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Advances in Wearable Photoplethysmography Applications in Health Monitoring

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 35660

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Dr. Mimma Nardelli

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Guest Editor

Bioengineering and Robotics Research Center E Piaggio, Università di Pisa, 56123 Pisa, Italy
Interests: biomedical signal processing; heart rate variability; complex systems; time series analysis; wearable systems
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Raquel Bailón

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Guest Editor

BSICOS, Aragón Institute for Engineering Research (I3A), IIS Aragón, University of Zaragoza, 50015 Zaragoza, Spain
Interests: biomedical signal processing; non-invasive autonomic nervous system assessment; wearable systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Photoplethysmography (PPG) is a low-cost optical technique that can provide useful information about the cardiovascular system, aiming to diagnose autonomic dysfunctions and peripheral vascular diseases. Due to its simplicity and versatility, this technology can be used to develop wearable and wireless devices for noninvasive and out-of-hospital monitoring of both healthy and pathological subjects.

Even if, in recent decades, technology has successfully increased the comfort of wearable PPG sensors, in terms of dimensions and battery life, scientific research is still working on several issues, e.g., poor sensor adhesion, which leads to acquiring signals corrupted by noise and motion artifacts, especially during physical activity. In this context, there are still many challenges related to PPG wearable device design and signal processing techniques to derive robust indices. The investigation of reliable PPG features, including rhythm and morphology features, is growing in interest, comprising novel signal processing methodologies.

This Special Issue will focus on original research papers and comprehensive reviews dealing with hardware and software advances in the development of robust and reliable biomarkers for the noninvasive monitoring of PPG. Topics of interest include, but are not limited to, cardiovascular pathologies, aging, stress and sport monitoring.

Dr. Mimma Nardelli
Prof. Dr. Raquel Bailón
Guest Editors

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Keywords

photoplethysmography (PPG)
pulse rate variability (PRV)
wearable monitoring systems
signal quality
PPG signal processing
time series analysis

Related Special Issue

Advances in Wearable Photoplethysmography Applications in Health Monitoring: 2nd Edition in Sensors (1 article)

Published Papers (14 papers)

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Editorial

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4 pages, 180 KiB

Open AccessEditorial

Advances in Wearable Photoplethysmography Applications in Health Monitoring

by Mimma Nardelli and Raquel Bailón

Sensors 2023, 23(16), 7064; https://doi.org/10.3390/s23167064 - 10 Aug 2023

Viewed by 899

Abstract

In the last few years, interest in wearable technology for physiological signal monitoring is rapidly growing, especially during and after the COVID-19 pandemic [...] Full article

(This article belongs to the Special Issue Advances in Wearable Photoplethysmography Applications in Health Monitoring)

Research

Jump to: Editorial

20 pages, 2973 KiB

Open AccessArticle

Experimental Exploration of Multilevel Human Pain Assessment Using Blood Volume Pulse (BVP) Signals

by Muhammad Umar Khan, Sumair Aziz, Niraj Hirachan, Calvin Joseph, Jasper Li and Raul Fernandez-Rojas

Sensors 2023, 23(8), 3980; https://doi.org/10.3390/s23083980 - 14 Apr 2023

Cited by 8 | Viewed by 1483

Abstract

Critically ill patients often lack cognitive or communicative functions, making it challenging to assess their pain levels using self-reporting mechanisms. There is an urgent need for an accurate system that can assess pain levels without relying on patient-reported information. Blood volume pulse (BVP) is a relatively unexplored physiological measure with the potential to assess pain levels. This study aims to develop an accurate pain intensity classification system based on BVP signals through comprehensive experimental analysis. Twenty-two healthy subjects participated in the study, in which we analyzed the classification performance of BVP signals for various pain intensities using time, frequency, and morphological features through fourteen different machine learning classifiers. Three experiments were conducted using leave-one-subject-out cross-validation to better examine the hidden signatures of BVP signals for pain level classification. The results of the experiments showed that BVP signals combined with machine learning can provide an objective and quantitative evaluation of pain levels in clinical settings. Specifically, no pain and high pain BVP signals were classified with 96.6% accuracy, 100% sensitivity, and 91.6% specificity using a combination of time, frequency, and morphological features with artificial neural networks (ANNs). The classification of no pain and low pain BVP signals yielded 83.3% accuracy using a combination of time and morphological features with the AdaBoost classifier. Finally, the multi-class experiment, which classified no pain, low pain, and high pain, achieved 69% overall accuracy using a combination of time and morphological features with ANN. In conclusion, the experimental results suggest that BVP signals combined with machine learning can offer an objective and reliable assessment of pain levels in clinical settings. Full article

