analysis of pulse wave parameters before APAP in the high-risk population was performed using k-means clustering. Linear regression was used to assess the associations of changes in cardiac risk composite parameter index and pulse wave parameters with clinical characteristics., Results: Eighty-two patients with OSA underwent APAP therapy. The cardiac risk composite parameter index after APAP was significantly lower than before APAP (0.38 ± 0.33 and 0.58 ± 0.31, respectively; P < .001). All pulse wave parameters (except irregular pulse) were significantly different ( P < .001) in patients with OSA and in the high-risk responders group after vs before APAP. The differences in pulse wave parameters after vs before APAP were not significant in the high-risk nonresponders group, except for the difference between the pulse rate acceleration index and the oxygen saturation index and pulse rate variability. Four clusters were obtained from the clustering analysis of pulse wave parameters before APAP in the high-risk responders group., Conclusions: APAP reduces the cardiac risk composite parameter index in patients with OSA by altering vascular behavior. Overnight photoplethysmography monitoring of pulse wave parameters can be used to assess whether patients with OSA will benefit from APAP., Citation: Zhu W, Xiang L, Cao L, Tian Y, Li W, He L. Evaluating the impact of automatic positive airway pressure therapy on cardiovascular risk index and vascular behavior in patients with obstructive sleep apnea: a study on heterogeneity in the therapeutic response. J Clin Sleep Med . 2024;20(9):1435-1444., (© 2024 American Academy of Sleep Medicine.)

Atrial Fibrillation (AF) screening from face videos has become popular with the trend of telemedicine and telehealth in recent years. In this study, the largest facial image database for camera-based AF detection is proposed. There are 657 participants from two clinical sites and each of them is recorded for about 10 minutes of video data, which can be further processed as over 10 000 segments around 30 seconds, where the duration setting is referred to the guideline of AF diagnosis. It is also worth noting that, 2 979 segments are segment-wise labeled, that is, every rhythm is independently labeled with AF or not. Besides, all labels are confirmed by the cardiologist manually. Various environments, talking, facial expressions, and head movements are involved in data collection, which meets the situations in practical usage. Specific to camera-based AF screening, a novel CNN-based architecture equipped with an attention mechanism is proposed. It is capable of fusing heartbeat consistency, heart rate variability derived from remote photoplethysmography, and motion features simultaneously to reliable outputs. With the proposed model, the performance of intra-database evaluation comes up to 96.62% of sensitivity, 90.61% of specificity, and 0.96 of AUC. Furthermore, to check the capability of adaptation of the proposed method thoroughly, the cross-database evaluation is also conducted, and the performance also reaches about 90% on average with the AUCs being over 0.94 in both clinical sites.

Video-based remote photoplethysmography (rPPG) has emerged as a promising technology for non-contact vital sign monitoring, especially under controlled conditions. However, the accurate measurement of vital signs in real-world scenarios faces several challenges, including artifacts induced by videocodecs, low-light noise, degradation, low dynamic range, occlusions, and hardware and network constraints. In this article, a systematic and comprehensive investigation of these issues is conducted, measuring their detrimental effects on the quality of rPPG measurements. Additionally, practical strategies are proposed for mitigating these challenges to improve the dependability and resilience of video-based rPPG systems. Methods for effective biosignal recovery in the presence of network limitations are detailed, along with denoising and inpainting techniques aimed at preserving video frame integrity. Compared to previous studies, this paper addresses a broader range of variables and demonstrates improved accuracy across various rPPG methods, emphasizing generalizability for practical applications in diverse scenarios with varying data quality. Extensive evaluations and direct comparisons demonstrate the effectiveness of these approaches in enhancing rPPG measurements under challenging environments, contributing to the development of more reliable and effective remote vital sign monitoring technologies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Objective: Oscillometric finger pressing is a smartphone-based blood pressure (BP) monitoring method. Finger photoplethysmography (PPG) oscillations and pressure are measured during a steady increase in finger pressure, and an algorithm computes systolic BP (SP) and diastolic BP (DP) from the measurements. The objective was to assess the impact of finger artery viscoelasticity on the BP computation., Methods: Nonlinear viscoelastic models relating transmural pressure (finger BP - applied pressure) to PPG oscillations during finger pressing were developed. The output of each model to a measured transmural pressure input was fitted to measured PPG oscillations from 15 participants. A parametric sensitivity analysis was performed via model simulations to elucidate the viscoelastic effect on the derivative-based BP computation algorithm., Results: A Wiener viscoelastic model comprising a first-order transfer function followed by a static sigmoidal function fitted the measured PPG oscillations better than an elastic model containing only the static function (median (IQR) error of 30.5% (25.6%-34.0%) vs 50.9% (46.7%-53.7%); p<0.01). In Wiener model simulations, the derivative algorithm underestimated SP, especially with high pulse pressure and low transfer function cutoff frequency (i.e., greater viscoelasticity). The mean of the normalized PPG waveform at the maximum oscillation beat was found to correlate with the cutoff frequency (r = -0.8) and could thus possibly be used to compensate for viscoelasticity., Conclusion: Finger artery viscoelasticity negatively impacts oscillometric BP computation algorithms but can potentially be compensated for using available measurements., Significance: These findings may help in converting smartphones into truly cuffless BP monitors for improving hypertension awareness and control.

