Modeling of the photoplethysmogram during atrial fibrillation

Simulating cardiac signals on 3D human models for photoplethysmography development

Introduction: Image-based heart rate estimation technology offers a contactless approach to healthcare monitoring that could improve the lives of millions of people. In order to comprehensively test or optimize image-based heart rate extraction methods, the dataset should contain a large number of factors such as body motion, lighting conditions, and physiological states. However, collecting high-quality datasets with complete parameters is a huge challenge. Methods: In this paper, we introduce a bionic human model based on a three-dimensional (3D) representation of the human body. By integrating synthetic cardiac signal and body involuntary motion into the 3D model, five well-known traditional and four deep learning iPPG (imaging photoplethysmography) extraction methods are used to test the rendered videos. Results: To compare with different situations in the real world, four common scenarios (stillness, expression/talking, light source changes, and physical activity) are created on each 3D human. The 3D human can be built with any appearance and different skin tones. A high degree of agreement is achieved between the signals extracted from videos with the synthetic human and videos with a real human-the performance advantages and disadvantages of the selected iPPG methods are consistent for both real and 3D humans. Discussion: This technology has the capability to generate synthetic humans within various scenarios, utilizing precisely controlled parameters and disturbances. Furthermore, it holds considerable potential for testing and optimizing image-based vital signs methods in challenging situations where real people with reliable ground truth measurements are difficult to obtain, such as in drone rescue.

Malignant arrhythmias detection using a synthesis-by-analysis modeling method of arterial blood pressure signal

Extreme bradycardia, extreme tachycardia, ventricular flutter fib, and ventricular tachycardia are four malignant arrhythmias (MAs) that lead to sudden cardiac death. It is very important to detect them in daily life. The arterial blood pressure (ABP) signal contains abundant pathological information about four MAs and is easy to be recorded under domestic conditions. Thus, a synthesis-by-analysis (SA) modeling method for ABP signal was proposed to detect the four MAs in this study. The average models of MAs and healthy subjects were obtained by SA modeling, and the change of each ABP wave was quantitively described by twelve parameters of wave models. Then, the probabilistic neural network (PNN) and random forest (RF) are trained to detect the MAs. The experimental data were employed from Fantasia and the 2015 PhysioNet/CinC Challenge databases. The SA modeling results show that some pathological and physiological changes could be extracted from the average models. The two-sample ks-test results between different groups are markedly different (h = 1, p < 0.05). The detection results show that the performances of PPN classifiers are less than that of RF. The kappa coefficients (KC) for the RF classifiers are 97.167 ± 1.46 %, 97.888 ± 0.808 %, 99.895 ± 0.545 %, 98.575 ± 1.683 % and 92.241 ± 1.517 %, respectively. The mean KC is 97.083 ± 0.67 %. Compared to the performance of some existing studies, the proposed method has better performance and is potential to diagnose MAs in m-health.

Extraction of cardiac-related signals from a suprasternal pressure sensor during sleep

Objective.The accurate detection of respiratory effort during polysomnography is a critical element in the diagnosis of sleep-disordered breathing conditions such as sleep apnea. Unfortunately, the sensors currently used to estimate respiratory effort are either indirect and ignore upper airway dynamics or are too obtrusive for patients. One promising alternative is the suprasternal notch pressure (SSP) sensor: a small element placed on the skin in the notch above the sternum within an airtight capsule that detects pressure swings in the trachea. Besides providing information on respiratory effort, the sensor is sensitive to small cardiac oscillations caused by pressure perturbations in the carotid arteries or the trachea. While current clinical research considers these as redundant noise, they may contain physiologically relevant information.Approach.We propose a method to separate the signal generated by cardiac activity from the one caused by breathing activity. Using only information available from the SSP sensor, we estimate the heart rate and track its variations, then use a set of tuned filters to process the original signal in the frequency domain and reconstruct the cardiac signal. We also include an overview of the technical and physiological factors that may affect the quality of heart rate estimation. The output of our method is then used as a reference to remove the cardiac signal from the original SSP pressure signal, to also optimize the assessment of respiratory activity. We provide a qualitative comparison against methods based on filters with fixed frequency cutoffs.Main results.In comparison with electrocardiography (ECG)-derived heart rate, we achieve an agreement error of 0.06 ± 5.09 bpm, with minimal bias drift across the measurement range, and only 6.36% of the estimates larger than 10 bpm.Significance.Together with qualitative improvements in the characterization of respiratory effort, this opens the development of novel portable clinical devices for the detection and assessment of sleep disordered breathing.

