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Photoplethysmography (PPG) allows various statements about the physiological state. It supports multiple recording setups, i.e., application to various body sites and different acquisition modes, rendering the technique a versatile tool for various situations. Owing to anatomical, physiological and metrological factors, PPG signals differ with the actual setup. Research on such differences can deepen the understanding of prevailing physiological mechanisms and path the way towards improved or novel methods for PPG analysis. The presented work systematically investigates the impact of the cold pressor test (CPT), i.e., a painful stimulus, on the morphology of PPG signals considering different recording setups. Our investigation compares contact PPG recorded at the finger, contact PPG recorded at the earlobe and imaging PPG (iPPG), i.e., non-contact PPG, recorded at the face. The study bases on own experimental data from 39 healthy volunteers. We derived for each recording setup four common morphological PPG features from three intervals around CPT. For the same intervals, we derived blood pressure and heart rate as reference. To assess differences between the intervals, we used repeated measures ANOVA together with paired t-tests for each feature and we calculated Hedges’ g to quantify effect sizes. Our analyses show a distinct impact of CPT. As expected, blood pressure shows a highly significant and persistent increase. Independently of the recording setup, all PPG features show significant changes upon CPT as well. However, there are marked differences between recording setups. Effect sizes generally differ with the finger PPG showing the strongest response. Moreover, one feature (pulse width at half amplitude) shows an inverse behavior in finger PPG and head PPG (earlobe PPG and iPPG). In addition, iPPG features behave partially different from contact PPG features as they tend to return to baseline values while contact PPG features remain altered. Our findings underline the importance of recording setup and physiological as well as metrological differences that relate to the setups. The actual setup must be considered in order to properly interpret features and use PPG. The existence of differences between recording setups and a deepened knowledge on such differences might open up novel diagnostic methods in the future.

Biometric authentication is a widely used method for verifying individuals’ identities using photoplethysmography (PPG) cardiac signals. The PPG signal is a non-invasive optical technique that measures the heart rate, which can vary from person to person. However, these signals can also be changed due to factors like stress, physical activity, illness, or medication. Ensuring the system can accurately identify and authenticate the user despite these variations is a significant challenge. To address these issues, the PPG signals were preprocessed and transformed into a 2-D image that visually represents the time-varying frequency content of multiple PPG signals from the same human using the scalogram technique. Afterward, the features fusion approach is developed by combining features from the hybrid convolution vision transformer (CVT) and convolutional mixer (ConvMixer), known as the CVT-ConvMixer classifier, and employing attention mechanisms for the classification of human identity. This hybrid model has the potential to provide more accurate and reliable authentication results in real-world scenarios. The sensitivity (SE), specificity (SP), F1-score, and area under the receiver operating curve (AUC) metrics are utilized to assess the model’s performance in accurately distinguishing genuine individuals. The results of extensive experiments on the three PPG datasets were calculated, and the proposed method achieved ACCs of 95%, SEs of 97%, SPs of 95%, and an AUC of 0.96, which indicate the effectiveness of the CVT-ConvMixer system. These results suggest that the proposed method performs well in accurately classifying or identifying patterns within the PPG signals to perform continuous human authentication.

In this paper, new features relevant to blood pressure (BP) estimation using photoplethysmography (PPG) are presented. A total of 195 features, including the proposed ones and those already known in the literature, have been calculated on a set composed of 50,000 pulses from 1080 different patients. Three feature selection methods, namely Correlation-based Feature Selection (CFS), RReliefF and Minimum Redundancy Maximum Relevance (MRMR), have then been applied to identify the most significant features for BP estimation. Some of these features have been extracted through a novel PPG signal enhancement method based on the use of the Maximal Overlap Discrete Wavelet Transform (MODWT). As a matter of fact, the enhanced signal leads to a reliable identification of the characteristic points of the PPG signal (e.g., systolic, diastolic and dicrotic notch points) by simple means, obtaining results comparable with those from purposely defined algorithms. For systolic points, mean and std of errors computed as the difference between the locations obtained using a purposely defined already known algorithm and those using the MODWT enhancement are, respectively, 0.0097 s and 0.0202 s; for diastolic points they are, respectively, 0.0441 s and 0.0486 s; for dicrotic notch points they are 0.0458 s and 0.0896 s. Hence, this study leads to the selection of several new features from the MODWT enhanced signal on every single pulse extracted from PPG signals, in addition to features already known in the literature. These features can be employed to train machine learning (ML) models useful for estimating systolic blood pressure (SBP) and diastolic blood pressure (DBP) in a non-invasive way, which is suitable for telemedicine health-care monitoring. [ABSTRACT FROM AUTHOR]

Continuous blood pressure (BP) measurement is vital in monitoring patients' health with a high risk of cardiovascular disease. The complex and dynamic nature of the cardiovascular system can influence BP through many factors, such as cardiac output, blood vessel wall elasticity, circulated blood volume, peripheral resistance, respiration, and emotional behavior. Yet, traditional BP measurement methods in continuously estimating the BP are cumbersome and inefficient. This paper presents a novel hybrid model by integrating a convolutional neural network (CNN) as a trainable feature extractor and support vector regression (SVR) as a regression model. This model can automatically extract features from the electrocardiogram (ECG) and photoplethysmography (PPG) signals and continuously estimates the systolic blood pressure (SBP) and diastolic blood pressure (DBP). The CNN takes the correct topology of input data and establishes the relationship between ECG and PPG features and BP. A total of 120 patients with available ECG, PPG, SBP, and DBP data are selected from the MIMIC III database to evaluate the performance of the proposed model. This novel model achieves an overall Mean Absolute Error (MAE) of 1.23 ± 2.45 mmHg (MAE ± STD) for SBP and 3.08 ± 5.67 for DBP, all of which comply with the accuracy requirements of the AAMI SP10 standard. [ABSTRACT FROM AUTHOR]

Recently, supplying healthcare services with wearable devices has been investigated. To realize this for true wireless stereo (TWS), which has limited resources (e.g. space, power consumption, and area), implementing multiple functions with one sensor simultaneously is required. The Photoplethysmography (PPG) sensor is a representative healthcare sensor that measures repeated data according to the heart rate. However, since the PPG data are biological, they are influenced by motion artifact and subject characteristics. Hence, noise reduction is needed for PPG data. In this paper, we propose the distance estimation algorithm for PPG signals of TWS. For distance estimation, we designed a waveform adjustment (WA) filter that minimizes noise while maintaining the relationship between before and after data, a lightweight deep learning model called MobileNet, and a PPG monitoring testbed. The number of criteria for distance estimation was set to three. In order to verify the proposed algorithm, we compared several metrics with other filters and AI models. The highest accuracy, precision, recall, and f1 score of the proposed algorithm were 92.5%, 92.6%, 92.8%, and 0.927, respectively, when the signal length was 15. Experimental results of other algorithms showed higher metrics than the proposed algorithm in some cases, but the proposed model showed the fastest inference time. [ABSTRACT FROM AUTHOR]