Background and Objective: Detection of the dicrotic notch (DN) within a cardiac cycle is essential for assessment of cardiac output, calculation of pulse wave velocity, estimation of left ventricular ejection time, and supporting feature-based machine learning models for noninvasive blood pressure estimation, and hypotension, or hypertension prediction. In this study, we present a new algorithm based on the iterative envelope mean (IEM) method to detect automatically the DN in arterial blood pressure (ABP) and photoplethysmography (PPG) waveforms., Methods: The algorithm was evaluated on both ABP and PPG waveforms from a large perioperative dataset (MLORD dataset) comprising 17,327 patients. The analysis involved a total of 1,171,288 cardiac cycles for ABP waveforms and 3,424,975 cardiac cycles for PPG waveforms. To evaluate the algorithm's performance, the systolic phase duration (SPD) was employed, which represents the duration from the onset of the systolic phase to the DN in the cardiac cycle. Correlation plots and regression analysis were used to compare the algorithm against marked DN detection, while box plots and Bland-Altman plots were used to compare its performance with both marked DN detection and an established DN detection technique (second derivative). The marking of the DN temporal location was carried out by an experienced researcher using the help of the 'find&#95;peaks' function from the scipy Python package, serving as a reference for the evaluation. The marking was visually validated by both an engineer and an anesthesiologist. The robustness of the algorithm was evaluated as the DN was made less visually distinct across signal-to-noise ratios (SNRs) ranging from -30 dB to -5 dB in both ABP and PPG waveforms., Results: The correlation between SPD estimated by the algorithm and that marked by the researcher is strong for both ABP (R 2 (87,343) =0.99, p<.001) and PPG (R 2 (86,764) =0.98, p<.001) waveforms. The algorithm had a lower mean error of DN detection (s): 0.0047 (0.0029) for ABP waveforms and 0.0046 (0.0029) for PPG waveforms, compared to 0.0693 (0.0770) for ABP and 0.0968 (0.0909) for PPG waveforms for the established 2nd derivative method. The algorithm has high rate of detectability of DN detection for SNR of >= -9 dB for ABP waveforms and >= -12 dB for PPG waveforms indicating robust performance in detecting the DN when it is less visibly distinct., Conclusion: Our proposed IEM- based algorithm can detect DN in both ABP and PPG waveforms with low computational cost, even in cases where it is not distinctly defined within a cardiac cycle of the waveform ('DN-less signals'). The algorithm can potentially serve as a valuable, fast, and reliable tool for extracting features from ABP and PPG waveforms. It can be especially beneficial in medical applications where DN-based features, such as SPD, diastolic phase duration, and DN amplitude, play a significant role., Competing Interests: Declaration of competing interest Dr. Cannesson is a consultant for Edwards Lifesciences and Masimo Corp, and has funded research from Edwards Lifesciences and Masimo Corp. He is also the founder of Sironis and Perceptive Medical and he owns patents and receives royalties for closed loop hemodynamic management technologies that have been licensed to Edwards Lifesciences., (Copyright © 2024. Published by Elsevier B.V.)