Log-Spectral Matching GAN: PPG-based Atrial Fibrillation Detection can be Enhanced by GAN-based Data Augmentation with Integration of Spectral Loss

Photoplethysmography (PPG) is a ubiquitous physiological measurement that detects beat-to-beat pulsatile blood volume changes and hence has a potential for monitoring cardiovascular conditions, particularly in ambulatory settings. A PPG dataset that is created for a particular use case is often imbalanced, due to a low prevalence of the pathological condition it targets to predict and the paroxysmal nature of the condition as well. To tackle this problem, we propose log-spectral matching GAN (LSM-GAN), a generative model that can be used as a data augmentation technique to alleviate the class imbalance in a PPG dataset to train a classifier. LSM-GAN utilizes a novel generator that generates a synthetic signal without a up-sampling process of input white noises, as well as adds the mismatch between real and synthetic signals in frequency domain to the conventional adversarial loss. In this study, experiments are designed focusing on examining how the influence of LSM-GAN as a data augmentation technique on one specific classification task - atrial fibrillation (AF) detection using PPG. We show that by taking spectral information into consideration, LSM-GAN as a data augmentation solution can generate more realistic PPG signals.

Training Convolutional Neural Networks on Simulated Photoplethysmography Data: Application to Bradycardia and Tachycardia Detection

Objective: To develop a method for detection of bradycardia and ventricular tachycardia using the photoplethysmogram (PPG).

Approach: The detector is based on a dual-branch convolutional neural network (CNN), whose input is the scalograms of the continuous wavelet transform computed in 5-s segments. Training and validation of the CNN is accomplished using simulated PPG signals generated from RR interval series extracted from public ECG databases. Manually annotated real PPG signals from the PhysioNet/CinC 2015 Challenge Database are used for performance evaluation. The performance is compared to that of a pulse-based reference detector.

Results: The sensitivity/specificity were found to be 98.1%/97.9 and 76.6%/96.8% for the CNN-based detector, respectively, whereas the corresponding results for the pulse-based detector were 94.7%/99.8 and 67.1%/93.8%, respectively.

Significance: The proposed detector may be useful for continuous, long-term monitoring of bradycardia and tachycardia using wearable devices, e.g., wrist-worn devices, especially in situations where sensitivity is favored over specificity. The study demonstrates that simulated PPG signals are suitable for training and validation of a CNN.

Effects of using different algorithms and fiducial points for the detection of interbeat intervals, and different sampling rates on the assessment of pulse rate variability from photoplethysmography

OBJECTIVE

Pulse Rate Variability (PRV) has been widely used as a surrogate of Heart Rate Variability (HRV). However, there are several technical aspects that may affect the extraction of PRV information from pulse wave signals such as the photoplethysmogram (PPG). The aim of this study was to evaluate the effects of changing the algorithm and fiducial points used for determining inter-beat intervals (IBIs), as well as the PPG sampling rate, from simulated PPG signals with known PRV content.

METHODS

PPG signals were simulated using a proposed model, in which PRV information can be modelled. Two independent experiments were performed. First, 5 IBIs detection algorithms and 8 fiducial points were used for assessing PRV information from the simulated PPG signals, and time-domain and Poincaré plot indices were extracted and compared to the expected values according to the simulated PRV. The best combination of algorithms and fiducial points were determined for each index, using factorial designs. Then, using one of the best combinations, PPG signals were simulated with varying sampling rates. PRV indices were extracted and compared to the expected values using Student t-tests or Mann-Whitney U-tests.