(This article belongs to the Special Issue Advances in Wearable Photoplethysmography Applications in Health Monitoring)

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17 pages, 5757 KiB

Open AccessArticle

Investigation of Optimal Light Source Wavelength for Cuffless Blood Pressure Estimation Using a Single Photoplethysmography Sensor

by Sogo Toda and Kenta Matsumura

Sensors 2023, 23(7), 3689; https://doi.org/10.3390/s23073689 - 02 Apr 2023

Cited by 1 | Viewed by 1506

Abstract

Routine blood pressure measurement is important for the early detection of various diseases. Recently, cuffless blood pressure estimation methods that do not require cuff pressurization have attracted attention. In this study, we investigated the effect of the light source wavelength on the accuracy of blood pressure estimation using only two physiological indices that can be calculated with photoplethysmography alone, namely, heart rate and modified normalized pulse volume. Using a newly developed photoplethysmography sensor that can simultaneously measure photoplethysmograms at four wavelengths, we evaluated its estimation accuracy for systolic blood pressure, diastolic blood pressure, and mean arterial pressure against a standard cuff sphygmomanometer. Mental stress tasks were used to alter the blood pressure of 14 participants, and multiple linear regression analysis showed the best light sources to be near-infrared for systolic blood pressure and blue for both diastolic blood pressure and mean arterial pressure. The importance of the light source wavelength for the photoplethysmogram in cuffless blood pressure estimation was clarified. Full article

(This article belongs to the Special Issue Advances in Wearable Photoplethysmography Applications in Health Monitoring)

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15 pages, 879 KiB

Open AccessArticle

Performance Assessment of Heartbeat Detection Algorithms on Photoplethysmograph and Functional NearInfrared Spectroscopy Signals

by Andrea Bizzego and Gianluca Esposito

Sensors 2023, 23(7), 3668; https://doi.org/10.3390/s23073668 - 31 Mar 2023

Cited by 1 | Viewed by 1281

Abstract

With wearable sensors, the acquisition of physiological signals has become affordable and feasible in everyday life. Specifically, Photoplethysmography (PPG), being a low-cost and highly portable technology, has attracted notable interest for measuring and diagnosing cardiac activity, one of the most important physiological and autonomic indicators. In addition to the technological development, several specific signal-processing algorithms have been designed to enable reliable detection of heartbeats and cope with the lower quality of the signals. In this study, we compare three heartbeat detection algorithms: Derivative-Based Detection (DBD), Recursive Combinatorial Optimization (RCO), and Multi-Scale Peak and Trough Detection (MSPTD). In particular, we considered signals from two datasets, namely, the PPG-DALIA dataset (N = 15) and the FANTASIA dataset (N = 20) which differ in terms of signal characteristics (sampling frequency and length) and type of acquisition devices (wearable and medical-grade). The comparison is performed both in terms of heartbeat detection performance and computational workload required to execute the algorithms. Finally, we explore the applicability of these algorithms on the cardiac component obtained from functional Near InfraRed Spectroscopy signals (fNIRS).The results indicate that, while the MSPTD algorithm achieves a higher F1 score in cases that involve body movements, such as cycling (MSPTD: Mean = 74.7, SD = 14.4; DBD: Mean = 54.4, SD = 21.0; DBD + RCO: Mean = 49.5, SD = 22.9) and walking up and down the stairs (MSPTD: Mean = 62.9, SD = 12.2; DBD: Mean = 50.5, SD = 11.9; DBD + RCO: Mean = 45.0, SD = 14.0), for all other activities the three algorithms perform similarly. In terms of computational complexity, the computation time of the MSPTD algorithm appears to grow exponentially with the signal sampling frequency, thus requiring longer computation times in the case of high-sampling frequency signals, where the usage of the DBD and RCO algorithms might be preferable. All three algorithms appear to be appropriate candidates for exploring the applicability of heartbeat detection on fNIRS data. Full article

(This article belongs to the Special Issue Advances in Wearable Photoplethysmography Applications in Health Monitoring)

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18 pages, 1243 KiB

Open AccessArticle

Contactless Cardiovascular Assessment by Imaging Photoplethysmography: A Comparison with Wearable Monitoring

by Valerie A. A. van Es, Richard G. P. Lopata, Enzo Pasquale Scilingo and Mimma Nardelli

