Wearable PPG Research Articles

Energy-Efficient Sleep Apnea Detection Using a Hyperdimensional Computing Framework Based on Wearable Bracelet Photoplethysmography.

Sleep apnea syndrome (SAS) is a common sleep disorder, which has been shown to be an important contributor to major neurocognitive and cardiovascular sequelae. Considering current diagnostic strategies are limited with bulky medical devices and high examination expenses, a large number of cases go undiagnosed. To enable large-scale screening for SAS, wearable photoplethysmography (PPG) technologies have been used as an early detection tool. However, existing algorithms are energy-intensive and require large amounts of memory resources, which are believed to be the major drawbacks for further promotion of wearable devices for SAS detection. In this paper, an energy-efficient method of SAS detection based on hyperdimensional computing (HDC) is proposed. Inspired by the phenomenon of chunking in cognitive psychology as a memory mechanism for improving working memory efficiency, we proposed a one-dimensional block local binary pattern (1D-BlockLBP) encoding scheme combined with HDC to preserve dominant dynamical and temporal characteristics of pulse rate signals from wearable PPG devices. Our method achieved 70.17 % accuracy in sleep apnea segment detection, which is comparable with traditional machine learning methods. Additionally, our method achieves up to 67× lower memory footprint, 68× latency reduction, and 93× energy saving on the ARM Cortex-M4 processor. The simplicity of hypervector operations in HDC and the novel 1D-BlockLBP encoding effectively preserve pulse rate signal characteristics with high computational efficiency. This work provides a scalable solution for long-term home-based monitoring of sleep apnea, enhancing the feasibility of consistent patient care.

Towards a machine-learning assisted non-invasive classification of dengue severity using wearable PPG data: a prospective clinical study

Dengue epidemics impose considerable strain on healthcare resources. Real-time continuous and non-invasive monitoring of patients admitted to the hospital could lead to improved care and outcomes. We evaluated the performance of a commercially available wearable (SmartCare) utilising photoplethysmography (PPG) to stratify clinical risk for a cohort of hospitalised patients with dengue in Vietnam. We performed a prospective observational study for adult and paediatric patients with a clinical diagnosis of dengue at the Hospital for Tropical Disease, Ho Chi Minh City, Vietnam. Patients underwent PPG monitoring early during admission alongside standard clinical care. PPG waveforms were analysed using machine learning models. Adult patients were classified between 3 severity classes: i) uncomplicated (ward-based), ii) moderate-severe (emergency department-based), and iii) severe (ICU-based). Data from paediatric patients were split into 2 classes: i) severe (during ICU stay) and ii) follow-up (14-21 days after the illness onset). Model performances were evaluated using standard classification metrics and 5-fold stratified cross-validation. We included PPG and clinical data from 132 adults and 15 paediatric patients with a median age of 28 (IQR, 21-35) and 12 (IQR, 9-13) years respectively. 1781h of PPG data were available for analysis. The best performing convolutional neural network models (CNN) achieved a precision of 0.785 and recall of 0.771 in classifying adult patients according to severity class and a precision of 0.891 and recall of 0.891 in classifying between disease and post-disease state in paediatric patients. We demonstrate that the use of a low-cost wearable provided clinically actionable data to differentiate between patients with dengue of varying severity. Continuous monitoring and connectivity to early warning systems could significantly benefit clinical care in dengue, particularly within an endemic setting. Work is currently underway to implement these models for dynamic risk predictions and assist in individualised patient care. EPSRC Centre for Doctoral Training in High-Performance Embedded and Distributed Systems (HiPEDS) (Grant: EP/L016796/1) and the Wellcome Trust (Grants: 215010/Z/18/Z and 215688/Z/19/Z).