RESULTS

From the first experiment, it was observed that AVNN and SD2 indices behaved similarly, and there was no significant influence of the fiducial points used. For other indices, there were several combinations that behaved similarly well, mostly based on the detection of the valleys of the PPG signal. There were differences according to the quality of the PPG signal. From the second experiment, it was observed that, for all indices but SDNN, the higher the sampling rate the better. AVNN and SD2 showed no statistical differences even at the lowest evaluated sampling rate (32 Hz), while RMSSD, pNN50, S, SD1 and SD1/SD2 showed good performance at sampling rates as low as 128 Hz.

CONCLUSION

The best combination of IBIs detection algorithms and fiducial points differs according to the application, but those based on the detection of the valleys of the PPG signal tend to show a better performance. The sampling rate of PPG signals for PRV analysis could be lowered to around 128 Hz, although it could be further lowered according to the application.

SIGNIFICANCE

The standardisation of PRV analysis could increase the reliability of this signal and allow for the comparison of results obtained from different studies. The obtained results allow for a first approach to establish guidelines for two important aspects in PRV analysis from PPG signals, i.e. the way the IBIs are segmented from PPG signals, and the sampling rate that should be used for these analyses. Moreover, a model for simulating PPG signals with PRV information has been proposed, which allows for the establishing of these guidelines while controlling for other variables, such as the quality of the PPG signal.

Diagnostic Features and Potential Applications of PPG Signal in Healthcare: A Systematic Review

Recent research indicates that Photoplethysmography (PPG) signals carry more information than oxygen saturation level (SpO2) and can be utilized for affordable, fast, and noninvasive healthcare applications. All these encourage the researchers to estimate its feasibility as an alternative to many expansive, time-wasting, and invasive methods. This systematic review discusses the current literature on diagnostic features of PPG signal and their applications that might present a potential venue to be adapted into many health and fitness aspects of human life. The research methodology is based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines 2020. To this aim, papers from 1981 to date are reviewed and categorized in terms of the healthcare application domain. Along with consolidated research areas, recent topics that are growing in popularity are also discovered. We also highlight the potential impact of using PPG signals on an individual's quality of life and public health. The state-of-the-art studies suggest that in the years to come PPG wearables will become pervasive in many fields of medical practices, and the main domains include cardiology, respiratory, neurology, and fitness. Main operation challenges, including performance and robustness obstacles, are identified.

Wearable Photoplethysmography for Cardiovascular Monitoring

Smart wearables provide an opportunity to monitor health in daily life and are emerging as potential tools for detecting cardiovascular disease (CVD). Wearables such as fitness bands and smartwatches routinely monitor the photoplethysmogram signal, an optical measure of the arterial pulse wave that is strongly influenced by the heart and blood vessels. In this survey, we summarize the fundamentals of wearable photoplethysmography and its analysis, identify its potential clinical applications, and outline pressing directions for future research in order to realize its full potential for tackling CVD.

Detection of Common Arrhythmias by the Watch-PAT: Expression of Electrical Arrhythmias by Pulse Recording

BACKGROUND

The WatchPAT (WP) device was shown to be accurate for the diagnosis of sleep apnea and is widely used worldwide as an ambulatory diagnostic tool. While it records peripheral arterial tone (PAT) and not electrocardiogram (ECG), the ability of it to detect arrhythmias is unknown and was not studied previously. Common arrhythmias such as atrial fibrillation (AF) or premature beats may be uniquely presented while recording PAT/pulse wave.

PURPOSE

To examine the potential detection of common arrhythmias by analyzing the PAT amplitude and pulse rate/volume changes.

PATIENTS AND METHODS

Patients with suspected sleep disordered breathing (SDB) were recruited with preference for patients with previously diagnosed AF or congestive heart failure (CHF). They underwent simultaneous WP and PSG studies in 11 sleep centers. A novel algorithm was developed to detect arrhythmias while measuring PAT and was tested on these patients. Manual scoring of ECG channel (recorded as part of the PSG) was blinded to the automatically analyzed WP data.

RESULTS

A total of 84 patients aged 57±16 (54 males) participated in this study. Their BMI was 30±5.7Kg/m2. Of them, 41 had heart failure (49%) and 17 (20%) had AF. The sensitivity and specificity of the WP to detect AF segments (of at least 60 seconds) were 0.77 and 0.99, respectively. The correlation between the WP derived detection of premature beats (events/min) to that of the PSG one was 0.98 (p<0.001).