The quality of heart rate (HR) measurements extracted from human photoplethysmography (PPG) signals are known to deteriorate under appreciable human motion. Auxiliary signals, such as accelerometer readings, are usually employed to detect and suppress motion artifacts. A 2019 study by Yifan Zhang and his coinvestigatorsused the noise components extracted from an infrared PPG signal to denoise a green PPG signal from which HR was extracted. Until now, this approach was only tested on "micro-motion" such as finger tapping. In this study, we extend this technique to allow accurate calculation of HR under high-intensity full-body repetitive "macro-motion". Our Dual Wavelength (DWL) framework was tested on PPG data collected from 14 human participants while running on a treadmill. The DWL method showed the following attributes: (1) it performed well under high-intensity full-body repetitive "macro-motion", exhibiting high accuracy in the presence of motion artifacts (as compared to the leading accelerometer-dependent HR calculation techniques TROIKA and JOSS); (2) it used only PPG signals; auxiliary signals such as accelerometer signals were not needed; and (3) it was computationally efficient, hence implementable in wearable devices. DWL yielded a Mean Absolute Error (M A E) of 1.22|0.57 BPM, Mean Absolute Error Percentage (M A E P) of 0.95|0.38%, and performance index (P I) (which is the frequency, in percent, of obtaining an HR estimate that is within ±5 BPM of the HR ground truth) of 95.88|4.9%. Moreover, DWL yielded a short computation period of 3.0|0.3 s to process a 360-second-long run. [ABSTRACT FROM AUTHOR]

Real-time detection of atrial fibrillation (AFib) is made possible by the quality assessment via a 1-D convolutional neural network (1D-CNN) in a processor of a photoplethysmography (PPG) sensor patch and a 2-D convolutional neural network (2D-CNN) for AFib detection in cloud. The 1D-CNN is able to filter out the unqualified PPG that is contaminated by motion artifacts and/or ambient light interference. The remaining qualified PPG is then inputted to another built 2D-CNN for detecting AFib. This 1D-CNN consists of four layers of convolutions and max pooling, one long short-term memory (LSTM), and an output dense layer. The 2D-CNN is pretrained based on the electrocardiography (ECG) data from multiparameter intelligent monitoring in intensive care (MIMIC) III database, for which the RR-intervals (RRIs) of ECG data are first extracted in Poincaré images and then regarded as input features to the model for training. This 2D-CNN has also four layers of convolutions and max pooling and four output dense layers. The pretrained model is next fine-tuned based on peak-to-peak intervals (PPIs) of PPG measured from wearable devices as input features for detecting AFib effectively. The quality-assessment 1D-CNN model is implemented in the wearable device to transmit only qualified data to the 2D-CNN model in cloud for AFib detection, achieving power efficiency. Both models are trained by the Adam optimizer. To validate the models, the PPIs of PPG were collected to evaluate the performance of the established models in real time. Experimental results show that the fine-tuned 2D-CNN for AFib detection achieves the accuracy, sensitivity, and specificity were 98.08%, 96.82%, and 98.86%, respectively, the most favorable as opposed to other reported works based on PPG. The models are able to not only assist clinicians in AFib detection but also provide a mechanism to detect AFib via wearable devices in real time. [ABSTRACT FROM AUTHOR]

The novel pulse oximetry measurement site of the neck is a promising location for multi-modal physiological monitoring. Specifically, in the context of respiratory monitoring, in which it is important to have direct information about airflow. The neck makes this possible, in contrast to common photoplethysmography (PPG) sensing sites. However, this PPG signal is susceptible to artifacts that critically impair the signal quality. To fully exploit neck PPG for reliable physiological parameters extraction and apneas monitoring, this paper aims to develop two classification algorithms for artifacts and apnea detection. Features from the time, correlogram, and frequency domains were extracted. Two SVM classifiers with RBF kernels were trained for different window (W) lengths and thresholds (Thd) of corruption. For artifacts classification, the maximum performance was attained for the parameters combination of [W = 6s-Thd= 20%], with an average accuracy= 85.84%(ACC), sensitivity= 85.43%(SE) and specificity= 86.26%(SP). For apnea detection, the model [W = 10s-Thd= 50%] maximized all the performance metrics significantly (ACC= 88.25%, SE= 89.03%, SP= 87.42%). The findings of this proof of concept are significant for denoising novel neck PPG signals, and demonstrate, for the first time, that it is possible to promptly detect apnea events from neck PPG signals in an instantaneous manner. This could make a big impact in crucial real-time applications, like devices to prevent sudden-unexpected-death-in-epilepsy (SUDEP). [ABSTRACT FROM AUTHOR]

The achievement of a healthy indoor environ- ment—an indispensable requirement for human well-being—relies on the establishment of good air quality conditions and thermal comfort levels. This study addresses the development of a healthcare-focused Internet-of-Things (IoT) system for the cardiac assessment using photoplethysmography (PPG) and ballistocardiography (BCG) that can be applied in any indoor environment. Results from its experimental application include the analysis of heart rate variability (HRV) and respiratory status from a group of young adults under different thermal conditions and relative humidity (RH) levels. The final goal was to investigate the effects of short-term exposure to different air temperature and humidity conditions on human thermal comfort based on the analysis of the autonomic nervous system (ANS) activity. The experiment took place in a regular and nonisothermal office environment. Simulations based on computational fluid dynamics (CFD) were conducted to estimate heat distribution and select the optimal locations for the environmental sensor nodes’ placement. Results indicated that activation of the parasympathetic system was more notable in short-term exposures to environments with different air temperatures (24 °C–30 °C) than with different levels of RH (50%–70%). Changes in ambient air temperature led to the activation of thermal regulatory reflexes and thermal discomfort, perceived by an increase in the ratio of low frequency to high frequency (LF/HF) at higher air temperature exposures. In addition, HRV parameters were used to train machine learning models on predicting if the user is in a thermally comfortable environment or a discomfortable environment (hot or humid), having achieved an 87% average accuracy. [ABSTRACT FROM AUTHOR]

: With advances in deepfake generating technology, it is getting increasingly difficult to detect deepfakes. Deepfakes can be used for many malpractices such as blackmail, politics, social media, etc. These can lead to widespread misinformation and can be harmful to an individual or an institution’s reputation. It has become important to be able to identify deepfakes effectively, while there exist many machine learning techniques to identify them, these methods are not able to cope up with the rapidly improving GAN technology which is used to generate deepfakes. Our project aims to identify deepfakes successfully using machine learning along with Heart Rate Analysis. The heart rate identified by our model is unique to each individual and cannot be spoofed or imitated by a GAN and is thus susceptible to improving GAN technology. To solve the deepfake detection problem we employ various machine learning models along with heart rate analysis to detect deepfakes. [ABSTRACT FROM AUTHOR]

Obstructive sleep apnea (OSA) is a common respiratory disorder associated with autonomic nervous system (ANS) dysfunction, resulting in abnormal heart rate variability (HRV). Capable of acquiring heart rate (HR) information with more convenience, wearable photoplethysmography (PPG) bracelets are proven to be a potential surrogate for electrocardiogram (ECG)-based devices. Meanwhile, bracelet-type PPG has been heavily marketed and widely accepted. This study aims to investigate the algorithm that can identify OSA with wearable devices. The information-based similarity of ordinal pattern sequences (OP_IBS), which is a modified version of the information-based similarity (IBS), has been proposed as a novel index to detect OSA based on wearable PPG signals. A total of 92 PPG recordings (29 normal subjects, 39 mild–moderate OSA subjects and 24 severe OSA subjects) were included in this study. OP_IBS along with classical indices were calculated. For severe OSA detection, the accuracy of OP_IBS was 85.9%, much higher than that of the low-frequency power to high-frequency power ratio (70.7%). The combination of OP_IBS, IBS, CV and LF/HF can achieve 91.3% accuracy, 91.0% sensitivity and 91.5% specificity. The performance of OP_IBS is significantly improved compared with our previous study based on the same database with the IBS method. In the Physionet database, OP_IBS also performed exceptionally well with an accuracy of 91.7%. This research shows that the OP_IBS method can access the HR dynamics of OSA subjects and help diagnose OSA in clinical environments. [ABSTRACT FROM AUTHOR]