PPG heart rate extraction algorithm based on the motion artifact intensity Classification and removal framework

When heart rate is extracted by wearable PPG, motion artifact noise causes serious interference. Since the accuracy of PPG heart rate extraction tends to be positively correlated with the complexity of heart rate extraction algorithm, it is challenging to extract heart rate via wearable PPG throughout the day by taking into account both algorithmic complexity and the accuracy of heart rate extraction. In this paper, a PPG heart rate extraction algorithm is proposed under the motion artifact intensity classification and removal (MAICR) framework. Firstly, it evaluates the motion artifact intensity using acceleration time-domain information and PPG frequency-domain information. Then, different heart rate estimation methods are selected according to motion artifact intensity, in ascending order. This corresponds to the use of the frequency-domain direct extraction method, the MNLMS adaptive filtering method, and the joint sparse spectral reconstruction method. Additionally, the overall framework of the algorithm (MAICR) is developed by considering the performance of various methods. Finally, experiments are conducted on the public dataset to demonstrate that the proposed algorithm is applicable to reduce the algorithmic complexity effectively while maintaining the accuracy in heart rate extraction at a certain degree.

Contact measurement of skin temperature using a wearable two-channel PPG optical sensor supplemented by thermometers

Abstract This paper describes realization, basic properties, testing, and experiments with a special prototype of a wearable two-channel photoplethysmography (PPG) sensor supplemented by contact thermometers that maps the skin temperature at the place where the optical part of the sensor touches a measured hand part (typically a wrist and fingers). Preliminary measurement confirms that proposed I2C thermometers have proper stability and precision, so can be successfully used in the developed PPG sensor. The performed main experiments show that the significant increase of temperature was always observed at the place of the worn PPG sensors during the whole measurement. This rise of temperature affects mainly the PPG signal range, thus it has also influence on the quality of the sensed PPG wave. The conclusion formulates the final recommendation about the necessity of at least 5-minute time delay between the PPG sensor placement on the hand and the start of the PPG signal acquisition. While the current measurements were realized in normal laboratory conditions, the whole wearable PPG sensor consists of non-ferromagnetic materials and all parts are fully shielded by aluminum boxes to enable measurement in a low magnetic field environment which, is our final long term research aim.

An Optical Signal Simulator for the Characterization of Photoplethysmographic Devices.

(1) Background: An optical simulator able to provide a repeatable signal with desired characteristics as an input to a photoplethysmographic (PPG) device is presented in order to compare the performance of different PPG devices and also to test the devices with PPG signals available in online databases. (2) Methods: The optical simulator consists of an electronic board containing a photodiode and LEDs at different wavelengths in order to simulate light reflected by the body; the PPG signal taken from the chosen database is reproduced by the electronic board, and the board is used to test a wearable PPG medical device in the form of earbuds. (3) Results: The PPG device response to different average and peak-to-peak signal amplitudes is shown in order to assess the device sensitivity, and the fidelity in tracking the actual heart rate is also investigated. (4) Conclusions: The developed optical simulator promises to be an affordable, flexible, and reliable solution to test PPG devices in the lab, allowing the testing of their actual performances thanks to the possibility of using PPG databases, thus gaining useful and significant information before on-the-field clinical trials.

Nocturnal Autonomic and Vasoconstriction Parameters By Wearable Wrist Photoplethysmography Predict Intensity of Imminent Pain in Subjects with Sickle Cell Disease