Sensors 2023, 23(3), 1505; https://doi.org/10.3390/s23031505 - 29 Jan 2023

Cited by 6 | Viewed by 2094

Abstract

Despite the notable recent developments in the field of remote photoplethysmography (rPPG), extracting a reliable pulse rate variability (PRV) signal still remains a challenge. In this study, eight image-based photoplethysmography (iPPG) extraction methods (GRD, AGRD, PCA, ICA, LE, SPE, CHROM, and POS) were compared in terms of pulse rate (PR) and PRV features. The algorithms were made robust for motion and illumination artifacts by using ad hoc pre- and postprocessing steps. Then, they were systematically tested on the public dataset UBFC-RPPG, containing data from 42 subjects sitting in front of a webcam (30 fps) while playing a time-sensitive mathematical game. The performances of the algorithms were evaluated by statistically comparing iPPG-based and finger-PPG-based PR and PRV features in terms of Spearman’s correlation coefficient, normalized root mean square error (NRMSE), and Bland–Altman analysis. The study revealed POS and CHROM techniques to be the most robust for PR estimation and the assessment of overall autonomic nervous system (ANS) dynamics by using PRV features in time and frequency domains. Furthermore, we demonstrated that a reliable characterization of the vagal tone is made possible by computing the Poincaré map of PRV series derived from the POS and CHROM methods. This study supports the use of iPPG systems as promising tools to obtain clinically useful and specific information about ANS dynamics. Full article

(This article belongs to the Special Issue Advances in Wearable Photoplethysmography Applications in Health Monitoring)

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22 pages, 1467 KiB

Open AccessArticle

Effects of Missing Data on Heart Rate Variability Metrics

by Diego Cajal, David Hernando, Jesús Lázaro, Pablo Laguna, Eduardo Gil and Raquel Bailón

Sensors 2022, 22(15), 5774; https://doi.org/10.3390/s22155774 - 02 Aug 2022

Cited by 6 | Viewed by 1774

Abstract

Heart rate variability (HRV) has been studied for decades in clinical environments. Currently, the exponential growth of wearable devices in health monitoring is leading to new challenges that need to be solved. These devices have relatively poor signal quality and are affected by numerous motion artifacts, with data loss being the main stumbling block for their use in HRV analysis. In the present paper, it is shown how data loss affects HRV metrics in the time domain and frequency domain and Poincaré plots. A gap-filling method is proposed and compared to other existing approaches to alleviate these effects, both with simulated (16 subjects) and real (20 subjects) missing data. Two different data loss scenarios have been simulated: (i) scattered missing beats, related to a low signal to noise ratio; and (ii) bursts of missing beats, with the most common due to motion artifacts. In addition, a real database of photoplethysmography-derived pulse detection series provided by Apple Watch during a protocol including relax and stress stages is analyzed. The best correction method and maximum acceptable missing beats are given. Results suggest that correction without gap filling is the best option for the standard deviation of the normal-to-normal intervals (SDNN), root mean square of successive differences (RMSSD) and Poincaré plot metrics in datasets with bursts of missing beats predominance (

p < 0.05

), whereas they benefit from gap-filling approaches in the case of scattered missing beats (

p < 0.05

). Gap-filling approaches are also the best for frequency-domain metrics (

p < 0.05

). The findings of this work are useful for the design of robust HRV applications depending on missing data tolerance and the desired HRV metrics. Full article

(This article belongs to the Special Issue Advances in Wearable Photoplethysmography Applications in Health Monitoring)

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18 pages, 3243 KiB

Open AccessArticle

Wearable Pulse Oximeter for Swimming Pool Safety

by Elżbieta Kałamajska, Jacek Misiurewicz and Jerzy Weremczuk

Sensors 2022, 22(10), 3823; https://doi.org/10.3390/s22103823 - 18 May 2022

Cited by 6 | Viewed by 2712

Abstract

The purpose of this research was to develop an algorithm for a wearable device that would prevent people from drowning in swimming pools. The device should detect pre-drowning symptoms and alert the rescue staff. The proposed detection method is based on analyzing real-time data collected from a set of sensors, including a pulse oximeter. The pulse oximetry technique is used for measuring the heart rate and oxygen saturation in the subject’s blood. It is an optical method; subsequently, the measurements obtained this way are highly sensitive to interference from the subject’s motion. To eliminate noise caused by the subject’s movement, accelerometer data were used in the system. If the acceleration sensor does not detect movement, a biosensor is activated, and an analysis of selected physiological parameters is performed. Such a setup of the algorithm allows the device to distinguish situations in which the person rests and does not move from situations in which the examined person has lost consciousness and has begun to drown. Full article