Objective: Evaluate the performance of a custom application developed for tonic-clonic seizure (TCS) monitoring on a consumer-wearable (Apple Watch) device., Methods: Participants with a history of convulsive epileptic seizures were recruited for either Epilepsy Monitoring Unit (EMU) or ambulatory (AMB) monitoring; participants without epilepsy (normal controls [NC]) were also enrolled in the AMB group. Both EMU and AMB participants wore an Apple Watch with a research app that continuously recorded accelerometer and photoplethysmography (PPG) signals, and ran a fixed-and-frozen tonic-clonic seizure detection algorithm during the testing period. This algorithm had been previously developed and validated using a separate training dataset. All EMU convulsive events were validated by video-electroencephalography (video-EEG); AMB events were validated by caregiver reporting and follow-ups. Device performance was characterized and compared to prior monitoring devices through sensitivity, false alarm rate (FAR; false-alarms per 24 h), precision, and detection delay (latency)., Results: The EMU group had 85 participants (4,279 h, 19 TCS from 15 participants) enrolled across four EMUs; the AMB group had 21 participants (13 outpatient, 8 NC, 6,735 h, 10 TCS from 3 participants). All but one AMB participant completed the study. Device performance in the EMU group included a sensitivity of 100 % [95 % confidence interval (CI) 79-100 %]; an FAR of 0.05 [0.02, 0.08] per 24 h; a precision of 68 % [48 %, 83 %]; and a latency of 32.07 s [standard deviation (std) 10.22 s]. The AMB group had a sensitivity of 100 % [66-100 %]; an FAR of 0.13 [0.08, 0.24] per 24 h; a precision of 22 % [11 %, 37 %]; and a latency of 37.38 s [13.24 s]. Notably, a single AMB participant was responsible for 8 of 31 false alarms. The AMB FAR excluding this participant was 0.10 [0.07, 0.14] per 24 h., Discussion: This study demonstrates the practicability of TCS monitoring on a popular consumer wearable (Apple Watch) in daily use for people with epilepsy. The monitoring app had a high sensitivity and a substantially lower FAR than previously reported in both EMU and AMB environments., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: This study was funded by the Band foundation, and the Johns Hopkins University Department of Neurology. EpiWatch, Inc., a company developing a non-EEG based seizure detection device, has obtained an exclusive license from the Johns Hopkins University to use this methodology in the future for commercial purposes. Under a license agreement between the Johns Hopkins University and EpiWatch, Inc., the University owns equity in EpiWatch, Inc. and the University, Mr. Shah, Dr. Krauss and Dr. Crone are entitled to royalty distributions related to technology described in the study discussed in this publication. Dr. Krauss and Dr. Crone are co-founders of EpiWatch. Mr. Shah, Dr. Krauss and Dr. Crone also own equity in the company. This arrangement has been reviewed and approved by the Johns Hopkins University in accordance with its conflict of interest policies. Dr. Gil-Nagel has received grants from Jazz Pharmaceutical, PTC Therapeutics and Zogenix. Dr. Gil-Nagel has also received fees from speakers bureau or advisory boards from: Bial, Biocodex, Eisai, Esteve, HealthInCode, Jazz Pharmaceutical, PTC Therapeutics, Stoke, Synaptia, UCB Pharma and Zogenix; Dr. Gil-Nagel owns shares of IO Track., (Copyright © 2024. Published by Elsevier Inc.)