Introduction: Sudden and unpredictable onset of pain is one of the biggest contributors to the morbidity of sickle cell disease (SCD), thought to occur due to obstruction of microvascular blood flow by rigid sickle-shaped red blood cells. Autonomic mediated vasoconstriction in the peripheral vasculature, with resulting decrease in tissue perfusion, is likely a triggering mechanism for the onset of vaso-occlusive pain, while the autonomic nervous system is also implicated in the pathogenesis of chronic neuropathic pain. Our analysis of 212 polysomnograms in SCD subjects showed that greater magnitude of nocturnal vasoconstriction (Mvasoc) is predictive of increased frequency of hospitalizations for vaso-occlusive pain crises (VOC) in the subsequent years (Chalacheva et al., Am J Hematol, 2020). In order to further develop this finding into a biomarker of more imminent pain, we monitored nocturnal photoplethysmography (PPG) at home with a wearable wristband device to measure vasoconstriction activity and heart rate variability, along with a daily electronic pain survey. Methods: 10 subjects with SCD were consented and underwent autonomic monitoring with a wristband device (Biostrap Inc) that uses an optical sensor to measure continuous PPG during sleep, with the raw PPG waveforms available from a cloud-based server. A unique biomarker (Mvasoc) was used to quantify the magnitude of vasoconstriction from the raw PPG signal, considering the frequency, magnitude, and duration of autonomic mediated spontaneous vasoconstrictions each night. Heart rate variability was derived from the pulse-to-pulse interval (PPI) of the PPG signal. Spectral indices of the PPI represent parasympathetic activity (high frequency power; HFP) and sympatho-vagal balance (Low to high Ratio; LHR = low frequency power(LFP)/HFP). Subjects concurrently documented daily pain incidence using an electronic text-based REDCap survey, with pain intensity measured on a scale of 1-10 if experiencing pain. Daily stress levels were also recorded on a scale of 1-10. Multivariate regression analysis was performed. Results: The subjects had a mean age of 25 years (range 14- 40 years) with an average hemoglobin of 8.8 g/dl . A total of 68 usable nocturnal PPG recordings were made (3-16 nights per subject). Therewere 25 incidents of pain on the day following autonomic monitoring with Biostrap, with the pain intensity ranging from 3-8. Multivariate analysis of nocturnal autonomic and vasoconstriction parameters revealed that a lower HFP ( p =0.0002) and a higher Mvasoc ( p=0.02) both preceded a higher intensity of pain the following day (Table 1). These results are consistent with a greater parasympathetic withdrawal and a higher magnitude of peripheral vasoconstriction predicting higher intensity of imminent pain. Age, gender, hemoglobin, stress levels and LHR were not predictive of pain the following day. Conclusion: Augmented nocturnal vasoconstriction and parasympathetic withdrawal noted prior to high intensity of pain imply that dysautonomia, with attendant changes in peripheral perfusion, has a significant role in pathogenesis of pain in SCD. Furthermore, autonomic vasoreactivity parameters were not significantly altered on the night after pain onset suggesting that the dysautonomia precedes pain. These results support the possibility of employing indices of nocturnal autonomic vasoreactivity as a predictive biomarker for imminent pain in SCD and provides a potential window for therapeutic intervention. This pilot study demonstrates the successful use of a wearable device PPG to remotely monitor nocturnal autonomic vasoreactivity and the ability to objectively quantify peripheral vasoconstriction responses. These findings will need to be applied in a larger cohort of SCD subjects with longer term monitoring for further validation and development of autonomic vasoreactivity as a predictive tool for pain in SCD.

Ultrathin Self-Powered Heavy-Metal-Free Cu-In-Se Quantum Dot Photodetectors for Wearable Health Monitoring.

Mechanically deformable photodetectors (PDs) are key device components for wearable health monitoring systems based on photoplethysmography (PPG). Achieving high detectivity, fast response time, and an ultrathin form factor in the PD is highly needed for next-generation wearable PPG systems. Self-powered operation without a bulky power-supply unit is also beneficial for point-of-care application. Here, we propose ultrathin self-powered PDs using heavy-metal-free Cu-In-Se quantum dots (QDs), which enable high-performance wearable PPG systems. Although the light-absorbing QD layer is extremely thin (∼40 nm), the developed PD exhibits excellent performance (specific detectivity: 2.10 × 1012 Jones, linear dynamic range: 102 dB, and spectral range: 250-1050 nm at zero bias), which is comparable to that of conventional rigid QD-PDs employing thick Pb-chalcogenide QD layers. This is attributed to material and device strategies─materials that include Cu-In-Se QDs, a MoS2-nanosheet-blended poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) hole transport layer, a ZnO nanoparticle electron transport layer, Ag and ITO electrodes, and an ultrathin form factor (∼120 nm except the electrodes) that enable excellent mechanical deformability. These allow the successful application of QD-PDs to a wearable system for real-time PPG monitoring, expanding their potential in the field of mobile bioelectronics.

Photoplethysmography in Wearable Devices: A Comprehensive Review of Technological Advances, Current Challenges, and Future Directions