Photoplethysmography (PPG) has recently become a popular method for heart rate estimation due to its simple acquisition technique. However, the main challenge in determining the heart rate from the PPG signals is its high vulnerability to motion artifacts (MA). In this paper, a new scheme is proposed for heart rate estimation through frame selective multistage adaptive noise cancellation (MANC). The frame selective approach determines the specific frames of PPG signal which are significantly interfered with MA, and the MA removal operation is only employed over those specific frames. The MANC scheme is implemented through the Least Mean Square (LMS) algorithm in which instead of the conventional approach of using accelerometer data directly, we propose to utilize mode-based decomposed 3-channel accelerometer data as reference signals independently in a sequential manner. The use of decomposed modes offers high degrees of controllability in the ANC scheme depending on the overlap between the spectra corresponding to MA and heart rate, thereby offers effective denoising. A peak searching algorithm is employed to estimate heart rate-related peaks from the resulting noise-reduced PPG signal. The novelty of the proposed scheme lies in the use of decomposed reference inputs to the MANC algorithm (named as DERMANC scheme) which is accomplished through both empirical mode decomposition (EMD) and variational mode decomposition (VMD). Performance of the proposed EMD and VMD based schemes (E-DERMANC and V-DERMANC) has been tested on a publicly available dataset and very satisfactory results are obtained in terms of estimation accuracy and computational time (0.95 and 1.10 BPM, respectively on 12 recordings) that makes the schemes worthy to be implemented in wearable devices.

Given that current cuffless blood pressure (BP) measurement technologies feature acceptable overall accuracy, this paper proposed a sufficiently accurate cuffless BP estimation method based on photoplethysmography (PPG) and electrocardiography (ECG) signals. This study used single-channel PPG and ECG signals to estimate heart rate (HR), diastolic BP (DBP), and systolic BP (SBP). A modified long-term recurrent convolutional network comprising a multi-scale convolution network and a long short-term memory (LSTM) network was used to develop a deep learning model for accurately estimating BP and HR. The PPG and ECG signal data of 1551 patients were obtained from the Data Sets-UCI Machine Learning Repository of the University of California, Irvine. The study dataset comprised ECG, PPG, and arterial BP (ABP) signals from the PhysioNet MIMIC II dataset. The original signals were processed by removing noise and artifacts. The aforementioned dataset contains 12,000 records in a hierarchical data format, with each record containing three signals, namely 125-Hz ECG signals from channel II (ECG lead II), 125-Hz PPG signals from the fingertip, and 125-Hz invasive ABP signals. To validate the stability and performance of the developed model, ten-fold cross-validation was conducted. The mean absolute error (MAE) (standard deviation (SD)) values of the developed model for predicting SBP, DBP, and HR were 2.24 mmHg (3.59 mmHg), 1.40 mmHg (2.56 mmHg), and 0.84 bpm (2.23 bpm), respectively. In addition, the estimated SBP and DBP values satisfied the standards of the British Hypertension Society and the Association for the Advancement of Medical Instrumentation. Compared with the methods proposed in other studies, the deep learning model developed in this study required a lower number of layers to provide accurate SBP, DBP, and HR estimations. The results of this study confirmed the effectiveness of the proposed deep learning architecture.

Electrocardiography (ECG) is generally deemed the golden standard for diagnosing cardiovascular diseases and photoplethysmography (PPG) is unobtrusive, low-cost, and convenient for continuous monitoring. However, PPG contains insufficient information to diagnose diseases. In this study, we propose a novel method to accurately convert PPG to ECG. The banded kernel ensemble method converts a low-quality source (PPG) to a high-quality destination (ECG). Unlike neural network solutions, our algorithm requires no computation burden in the conversion task after a trained model is obtained. The proposed algorithm is then tested on a publicly available MIMIC III database. Our prediction shows excellent accuracy in the validation dataset. It offers the testing performance of under 0.314 and above 0.55 in rrmse (relative root mean squared error) and KGE (Kling–Gupta efficiency), respectively, under the scenarios of three prevalent heart diseases. The reconstructed ECG can be further used to perform heart disease detection and we obtained an average correctness rate of 81%. Our method can help a large population of high-risk, believed-healthy persons to walk into doctors’ offices before the situation becomes irreversible.

There has been a growing interest in developing cuff-less blood pressure (BP) estimation methods to enable continuous BP monitoring from electrocardiogram (ECG) and/or photoplethysmogram (PPG) signals. The majority of these methods have been evaluated using publicly-available datasets, however, there exist significant discrepancies across studies with respect to the size, the number of subjects, and the applied pre-processing steps for the data that is eventually used for training and testing the models. Such differences make conducting performance comparison across models largely unfair, and mask the generalization capability of various BP estimation methods. To fill this important gap, this paper presents “PulseDB,” the largest cleaned dataset to date, for benchmarking BP estimation models that also fulfills the requirements of standardized testing protocols. PulseDB contains 1) 5,245,454 high-quality 10-s segments of ECG, PPG, and arterial BP (ABP) waveforms from 5,361 subjects retrieved from the MIMIC-III waveform database matched subset and the VitalDB database; 2) subjects’ identification and demographic information, that can be utilized as additional input features to improve the performance of BP estimation models, or to evaluate the generalizability of the models to data from unseen subjects; and 3) positions of the characteristic points of the ECG/PPG signals, making PulseDB directly usable for training deep learning models with minimal data pre-processing. Additionally, using this dataset, we conduct the first study to provide insights about the performance gap between calibration-based and calibration-free testing approaches for evaluating generalizability of the BP estimation models. We expect PulseDB, as a user-friendly, large, comprehensive and multi-functional dataset, to be used as a reliable source for the evaluation of cuff-less BP estimation methods.

Negative and positive emotions are the risk and protective factors for the cause and prognosis of hypertension. This study aimed to use five photoplethysmography (PPG) waveform indices and affective computing (AC) to discriminate the emotional states in patients with hypertension. Forty-three patients with essential hypertension were measured for blood pressure and PPG signals under baseline and four emotional conditions (neutral, anger, happiness, and sadness), and the PPG signals were transformed into the mean standard deviation of five PPG waveform indices. A support vector machine was used as a classifier. The performance of the classifier was verified by using resubstitution and six-fold cross-validation (CV) methods. Feature selectors, including full search and genetic algorithm (GA), were used to select effective feature combinations. Traditional statistical analyses only differentiated between the emotional states and baseline, whereas AC achieved 100% accuracy in distinguishing between the emotional states and baseline by using the resubstitution method. AC showed high accuracy rates when used with 10 waveform features in distinguishing the records into two, three, and four classes by applying a six-fold CV. The GA feature selector further boosted the accuracy to 78.97%, 74.22%, and 67.35% in two-, three-, and four-class differentiation, respectively. The proposed AC achieved high accuracy in categorizing PPG records into distinct emotional states with features extracted from only five waveform indices. The results demonstrated the effectiveness of the five indices and the proposed AC in patients with hypertension. [ABSTRACT FROM AUTHOR]

