Search

The Photoplethysmography (PPG) has already proved its importance as an auxiliary vital signal measurement technique. The PPG is predominantly a quasi-periodic signal for small timescales. This property allows the reconstructing of a phase space and, therefore, the attractor of PPG segments which presents good insights on dynamics of physiological systems. However, the assessment of PPG waveforms is a difficult task due to motion artifacts, biological tissue differences and other sources of noise. In the same direction, protocols for PPG signals acquisition have its development in a slower step. To contribute against this scenario, this work proposes and describes a method using the attractor reconstruction analysis to provide a quality assessment of PPG signals. The method presented good results in classifying into good and bad quality PPG signals segments achieving F1-score of 97%. The method is totally analytical, thus, it is suitable for real-time applications. No training process is required, it is independent of the subject physiology and no calibration is needed. • This work proposes a method to classify PPG signal segments of good quality and bad quality. • The method is totally analytical based on the attractor reconstruction analysis. • Thus, it is suitable for real-time applications and no training process is required. • The method is independent of the subject physiology and no calibration is needed. [ABSTRACT FROM AUTHOR]

The intelligent wearable heart rate measurement requirement has attracted more and more attention, and the related applications of Internet of Things are emerging. However, under intensive physical exercises, motion artifacts are strong interference sources for wrist-type photoplethysmography (PPG) sensor signals, thus significantly affecting the accurate estimation of heart rate and other physiological parameters. Currently, how to effectively remove the motion artifacts from PPG sensor signals is becoming an active and challenging research realm. In this paper, we propose a multi-channel spectral matrix decomposition (MC-SMD) model to accurately estimate heart rate in the presence of intensive physical activities. Motivated by the observation that the PPG signal spectrum and the acceleration spectrum have almost the same spectral peak positions in the frequency domain, we first model the removal of motion artifacts as a spectral matrix decomposition optimization problem. After removing motion artifacts, we propose a new spectral peak tracking method for estimating heart rate. Experimental results on the well-known PPG data sets recorded from 12 subjects during intensive movements demonstrate that MC-SMD can efficiently remove the motion artifacts and retrieve an accurate heart rate using multi-channel PPG sensor signals.

Heart rate (HR) as an important physiological indicator could properly describe global subject’s physical status. Photoplethysmographic (PPG) sensors are catching on in field of wearable sensors, combining the advantages in costs, weight and size. Nevertheless, accuracy in HR readings is unreliable specifically during physical activity. Among several identified sources that affect PPG recording, contact pressure (CP) between the PPG sensor and skin greatly influences the signals. Methods: In this study, the accuracy of HR measurements of a PPG sensor at different CP was investigated when compared with a commercial ECG-based chest strap used as a test control, with the aim of determining the optimal CP to produce a reliable signal during physical activity. Seventeen subjects were enrolled for the study to perform a physical activity at three different rates repeated at three different contact pressures of the PPG-based wristband. Results: The results show that the CP of 54 mmHg provides the most accurate outcome with a Pearson correlation coefficient ranging from 0.81 to 0.95 and a mean average percentage error ranging from 3.8% to 2.4%, based on the physical activity rate. Conclusion: Authors found that changes in the CP have greater effects on PPG-HR signal quality than those deriving from the intensity of the physical activity and specifically, the individual best CP for each subject provided reliable HR measurements even for a high intensity of physical exercise with a Bland–Altman plot within ±11 bpm. Although future studies on a larger cohort of subjects are still needed, this study could contribute a profitable indication to enhance accuracy of PPG-based wearable devices.

The chicken embryo is a widely used experimental animal model in many studies, including in the field of developmental biology, of the physiological responses and adaptation to altered environments, and for cancer and neurobiology research. The embryonic heart rate is an important physiological variable used as an index reflecting the embryo’s natural activity and is considered one of the most difficult parameters to measure. An acceptable measurement technique of embryonic heart rate should provide a reliable cardiac signal quality while maintaining adequate gas exchange through the eggshell during the incubation and embryonic developmental period. In this paper, we present a detailed design and methodology for a non-invasive photoplethysmography (PPG)-based prototype (Egg-PPG) for real-time and continuous monitoring of embryonic heart rate during incubation. An automatic embryonic cardiac wave detection algorithm, based on normalised spectral entropy, is described. The developed algorithm successfully estimated the embryonic heart rate with 98.7% accuracy. We believe that the system presented in this paper is a promising solution for non-invasive, real-time monitoring of the embryonic cardiac signal. The proposed system can be used in both experimental studies (e.g., developmental embryology and cardiovascular research) and in industrial incubation applications.

As photoplethysmographic (PPG) signals are comprised of numerous pieces of important physiological information, they have been widely employed to measure many physiological parameters. However, only a high-quality PPG signal can provide a reliable physiological assessment. Unfortunately, PPG signals are easily corrupted by motion artifacts and baseline drift during recording. Although several rule-based algorithms have been developed for evaluating the quality of PPG signals, few artificial intelligence-based algorithms have been presented. Thus, this study aims to classify the quality of PPG signals by using two two-dimensional deep convolution neural networks (DCNN) when the PPG pulse is used to measure cardiac stroke volume (SV) by impedance cardiography. An image derived from a PPG pulse and its differential pulse is used as the input to the two DCNN models. To quantify the quality of individual PPG pulses, the error percentage of the beat-to-beat SV measured by our device and medis® CS 2000 synchronously is used to determine whether the pulse quality is high, middle, or low. Fourteen subjects were recruited, and a total of 3135 PPG pulses (1342 high quality, 73 middle quality, and 1720 low quality) were obtained. We used a traditional DCNN, VGG-19, and a residual DCNN, ResNet-50, to determine the quality levels of the PPG pulses. Their results were all better than the previous rule-based methods. The accuracies of VGG-19 and ResNet-50 were 0.895 and 0.925, respectively. Thus, the proposed DCNN may be applied for the classification of PPG quality and be helpful for improving the SV measurement in impedance cardiography.