Patients with surgical, pulmonary, and cardiac problems, continual monitoring of Oxygen Saturation of a Person (SpO2) and Respiratory Rate (RR) is essential. Similarly, the persons with cardiopulmonary health issues, RR estimation is crucial. The performance of the ventilator assistance and lung medicines are evaluated using SpO2 and RR. For the persons, those who are living alone with respiratory illnesses need a compulsory estimation of RR. In case of serious illness, the RR might face abrupt changes. The immobility of the disturbance and RR makes the RR evaluation from the PhotoPlethysmoGraphic (PPG) signals is a difficult challenge. So, an efficient RR and SpO2 estimation framework from the PPG signal using the deep learning method is developed in this paper. At first, the PPG signal is collected from standard data sources. The collected PPG signals undergo signal pre-processing. The pre-processing procedures include Motion Artifacts (MA) removal and filtering techniques. The pre-processed signals are split into distinct windows. From the split windows of the signals, the spectral features, RR, and Respiratory Peak Variance (RPV) features are extracted. The retrieved features are selected optimally with the help of Advanced Golden Tortoise Beetle Optimizer (AGTBO). The weights are chosen optimally with the same AGTBO. The optimally selected features are fused with the optimal features to get the weighted optimal features. These weighted optimal features are fed into the Ensemble Learning-based RR and SpO2 Estimation Network (ELRR-SpO2EN). The ensemble learning model is developed by combining Multilayer Perceptron (MLP), AdaBoost, and Attention-based Long Short Term Memory (A-LSTM). The performance of the developed RR and SpO2 estimation model is compared with other existing techniques. The experimental analysis results revealed that the proposed AGTBO-ELRR-SpO2EN model attained 96 % accuracy for the second dataset, which is higher than the conventional models such as MLP (90 %), Adaboost (92 %), A-LSTM (92 %), and MLP-ADA-ALSTM (94 %). Thus, it has been confirmed that the designed RR and SpO2 estimation framework from PPG signals is more efficient than the other conventional models., Competing Interests: Declaration of competing interest The authors declare no conflict of interest., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Objective: The decline in vascular elasticity with aging can be manifested in the shape of pulse wave. The study investigated the pulse wave features that are sensitive to age and the pattern of these features change with increasing age were examined., Methods: Five features were proposed and extracted from the photoplethysmography (PPG)-based pulse wave or its first derivative wave. The correlation between these PPG features and ages was studied in 100 healthy subjects with a wide range of ages (20-71 years). Piecewise regression coefficients were calculated to examine the rates of change of the PPG features with age at different age stages., Results: The proposed PPG features obtained from the finger showed a strong and significant correlation with age (with r = 0.76 - 0.77, p < 0.01), indicating higher sensitivity to age changes compared to the PPG features reported in previous studies (with r = 0.66 - 0.75). The correlation remained significant even after correcting for other clinical variables. The rate of change of the PPG feature values was found to be significantly faster in subjects aged ≥40 years compared to those aged < 40 years in the healthy population. This rate of change was similar to the age-related progression of arterial stiffness evaluated by pulse wave velocity (PWV), which is considered a gold standard for evaluating vascular stiffness., Conclusions: The proposed PPG features showed a high correlation with chronological age in healthy subjects and exhibited a similar age-related change trend as PWV., Significance: With the convenience of PPG measures, the proposed age-related features have the potential to be used as biomarkers for vascular aging and estimating the risk of cardiovascular disease.

Background: Coronary perfusion pressure (CPP) indicates spontaneous return of circulation and is recommended for high-quality cardiopulmonary resuscitation (CPR). This study aimed to investigate a method for non-invasive estimation of CPP using electrocardiography (ECG) and photoplethysmography (PPG) during CPR., Methods: Nine pigs were used in this study. ECG, PPG, invasive arterial blood pressure (ABP), and right atrial pressure (RAP) signals were simultaneously recorded. The CPPs were estimated using three datasets: (a) ECG, (b) PPG, and (c) ECG and PPG, and were compared with invasively measured CPPs. Four machine-learning algorithms, namely support vector regression, random forest (RF), K-nearest neighbor, and gradient-boosted regression tree, were used for estimation of CPP., Results: The RF model with a combined ECG and PPG dataset achieved better estimation of CPP than the other algorithms. Specifically, the mean absolute error was 4.49 mmHg, the root mean square error was 6.15 mm Hg, and the adjusted R 2 was 0.75. A strong correlation was found between the non-invasive estimation and invasive measurement of CPP (r = 0.88), which supported our hypothesis that machine-learning-based analysis of ECG and PPG parameters can provide a non-invasive estimation of CPP for CPR., Conclusions: This study proposes a novel estimation of CPP using ECG and PPG with machine-learning-based algorithms. Non-invasively estimated CPP showed a high correlation with invasively measured CPP and may serve as an easy-to-use physiological indicator for high-quality CPR treatment., Competing Interests: Declaration of competing interest None of the authors have any financial or personal relationship with other people or organizations that could inappropriately influence (bias) this work., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Despite the growing adoption of wearable photoplethysmography (PPG) devices in personal health management, their measurement accuracy remains limited due to susceptibility to noise. This paper proposes a novel signal completion technique using generative adversarial networks that ensures both global and local consistency. Our approach innovatively addresses both short- and long-term PPG variations to restore waveforms while maintaining waveform consistency within and between pulses. We evaluated our model by removing up to 50 % of segments from segmented PPG waveforms and comparing the original and reconstructed waveforms, including systolic peak information. The results demonstrate that our method accurately reconstructs waveforms with high fidelity, producing natural and seamless transitions without discontinuities at reconstructed boundaries. Additionally, the reconstructed waveforms preserve typical PPG shapes with minimal distortion, underscoring the effectiveness and novelty of our technique., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Getting prompt insights about health and well-being in a non-invasive way is one of the most popular features available on wearable devices. Among all vital signs available, heart rate (HR) monitoring is one of the most important since other measurements are based on it. Real-time HR estimation in wearables mostly relies on photoplethysmography (PPG), which is a fair technique to handle such a task. However, PPG is vulnerable to motion artifacts (MA). As a consequence, the HR estimated from PPG signals is strongly affected during physical exercises. Different approaches have been proposed to deal with this problem, however, they struggle to handle exercises with strong movements, such as a running session. In this paper, we present a new method for HR estimation in wearables that uses an accelerometer signal and user demographics to support the HR prediction when the PPG signal is affected by motion artifacts. This algorithm requires a tiny memory allocation and allows on-device personalization since the model parameters are finetuned in real time during workout executions. Also, the model may predict HR for a few minutes without using a PPG, which represents a useful contribution to an HR estimation pipeline. We evaluate our model on five different exercise datasets - performed on treadmills and in outdoor environments - and the results show that our method can improve the coverage of a PPG-based HR estimator while keeping a similar error performance, which is particularly useful to improve user experience.