CONCLUSION

The novel automatic algorithm of the WP can reasonably detect AF and premature beats. We suggest that when the algorithm raises a flag for arrhythmia, the patients should shortly undergo ECG and/or Holter ECG study.

Arrhythmia detection and classification using ECG and PPG techniques: a review

Electrocardiogram (ECG) and photoplethysmograph (PPG) are non-invasive techniques that provide electrical and hemodynamic information of the heart, respectively. This information is advantageous in the diagnosis of various cardiac abnormalities. Arrhythmia is the most common cardiovascular disease, manifested as single or multiple irregular heartbeats. However, due to the continuous manual observation, it becomes troublesome for experts sometimes to identify the paroxysmal nature of arrhythmia correctly. Moreover, due to advancements in technology, there is an inclination towards wearable sensors which monitor such patients continuously. Thus, there is a need for automatic detection techniques for the identification of arrhythmia. In the presented work, ECG and PPG-based state-of-the-art methods have been described, including preprocessing, feature extraction, and classification techniques for the detection of various arrhythmias. Additionally, this review exhibits various wearable sensors used in the literature and public databases available for the evaluation of results. The study also highlights the limitations of the current techniques and pragmatic solutions to improvise the ongoing effort.

Considerations on Performance Evaluation of Atrial Fibrillation Detectors

OBJECTIVE

A large number of atrial fibrillation (AF) detectors have been published in recent years, signifying that the comparison of detector performance plays a central role, though not always consistent. The aim of this study is to shed needed light on aspects crucial to the evaluation of detection performance.

METHODS

Three types of AF detector, using either information on rhythm, rhythm and morphology, or segments of ECG samples, are implemented and studied on both real and simulated ECG signals. The properties of different performance measures are investigated, for example, in relation to dataset imbalance.

RESULTS

The results show that performance can differ considerably depending on the way detector output is compared to database annotations, i.e., beat-to-beat, segment-to-segment, or episode-to-episode comparison. Moreover, depending on the type of detector, the results substantiate that physiological and technical factors, e.g., changes in ECG morphology, rate of atrial premature beats, and noise level, can have a considerable influence on performance.

CONCLUSION

The present study demonstrates overall strengths and weaknesses of different types of detector, highlights challenges in AF detection, and proposes five recommendations on how to handle data and characterize performance.

PPGTempStitch: A MATLAB Toolbox for Augmenting Annotated Photoplethsmogram Signals

An annotated photoplethysmogram (PPG) is required when evaluating PPG algorithms that have been developed to detect the onset and systolic peaks of PPG waveforms. However, few publicly accessible PPG datasets exist in which the onset and systolic peaks of the waveforms are annotated. Therefore, this study developed a MATLAB toolbox that stitches predetermined annotated PPGs in a random manner to generate a long, annotated PPG signal. With this toolbox, any combination of four annotated PPG templates that represent regular, irregular, fast rhythm, and noisy PPG waveforms can be stitched together to generate a long, annotated PPG. Furthermore, this toolbox can simulate real-life PPG signals by introducing different noise levels and PPG waveforms. The toolbox can implement two stitching methods: one based on the systolic peak and the other on the onset. Additionally, cubic spline interpolation is used to smooth the waveform around the stitching point, and a skewness index is used as a signal quality index to select the final signal output based on the stitching method used. The developed toolbox is free and open-source software, and a graphical user interface is provided. The method of synthesizing by stitching introduced in this paper is a data augmentation strategy that can help researchers significantly increase the size and diversity of annotated PPG signals available for training and testing different feature extraction algorithms.