(This article belongs to the Special Issue Advances in Wearable Photoplethysmography Applications in Health Monitoring)

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18 pages, 2573 KiB

Open AccessArticle

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

by Paolo Castiglioni, Paolo Meriggi, Marco Di Rienzo, Carolina Lombardi, Gianfranco Parati and Andrea Faini

Sensors 2022, 22(8), 2891; https://doi.org/10.3390/s22082891 - 09 Apr 2022

Cited by 5 | Viewed by 2543

Abstract

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. Full article

(This article belongs to the Special Issue Advances in Wearable Photoplethysmography Applications in Health Monitoring)

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21 pages, 1713 KiB

Open AccessArticle

Proposal for a Home Sleep Monitoring Platform Employing a Smart Glove

by Remo Lazazzera, Pablo Laguna, Eduardo Gil and Guy Carrault

Sensors 2021, 21(23), 7976; https://doi.org/10.3390/s21237976 - 29 Nov 2021

Cited by 8 | Viewed by 2679

Abstract

The present paper proposes the design of a sleep monitoring platform. It consists of an entire sleep monitoring system based on a smart glove sensor called UpNEA worn during the night for signals acquisition, a mobile application, and a remote server called AeneA [...] Read more.

_{2}

) signal from the index finger. Overnight recordings are sent from the hardware to a mobile application and then transferred to AeneA. After cloud computing, the results are shown in a web application, accessible for the user and the clinician. The AeneA sleep monitoring activity performs different tasks: sleep stages classification and oxygen desaturation assessment; heart rate and respiration rate estimation; tachycardia, bradycardia, atrial fibrillation, and premature ventricular contraction detection; and apnea and hypopnea identification and classification. The PPG breathing rate estimation algorithm showed an absolute median error of 0.5 breaths per minute for the 32 s window and 0.2 for the 64 s window. The apnea and hypopnea detection algorithm showed an accuracy (Acc) of 75.1%, by windowing the PPG in one-minute segments. The classification task revealed 92.6% Acc in separating central from obstructive apnea, 83.7% in separating central apnea from central hypopnea and 82.7% in separating obstructive apnea from obstructive hypopnea. The novelty of the integrated algorithms and the top-notch cloud computing products deployed, encourage the production of the proposed solution for home sleep monitoring. Full article

(This article belongs to the Special Issue Advances in Wearable Photoplethysmography Applications in Health Monitoring)

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19 pages, 6091 KiB

Open AccessArticle

Identification of Ictal Tachycardia in Focal Motor- and Non-Motor Seizures by Means of a Wearable PPG Sensor

by Martin Glasstetter, Sebastian Böttcher, Nicolas Zabler, Nino Epitashvili, Matthias Dümpelmann, Mark P. Richardson and Andreas Schulze-Bonhage

Sensors 2021, 21(18), 6017; https://doi.org/10.3390/s21186017 - 08 Sep 2021

Cited by 9 | Viewed by 2933

Abstract

Photoplethysmography (PPG) as an additional biosignal for a seizure detector has been underutilized so far, which is possibly due to its susceptibility to motion artifacts. We investigated 62 focal seizures from 28 patients with electrocardiography-based evidence of ictal tachycardia (IT). Seizures were divided into subgroups: those without epileptic movements and those with epileptic movements not affecting and affecting the extremities. PPG-based heart rate (HR) derived from a wrist-worn device was calculated for sections with high signal quality, which were identified using spectral entropy. Overall, IT based on PPG was identified in 37 of 62 (60%) seizures (9/19, 7/8, and 21/35 in the three groups, respectively) and could be found prior to the onset of epileptic movements affecting the extremities in 14/21 seizures. In 30/37 seizures, PPG-based IT was in good temporal agreement (<10 s) with ECG-based IT, with an average delay of 5.0 s relative to EEG onset. In summary, we observed that the identification of IT by means of a wearable PPG sensor is possible not only for non-motor seizures but also in motor seizures, which is due to the early manifestation of IT in a relevant subset of focal seizures. However, both spontaneous and epileptic movements can impair PPG-based seizure detection. Full article

(This article belongs to the Special Issue Advances in Wearable Photoplethysmography Applications in Health Monitoring)

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21 pages, 880 KiB

Open AccessArticle

Transcending Conventional Biometry Frontiers: Diffusive Dynamics PPG Biometry

by Javier de Pedro-Carracedo, David Fuentes-Jimenez, Ana María Ugena and Ana Pilar Gonzalez-Marcos

Sensors 2021, 21(16), 5661; https://doi.org/10.3390/s21165661 - 23 Aug 2021

Cited by 4 | Viewed by 2485

Abstract

This paper presents the first photoplethysmographic (PPG) signal dynamic-based biometric authentication system with a Siamese convolutional neural network (CNN). Our method extracts the PPG signal’s biometric characteristics from its diffusive dynamics, characterized by geometric patterns in the

(p, q)

-planes [...] Read more.