Pulse arrival time (PAT), Pulse transit time (PTT), and Pulse Wave Velocity (PWV) have all been used as metrics for assessing a number of cardiovascular applications, including arterial stiffness and cuffless blood pressure monitoring. These have been measured using various sensing methods, including electrocardiogram (ECG) with photoplethysmogram (PPG), two PPG sensors, or two Bioimpedance (BioZ) sensors. Our study addresses the mathematical inaccuracies of previous bioimpedance approaches and incorporates PTT weights for the peak-peak (PTTpp), middle-middle (PTTmm), and foot-foot (PTTff) segments of the sensing signal into a single neuron model to determine a more accurate and stable PWV. In addition, we developed a tetherless bioimpedance device and compared our PTT estimation approaches, which yielded PWV across six subjects and two different arteries. Specifically, using our model, we found that the most reliable combination of weights corresponding to PTTpp, PTTmm, and PTTff was (0.260, 0.704, 0.036) for the brachial artery and (0.104, 0.858, 0.038) for radial artery. This model consistently yielded stable values across repetitions, with PWV values of 5.2 m/s, 5.3 m/s, and 5.9 m/s for the brachial artery and values of 5.8 m/s, 6.6 m/s, and 6.5 m/s for the radial artery. This system and model offer the possibility of obtaining higher reliability PTT and PWV values yielding better monitoring of cardiovascular health measures such as blood pressure and arterial stiffness., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

This article reviews the evolution of noninvasive hemodynamic monitoring technologies, highlighting their importance in perioperative and critical care settings. Initially dominated by invasive methods, the field has shifted toward noninvasive techniques to reduce risks and improve patient safety. These advancements encompass various technologies, including bioimpedance/bioreactance, pulse contour analysis, and photoplethysmography, offering anesthesiologists dynamic tools for patient management. The article explores historical developments, traditional and advanced noninvasive monitors, and future trends, emphasizing the potential of integrating artificial intelligence and wearable technology in patient care., Competing Interests: Disclosure The authors declare that there are no direct conflicts of interest regarding the topics discussed in this article. The author, Bret Alvis, is the CMO and an inventor on intellectual property in the field of venous waveform analysis assigned to Vanderbilt and licensed to VoluMetrix and is married to the COO of VoluMetrix., (Crown Copyright © 2024. Published by Elsevier Inc. All rights reserved.)