Photoplethysmography (PPG) is an affordable and straightforward optical technique used to detect changes in blood volume within tissue microvascular beds. PPG technology has found widespread application in commercial medical devices, enabling measurements of oxygen saturation, blood pressure, and cardiac output; the assessment of autonomic nerve function; and the diagnosis of peripheral vascular disease. Recently, the growing demand for non-invasive, portable, cost-effective technology, along with advancements in small semiconductor components, has led to the integration of PPG into various wrist-worn wearable devices. Multiple sensor structures have been proposed and, through appropriate signal processing and algorithmic application, these wearable devices can measure a range of health indicators during daily life. This paper begins by addressing the market status of wrist-worn wearable devices, followed by an explanation of the fundamental principles underlying light operation and its interaction with living tissue for PPG measurements. Moving on to technological advancements, the paper addresses the analog front end for the measurement of the PPG signal, sensor configurations with multiple light emitters and receivers, the minimum sampling rate required for low-power systems, and the measurement of stress, sleep, blood pressure, blood glucose, and activity using PPG signals. Several challenges in the field are also identified, including selecting the appropriate wavelength for the PPG sensor’s light source, developing low-power interpolation methods to extract high-resolution inter-beat intervals at a low sampling rate, and exploring the measurement of physiological phenomena using multi-wavelength PPG signals simultaneously collected at the same location. Lastly, the paper presents future research directions, which encompass the development of new, reliable parameters specific to wearable PPG devices and conducting studies in real-world scenarios, such as 24-h long-term measurements.

Feasibility of Wearable PPG for Simultaneous Hand Gesture and Force Level Classification

Simultaneous decoding of hand gestures and force levels has the potential to unlock a broad range of home-based application scenarios in post-stroke rehabilitation. However, current wearable approaches cannot struggle to balance wearability and functionality well. We thus propose and investigate the feasibility of wearable photoplethysmography (PPG) for simultaneous hand gesture and force level classification. A customized wristband embedded with green, red, and infrared light PPG sensors was developed. Fourteen subjects wore this wristband and performed four pinching gestures at 10%, 40%, and 70% maximum volunteer contraction (MVC) force levels. The results showed that the proposed method achieved satisfactory recognition accuracy, and the combination of three reflected light PPG signals was more accurate than any single signal or two-signal combinations. Force level classification accuracy within each hand gesture was less robust to the variety of hand gestures, and hand gesture classification accuracy at different force levels decreased as the force level increased. In addition, the proposed method still achieved reasonable recognition accuracy, with a relatively small amount of training data, potentially saving time in practical applications. These results demonstrate the feasibility of a wearable, low-cost PPG approach for simultaneous hand gesture and force level classification, which could facilitate more widespread hand mobility and strength training both inside and outside the clinic.

On-Device Multi-Level Signal Quality Aware Compression for Energy-Efficient Wearable PPG Sensing

On-device computing in biomedical sensors has become attractive for developing wearable health monitoring systems. The challenge is to make a compromise between the latency and complexity in a resource-constrained implementation. This article describes an on-device implementation of multi-level signal quality aware and quality controlled compression (MSQQCC) that enhances the compression factor while preserving the clinical features in a wearable photoplethysmography (PPG) sensing application. The multi-level quality assessment (QA) provides three eligible PPG qualities, viz., “excellent,” “good,” and “average,” based on which corresponding upper limits are set for further compression using a discrete wavelet transform, while the “corrupted” segments are discarded. A pretrained multilayer perceptron neural network (MLPNN) provides the optimal quantization level of coefficients. The residual data is separately compressed using an autoencoder (AE). MSQQCC was evaluated with 300 min of PPG data from three public data sets and 110 min of data collected at a laboratory. The end-to-end pipeline was implemented in a standalone system with an ARM Cortex A53 controller, requiring 35.51 kB of memory and 1.8 s latency to process 4 s PPG data. The on-device QA achieved 98.45% overall accuracy (Ac), which outperforms published works on PPG QA. The mean deviation of PPG clinical features by 5%, with overall compression ratio (CR) and percentage root mean squared difference (PRD) were 40.85 and 2.52, which are superior to many published works. Real-time transmission over Bluetooth shows an improvement of energy efficiency by a significant factor and a 34% extended battery life for wearable PPG sensors. The results are encouraging for the adoption of MSQQCC in wearable biomedical health monitoring.