Animal welfare remains a very important issue in the livestock sector, but monitoring animal welfare in an objective and continuous way remains a serious challenge. Monitoring animal welfare, based upon physiological measurements instead of the audio–visual scoring of behaviour, would be a step forward. One of the obvious physiological signals related to welfare and stress is heart rate. The objective of this research was to measure heart rate (beat per minutes) in pigs with technology that soon will be affordable. Affordable heart rate monitoring is done today at large scale on humans using the Photo Plethysmography (PPG) technology. We used PPG sensors on a pig′s body to test whether it allows the retrieval of a reliable heart rate signal. A continuous wavelet transform (CWT)-based algorithm is developed to decouple the cardiac pulse waves from the pig. Three different wavelets, namely second, fourth and sixth order Derivative of Gaussian (DOG), are tested. We show the results of the developed PPG-based algorithm, against electrocardiograms (ECG) as a reference measure for heart rate, and this for an anaesthetised versus a non-anaesthetised animal. We tested three different anatomical body positions (ear, leg and tail) and give results for each body position of the sensor. In summary, it can be concluded that the agreement between the PPG-based heart rate technique and the reference sensor is between 91% and 95%. In this paper, we showed the potential of using the PPG-based technology to assess the pig′s heart rate.

Diabetic peripheral neuropathy (DPN) is a very common neurological disorder in diabetic patients. This study presents a new percussion-based index for predicting DPN by decomposing digital volume pulse (DVP) signals from the fingertip. In this study, 130 subjects (50 individuals 44 to 89 years of age without diabetes and 80 patients 37 to 86 years of age with type 2 diabetes) were enrolled. After baseline measurement and blood tests, 25 diabetic patients developed DPN within the following five years. After removing high-frequency noise in the original DVP signals, the decomposed DVP signals were used for percussion entropy index (PEIDVP) computation. Effects of risk factors on the incidence of DPN in diabetic patients within five years of follow-up were tested using binary logistic regression analysis, controlling for age, waist circumference, low-density lipoprotein cholesterol, and the new index. Multivariate analysis showed that patients who did not develop DPN in the five-year period had higher PEIDVP values than those with DPN, as determined by logistic regression model (PEIDVP: odds ratio 0.913, 95% CI 0.850 to 0.980). This study shows that PEIDVP can be a major protective factor in relation to the studied binary outcome (i.e., DPN or not in diabetic patients five years after baseline measurement).

For all homoeothermic living organisms, heart rate (HR) is a core variable to control the metabolic energy production in the body, which is crucial to realize essential bodily functions. Consequently, HR monitoring is becoming increasingly important in research of farm animals, not only for production efficiency, but also for animal welfare. Real-time HR monitoring for humans has become feasible though there are still shortcomings for continuously accurate measuring. This paper is an effort to estimate whether it is realistic to get a continuous HR sensor for livestock that can be used for long term monitoring. The review provides the reported techniques to monitor HR of living organisms by emphasizing their principles, advantages, and drawbacks. Various properties and capabilities of these techniques are compared to check the potential to transfer the mostly adequate sensor technology of humans to livestock in term of application. Based upon this review, we conclude that the photoplethysmographic (PPG) technique seems feasible for implementation in livestock. Therefore, we present the contributions to overcome challenges to evolve to better solutions. Our study indicates that it is realistic today to develop a PPG sensor able to be integrated into an ear tag for mid-sized and larger farm animals for continuously and accurately monitoring their HRs.

Arterial stiffness is considered an index of vascular aging. The brachial–ankle pulse wave velocity (baPWV) method is widely used because of its proven effectiveness; and the pulse wave velocity measurement method using both electrocardiogram (ECG) and photoplethysmogram (PPG) is actively being studied due to the convenience of measurement and the possibility of miniaturization. The aim of this study was to evaluate and compare the effects of age and gender in Korean adults using both the baPWV method and the PWV method with ECG and finger PPG (heart–finger PWV). The measurements have been carried out for 185 healthy subjects of Korean adults, and the results showed that the baPWV was highly correlated with age in both genders (r = 0.94 for both males and females). However, the correlation values in heart–finger PWV measurement were significantly lower than those of baPWV (r = 0.37 for males and r = 0.71 for females). Although the heart–finger PWV method is suitable for mobile applications because it can be easily miniaturized while maintaining its signal quality, these results show that the heart–finger PWV method is not as effective as baPWV at evaluating the arterial stiffness.

Cardiovascular disease is a leading cause of death. Several markers have been proposed to predict cardiovascular morbidity. The ankle-brachial index (ABI) marker is defined as the ratio between the ankle and the arm systolic blood pressures, and it is generally assessed through sphygmomanometers. An alternative tool for cardiovascular status assessment is Photoplethysmography (PPG). PPG is a non-invasive optical technique that measures volumetric blood changes induced by pulse pressure propagation within arteries. However, PPG does not provide absolute pressure estimation, making assessment of cardiovascular status less direct. The capability of a multivariate data-driven approach to predict ABI from peculiar PPG features was investigated here. ABI was measured using a commercial instrument (Enverdis Vascular Explorer, VE-ABI), and it was then used for a General Linear Model estimation of ABI from multi-site PPG in a supervised learning framework (PPG-ABI). A Receiver Operating Characteristic (ROC) analysis allowed to investigate the capability of PPG-ABI to discriminate cardiovascular impairment as defined by VE-ABI. Findings suggested that ABI can be estimated form PPG (r = 0.79) and can identify pathological cardiovascular status (AUC = 0.85). The advantages of PPG are simplicity, speed and operator-independency, allowing extensive screening of cardiovascular status and associated cardiovascular risks.