Background: Cuffless blood pressure measurement technologies have attracted significant attention for their potential to transform cardiovascular monitoring. Methods: This updated narrative review thoroughly examines the challenges, opportunities, and limitations associated with the implementation of cuffless blood pressure monitoring systems. Results: Diverse technologies, including photoplethysmography, tonometry, and ECG analysis, enable cuffless blood pressure measurement and are integrated into devices like smartphones and smartwatches. Signal processing emerges as a critical aspect, dictating the accuracy and reliability of readings. Despite its potential, the integration of cuffless technologies into clinical practice faces obstacles, including the need to address concerns related to accuracy, calibration, and standardization across diverse devices and patient populations. The development of robust algorithms to mitigate artifacts and environmental disturbances is essential for extracting clear physiological signals. Based on extensive research, this review emphasizes the necessity for standardized protocols, validation studies, and regulatory frameworks to ensure the reliability and safety of cuffless blood pressure monitoring devices and their implementation in mainstream medical practice. Interdisciplinary collaborations between engineers, clinicians, and regulatory bodies are crucial to address technical, clinical, and regulatory complexities during implementation. In conclusion, while cuffless blood pressure monitoring holds immense potential to transform cardiovascular care. The resolution of existing challenges and the establishment of rigorous standards are imperative for its seamless incorporation into routine clinical practice. Conclusion: The emergence of these new technologies shifts the paradigm of cardiovascular health management, presenting a new possibility for non-invasive continuous and dynamic monitoring. The concept of cuffless blood pressure measurement is viable and more finely tuned devices are expected to enter the market, which could redefine our understanding of blood pressure and hypertension.

Preterm birth (PTB) remains a global health concern, impacting neonatal mortality and lifelong health consequences. Traditional methods for estimating PTB rely on electronic health records or biomedical signals, limited to short-term assessments in clinical settings. Recent studies have leveraged wearable technologies for in-home maternal health monitoring, offering continuous assessment of maternal autonomic nervous system (ANS) activity and facilitating the exploration of PTB risk. In this paper, we conduct a longitudinal study to assess the risk of PTB by examining maternal ANS activity through heart rate (HR) and heart rate variability (HRV). To achieve this, we collect long-term raw photoplethysmogram (PPG) signals from 58 pregnant women (including seven preterm cases) from gestational weeks 12-15 to three months post-delivery using smartwatches in daily life settings. We employ a PPG processing pipeline to accurately extract HR and HRV, and an autoencoder machine learning model with SHAP analysis to generate explainable abnormality scores indicative of PTB risk. Our results reveal distinctive patterns in PTB abnormality scores during the second pregnancy trimester, indicating the potential for early PTB risk estimation. Moreover, we find that HR, average of interbeat intervals (AVNN), SD1SD2 ratio, and standard deviation of interbeat intervals (SDNN) emerge as significant PTB indicators., (© 2024. The Author(s).)

Objective . The widespread adoption of Photoplethysmography (PPG) as a non-invasive method for detecting blood volume variations and deriving vital physiological parameters reflecting health status has surged, primarily due to its accessibility, cost-effectiveness, and non-intrusive nature. This has led to extensive research around this technique in both daily life and clinical applications. Interestingly, despite the existence of contradictory explanations of the underlying mechanism of PPG signals across various applications, a systematic investigation into this crucial matter has not been conducted thus far. This gap in understanding hinders the full exploitation of PPG technology and undermines its accuracy and reliability in numerous applications. Approach . Building upon a comprehensive review of the fundamental principles and technological advancements in PPG, this paper initially attributes the origin of PPG signals to a combination of physical and physiological transmission processes. Furthermore, three distinct models outlining the concerned physiological transmission processes are synthesized, with each model undergoing critical examination based on theoretical underpinnings, empirical evidence, and constraints. Significance . The ultimate objective is to form a fundamental framework for a better understanding of physiological transmission processes in PPG signal generation and to facilitate the development of more reliable technologies for detecting physiological signals., (© 2024 Institute of Physics and Engineering in Medicine. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)

The performance of a pulse oximeter based on photoelectric detection is greatly affected by motion noise (MA) in the photoplethysmographic (PPG) signal. This paper presents an algorithm for detecting motion oxygen saturation, which reconstructs a motion noise reference signal using ensemble of complete adaptive noise and empirical mode decomposition combined with multi-scale permutation entropy, and eliminates MA in the PPG signal using a convex combination least mean square adaptive filters to calculate dynamic oxygen saturation. The test results show that, under simulated walking and jogging conditions, the mean absolute error (MAE) of oxygen saturation estimated by the proposed algorithm and the reference oxygen saturation are 0.05 and 0.07, respectively, with means absolute percentage error (MAPE) of 0.05% and 0.07%, respectively. The overall Pearson correlation coefficient reaches 0.971 2. The proposed scheme effectively reduces motion artifacts in the corrupted PPG signal and is expected to be applied in portable photoelectric pulse oximeters to improve the accuracy of dynamic oxygen saturation measurement.