People’s daily life is easily affected by mental stress, which can lead to mental illness in the long term. The current mental stress detection process is cumbersome, and the development of rapid assessment methods will make a great contribution to medical care. In view of this, this study used a pulse oximeter to obtain noninvasive photoplethysmography (PPG) signals, the measurement information was analyzed using heart rate variability (HRV), and the Poincaré plot of the heartbeat cycle was the output. Poincaré maps are used as input to deep learning (DL) to perform conditional prediction of mental stress. Finally, the results of conventional HRV and DL are compared. From the experimental results, the classification of the feature of the PPG signal (Poincaré plot) by the model is a meaningful and good result. [ABSTRACT FROM AUTHOR]

Wrist-based hand gesture recognition has the potential to unlock naturalistic human-computer interaction for a vast array of virtual and augmented reality applications. Photoplethysmography (PPG), force myography (FMG), and accelerometry (ACC) have generally been proposed as isolated single sensing modalities for gesture recognition, but any of these alone is inherently limited in the amount of biological information it can collect during finger and hand movements. We thus propose a novel, wrist-based, PPG-FMG-ACC combined sensing approach based on a multi-head attention mechanism fusion convolutional neural network (CNN-AF) for gesture recognition. Nine subjects performed twelve hand gestures involving various wrist and finger postures. Experimental results showed that multi-modal fusion improved classification performance significantly ($p$ $< $ 0.01) compared to any single sensing modality, and the F1-score of the combined PPG-FMG-ACC approach was 40.1% higher than PPG alone, 27.4% higher than ACC alone, and 11.9% higher than FMG alone. To the best of our knowledge, this paper is the first to combine wrist-based PPG, FMG, and ACC signals for hand gesture recognition. These results could serve to inform wrist-based gesture recognition design (e.g., via a smartwatch) and thus expand the capabilities of intuitive and ubiquitous human-machine interaction. [ABSTRACT FROM AUTHOR]

Study Objectives: From 2019-2023, the United States Food and Drug Administration has cleared 9 novel obstructive sleep apnea-detecting wearables for home sleep apnea testing, with many now commercially available for sleep clinicians to integrate into their clinical practices. To help clinicians comprehend these devices and their functionalities, we meticulously reviewed their operating mechanisms, sensors, algorithms, data output, and related performance evaluation literature., Methods: We collected information from PubMed, United States Food and Drug Administration clearance documents, ClinicalTrials.gov, and web sources, with direct industry input whenever feasible., Results: In this "device-centered" review, we broadly categorized these wearables into 2 main groups: those that primarily harness photoplethysmography data and those that do not. The former include the peripheral arterial tonometry-based devices. The latter was further broken down into 2 key subgroups: acoustic-based and respiratory effort-based devices. We provided a performance evaluation literature review and objectively compared device-derived metrics and specifications pertinent to sleep clinicians. Detailed demographics of study populations, exclusion criteria, and pivotal statistical analyses of the key validation studies are summarized., Conclusions: In the foreseeable future, these novel obstructive sleep apnea-detecting wearables may emerge as primary diagnostic tools for patients at risk for moderate-to-severe obstructive sleep apnea without significant comorbidities. While more devices are anticipated to join this category, there remains a critical need for cross-device comparison studies as well as independent performance evaluation and outcome research in diverse populations. Now is the moment for sleep clinicians to immerse themselves in understanding these emerging tools to ensure our patient-centered care is improved through the appropriate implementation and utilization of these novel sleep technologies., Citation: Chiang AA, Jerkins E, Holfinger S, et al. OSA diagnosis goes wearable: are the latest devices ready to shine? J Clin Sleep Med . 2024;20(11):1823-1838., (© 2024 American Academy of Sleep Medicine.)

Background and Objective: Photoplethysmography (PPG) signals provide a non-invasive method for monitoring cardiovascular health, including blood pressure levels, which are critical for the early detection and management of hypertension. This study leverages wavelet transformation and special purpose deep learning model, enhanced by signal processing and normalization, to classify blood pressure stages from PPG signals. The primary objective is to advance non-invasive hypertension monitoring, improving the accuracy and efficiency of these assessments., Methods: The study employed continuous wavelet transform (CWT) to prepare PPG signals for analysis using a special purpose PPG-NET designed by applying advanced deep-learning models. PPG-NET was verified by applying several pre-trained models, including Inception, MobileNetV2, InceptionResNetV2, and others to the PPG data. Rigorously five-fold cross-validated models were conducted to obtain the models' performance to ensure robustness and repeatability of results., Results: The PPG-NET model demonstrated superior performance, achieving a perfect accuracy of 100 % in classifying the four stages of hypertension-normal, prehypertension, stage 1, and stage 2. The evaluation metrics reported include precision, sensitivity, and specificity, with the PPG-NET model achieving 100 % across all metrics. Other models showed varying levels of accuracy, with InceptionV3 also reaching 91.5 %, while some, like VGG-19, underperformed significantly., Conclusions: Integrating CWT and PPG-NET offers a promising avenue for enhancing non-invasive blood pressure monitoring. The PPG-NET model, in particular, showed potential for clinical application due to its high accuracy and reliability. This study showed the effectiveness of combining advanced computational techniques with traditional PPG analysis, potentially leading to more personalized and accessible hypertension management strategies., Competing Interests: 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., (© 2024 The Authors.)

The purpose of this study was to examine the accuracy of HR measurements by the PTN-104 sensor in comparison to the fingertip pulse oximeter, which is a photoplethysmographic sensor (PPG). Twelve healthy participants underwent the same protocol during a single visit. Measurements were taken after each participant completed an initial rest period of 5 minutes and after 1-minute of exercising comprising of 30 squats. Each subject had the PTN-104 sensor attached to the index finger and a fingertip pulse oximeter to the opposite one. When examining the data in aggregate, there was a strong correlation between the PTN-104 sensor and PPG for HR (r = 0.988) with a mean bias of −2.55 bpm (95 % LoA +5.0, −10.1). The PTN-104 sensor satisfied validity criteria for HR monitors, however, showed a lower accuracy for measurements at rest, which is surprising. Due to the noticed limitations, this study should be repeated with a larger group of subjects and the PTN-104 sensor should be compared to the gold standard method for measuring HR, which is ECG.