Diabetic peripheral neuropathy (DPN) is one of the most common chronic complications of diabetes. It has become an essential public health crisis, especially for care in the home. Synchronized electrocardiogram (ECG) and photoplethysmography (PPG) signals were obtained from healthy non-diabetic (n = 37) and diabetic (n = 85) subjects without peripheral neuropathy, recruited from the diabetic outpatient clinic. The conventional parameters, including low-/high-frequency power ratio (LHR), small-scale multiscale entropy index (MEISS), large-scale multiscale entropy index (MEILS), electrocardiogram-based pulse wave velocity (PWVmean), and percussion entropy index (PEI), were computed as baseline and were then followed for six years after the initial PEI measurement. Three new diabetic subgroups with different PEI values were identified for the goodness-of-fit test and Cox proportional Hazards model for relative risks analysis. Finally, Cox regression analysis showed that the PEI value was significantly and independently associated with the risk of developing DPN after adjustment for some traditional risk factors for diabetes (relative risks = 4.77, 95% confidence interval = 1.87 to 6.31, p = 0.015). These findings suggest that the PEI is an important risk parameter for new-onset DPN as a result of a chronic complication of diabetes and, thus, a smaller PEI value can provide valid information that may help identify type 2 diabetic patients at a greater risk of future DPN.

Persistent cardiac arrhythmias are readily amenable to detection by performing a standard electrocardiogram (ECG), but detection of transient (paroxysmal) arrhythmias has long been a significant cause of frustration to both doctors and patients. Often a significantly symptomatic arrhythmia is experienced by the patient but terminates before an ECG can be recorded to allow diagnosis. Prognostically important treatment is often delayed, and recurrent symptomatic attacks represent a high morbidity in patients' lives and result in a burden on emergency services, who often arrive after the arrhythmia has terminated with no resultant progress in making a diagnosis. Another area of concern has been the presence of asymptomatic, but clinically important, arrhythmias that can go unnoticed by people experiencing them and may result in permanent harm; asymptomatic paroxysmal atrial fibrillation in patients with high CHA 2 DS 2 -VASc scores being the most common example. Both these issues are now being importantly addressed by the widespread availability of portable ECG recording devices, which patients can either manually activate themselves or program to automatically detect abnormal arrhythmias. Information on the range of devices available and their strengths and weaknesses is limited. This article aims to provide a helpful overview for patients and doctors advising them., Competing Interests: Conflicts of interest None declared., (Copyright © 2023 Medinews (Cardiology) Limited.)

The growing global prevalence of heart failure (HF) necessitates innovative methods for early diagnosis and classification of myocardial dysfunction. In recent decades, non-invasive sensor-based technologies have significantly advanced cardiac care. These technologies ease research, aid in early detection, confirm hemodynamic parameters, and support clinical decision-making for assessing myocardial performance. This discussion explores validated enhancements, challenges, and future trends in heart failure and dysfunction modeling, all grounded in the use of non-invasive sensing technologies. This synthesis of methodologies addresses real-world complexities and predicts transformative shifts in cardiac assessment. A comprehensive search was performed across five databases, including PubMed, Web of Science, Scopus, IEEE Xplore, and Google Scholar, to find articles published between 2009 and March 2023. The aim was to identify research projects displaying excellence in quality assessment of their proposed methodologies, achieved through a comparative criteria-based rating approach. The intention was to pinpoint distinctive features that differentiate these projects from others with comparable objectives. The techniques identified for the diagnosis, classification, and characterization of heart failure, systolic and diastolic dysfunction encompass two primary categories. The first involves indirect interaction with the patient, such as ballistocardiogram (BCG), impedance cardiography (ICG), photoplethysmography (PPG), and electrocardiogram (ECG). These methods translate or convey the effects of myocardial activity. The second category comprises non-contact sensing setups like cardiac simulators based on imaging tools, where the manifestations of myocardial performance propagate through a medium. Contemporary non-invasive sensor-based methodologies are primarily tailored for home, remote, and continuous monitoring of myocardial performance. These techniques leverage machine learning approaches, proving encouraging outcomes. Evaluation of algorithms is centered on how clinical endpoints are selected, showing promising progress in assessing these approaches' efficacy., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Al Younis, Hadjileontiadis, Stefanini and Khandoker.)

Low-amplitude PPG signals are more affected by noise contamination and other undesirable effects because the signal strength is comparable to noise power. Although several authors claim that decreases in the amplitude of the PPG wave should be addressed from signal acquisition and conditioning stages such decreases can also be associated with changes in the patient condition. In that instance, it is important to ensure continuous and reliable HR monitoring which, in turn, depends on how robust is the peak detection method. Numerous efforts have been made to develop algorithms for accurate PPG peak detection under high motion artefact conditions. However, little has been done regarding peak detection in low-amplitude PPG signals. In an attempt to address this issue, a novel and simple peak detection algorithm for PPG signals was proposed. Results show that our method could be a good contribution for robust strategies that can dynamically adapt their peak detection method to circumstances in which a decrease in the amplitude of the PPG signal is expected. Still, more extensive testing under a wide range of conditions (e.g. intensive physical exercise) is needed to perform a rigorous validation. [ABSTRACT FROM AUTHOR]