Parameter Estimation of Hemodynamic Cardiovascular Model for Synthesis of Photoplethysmogram Signal

Synthesis of accurate, personalize photoplethysmogram (PPG) signal is important to interpret, analyze and predict cardiovascular disease progression. Generative models like Generative Adversarial Networks (GANs) can be used for signal synthesis, however, they are difficult to map to the underlying pathophysiological conditions. Hence, we propose a PPG synthesis strategy that has been designed using a cardiovascular system, modeled through the hemodynamic principle. The modeled architecture is composed of a two-chambered heart along with the systemic-pulmonic blood circulation and a baroreflex auto-regulation mechanism to control the arterial blood pressure. The comprehensive PPG signal is synthesized from the cardiac pressure-flow dynamics. In order to tune the modeled cardiac parameters with respect to a measured PPG data, a novel feature extraction strategy has been employed along with the particle swarm optimization heuristics. Our results demonstrate that the synthesized PPG is accurately followed the morphological changes of the ground truth (GT) signal with an RMSE of 0.003 occurring due to the Coronary Artery Disease (CAD) which is caused by an obstruction in the artery.

Synthetic photoplethysmogram generation using two Gaussian functions

Evaluating the performance of photoplethysmogram (PPG) event detection algorithms requires a large number of PPG signals with different noise levels and sampling frequencies. As publicly available PPG databases provide few options, artificially constructed PPG signals can also be used to facilitate this evaluation. Here, we propose a dynamic model to synthesize PPG over specified time durations and sampling frequencies. In this model, a single pulse was simulated by two Gaussian functions. Additionally, the beat-to-beat intervals were simulated using a normal distribution with a specific mean value and a specific standard deviation value. To add periodicity and to generate a complete signal, the circular motion principle was used. We synthesized three classes of pulses by emulating three different templates: excellent (systolic and diastolic waves are salient), acceptable (systolic and diastolic waves are not salient), and unfit (systolic and diastolic waves are noisy). The optimized model fitting of the Gaussian functions to the templates yielded 0.99, 0.98, and 0.85 correlations between the template and synthetic pulses for the excellent, acceptable, and unfit classes, respectively, with mean square errors of 0.001, 0.003, and 0.017, respectively. By comparing the heart rate variability of real PPG and randomly synthesized PPG for 5 min in 116 records from the MIMIC III database, strong correlations were found in SDNN, RMSSD, LF, HF, SD1, and SD2 (0.99, 0.89, 0.84, 0.89, 0.90 and 0.95, respectively).

Atrial fibrillation detection using photoplethysmographic signal: the effect of the observation window

A correct and early diagnosis of cardiac arrhythmias could improve patients' quality of life. The aim of this study is to classify the cardiac rhythm (atrial fibrillation, AF, or normal sinus rhythm NSR) from the photoplethysmographic (PPG) signal and assess the effect of the observation window length. Simulated signals are generated with a PPG simulator previously proposed. The different window lengths taken into account are 20, 30, 40, 50, 100, 150, 200, 250 and 300 beats. After systolic peak detection algorithm, 10 features are computed on the inter-systolic interval series, assessing variability and irregularity of the series. Then, feature selection was performed (using the sequential forward floating search algorithm) which identified two variability parameters (Mean and rMSSD) as the best selection. Finally, the classification by linear support vector machine was performed. Using only two features, accuracy was very high for all the analyzed observation window lengths, going from 0.913±0.055 for length equal to 20 to 0.995±0.011 for length equal to 300 beats.Clinical relevance These preliminary results show that short PPG signals (20 beats) can be used to correctly detect AF.

A Review of Atrial Fibrillation Detection Methods as a Service

Atrial Fibrillation (AF) is a common heart arrhythmia that often goes undetected, and even if it is detected, managing the condition may be challenging. In this paper, we review how the RR interval and Electrocardiogram (ECG) signals, incorporated into a monitoring system, can be useful to track AF events. Were such an automated system to be implemented, it could be used to help manage AF and thereby reduce patient morbidity and mortality. The main impetus behind the idea of developing a service is that a greater data volume analyzed can lead to better patient outcomes. Based on the literature review, which we present herein, we introduce the methods that can be used to detect AF efficiently and automatically via the RR interval and ECG signals. A cardiovascular disease monitoring service that incorporates one or multiple of these detection methods could extend event observation to all times, and could therefore become useful to establish any AF occurrence. The development of an automated and efficient method that monitors AF in real time would likely become a key component for meeting public health goals regarding the reduction of fatalities caused by the disease. Yet, at present, significant technological and regulatory obstacles remain, which prevent the development of any proposed system. Establishment of the scientific foundation for monitoring is important to provide effective service to patients and healthcare professionals.