AimsSarcopenia is significantly associated with the number of cardiovascular and metabolic diseases, however, the underlying pathophysiological processes are largely unknown. This study performed harmonic index of finger photoplethysmography (PPG) waveforms with the aims of distinguishing different arterial pulse waveform signals between sarcopenia, presarcopenia, dynapenia, and healthy subjects.MethodsSixty-eight subjects were enrolled and obtained 1-min PPG signals, then were assigned to four age-matched groups: control, dynapenia, presarcopenia, and sarcopenia which definition according to Asian Working Group for Sarcopenia (AWGS): 2019 Consensus Update on Sarcopenia Diagnosis and Treatment. Harmonics 1–10 of the PPG waveform were obtained and calculated each of the amplitude proportions (Cn), standard deviations (SDn), coefficients of variations (CVn), and vascular elasticity index (VEI) for to evaluating the blood-pressure harmonic variability.ResultsThe prevalence of sarcopenia in women gender (8 out of 9, 88.9%, p = 0.046) and osteoporosis in dynapenia (7 out of 16, 43.8%, p = 0.005) were significant higher. Among the four groups, compared with control, dynapenia, and presarcopenia, sarcopenia had largest SDn-values for harmonics 1, 2, 3, and 5 (ratio 1, 2, 3, 5 = 0.354, 0.209, 0.137, 0.074); whereas sarcopenia had largest coefficients of variations (CVn) values for harmonics 1, 2, 3 and 10 (ratio 1, 2, 3, 10 = 0.263, 0.310, 0.402, 0.791). Besides, the Δ odds ratio of ratio 3, 4,and 6 tertile values were significantly increased in sarcopenia and possible sarcopenia group compared with control group. Subjects with sarcopenia had significantly higher VEI in mean, SD, and CV of the PPG waveform (mean = 2.332, SD = 1.479, CV = 0.634, p = 0.007) among the groups and the results of binary logistic regression analysis in the tertiles met statistical significance between the sarcopenia and non-sarcopenia groups whether adjusted or unadjusted (adjusted odds ratio 6.956, p = 0.030, unadjusted odds ratio 3.937, p = 0.039).ConclusionsThe elasticity of vessels among sarcopenia groups in lower-frequency components of harmonic ratio in which we defined as VEI showed a significantly highest VEI mean, SD, and CV in sarcopenia indicates the poorer elasticity of the arteries. The present findings showed finger PPG waveform measurements may be useful for early detection of vascular diseases with patients with sarcopenia in a non-invasive and easy-to-perform technique which may expand the clinical applicability in the future.

Fatigue is a prevalent subjective sensation, affecting both office workers and a significant global population. In Taiwan alone, over 2.6 million individuals—around 30% of office workers—experience chronic fatigue. However, fatigue transcends workplaces, impacting people worldwide and potentially leading to health issues and accidents. Gaining insight into one’s fatigue status over time empowers effective management and risk reduction associated with other ailments. Utilizing photoplethysmography (PPG) signals brings advantages due to their easy acquisition and physiological insights. This study crafts a specialized preprocessing and peak detection methodology for PPG signals. A novel fatigue index stems from PPG signals, focusing on the dicrotic peak’s position. This index replaces subjective data from the brief fatigue index (BFI)-Taiwan questionnaire and heart rate variability (HRV) indices derived from PPG signals for assessing fatigue levels. Correlation analysis, involving sixteen healthy adults, highlights a robust correlation (R > 0.53) between the new fatigue index and specific BFI questions, gauging subjective fatigue over the last 24 h. Drawing from these insights, the study computes an average of the identified questions to formulate the evaluated fatigue score, utilizing the newfound fatigue index. The implementation of linear regression establishes a robust fatigue assessment system. The results reveal an impressive 91% correlation coefficient between projected fatigue levels and subjective fatigue experiences. This underscores the remarkable accuracy of the proposed fatigue prediction in evaluating subjective fatigue. This study further operationalized the proposed PPG processing, peak detection method, and fatigue index using C# in a computer environment alongside a PPG device, thereby offering real-time fatigue indices to users. Timely reminders are employed to prompt users to take notice when their index exceeds a predefined threshold, fostering greater attention to their physical well-being.

Researchers commonly use continuous noninvasive blood-pressure measurement (cNIBP) based on photoplethysmography (PPG) signals to monitor blood pressure conveniently. However, the performance of the system still needs to be improved. Accuracy and precision in blood-pressure measurements are critical factors in diagnosing and managing patients’ health conditions. Therefore, we propose a convolutional long short-term memory neural network (CNN–LSTM) with grid search ability, which provides a robust blood-pressure estimation system by extracting meaningful information from PPG signals and reducing the complexity of hyperparameter optimization in the proposed model. The multiparameter intelligent monitoring for intensive care III (MIMIC III) dataset obtained PPG and arterial-blood-pressure (ABP) signals. We obtained 75,226 signal segments, with 60,180 signals allocated for training data, 12,030 signals allocated for the validation set, and 15,045 signals allocated for the test data. During training, we applied five-fold cross-validation with a grid-search method to select the best model and determine the optimal hyperparameter settings. The optimized configuration of the CNN–LSTM layers consisted of five convolutional layers, one long short-term memory (LSTM) layer, and two fully connected layers for blood-pressure estimation. This study successfully achieved good accuracy in assessing both systolic blood pressure (SBP) and diastolic blood pressure (DBP) by calculating the standard deviation (SD) and the mean absolute error (MAE), resulting in values of 7.89 ± 3.79 and 5.34 ± 2.89 mmHg, respectively. The optimal configuration of the CNN–LSTM provided satisfactory performance according to the standards set by the British Hypertension Society (BHS), the Association for the Advancement of Medical Instrumentation (AAMI), and the Institute of Electrical and Electronics Engineers (IEEE) for blood-pressure monitoring devices.

Blood pressure (BP) is known as an indicator of human health status, and regular measurement is helpful for early detection of cardiovascular diseases. Traditional techniques for measuring BP are either invasive or cuff-based and thus are not suitable for continuous measurement. Aiming at the deficiencies in existing studies, a novel cuffless BP estimation framework of Receptive Field Parallel Attention Shrinkage Network (RFPASN) and BP range constraint is proposed. Firstly, RFPASN uses the multi-scale large receptive field convolution module to capture the long-term dynamics in the photoplethysmography (PPG) signal without using long short-term memory (LSTM). On this basis, the features acquired by the parallel mixed domain attention module are used as thresholds, and the soft threshold function is used to screen the input features to enhance the discriminability and robustness of features, which can significantly improve the prediction accuracy of diastolic blood pressure (DBP) and systolic blood pressure (SBP). Finally, in order to prevent large fluctuations in the prediction results of RFPASN, RFPASN based on BP range constraint is proposed to make the prediction results of RFPASN more accurate and reasonable. The performance of the proposed method is demonstrated on a publically available MIMIC-II database. The database contains normal, hypertensive and hypotensive people. We have achieved MAE of 1.63/1.59 (DBP) and 2.26/2.15 (SBP) mmHg for BP on total population of 1562 subjects. A comparative study shows that the proposed algorithm is more promising than the state-of-the-art. • In order to effectively extract the information that can represent BP changes in the PPG signal, the BP prediction model based on Receptive Field Parallel Attention Shrinkage Network (RFPASN) is built in this paper. It is an "end-to-end" BP estimation method. • We propose Receptive Field Parallel Attention Shrinkage Block (RFPASB), which can capture the long-term dynamics in the PPG signal. Then, the features with strong discriminability are extracted by parallel mixed domain attention module, which is used as the threshold of soft threshold function, so as to retain important features and eliminate redundant information. • In order to prevent unreasonable prediction results from RFPASN and limit BP prediction results to a reasonable range, we construct abnormal BP discriminative model to limit the range of RFPASN prediction results to further improve the accuracy and reasonableness of BP prediction results. [ABSTRACT FROM AUTHOR]