Objective: Respiratory rate (RR) estimation algorithms based on the photoplethymogram (PPG) and electrocardiogram (ECG) lack clinical robustness. This is because the PPG and ECG respiratory modulations are dependent on patient physiology, regardless of general signal quality. The present work describes an RR estimation algorithm using respiratory quality indices (RQIs) that assess the presence or absence of the PPG- and ECG-derived respiratory modulations. Methods: Six respiratory waveforms are derived from the amplitude modulation, frequency modulation, and baseline wander of the PPG and ECG. The respiratory quality of each modulation is assessed by using RQIs based on the fast Fourier transform, autoregression, and autocorrelation. The individual RQIs are fused to obtain a single RQI per modulation per time window. Based on a tunable threshold, the RQIs are used to discard poor modulations and weight the remaining modulations to provide a single RR estimation per time window. Results: The proposed method was tested on two independent datasets and found that using a conservative threshold, the mean absolute error was 0.71 $\pm$ 0.89 and 3.12 $\pm$ 4.39 brpm while discarding only 1.3% and 23.2% of all time windows, for each dataset, respectively. Conclusion: These errors are either better than or comparable to current methods, and the number of windows discarded is far lower demonstrating improved robustness. Significance: This work describes a novel preprocessing algorithm that can be implemented in conjunction with other RR estimation techniques to improve robustness by specifically considering the quality of the respiratory information. [ABSTRACT FROM AUTHOR]

This study presents a vision-based approach that employs RGB video images as the sole source for inferring thermoregulation states in the human body in response to thermal condition variations in indoor environments. The primary objective is to contribute to our envisioned thermoregulation-based HVAC control that leverages actual thermal demands from end-users’ thermoregulation states for increased energy efficiency. Given that the envisioned control system calls for measurement techniques under four constraints of non-intrusiveness, applicability, sensitivity, and ubiquity (i.e., feasibility and scalability), this study investigated the potentials of ubiquitously obtainable RGB video images (through webcams or smartphones). Using photoplethysmography (PPG), a well-known optical technique for measuring blood volume changes in the microvascular bed of skin, we have leveraged the mechanism of blood flow control to the skin surface (blood vessels' dilation and constriction) for heat dissipation regulations, reflected in PPG signal's amplitude. Given the subtle variations of PPG signals and their susceptibility to noise, we proposed a framework that uses a combination of independent component analysis and adaptive filtering to reduce unwanted and in-band artifacts while preserving the amplitude information of PPG signals that indicates thermophysiological states. The framework was experimentally evaluated using transient thermal conditions to account for applicability and sensitivity attributes. Therefore, without considering an acclimation time for stabilized thermoregulation states, human subjects were exposed to varying temperatures (∼20–30 °C) while reporting their thermal sensations. In total, for 10 human subjects out of 15, a positive correlation between vision-based indicators, skin temperature, and thermal sensations were observed demonstrating promising potential in inferring thermal sensations of occupants with sufficient sensitivity. [ABSTRACT FROM AUTHOR]

In this paper, we present the design, development, and validation of a ‘modular photoplethysmography (PPG) system called ZenPPG. This portable, dual-channel system has the capability to produce “raw” PPG signals at two different wavelengths using commercial and/or custom-made PPG sensors. The system consists of five modules, each consisting of circuitry required to perform specific tasks, and are all interconnected by a system bus. The ZenPPG system also facilitates the acquisition of other physiological signals on-demand including electrocardiogram (ECG), respiration, and temperature signals. This report describes the technical details and the evaluation of the ZenPPG along with results from a pilot in vivo study on healthy volunteers. The results from the technical evaluations demonstrate the superiority and flexibility of the system. Also, the systems’ compatibility with commercial pulse oximetry sensors such as the Masimo reusable sensors was demonstrated, where good quality raw PPG signals were recorded with the signal-to-noise ratio (SNR) of 50.65 dB. The estimated arterial oxygen saturation (SpO2) values from the system were also in close agreement with commercial pulse oximeters, although the accuracy of the reported SpO2 value is dependent on the calibration function used. Future work is targeted toward the development of variations of each module, including the laser driver and fiber optic module, onboard data acquisition and signal processing modules. The availability of this system will help researchers from a wide range of disciplines to customize and integrate the ZenPPG system to their research needs and will most definitely enhance research in related fields. [ABSTRACT FROM AUTHOR]

Remote camera-based measurement of physiology has great potential for healthcare and affective computing. Recent advances in computer vision and signal processing have enabled photoplethysmography (PPG) measurement using commercially available cameras. However, there remain challenges in recovering accurate noncontact PPG measurements in the presence of rigid head motion. When a subject is moving, their face may be turned away from one camera, be obscured by an object, or move out of the frame resulting in missing observations. As the calculation of pulse rate variability (PRV) requires analysis over a time window of several minutes, the effect of missing observations on such features is deleterious. We present an approach for fusing partial color-channel signals from an array of cameras that enable physiology measurements to be made from moving subjects, even if they leave the frame of one or more cameras, which would not otherwise be possible with only a single camera. We systematically test our method on subjects ( N=25) using a set of six, 5-min tasks (each repeated twice) involving different levels of head motion. This results in validation across 25 h of measurement. We evaluate pulse rate and PRV parameter estimation including statistical, geometric, and frequency-based measures. The median absolute error in pulse rate measurements was 0.57 beats-per-minute (BPM). In all but two tasks with the greatest motion, the median error was within 0.4 BPM of that from a contact PPG device. PRV estimates were significantly improved using our proposed approach compared to an alternative not designed to handle missing values and multiple camera signals; the error was reduced by over 50%. Without our proposed method, errors in pulse rate would be very high, and estimation of PRV parameters would not be feasible due to significant data loss. [ABSTRACT FROM AUTHOR]