A Pulse Signal Preprocessing Method Based on the Chauvenet Criterion

Pulse signals are widely used to evaluate the status of the human cardiovascular, respiratory, and circulatory systems. In the process of being collected, the signals are usually interfered by some factors, such as the spike noise and the poor-sensor-contact noise, which have severely affected the accuracy of the subsequent detection models. In recent years, some methods have been applied to processing the above noisy signals, such as dynamic time warping, empirical mode decomposition, autocorrelation, and cross-correlation. Effective as they are, those methods are complex and difficult to implement. It is also found that the noisy signals are tightly related to gross errors. The Chauvenet criterion, one of the gross error discrimination criterions, is highly efficient and widely applicable for being without the complex calculations like decomposition and reconstruction. Therefore, in this study, based on the Chauvenet criterion, a new pulse signal preprocessing method is proposed, in which adaptive thresholds are designed, respectively, to discriminate the abnormal signals caused by spike noise and poor-sensor-contact noise. 81 hours of pulse signals (with a sleep apnea annotated every 30 seconds and 9,720 segments in total) from the MIT-BIH Polysomnographic Database are used in the study, including 35 minutes of poor-sensor-contact noises and 25 minutes of spike noises. The proposed method was used to preprocess the pulse signals, in which 9,684 segments out of a total of 9,720 were correctly discriminated, and the accuracy of the method reached 99.63%. To quantitatively evaluate the noise removal effect, a simulation experiment is conducted to compare the Jaccard Similarity Coefficient (JSC) calculated before and after the noise removal, respectively, and the results show that the preprocessed signal obtains higher JSC, closer to the reference signal, which indicates that the proposed method can effectively improve the signal quality. In order to evaluate the method, three back-propagation (BP) sleep apnea detection models with the same network structure and parameters were established, respectively. Through comparing the recognition rate and the prediction rate of the models, higher rates were obtained by using the proposed method. To prove the efficiency, the comparison experiment between the proposed Chauvenet-based method and a Romanovsky-based method was conducted, and the execution time of the proposed method is much shorter than that of the Romanovsky method. The results suggest that the superiority in execution time of the Chauvenet-based method becomes more significant as the date size increases.

The potential of photoplethysmogram and galvanic skin response in emotion recognition using nonlinear features

Recently, developing an accurate automatic emotion recognition system using a minimum number of bio-signals has become a challenging issue in "affective computing." This study aimed to propose a reliable system by examining nonlinear dynamics of photoplethysmogram (PPG) and galvanic skin response (GSR). To address this goal, two strategies were adopted. First, the efficiency of each signal in valence/arousal based emotion categorization was examined. Then, the proficiency of a hybrid feature, by combining both GSR and PPG features was studied. Lyapunov exponents, lagged Poincare's measures, and approximate entropy were extracted to characterize the irregularity and chaotic behavior of the phase space. To discriminate two levels of arousal and two levels of the valence, a probabilistic neural network (PNN) with different sigma adjustment parameter was examined. The results showed that the phase space geometry and consequently, the signal dynamics are influenced by the emotional music video. Additionally, distinctive patterns of the phase space behavior were observed under the influence of different lags. For both signals, the most irregularity was observed during the high valence, and the least irregularity was seen during the low valence. Consequently, signals' irregularity is affected by the valence dimension. The results showed that the fusion has more potential for emotion recognition than that of using each signal separately. For sigma = 0.1, the highest recognition rate was 100% in a subject-dependent mode. In a subject-independent mode, the maximum accuracies of 88.57 and 86.8% were obtained for arousal and valence dimensions, respectively.

Detection of atrial fibrillation using a wrist-worn device

OBJECTIVE

This study proposes an algorithm for the detection of atrial fibrillation (AF), designed to operate on extended photoplethysmographic (PPG) signals recorded using a wrist-worn device of own design.