Unified Quality-Aware Compression and Pulse-Respiration Rates Estimation Framework for Reducing Energy Consumption and False Alarms of Wearable PPG Monitoring Devices

Objective: Due to the high demands of tiny, compact, lightweight and low-cost photoplethysmogram (PPG) monitoring devices, these devices are resource-constrained including limited battery power. Consequently, it highly demands frequent charge or battery replacement in the case of continuous PPG sensing and transmission. Further, PPG signals are often corrupted severely under ambulatory and exercise recording conditions that leads to frequent false alarms. Method: In this paper, we propose a unified quality-aware compression and pulse-respiration rates estimation framework for reducing energy consumption and false alarms of wearable and edge PPG monitoring devices by exploring predictive coding technique for jointly performing signal quality assessment (SQA), data compression and pulse rate (PR) and respiration rate (RR) estimation without use of different domains of signal processing techniques that can be achieved by using the features extracted from the smoothed prediction error signal. Results: By using the five standard PPG databases, the performance of the proposed unified framework is evaluated in terms of compression ratio (CR), mean absolute error (MAE), false alarm reduction rate (FARR), processing time (PT) and energy saving (ES). The compression, PR and RR estimation and SQA results are compared with that of the existing methods and also with results of uncompressed PPG signals with sampling rates of 125 Hz and 25 Hz. Conclusion: The proposed unified quality-aware framework achieves an average CR of 4%, SQA (Se of 92.00%, FARR of 84.87%), PR (MAE: 0.46 ±1.20) and RR (MAE: 1.75 (0.65-4.45), PT (sec) of 15.34 ±0.01) and ES of 70.28% which outperforms the results of uncompressed PPG signal with a sampling rate of 125 Hz. Significance: Arduino Due computing platform based implementation demonstrates the real-time feasibility of the proposed unified quality-aware PR-RR estimation and data compression and transmission framework on the limited computational resources. Thus, it has great potential in improving energy-efficiency and trustworthiness of wearable and edge PPG monitoring devices.

A Review of Wearable Multi-wavelength Photoplethysmography.

Optical pulse detection photoplethysmography (PPG) provides a means of low cost and unobtrusive physiological monitoring that is popular in many wearable devices. However, the accuracy, robustness and generalizability of single-wavelength PPG sensing are sensitive to biological characteristics as well as sensor configuration and placement; this is significant given the increasing adoption of single-wavelength wrist-worn PPG devices in clinical studies and healthcare. Since different wavelengths interact with the skin to varying degrees, researchers have explored the use of multi-wavelength PPG to improve sensing accuracy, robustness and generalizability. This paper contributes a novel and comprehensive state-of-the-art review of wearable multi-wavelength PPG sensing, encompassing motion artifact reduction and estimation of physiological parameters. The paper also encompasses theoretical details about multi-wavelength PPG sensing and the effects of biological characteristics. The review findings highlight the promising developments in motion artifact reduction using multi-wavelength approaches, the effects of skin temperature on PPG sensing, the need for improved diversity in PPG sensing studies and the lack of studies that investigate the combined effects of factors. Recommendations are made for the standardization and completeness of reporting in terms of study design, sensing technology and participant characteristics.

Cuffless Blood Pressure Estimation During Moderate- and Heavy-Intensity Exercise Using Wearable ECG and PPG.

To develop and evaluate an accurate method for cuffless blood pressure (BP) estimation during moderate- and heavy-intensity exercise. Twelve participants performed three cycling exercises: a ramp-incremental exercise to exhaustion, and moderate and heavy pseudorandom binary sequence exercises on an electronically braked cycle ergometer over the course of 21 minutes. Subject-specific and population-based nonlinear autoregressive models with exogenous inputs (NARX) were compared with feedforward artificial neural network (ANN) models and pulse arrival time (PAT) models. Population-based NARX models, (applying leave-one-subject-out cross-validation), performed better than the other models and showed good capability for estimating large changes in mean arterial pressure (MAP). The models were unable to track consistent decreases in BP during prolonged exercise caused by reduction in peripheral vascular resistance, since this information is apparently not encoded in the employed proxy physiological signals (electrocardiography and forehead PPG) used for BP estimation. Nevertheless, the population-based NARX model had an error standard deviation of 11.0 mmHg during the entire exercise window, which improved to 9.0 mmHg when the model was periodically calibrated every 7 minutes. Population-based NARX models can estimate BP during moderate- and heavy-intensity exercise but need periodic calibration to account for the change in vascular resistance during exertion. MAP can be continuously tracked during exercise using only wearable sensors, making monitoring exercise physiology more convenient and accessible.