Acquiring a precise percentage of oxygen saturation (SpO2) from a finger-probe pulse oximeter is dependent on both artifact-free red and infrared photoplethysmoghaphic (PPG) signals. Nonetheless, in real-life situations, these PPG signals are corrupted by a motion artifact (MA) signal that is generated from either finger or hand movement. To resolve this MA interference, the cause of the adulteration of PPG signals by the MA signal is examined. The MA signal is found to behave like an additive noise. Additionally, the frequency responses of the MA and PPG signals show that these signals are in the same frequency band. Hence, instead of direct current, a sinusoidal wave alternating current is proposed to drive an LED source in order to shift the PPG frequency band away from the MA frequency band. Experimentally, a commercial finger-probe pulse oximeter is employed. To determine the performance of the presented scheme, the resulting PPG signals are compared with those from employing the old-fashioned LED-driving method. In addition, the accuracy is verified by computing the SpO2 value. The results reveal that the proposed approach successfully retains the fundamental morphologies of the PPG structures when motion occurs. Moreover, the calculated SpO2 values from the proposed technique provide an average error of approximately 1.4%, whereas the conventional method yields a mean error approximately 4.2%. [ABSTRACT FROM AUTHOR]

We report VivoSoC, a system-on-chip realized in 130-nm CMOS for miniaturized medical instrumentation as used in mobile health devices or implantable telemetry systems for animal experiments. It features six neural stimulation channels and acquisition circuits for 9 $\times $ electrode-based recordings, 4-channel/32-LED photoplethysmography (PPG), bioimpedance, and temperature. A 34- $\mu \text{W}$ /MHz quad-core processing unit with sophisticated data, power, and clock management techniques enables on-chip feature extraction, which can dramatically reduce transmission or storage data rates in a system—crucial for overall power consumption and exemplarily demonstrated on preliminary work on implantable PPG-based vital signs monitoring. [ABSTRACT FROM AUTHOR]

This paper evaluates the use of accelerometers for continuous monitoring of respiratory rate (RR), which is an important vital sign in post-intensive care patients or those inside the intensive care unit (ICU). The respiratory rate can be estimated from accelerometer and photoplethysmography (PPG) signals for patients following ICU discharge. Due to sensor faults, sensor detachment, and various artifacts arising from motion, RR estimates derived from accelerometry and PPG may not be sufficiently reliable for use with existing algorithms. This paper described a case study of 10 selected patients, for which fewer RR estimates have been obtained from PPG signals in comparison to those from accelerometry. We describe an algorithm for which we show a maximum mean absolute error between estimates derived from PPG and accelerometer of 2.56 breaths/min. Our results obtained using the 10 selected patients are highly promising for estimation of RR from accelerometers, where significant agreements have been observed with the PPG-based RR estimates in many segments and across various patients. We present this research as a step towards producing reliable RR monitoring systems using low-cost mobile accelerometers for monitoring patients inside the ICU or on the ward (post-ICU). [ABSTRACT FROM PUBLISHER]

Remote photoplethysmography (rPPG) can achieve contactless human vital signs monitoring, but its signal quality is limited by the remote operation nature. In practical applications, improving the rPPG signal quality becomes an essential task. As a remote imaging technique, rPPG utilizes a camera to capture a video of a skin area, especially the facial area, then focuses on a particular sub-area as the region of interest (ROI). In this paper, we investigated a novel adaptive ROI (AROI) approach for improving the rPPG signal quality. In this approach, block-based spatial-temporal division is performed on a captured face video. Based on these segmented video pipelines, the spatial-temporal quality distribution of the rPPG signals is estimated using a signal-to-noise ratio (SNR) feature. Afterwards, AROIs are calculated through mean-shift clustering and adaptive thresholding in SNR maps. As the AROI can be dynamically adjusted according to the spatial-temporal quality distribution of rPPG signals on the face, the quality of the final recovered rPPG signal is improved. The performance of the proposed AROI approach was evaluated with both still and moving subjects. Compared to conventional ROI methods for rPPG, the proposed AROI obtained a higher accuracy in heart rate measurement. And the state-of-the-art motion-resistant rPPG techniques can be effectively enhanced through being integrated with the AROI. [ABSTRACT FROM AUTHOR]

Heart rate (HR) monitoring using photoplethysmographic (PPG) signals recorded from wearers’ wrist greatly facilitates design of wearable devices and maximizes user experience. However, placing PPG sensors in wrist causes much stronger and complicated motion artifacts (MA) due to loose interface between sensors and skin. Therefore, developing robust HR estimation algorithms for wrist-type PPG signals has significant commercial values. In this paper, we propose a robust HR estimation algorithm for wrist-type PPG signals using multiple reference adaptive noise cancellation (ANC) technique—termed here as “MURAD.” The main challenge of using ANC for MA reduction is to devise a qualified reference noise signal (RNS) to the adaptive filter. We propose a novel solution by using four RNSs, namely, the three-axis accelerometer data and the difference signal between the two PPG signals. For each RNS, we get a different version of the cleaned PPG signal. Then, a set of probable HR values is estimated using all of the cleaned PPG signals, and then, the value that is closest to the estimated HR of the previous time window is chosen to be the HR estimate of the current window. Then, some peak verification techniques are employed to ensure accurate HR estimations. The proposed technique gives lower average absolute error compared to state-of-the art methods. So, MURAD method provides a promising solution to the challenge of HR monitoring using PPG in wearable devices during severe MA conditions. [ABSTRACT FROM PUBLISHER]

The photoplethysmographic (PPG) signal is an important source of information for estimating heart rate (HR). However, the PPG signal could be strongly contaminated by the motion artifact of the subjects, making HR estimation a particularly difficult problem. In this paper, we propose PARHELIA, a PARticle filter-based algorithm for HEart rate estimation using photopLethysmographIc signAls. The proposed method employs a particle filter, and utilizes the simultaneously recorded acceleration signals from a wrist-type sensor, to keep track of multiple HR candidates. This achieves quick recovery from incorrect HR estimations under the strong influence of the MA. Experimental results for a dataset of 12 subjects recorded during fast running showed that the average absolute estimation error was 1.17 beats per minute (BPM) whereas that of the best-known conventional method, JOSS, is 1.28 BPM. Furthermore, the estimation time of PARHELIA is 20 times shorter than JOSS. [ABSTRACT FROM PUBLISHER]