As a kind of physical signals that could be easily acquired in daily life, photoplethysmography (PPG) signal becomes a promising solution to biometric identification for daily access management system (AMS). State-of-the-art PPG-based identification systems are susceptible to the form of motions and physical conditions of the subjects. In this work, to exploit the advantage of deep learning, we developed an improved deep convolutional neural network (CNN) architecture by using the Gram matrix (GM) technique to convert time-serial PPG signals to two-dimensional images with a temporal dependency to improve accuracy under different forms of motions. To ensure a fair evaluation, we have adopted cross-validation method and "training and testing" dataset splitting method on the TROIKA dataset collected in ambulatory conditions. As a result, the proposed GM-CNN method achieved accuracy improvement from 69.5% to 92.4%, which is the best result in terms of multi-class classification compared with state-of-the-art models. Based on average five-fold cross-validation, we achieved an accuracy of 99.2%, improved the accuracy by 3.3% compared with the best existing method for the binary-class. [ABSTRACT FROM AUTHOR]

Recent trends in research and the market show high demand for frequent monitoring of glucose to estimate the blood sugar level non-invasively which can replace the conventional finger-prick glucometer for daily use. This paper presents a high precision near-infrared Photoplethysmography (PPG) based noninvasive glucose monitoring System on Chip (SoC). The proposed system implements a fully differential Analog Frontend (AFE) with nonlinear medium Gaussian support-vector-regression (NMG-SVR) for glucose estimation. The AFE design incorporates chopping which enables the reduction of the integrated input-referred current noise to 9.4pArms thus achieving a dynamic range of 115dB. The glucose prediction processor (GPP) removes noise from the PPG signal, extracts ten unique features, and estimates the blood glucose level using a trained customized NMG-SVR model that minimizes the hardware cost by 25%. The extracted features are carefully designed and implemented to ensure inter-feature dependency, which helps to reduce the overall area by more than 40%. Moreover, GPP is implemented using power and clock gating techniques to minimize both static and dynamic power consumption. The proposed SoC is realized with $0.18~\mu \text{m}$ CMOS technology and occupies an area of 6 mm2. It dissipates a power of $186~\mu \text{W}$ and achieves a mean absolute relative difference (mARD) of 6.9% verified on 200 subjects. [ABSTRACT FROM AUTHOR]

Recent advances in the field of cognitive sciences have provided evidence to classify activities with different cognitive engagement levels. High cognitive load and sustained attention may predispose an individual to psychological and physical stress and may result in a lack of performance. Cognitive load monitoring of professionals in high precision fields has gathered significant attention in the current research scenario. The proposed work in continuity has tried to demonstrate the use of two non‐invasive physiological sensing modalities viz. galvanic skin response (GSR) and photoplethysmography (PPG) to elucidate effectiveness in picking up differences of two induced cognitive state categories based on machine learning and statistical methods. This paper demonstrates how GSR and PPG signals data can be effectively utilized to examine the shift in cognitive load. The present work proposes the use of general linear chirplet transform (GLCT) to evaluate the time‐frequency characteristic of GSR and PPG signals and utilize the statistical features in classification. Random Forest, Decision Tree, and k‐Nearest Neighbours demonstrated an accuracy of 92.13%, 88.0%, and 86.13% respectively on a dataset of 20 subjects against an optimized feature set, thus demonstrating the effectiveness of the proposed methodology for differentiating pre‐defined categories of cognitive load. The study shows the potential of GSR and PPG signals attributes and time‐frequency representation using GLCT to monitor cognitive load in real‐life conditions. The proposed work also indicates the possibility to extend the same to attention‐demanding fields such as aviation, medicine, and manufacturing for effective remediation of fatigue periods with increased cognitive demand, which, if sustained, may lead to cognitive decline in the long run. [ABSTRACT FROM AUTHOR]

The past few decades have seen ongoing development of continuous glucose monitoring (CGM) systems that are noninvasive and accurately measure blood glucose levels. The conventional finger-prick method, though accurate, is not feasible for use multiple times a day, as it is painful and test strips are expensive. Although minimally invasive and noninvasive CGM systems have been introduced into the market, they are expensive and require finger-prick calibrations. As the diabetes trend is high in low- and middle-income countries, a cost-effective and easy-to-use noninvasive glucose monitoring device is the need of the hour. This review paper briefly discusses the noninvasive glucose measuring technologies and their related research work. The technologies discussed are optical, transdermal, and enzymatic. The paper focuses on Near Infrared (NIR) technology and NIR Photoplethysmography (PPG) for blood glucose prediction. Feature extraction from PPG signals and glucose prediction with machine learning methods are discussed. The review concludes with key points and insights for future development of PPG NIR-based blood glucose monitoring systems. [ABSTRACT FROM AUTHOR]

Heart Rate Variability (HRV) has become an important risk assessment tool when diagnosing illnesses related to heart health. HRV is typically measured with an electrocardiogram; however, there are multiple studies that use Photoplethysmography (PPG) instead. Measuring HRV with video is beneficial as a non-invasive, hands-free alternative and represents a more accessible approach. We developed a methodology to extract HRV from video based on face detection algorithms and color augmentation. We applied this methodology to 45 samples. Signals obtained from PPG and video recorded an average mean error of less than 1 bpm when measuring the heart rate of all subjects. Furthermore, utilizing PPG and video, we computed 61 variables related to HRV. We compared each of them with three correlation metrics (i.e., Kendall, Pearson, and Spearman), adjusting them for multiple comparisons with the Benjamini–Hochberg method to control the false discovery rate and to retrieve the q-value when considering statistical significance lower than 0.5. Using these methods, we found significant correlations for 38 variables (e.g., Heart Rate, 0.991; Mean NN Interval, 0.990; and NN Interval Count, 0.955) using time-domain, frequency-domain, and non-linear methods. [ABSTRACT FROM AUTHOR]

This study proposed a noninvasive blood glucose estimation system based on dual-wavelength photoplethysmography (PPG) and bioelectrical impedance measuring technology that can avoid the discomfort created by conventional invasive blood glucose measurement methods while accurately estimating blood glucose. The measured PPG signals are converted into mean, variance, skewness, kurtosis, standard deviation, and information entropy. The data obtained by bioelectrical impedance measuring consist of the real part, imaginary part, phase, and amplitude size of 11 types of frequencies, which are converted into features through principal component analyses. After combining the input of seven physiological features, the blood glucose value is finally obtained as the input of the back-propagation neural network (BPNN). To confirm the robustness of the system operation, this study collected data from 40 volunteers and established a database. From the experimental results, the system has a mean squared error of 40.736, a root mean squared error of 6.3824, a mean absolute error of 5.0896, a mean absolute relative difference of 4.4321%, and a coefficient of determination (R Squared, R2) of 0.997, all of which fall within the clinically accurate region A in the Clarke error grid analyses. [ABSTRACT FROM AUTHOR]

Objective: Many calibration models for cuff-less blood pressure (BP) measurement must be periodically updated with cuff BP values to account for vascular aging. However, the time period required for these “cuff re-calibrations” is largely unknown. The impact of one year of aging on several calibration models was assessed. Methods: Ten humans (6 males, 57±18 years, 3 hypertensives) were studied during multiple recording sessions that occurred one year apart. In each session, electrocardiography (ECG), ear photoplethysmography (PPG), finger PPG, and toe PPG waveforms and manual cuff BP were recorded before and after slow breathing, mental arithmetic, cold pressor, and nitroglycerin. Linear models based on each PPG waveform, which were previously shown to offer value in predicting the intervention-induced BP changes in a larger subject cohort, were employed. The model coefficients were determined for each subject via one session, and the fully-defined, subject-specific calibration models were then evaluated in the corresponding subjects via the session one year later. Results: Only a linear model relating toe pulse arrival time (PAT) – time delay between ECG R-wave and toe PPG foot – to systolic BP (SBP) remained useful. After the year, this model changed little on average (root-mean-squared-error (RMSE) = 1.5 mmHg) and predicted the cuff BP values better than the average of the initial cuff BP values of the subject (RMSE = 9.6±0.8 mmHg vs. 12.7±1.0 mmHg; p < 0.05). Conclusion: These results suggest annual cuff recalibrations for the toe PAT-SBP model. Significance: Toe PAT may offer a practical recalibration period that fosters user adherence. [ABSTRACT FROM AUTHOR]