Continuous monitoring of physiological signals such as electrocardiogram (ECG) in driving environments has the potential to reduce the need for frequent health check-ups by providing real-time information on cardiovascular health. However, capturing ECG from sensors mounted on steering wheels creates difficulties due to motion artifacts, noise, and dropouts. To address this, we propose a novel method for reliable and accurate detection of heartbeats using sensor fusion with a bidirectional long short-term memory (BiLSTM) model. Our dataset contains reference ECG, steering wheel ECG, photoplethysmogram (PPG), and imaging PPG (iPPG) signals, which are more feasible to capture in driving scenarios. We combine these signals for R-wave detection. We conduct experiments with individual signals and signal fusion techniques to evaluate the performance of detected heartbeat positions. The BiLSTMs model achieves a performance of 62.69% in the driving scenario city. The model can be integrated into the system to detect heartbeat positions for further analysis.

Integrating continuous monitoring into everyday objects enables the early detection of diseases. This paper presents a novel approach to heartbeat monitoring on eScooters using multi-modal signal fusion. We explore heartbeat monitoring using electrocardiography (ECG) and photoplethysmography (PPG) and evaluate four signal fusion approaches based on convolutional neural network (CNN) and long short-term memory (LSTM) architectures. We perform an evaluation study using skin-attached ECG electrodes for ground truth generation. The CNN+LSTM late fusion accurately measures the heartbeat for 76.17% of the driving time.

Peripheral Capillary Oxygen Saturation (SpO 2 ) has received increasing attention during the COVID-19 pandemic. Clinical investigations have demonstrated that individuals afflicted with COVID-19 exhibit notably reduced levels of SpO 2 before the deterioration of their health status. To cost-effectively enable individuals to monitor their SpO 2 , this paper proposes a novel neural network model named "ITSCAN" based on Temporal Shift Module. Benefiting from the widespread use of smartphones, this model can assess an individual's SpO 2 in real time, utilizing standard facial video footage, with a temporal granularity of seconds. The model is interweaved by two distinct branches: the motion branch, responsible for extracting spatiotemporal data features and the appearance branch, focusing on the correlation between feature channels and the location information of feature map using coordinate attention mechanisms. Accordingly, the SpO 2 estimator generates the corresponding SpO 2 value. This paper summarizes for the first time 5 loss functions commonly used in the SpO 2 estimation model. Subsequently, a novel loss function has been contributed through the examination of various combinations and careful selection of hyperparameters. Comprehensive ablation experiments analyze the independent impact of each module on the overall model performance. Finally, the experimental results based on the public dataset (VIPL-HR) show that our model has obvious advantages in MAE (1.10%) and RMSE (1.19%) compared with related work, which implies more accuracy of the proposed method to contribute to public health., (© 2024. The Author(s).)

Atrial fibrillation (AF) is the most prevalent arrhythmia characterized by intermittent and asymptomatic episodes. However, traditional detection methods often fail to capture the sporadic and intricate nature of AF, resulting in an increased risk of false-positive diagnoses. To address these challenges, this study proposes an intelligent AF detection and diagnosis method that integrates Complementary Ensemble Empirical Mode Decomposition, Power-Normalized Cepstral Coefficients, Bi-directional Long Short-term Memory (CEPNCC-BiLSTM), and photoelectric volumetric pulse wave technology to enhance accuracy in detecting AF. Compared to other approaches, the proposed method demonstrates faster preprocessing efficiency and higher sensitivity in detecting AF while effectively filtering out false alarms from photoplethysmography (PPG) recordings of non-AF patients. Considering the limitations of conventional AF detection evaluation systems that lack a comprehensive assessment of efficiency and accuracy, this study proposes the ET-score evaluation system based on F-measurement, which incorporates both computational speed and accuracy to provide a holistic assessment of overall performance. Evaluated with the ET-score , the CEPNCC-BiLSTM method outperforms EEMD-based improved Power-Normalized Cepstral Coefficients and Bi-directional Long Short-term Memory (EPNCC-BiLSTM), Support Vector Machine (SVM), EPNCC-SVM, and CEPNCC-SVM methods. Notably, this approach achieves an outstanding accuracy rate of up to 99.2% while processing PPG recordings within 5 s, highlighting its potential for long-term AF monitoring.