Healthcare is expected to increasingly shift care out of inpatient settings thanks to wearable monitoring systems. Photoplethysmography (PPG) is an optical technique already integrated into wrist-worn commercial products which presents significant advantages in terms of cost and dimensions. PPG-based devices, despite their ability to detect multiple cardiovascular parameters, are affected by several influencing conditions that depend both on technological or environmental variables, and on intra- and inter-subject variability that influences the whole measurement chain and reliability, hindering an objective characterization of PPG devices. Plus, the lack of standardization for data collection and processing leads to the lack of generalizability and reproducibility of results, preventing the full exploitation of the potential prognostic capacity of this technology. Thus, this review aims not only to summarize the main influencing parameters of PPG technology, which should be addressed when testing the sensor, but also to suggest tentative guidelines for a possible future standardization initiative. [ABSTRACT FROM AUTHOR]

It has been well established that change in emotion state is associated with the change in physiological signals. This paper aimed to investigate the differences of finger photoplethysmography (PPG) morphological features and feature time series between happiness and sadness emotion states. Fifty-three volunteers were enrolled. Finger PPG signals were recorded under two emotion states with a random measurement order (first happiness emotion measurement then sadness or reverse). Seven morphological features were extracted, including three temporal features (T, T1 and T2), three area features (A, A1 and A2) and one amplitude feature (Amp). Five variability indices from the 5-min feature time series were calculated, including two time-domain indices (SDNN and RMSSD) and three frequency-domain indices (LFn, HFn and LF/HF). Results showed that temporal features T2 and T were critical features for identifying the two emotion states since not only they themselves but also their three frequency-domain variability indices had significant differences between the two emotion states. For area features, only two frequency-domain variability indices of LFn and HFn for A1 feature time series reported significant differences. Amplitude feature Amp itself, as well as its variability indices, did not had significant differences between the two emotion states. These results indicated that temporal features were more sensitive to response to emotion change than area and amplitude features. Moreover, compared with time-domain variability indices, frequency-domain variability indices were more suitable for short-term 5-min time series analysis for exploring the inherent but slight change due to the emotion effect.

Photoplethysmography (PPG) is a non-invasive technique that employs near infrared light to estimate periodic oscillations in blood volume within arteries caused by the pulse pressure wave. Importantly, combined Electrocardiography (ECG) and PPG can be employed to quantify arterial stiffness. The capabilities of a home-made multi-channel PPG-ECG device (7 PPG probes, 4 ECG derivations) to evaluate arterial ageing were assessed. The high numerosity of channels allowed to estimate arterial stiffness at multiple body locations, without supra-systolic cuff occlusion, providing a fast and accurate examination of cardiovascular status and potentially allowing large scale clinical screening of cardiovascular risk.

With photoplethysmograph (PPG) sensors showing increasing potential in wearable health monitoring, the challenging problem of motion artifact (MA) removal during intensive exercise has become a popular research topic. In this study, a novel method that combines heart rate frequency (HRF) estimation and notch filtering is proposed. The proposed method applies a cascaded adaptive noise cancellation (ANC) based on the least mean squares (LMS)-Newton algorithm for preliminary motion artifacts reduction, and further adopts special heart rate frequency tracking and correction schemes for accurate HRF estimation. Finally, notch filters are employed to restore the PPG signal with estimated HRF based on its quasi-periodicity. On an open source data set that features intensive running exercise, the proposed method achieves a competitive mean average absolute error (AAE) result of 0.92 bpm for HR estimation. The practical experiments are carried out with the PPG evaluation platform developed by ourselves. Under three different intensive motion patterns, a 0.89 bpm average AAE result is achieved with the average correlation coefficient between recovered PPG signal and reference PPG signal reaching 0.86. The experimental results demonstrate the effectiveness of the proposed method for accurate HR estimation and robust MA removal in PPG during intensive exercise.

Photoplethysmography (PPG) is a widely available non-invasive optical technique to visualize pressure pulse waves (PWs). Pulse transit time (PTT) is a physiological parameter that is often derived from calculations on ECG and PPG signals and is based on tightly defined characteristics of the PW shape. PPG signals are sensitive to artefacts. Coughing or movement of the subject can affect PW shapes that much that the PWs become unsuitable for further analysis. The aim of this study was to develop an algorithm that automatically and objectively eliminates unsuitable PWs. In order to develop a proper algorithm for eliminating unsuitable PWs, a literature study was conducted. Next, a '7Step PW-Filter' algorithm was developed that applies seven criteria to determine whether a PW matches the characteristics required to allow PTT calculation. To validate whether the '7Step PW-Filter' eliminates only and all unsuitable PWs, its elimination results were compared to the outcome of manual elimination of unsuitable PWs. The '7Step PW-Filter' had a sensitivity of 96.3% and a specificity of 99.3%. The overall accuracy of the '7Step PW-Filter' for detection of unsuitable PWs was 99.3%. Compared to manual elimination, using the '7Step PW-Filter' reduces PW elimination times from hours to minutes and helps to increase the validity, reliability and reproducibility of PTT data. [ABSTRACT FROM AUTHOR]