Information-Based Similarity of Ordinal Pattern Sequences as a Novel Descriptor in Obstructive Sleep Apnea Screening Based on Wearable Photoplethysmography Bracelets.

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.

A dynamic reconfigurable wearable device to acquire high quality PPG signal and robust heart rate estimate based on deep learning algorithm for smart healthcare system

Photoplethysmography (PPG) is a noninvasively technique used to detect vital signs such as heart rate (HR), saturation of peripheral oxygen (SpO2) and blood pressure (BP). Recently, wearable finger-type PPG devices are increasingly developed toward convenience of person under monitoring (PUM). Two most critical features of wearable PPG devices are high accuracy and long operation time. To enhance these functions, this paper proposes a new architecture to select, process and transfer only high quality PPG signals. Hence, data quality is significantly improved and power consumption on wireless module is minimized. In the proposed architecture, parameters of PPG sensor are reconfigured in real-time being suitable with skin characteristics of PUM. Moreover, the adaptive LED current control algorithm is proposed to dynamically change the LED current to remove various motion artifacts (MA) and get high amplitude PPG signal. We also develop a heart rate (HR) estimation framework utilizing deep learning (DL) model based on convolutional neural network (CNN) and long short-term memory (LSTM) network. The combination of CNN-LSTM can extract features from both spatial and temporal correlation. A light-weight model with two CNN-LSTM layers is built to estimate HR with in only 5s. For validation, we conduct experiments with volunteers doing various physical exercises. The results show PPG signals with high amplitude and signal-to-noise ratio (SNR). HR estimation is more accurate even during irregular and muscle strength exercises. The proposed adaptive architecture and DL-based HR estimation can overcome MA and minimize the power consumption on wireless transfer module of wearable PPG devices.

Accurate detection of heart rate using in-ear photoplethysmography in a clinical setting.

BackgroundRecent research has shown that photoplethysmography (PPG) based wearable sensors offer a promising potential for chronic disease monitoring. The aim of the present study was to assess the performance of an in-ear wearable PPG sensor in acquiring valid and reliable heart rate measurements in a clinical setting, with epileptic patients.MethodsPatients undergoing video-electroencephalography (EEG) monitoring with concomitant one-lead electrocardiographic (ECG) recordings were equipped with an in-ear sensor developed by cosinuss°.ResultsIn total, 2,048 h of recording from 97 patients with simultaneous ECG and in-ear heart rate data were included in the analysis. The comparison of the quality-filtered in-ear heart rate data with the reference ECG resulted in a bias of 0.78 bpm with a standard deviation of ±2.54 bpm; Pearson’s Correlation Coefficient PCC = 0.83; Intraclass Correlation Coefficient ICC = 0.81 and mean absolute percentage error MAPE = 2.57.ConclusionThese data confirm that the in-ear wearable PPG sensor provides accurate heart rate measurements in comparison with ECG under realistic clinical conditions, especially with a signal quality indicator. Further research is required to investigate whether this technology is helpful in identifying seizure-related cardiovascular changes.

Wearable In-Ear PPG: Detailed Respiratory Variations Enable Classification of COPD.

An ability to extract detailed spirometry-like breathing waveforms from wearable sensors promises to greatly improve respiratory health monitoring. Photoplethysmography (PPG) has been researched in depth for estimation of respiration rate, given that it varies with respiration through overall intensity, pulse amplitude and pulse interval. We compare and contrast the extraction of these three respiratory modes from both the ear canal and finger and show a marked improvement in the respiratory power for respiration induced intensity variations and pulse amplitude variations when recording from the ear canal. We next employ a data driven multi-scale method, noise assisted multivariate empirical mode decomposition (NA-MEMD), which allows for simultaneous analysis of all three respiratory modes to extract detailed respiratory waveforms from in-ear PPG. For rigour, we considered in-ear PPG recordings from healthy subjects, both older and young, patients with chronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis (IPF) and healthy subjects with artificially obstructed breathing. Specific in-ear PPG waveform changes are observed for COPD, such as a decreased inspiratory duty cycle and an increased inspiratory magnitude, when compared with expiratory magnitude. These differences are used to classify COPD from healthy and IPF waveforms with a sensitivity of 87% and an overall accuracy of 92%. Our findings indicate the promise of in-ear PPG for COPD screening and unobtrusive respiratory monitoring in ambulatory scenarios and in consumer wearables.