• Performance analysis on the estimation of Blood Pressure. • Selection of suitable features from the photoplethysmography (PPG) signal. • Comparison of the performance of different ML algorithms. • Analysis of the impact on accuracy and training times on variation of machine learning parameters. • Identification of an empirical relationship on achievable RMSE as a function of training set size. This paper deals with an in-depth performance analysis on the estimation of Systolic Blood Pressure (SBP) and Diastolic Blood Pressure (DBP) by using features from the photoplethysmography (PPG) signal enhanced using the Maximal Overlap Discrete Wavelet Transform (MODWT), to train many machine learning (ML) regression models, including eXtreme Gradient Boost (XGBoost). The impact of different features selections methods, ML methods and training set sizes has been analyzed. One result is on the achievable improvements using features extracted from MODWT enhanced PPG signals. The most significant features have been selected using three different algorithms, namely RReliefF, Minimum Redundancy Maximum Relevance (MRMR) and Correlation-based Feature Selection (CFS). This comparison has been critical to underline that the exploitation of the new features allows to improve SBP and DBP estimation. Moreover, the authors have trained several ML algorithms to provide a comparison of their accuracy and training time, showing the Pareto frontier. RReliefF and MRMR selections algorithms, and several ML algorithms such as XGBoost, Gaussian Process Regression (GPR) and Ensemble stood out for their performance, with a different compromise between prediction error and training time. In addition, a further result has been obtained by varying the dimension of the dataset to understand the impact on Root Mean Square Error (RMSE) for models that have shown better performance, giving an empirical relationship on achievable RMSE as a function of training set size. From that relationship it has been extrapolated an upper boundary of the set size over which no further RMSE improvements are expected. [ABSTRACT FROM AUTHOR]

Cuff-less blood pressure measurement plays a pivotal part in hypertension diagnosis and prevention. By employing deep learning algorithms, blood pressure estimation applying photoplethysmography (PPG) signals has achieved remarkable improvements and attracted growing attention. However, most existing methods utilize the PPG signal emphasized on localized feature learning, often neglecting the global features in the temporal data. To tackle the issue, we propose a novel architecture. It leverages a parallel processing approach by using Convolutional Neural Network for extracting local features and simultaneously employing Transformer to capture global features. The feature fusion block integrates the features using spatial and channel attention. The blood pressure values are regressed by two fully connected layers. To verify the performance of our method, we conduct evaluations on the Medical Information Mart for Intensive Care (MIMIC) database. On the MIMIC database, our algorithm demonstrates exceptional performance. For Diastolic Pressure, Mean Arterial Pressure, and Systolic Pressure, the mean absolute errors are 2.36 mmHg, 2.03 mmHg, and 4.44 mmHg, respectively. This performance aligns with the rigorous criteria required by an Association for the Advancement of Medical Instrumentation (AAMI) and receives a Grade A rating in accordance with the British Hypertension Society (BHS) standard. • This is the first attempt to use Transformer for cuff-less BP estimation via PPG. • We present a novel fusion strategy to improve model performance. • Extensive evaluations on a public dataset demonstrate our method's effectiveness. [ABSTRACT FROM AUTHOR]

Understanding the complexity of living systems in both health and disease poses a substantial challenge in biomedical science. Traditional models and statistical analyses often prove inadequate in capturing the intricate workings of our bodies, resulting in unforeseen discoveries and limitations in drug development and public health strategies. Multiscale entropy (MSE) measures have emerged as a promising tool for assessing the complexity of time series data, considering the diverse time scales inherent in biological systems. This review paper aims to explore the utility of MSE in conjunction with electrocardiography, electromyography, electroencephalography and photoplethysmography signals for predicting and comprehending the behavior of healthy biological systems and their transformations with aging and disease. Emphasis would be placed on the significance of employing adaptive data analysis techniques, underscoring the limitations of fragmenting complex systems, a common practice in scientific investigations. The primary focus will center on the applications of MSE in analyzing human physiological signals, encompassing both one-dimensional and two-dimensional aspects, across various clinical scenarios such as heart diseases, type 2 diabetes, sleep apnea, bladder obstruction, and others. The objective of this review is also to illuminate the potential of MSE in aiding a more profound understanding of intervention effects, including carotenoid-containing supplements or clinical treatments, with substantial implications for clinical practice and research. [ABSTRACT FROM AUTHOR]

• A novel Deep learning-based approach to reconstruct the EEG signal while preserving its morphology by removing the EOG artifacts. • Provide comprehensive multimodal performance analysis of EEG, ECG and PPG in detecting emotions. • Investigate a large number of time, frequency, and time–frequency features for emotion recognition. • Novel multimodal network for emotion recognition outperforming the state-of-the-art reported performance. Emotion Recognition Systems (ERS) play a pivotal role in facilitating naturalistic Human-Machine Interactions (HMI). The research has utilized a dataset with diverse physiological signals, including Electroencephalogram (EEG), Photoplethysmography (PPG), and Electrocardiogram (ECG), to detect emotions evoked by video stimuli. The study has addressed challenges with EEG data, particularly prefrontal channels contaminated by eye blink artifacts. To tackle this, a novel 1D deep learning model, MultiResUNet3p, effectively generated clean EEG signals. Extensive features have been extracted from each modality (TD, FD, TFD), and the study identified that combining 112 features from EEG and ECG achieved the highest accuracy. The emotion classification task encompassed six emotions, and the model demonstrates outstanding performance with 96.12% accuracy in binary classification (Positive vs. Negative) and 94.25% accuracy in a complex multiclass classification of six emotions (Happy, Anger, Disgust, Fear, Neutral, and Sad). This research underscores the potential of integrating multiple physiological signals and advanced techniques to significantly improve emotion recognition accuracy, particularly in real-world scenarios involving naturalistic Human-Machine Interactions (HMI). [ABSTRACT FROM AUTHOR]

• The significance of incorporating multiple modalities, namely PPG, electrodermal activity (EDA), and skin temperature (ST), in contrast to relying on a single modality for blood glucose estimation. • Exploration of the utilization of food logs as a valuable data source to enhance the accuracy of blood glucose estimation. • Introduction of a multi-stream neural network architecture augmented with cross-modality attention mechanisms, a novel approach that substantially improves the precision of blood glucose level estimation. In the context of effective disease management for hyperglycemia patients, regular monitoring of blood glucose levels is imperative. However, traditional glucose monitoring methods suffer from invasiveness, discomfort, and potential infection. This paper introduces an innovative approach that utilizes non-invasive data sources derived from wearable devices, namely Photoplethysmography (PPG), Electrodermal Activity (EDA), and skin temperature (ST), in combination with user-provided food logs. The proposed model, MMG-Net, uses the three waveforms along with food features extracted from food logs to estimate blood glucose levels. MMG-Net delivers exceptional performance metrics, achieving a Mean Absolute Error of 13.51 mg/dL, a Mean Absolute Percentage Error of 12.57 %, and a Root Mean Square Error of 17.26 mg/dL. Notably, MMG-Net outperforms existing solutions in the estimation of blood glucose levels, solidifying its status as an innovative approach. The model's clinical precision is substantiated through Clarke Error Grid analysis, with a remarkable 99.43 % of predictions falling within clinically acceptable ranges. This paper presents a substantial advancement in non-invasive blood glucose monitoring, offering a promising avenue for enhanced disease management among hyperglycemic patients with only wearable devices. [ABSTRACT FROM AUTHOR]