Smartphones and tablets are widely used in medical fields, which can improve healthcare and reduce healthcare costs. Many medical applications for smartphones and tablets have already been developed and widely used by both health professionals and patients. Specifically, video recordings of fingertips made using a smartphone camera contain a pulsatile component caused by the cardiac pulse equivalent to that present in a photoplethysmographic signal. By performing peak detection on the pulsatile signal, it is possible to estimate a continuous heart rate and a respiratory rate. To estimate the heart rate and respiratory rate accurately, which pixel regions of the color bands give the most optimal signal quality should be investigated. In this paper, we investigate signal quality to determine the best signal quality by the largest amplitude values for three different smartphones under different conditions. We conducted several experiments to obtain reliable PPG signals and compared the PPG signal strength in the three color bands when the flashlight was both on and off. We also evaluated the intensity changes of PPG signals obtained from the smartphones with motion artifacts and fingertip pressure force. Furthermore, we have compared the PSNR of PPG signals of the full-size images with that of the region of interests (ROIs). [ABSTRACT FROM AUTHOR]

Most of the wrist-worn devices on the market provide a continuous heart rate measurement function using photoplethysmography, but have not yet provided a function to measure the continuous heart rate variability (HRV) using beat-to-beat pulse interval. The reason for such is the difficulty of measuring a continuous pulse interval during movement using a wearable device because of the nature of photoplethysmography, which is susceptible to motion noise. This study investigated the effect of missing heart beat interval data on the HRV analysis in cases where pulse interval cannot be measured because of movement noise. First, we performed simulations by randomly removing data from the RR interval of the electrocardiogram measured from 39 subjects and observed the changes of the relative and normalized errors for the HRV parameters according to the total length of the missing heart beat interval data. Second, we measured the pulse interval from 20 subjects using a wrist-worn device for 24 h and observed the error value for the missing pulse interval data caused by the movement during actual daily life. The experimental results showed that mean NN and RMSSD were the most robust for the missing heart beat interval data among all the parameters in the time and frequency domains. Most of the pulse interval data could not be obtained during daily life. In other words, the sample number was too small for spectral analysis because of the long missing duration. Therefore, the frequency domain parameters often could not be calculated, except for the sleep state with little motion. The errors of the HRV parameters were proportional to the missing data duration in the presence of missing heart beat interval data. Based on the results of this study, the maximum missing duration for acceptable errors for each parameter is recommended for use when the HRV analysis is performed on a wrist-worn device. [ABSTRACT FROM AUTHOR]

Objective: Recently numerous methods have been proposed for estimating average heart rate using photoplethysmography (PPG) during physical activity, overcoming the significant interference that motion causes in PPG traces. We propose a new algorithm framework for extracting instantaneous heart rate from wearable PPG and Electrocardiogram (ECG) signals to provide an estimate of heart rate variability during exercise. Methods: For ECG signals, we propose a new spectral masking approach which modifies a particle filter tracking algorithm, and for PPG signals constrains the instantaneous frequency obtained from the Hilbert transform to a region of interest around a candidate heart rate measure. Performance is verified using accelerometry and wearable ECG and PPG data from subjects while biking and running on a treadmill. Results: Instantaneous heart rate provides more information than average heart rate alone. The instantaneous heart rate can be extracted during motion to an accuracy of 1.75 beats per min (bpm) from PPG signals and 0.27 bpm from ECG signals. Conclusion: Estimates of instantaneous heart rate can now be generated from PPG signals during motion. These estimates can provide more information on the human body during exercise. Significance: Instantaneous heart rate provides a direct measure of vagal nerve and sympathetic nervous system activity and is of substantial use in a number of analyzes and applications. Previously it has not been possible to estimate instantaneous heart rate from wrist wearable PPG signals. [ABSTRACT FROM AUTHOR]

Goal Monitoring heart rate (HR) from intense motion artifact (MA) corrupted photoplethysmography (PPG) signals is a challenging problem. Accurate HR estimation from intense MA corrupted PPG signals can be very useful in HR monitoring programs during physical exercises. Method In this paper, we present a constrained recursive least-squares (cRLS) based denoising technique to effectively eliminate MAs from a composite PPG signal, and a coupled wavelet-Fourier based frequency estimation technique to estimate the HR more precisely. To separate the clean PPG, the MA corrupted composite PPG signal is decomposed using the ensemble empirical mode decomposition (EEMD) based signal separation technique. When EEMD fails to effectively separate the clean PPG from MAs, the new cRLS based denoising is used that can eliminate MAs even when there is an acceleration peak close to the HR peak. In HR estimation from the denoised signal, a novel wavelet based frequency estimation technique is incorporated along with the conventional Fourier based technique that improves the precision of HR tracking, especially from nonstationary PPG window frames. The autoregressive (AR) model based predictor and some heuristic decision based approaches are included for further reduction of the HR estimation error. Result The method is tested on 12 different subjects running on a treadmill with a peak velocity upto 15 km/h and we have obtained a mean absolute error of 0.81–0.84 beat per minute (BPM) with a standard deviation of ±0.82–0.96 BPM. Conclusion The proposed technique has achieved a benchmark result on these subjects which is much better as compared to the recently reported techniques in the literature. Significance The proposed method is expected to find a good application in wearable gadgets to monitor HRs accurately during fitness programs and other activities with intense physical motion. [ABSTRACT FROM AUTHOR]