Heart Rate Variability from Wearable Photoplethysmography Systems: Implications in Sleep Studies at High Altitude.

The interest in photoplethysmography (PPG) for sleep monitoring is increasing because PPG may allow assessing heart rate variability (HRV), which is particularly important in breathing disorders. Thus, we aimed to evaluate how PPG wearable systems measure HRV during sleep at high altitudes, where hypobaric hypoxia induces respiratory disturbances. We considered PPG and electrocardiographic recordings in 21 volunteers sleeping at 4554 m a.s.l. (as a model of sleep breathing disorder), and five alpine guides sleeping at sea level, 6000 m and 6800 m a.s.l. Power spectra, multiscale entropy, and self-similarity were calculated for PPG tachograms and electrocardiography R–R intervals (RRI). Results demonstrated that wearable PPG devices provide HRV measures even at extremely high altitudes. However, the comparison between PPG tachograms and RRI showed discrepancies in the faster spectral components and at the shorter scales of self-similarity and entropy. Furthermore, the changes in sleep HRV from sea level to extremely high altitudes quantified by RRI and PPG tachograms in the five alpine guides tended to be different at the faster frequencies and shorter scales. Discrepancies may be explained by modulations of pulse wave velocity and should be considered to interpret correctly autonomic alterations during sleep from HRV analysis.

A Teenager Physical Fitness Evaluation Model Based on 1D-CNN with LSTM and Wearable Running PPG Recordings.

People attach greater importance to the physical health of teenagers because adolescence is a critical period for the healthy development of the human body. With the progress of biosensing technologies and artificial intelligence, it is feasible to apply wearable devices to continuously record teenagers’ physiological signals and make analyses based on modern advanced methods. To solve the challenge that traditional methods of monitoring teenagers’ physical fitness lack accurate computational models and in-depth data analyses, we propose a novel evaluation model for predicting the physical fitness of teenagers. First, we collected 1024 teenagers’ PPGs under the guidance of the proposed three-stage running paradigm. Next, we applied the median filter and wavelet transform to denoise the original signals and obtain HR and SpO2. Then, we used the Pearson correlation coefficient method to finalize the feature set, based on the extracted nine physical features. Finally, we built a 1D-CNN with LSTM model to classify teenagers’ physical fitness condition into four levels: excellent, good, medium, and poor, with an accuracy of 98.27% for boys’ physical fitness prediction, and 99.26% for girls’ physical fitness prediction. The experimental results provide evidence supporting the feasibility of predicting teenagers’ physical fitness levels by their running PPG recordings.

Continuous 24-h Photoplethysmogram Monitoring Enables Detection of Atrial Fibrillation.

Aim: Atrial fibrillation (AF) detection is challenging because it is often asymptomatic and paroxysmal. We evaluated continuous photoplethysmogram (PPG) for signal quality and detection of AF.Methods: PPGs were recorded using a wrist-band device in 173 patients (76 AF, 97 sinus rhythm, SR) for 24 h. Simultaneously recorded 3-lead ambulatory ECG served as control. The recordings were split into 10-, 20-, 30-, and 60-min time-frames. The sensitivity, specificity, and F1-score of AF detection were evaluated for each time-frame. AF alarms were generated to simulate continuous AF monitoring. Sensitivities, specificities, and positive predictive values (PPVs) of the alarms were evaluated. User experiences of PPG and ECG recordings were assessed. The study was registered in the Clinical Trials database (NCT03507335).Results: The quality of PPG signal was better during night-time than in daytime (67.3 ± 22.4% vs. 30.5 ± 19.4%, p < 0.001). The 30-min time-frame yielded the highest F1-score (0.9536), identifying AF correctly in 72/76 AF patients (sensitivity 94.7%), only 3/97 SR patients receiving a false AF diagnosis (specificity 96.9%). The sensitivity and PPV of the simulated AF alarms were 78.2 and 97.2% at night, and 49.3 and 97.0% during the daytime. 82% of patients were willing to use the device at home.Conclusion: PPG wrist-band provided reliable AF identification both during daytime and night-time. The PPG data’s quality was better at night. The positive user experience suggests that wearable PPG devices could be feasible for continuous rhythm monitoring.