Photoplethysmography (PPG) signals have been widely used in evaluating cardiovascular biomarkers, however, there is a lack of in-depth understanding of the remote usage of this technology and its viability for underdeveloped countries. This study aims to quantitatively evaluate the performance of a low-cost wireless PPG device in detecting ultra-short-term time-domain pulse rate variability (PRV) parameters in different postures and breathing patterns. A total of 30 healthy subjects were recruited. ECG and PPG signals were simultaneously recorded in 3 min using miniaturized wearable sensors. Four heart rate variability (HRV) and PRV parameters were extracted from ECG and PPG signals, respectively, and compared using analysis of variance (ANOVA) or Scheirer–Ray–Hare test with post hoc analysis. In addition, the data loss was calculated as the percentage of missing sampling points. Posture did not present statistical differences across the PRV parameters but a statistical difference between indicators was found. Strong variation was found for the RMSSD indicator in the standing posture. The sitting position in both breathing patterns demonstrated the lowest data loss (1.0 ± 0.6 and 1.0 ± 0.7) and the lowest percentage of different factors for all indicators. The usage of commercial PPG and BLE devices can allow the reliable extraction of the PPG signal and PRV indicators in real time.

(1) Background: Traumatic brain injuries (TBI) result in high fatality and lifelong disability rates. Two of the primary biomarkers in assessing TBI are intracranial pressure (ICP) and brain oxygenation. Both are assessed using standalone techniques, out of which ICP can only be assessed utilizing invasive techniques. The motivation of this research is the development of a non-invasive optical multimodal monitoring technology for ICP and brain oxygenation which will enable the effective management of TBI patients. (2) Methods: a multiwavelength optical sensor was designed and manufactured so as to assess both parameters based on the pulsatile and non-pulsatile signals detected from cerebral backscatter light. The probe consists of four LEDs and three photodetectors that measure photoplethysmography (PPG) and near-infrared spectroscopy (NIRS) signals from cerebral tissue. (3) Results: The instrumentation system designed to acquire these optical signals is described in detail along with a rigorous technical evaluation of both the sensor and instrumentation. Bench testing demonstrated the right performance of the electronic circuits while a signal quality assessment showed good indices across all wavelengths, with the signals from the distal photodetector being of highest quality. The system performed well within specifications and recorded good-quality pulsations from a head phantom and provided non-pulsatile signals as expected. (4) Conclusions: This development paves the way for a multimodal non-invasive tool for the effective assessment of TBI patients.

The increasing interest in innovative solutions for health and physiological monitoring has recently fostered the development of smaller biomedical devices. These devices are capable of recording an increasingly large number of biosignals simultaneously, while maximizing the user’s comfort. In this study, we have designed and realized a novel wearable multisensor ring-shaped probe that enables synchronous, real-time acquisition of photoplethysmographic (PPG) and galvanic skin response (GSR) signals. The device integrates both the PPG and GSR sensors onto a single probe that can be easily placed on the finger, thereby minimizing the device footprint and overall size. The system enables the extraction of various physiological indices, including heart rate (HR) and its variability, oxygen saturation (SpO2), and GSR levels, as well as their dynamic changes over time, to facilitate the detection of different physiological states, e.g., rest and stress. After a preliminary SpO2 calibration procedure, measurements have been carried out in laboratory on healthy subjects to demonstrate the feasibility of using our system to detect rapid changes in HR, skin conductance, and SpO2 across various physiological conditions (i.e., rest, sudden stress-like situation and breath holding). The early findings encourage the use of the device in daily-life conditions for real-time monitoring of different physiological states.

Sleep apnea syndrome (SAS) is a common but underdiagnosed health problem related to impaired quality of life and increased cardiovascular risk. In order to solve the problem of complicated and expensive operation procedures for clinical diagnosis of sleep apnea, here we propose a small and low-cost wearable apnea diagnostic system. The system uses a photoplethysmography (PPG) optical sensor to collect human pulse wave signals and blood oxygen saturation synchronously. Then multiscale entropy and random forest algorithms are used to process the PPG signal for analysis and diagnosis of sleep apnea. The SAS determination is based on the comprehensive diagnosis of the PPG signal and blood oxygen saturation signal, and the blood oxygen is used to exclude the error induced by non-pathological factors. The performance of the system is compared with the Compumedics Grael PSG (Polysomnography) sleep monitoring system. This simple diagnostic system provides a feasible technical solution for portable and low-cost screening and diagnosis of SAS patients with a high accuracy of over 85%.

Frequency estimation plays a critical role in vital sign monitoring. Methods based on Fourier transform and eigen-analysis are commonly adopted techniques for frequency estimation. Because of the nonstationary and time-varying characteristics of physiological processes, time-frequency analysis (TFA) is a feasible way to perform biomedical signal analysis. Among miscellaneous approaches, Hilbert–Huang transform (HHT) has been demonstrated to be a potential tool in biomedical applications. However, the problems of mode mixing, unnecessary redundant decomposition and boundary effect are the common deficits that occur during the procedure of empirical mode decomposition (EMD) or ensemble empirical mode decomposition (EEMD). The Gaussian average filtering decomposition (GAFD) technique has been shown to be appropriate in several biomedical scenarios and can be an alternative to EMD and EEMD. This research proposes the combination of GAFD and Hilbert transform that is termed the Hilbert–Gauss transform (HGT) to overcome the conventional drawbacks of HHT in TFA and frequency estimation. This new method is verified to be effective for the estimation of respiratory rate (RR) in finger photoplethysmography (PPG), wrist PPG and seismocardiogram (SCG). Compared with the ground truth values, the estimated RRs are evaluated to be of excellent reliability by intraclass correlation coefficient (ICC) and to be of high agreement by Bland–Altman analysis.

This study presents an ear-mounted photoplethysmography (PPG) system that is designed to detect mental stress. Mental stress is a prevalent condition that can negatively impact an individual’s health and well-being. Early detection and treatment of mental stress are crucial for preventing related illnesses and maintaining overall wellness. The study used data from 14 participants that were collected in a controlled environment. The participants were subjected to stress-inducing tasks such as the Stroop color–word test and mathematical calculations. The raw PPG signal was then preprocessed and transformed into scalograms using continuous wavelet transform (CWT). A convolutional neural network classifier was then used to classify the transformed signals as stressed or non-stressed. The results of the study show that the PPG system achieved high levels of accuracy (92.04%) and F1-score (90.8%). Furthermore, by adding white Gaussian noise to the raw PPG signals, the results were improved even more, with an accuracy of 96.02% and an F1-score of 95.24%. The proposed ear-mounted device shows great promise as a reliable tool for the early detection and treatment of mental stress, potentially revolutionizing the field of mental health and well-being.