In this paper, we present an unobtrusive cuffless blood pressure (BP) monitoring system based on pulse arrival time (PAT) for facilitating long-term home BP monitoring. The proposed system consists of an electrocardiograph (ECG), a photoplethysmograph (PPG), and a control circuit with a Bluetooth module, all of which are mounted on a common armchair to measure ECG and PPG signals from users while sitting on the armchair in order to calculate continuous PAT. Considering the good linear correlation of systolic BP (SBP) and the nonlinear correlation of diastolic BP (DBP) with PAT, a new BP estimation method was proposed. Ten subjects underwent BP monitoring experiments involving stationary sitting on a chair, lying on a bed, and pedaling using an ergometer in order to assess the accuracy of the estimated BP. A cuff-type BP monitor was used as reference in the experiments. Results showed that the mean difference of the estimated SBP and DBP was within 0.2 ± 5.8 mmHg ($p$ < 0.00001) and 0.4 ± 5.7 mmHg ($p$ < 0.00001), respectively, and the mean absolute difference of the estimated SBP and DBP were 4.4 and 4.6 mmHg, respectively, compared to references. Additionally, five subjects participated in data collections consisting of sitting on a chair twice a day for one month. Compared to the reference, the difference did not obviously increase along with time, even though individualized calibration was executed only once at the beginning. These results suggest that the proposed system has quite the potential for long-term home BP monitoring. [ABSTRACT FROM AUTHOR]

Photoplethysmography (PPG) is a popular technique utilized in pulse oximeter. Several researches have shown that PPG signal possesses the respiratory-induced intensity variation (RIIV) component, which implies that arterial oxygen saturation, heart rate (HR) and respiratory rate (RR) can be acquired by a single device. The commercial pulse oximeter generally provides the values of arterial oxygen saturation (SpO 2 ) and HR. To successfully add the function of RR estimation to pulse oximeter, an algorithm requiring fewer resources plays a critical role. This paper presents a wavelet-based algorithm for RR estimation from PPG signal that can be implemented in the micro-controller (MCU) of pulse oximeter. The algorithm has been coded in C language and tested in a 32-bit MCU. The estimation results derived by the algorithm agree well with those from usual spectrum analysis methods. The RR estimations derived by PPG and respiratory signal are analyzed by Bland–Altman method. The RR estimations for long-term trace, breath-holding and paced-breathing experiments are also conducted to verify the performance of the proposed algorithm. The experimental results indicate that the proposed algorithm is highly reliable and is feasible to be incorporated in the commercial pulse oximeter. [ABSTRACT FROM AUTHOR]

In this article, a wearable Doppler radar unit with radio data link in 2.4 GHz for use in portable patient monitoring and emergency response is presented. By using a rotated E-shaped strip in the radiating patch, a new resonance at lower frequencies (2.4 GHz) can be achieved. Also, by embedding cutting a rectangular slot with protruded interdigital strip inside the slot in the feed line a frequency band-stop performance can be achieved. The proposed structure has a major advantage in high harmonic rejection. In order to generate efficient RF to dc conversion, we use a pair of gap distances in the microstrip loop. Unlike portable electrocardiograms (ECG) or photoplethysmography, the near-field Doppler unit enables monitoring of the person’s heart rate without the need for electrical contact or optical access to the patient’s skin. The Doppler unit is designed to be embedded in a clothing garment, such as a shirt or vest, or used by medical emergency personnel in an instrumented blanket or medical stretcher. Since the Doppler unit is placed directly on or behind the patient’s torso, the extraneous signals due to relative motion artifacts are greatly reduced. Low-cost design is achieved by employing pulse wideband microstrip elements for the integrated patch antenna, microwave oscillator, and tuning elements. Also, since the distance between the Doppler unit and the patient is fixed, it was possible to tune the detection phase to enable the use of a single mixer diode and eliminate the need for quadrature detection. This approach is demonstrated using a simple direct conversion radar circuit implemented in a microstrip operating at 2.460 GHz. Heart rate data is shown for detection distances of 0.5 meter. Measured heart data from this technique shows a clear waveform substructure similar to the Personal Questionnaire Rapid Scaling Technique complex features found in captured ECG data. [ABSTRACT FROM PUBLISHER]

It has been well established that change in emotion state is associated with the change in physiological signals. This paper aimed to investigate the differences of finger photoplethysmography (PPG) morphological features and feature time series between happiness and sadness emotion states. Fifty-three volunteers were enrolled. Finger PPG signals were recorded under two emotion states with a random measurement order (first happiness emotion measurement then sadness or reverse). Seven morphological features were extracted, including three temporal features (T, T 1 and T 2 ), three area features (A, A 1 and A 2 ) and one amplitude feature (Amp). Five variability indices from the 5-min feature time series were calculated, including two time-domain indices (SDNN and RMSSD) and three frequency-domain indices (LFn, HFn and LF/HF). Results showed that temporal features T 2 and T were critical features for identifying the two emotion states since not only they themselves but also their three frequency-domain variability indices had significant differences between the two emotion states. For area features, only two frequency-domain variability indices of LFn and HFn for A 1 feature time series reported significant differences. Amplitude feature Amp itself, as well as its variability indices, did not had significant differences between the two emotion states. These results indicated that temporal features were more sensitive to response to emotion change than area and amplitude features. Moreover, compared with time-domain variability indices, frequency-domain variability indices were more suitable for short-term 5-min time series analysis for exploring the inherent but slight change due to the emotion effect. [ABSTRACT FROM AUTHOR]

A new digital filter, AMARS (aligning minima of alternating random signal) has been derived using trigonometry to regulate signal pulsations inline. The pulses are randomly presented in continuous signals comprising frequency band lower than the signal's mean rate. Frequency selective filters are conventionally employed to reject frequencies undesired by specific applications. However, these conventional filters only reduce the effects of the rejected range producing a signal superimposed by some baseline wander (BW). In this work, filters of different ranges and techniques were independently configured to preprocess a photoplethysmogram, an optical biosignal of blood volume dynamics, producing wave shapes with several BWs. The AMARS application effectively removed the encountered BWs to assemble similarly aligned trends. The removal implementation was found repeatable in both ear and finger photoplethysmograms, emphasizing the importance of BW removal in biosignal processing in retaining its structural, functional and physiological properties. We also believe that AMARS may be relevant to other biological and continuous signals modulated by similar types of baseline volatility. [ABSTRACT FROM PUBLISHER]