APPROACH

Robustness against false alarms is achieved by means of signal quality assessment and different techniques for suppression of ectopic beats, bigeminy, and respiratory sinus arrhythmia. The decision logic is based on our previously proposed RR interval-based AF detector, but modified to account for differences between interbeat intervals in the ECG and the PPG. The detector is evaluated on simulated PPG signals as well as on clinical PPG signals recorded during cardiac rehabilitation after myocardial infarction.

MAIN RESULTS

Analysis of the clinical signals showed that 1.5 false alarms were on average produced per day with a sensitivity of 72.0% and a specificity of 99.7% when 89.2% of the database was available for analysis, whereas as many as 15 when the RR interval-based AF detector, boosted by accelerometer information for signal quality assessment, was used. However, a sensitivity of 97.2% and a specificity of 99.6% were achieved when increasing the demands on signal quality so that 50% was available for analysis.

SIGNIFICANCE

The proposed detector offers promising performance and is particularly well-suited for implementation in low-power wearable devices, e.g. wrist-worn devices, with significance in mass screening applications.

Sinus or not: a new beat detection algorithm based on a pulse morphology quality index to extract normal sinus rhythm beats from wrist-worn photoplethysmography recordings

OBJECTIVE

Wrist-worn photoplethysmography (PPG) can enable free-living physiological monitoring of people during diverse activities, ranging from sleep to physical exercise. In many applications, it is important to remove the pulses not related to sinus rhythm beats from the PPG signal before using it as a cardiovascular descriptor. In this manuscript, we propose an algorithm to assess the morphology of the PPG signal in order to reject non-sinus rhythm pulses, such as artefacts or pulses related to arrhythmic beats.

APPROACH

The algorithm segments the PPG signal into individual pulses and dynamically evaluates their morphological likelihood of being normal sinus rhythm pulses via a template-matching approach that accounts for the physiological variability of the signal. The normal sinus rhythm likelihood of each pulse is expressed as a quality index that can be employed to reject artefacts and pulses related to arrhythmic beats.

MAIN RESULTS

Thresholding the pulse quality index enables near-perfect detection of normal sinus rhythm beats by rejecting most of the non-sinus rhythm pulses (positive predictive value 98%-99%), both in healthy subjects and arrhythmic patients. The rejection of arrhythmic beats is almost complete (sensitivity to arrhythmic beats 7%-3%), while the sensitivity to sinus rhythm beats is not compromised (96%-91%).

SIGNIFICANCE

The developed algorithm consistently detects normal sinus rhythm beats in a PPG signal by rejecting artefacts and, as a first of its kind, arrhythmic beats. This increases the reliability in the extraction of features which are adversely influenced by the presence of non-sinus pulses, whether related to inter-beat intervals or to pulse morphology, from wrist-worn PPG signals recorded in free-living conditions.

Wearable sensors: can they benefit patients with chronic kidney disease?

INTRODUCTION

This article ponders upon wearable medical measurement devices in relation to Chronic Kidney Disease (CKD) and its' associated comorbidities - and whether these might benefit CKD-patients. We aimed to map the intersection(s) of nephrology and wearable sensor technology to help technologists understand medical aspects, and clinicians to understand technological possibilities that are available (or soon will become so). Areas covered: A structured literature search on main comorbidities and complications CKD patients suffer from, was used to steer mini-reviews on wearable sensor technologies clustered around 3 themes being: Cardiovascular-related, diabetes-related and physical fitness/frailty. This review excludes wearable dialysis - although also strongly enabled by miniaturization - because that highly important theme deserves separate in-depth reviewing. Expert commentary: Continuous progress in integrated electronics miniaturization enormously lowered price, size, weight and energy consumption of electronic sensors, processing power, memory and wireless connectivity. These combined factors boost opportunities for wearable medical sensors. Such devices can be regarded as enablers for: Remote monitoring, influencing human behaviour (exercise, dietary), enhanced home care, remote consults, patient education and peer networks. However, to make wearable medical devices succeed, the challenge to fit them into health care structures will be dominant over the challenge to realize the bare technologies themselves.