(p, q)

-planes specific to the 0–1 test. PPG signal diffusive dynamics are strongly dependent on the vascular bed’s biostructure, unique to each individual. The dynamic characteristics of the PPG signal are more stable over time than its morphological features, particularly in the presence of psychosomatic conditions. Besides its robustness, our biometric method is anti-spoofing, given the complex nature of the blood network. Our proposal trains using a national research study database with 40 real-world PPG signals measured with commercial equipment. Biometric system results for input data, raw and preprocessed, are studied and compared with eight primary biometric methods related to PPG, achieving the best equal error rate (ERR) and processing times with a single attempt, among all of them. Full article

(This article belongs to the Special Issue Advances in Wearable Photoplethysmography Applications in Health Monitoring)

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17 pages, 2813 KiB

Open AccessArticle

Multimodal Assessment of the Pulse Rate Variability Analysis Module of a Photoplethysmography-Based Telemedicine System

by Flóra Antali, Dániel Kulin, Konrád István Lucz, Balázs Szabó, László Szűcs, Sándor Kulin and Zsuzsanna Miklós

Sensors 2021, 21(16), 5544; https://doi.org/10.3390/s21165544 - 18 Aug 2021

Cited by 11 | Viewed by 3139

Abstract

Alterations of heart rate variability (HRV) are associated with various (patho)physiological conditions; therefore, HRV analysis has the potential to become a useful diagnostic module of wearable/telemedical devices to support remote cardiovascular/autonomic monitoring. Continuous pulse recordings obtained by photoplethysmography (PPG) can yield pulse rate variability (PRV) indices similar to HRV parameters; however, it is debated whether PRV/HRV parameters are interchangeable. In this study, we assessed the PRV analysis module of a digital arterial PPG-based telemedical system (SCN4ALL). We used Bland–Altman analysis to validate the SCN4ALL PRV algorithm to Kubios Premium software and to determine the agreements between PRV/HRV results calculated from 2-min long PPG and ECG captures recorded simultaneously in healthy individuals (n = 33) at rest and during the cold pressor test, and in diabetic patients (n = 12) at rest. We found an ideal agreement between SCN4ALL and Kubios outputs (bias < 2%). PRV and HRV parameters showed good agreements for interbeat intervals, SDNN, and RMSSD time-domain variables, for total spectral and low-frequency power (LF) frequency-domain variables, and for non-linear parameters in healthy subjects at rest and during cold pressor challenge. In diabetics, good agreements were observed for SDNN, LF, and SD2; and moderate agreement was observed for total power. In conclusion, the SCN4ALL PRV analysis module is a good alternative for HRV analysis for numerous conventional HRV parameters. Full article

(This article belongs to the Special Issue Advances in Wearable Photoplethysmography Applications in Health Monitoring)

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20 pages, 1379 KiB

Open AccessArticle

Coherence between Decomposed Components of Wrist and Finger PPG Signals by Imputing Missing Features and Resolving Ambiguous Features

by Pei-Yun Tsai, Chiu-Hua Huang, Jia-Wei Guo, Yu-Chuan Li, An-Yeu Andy Wu, Hung-Ju Lin and Tzung-Dau Wang

Sensors 2021, 21(13), 4315; https://doi.org/10.3390/s21134315 - 24 Jun 2021

Cited by 10 | Viewed by 3200

Abstract

B a c k g r o u n d

: Feature extraction from photoplethysmography (PPG) signals is an essential step to analyze vascular and hemodynamic information. Different morphologies of PPG waveforms from different measurement sites appear. Various phenomena of missing or ambiguous [...] Read more.

B a c k g r o u n d

M e t h o d s

: The reasons that cause missing or ambiguous features of finger and wrist PPG pulses are analyzed based on the concept of component waves from pulse decomposition. Then, a systematic approach for missing-feature imputation and ambiguous-feature resolution is proposed.

R e s u l t s

: From the experimental results, with the imputation and ambiguity resolution technique, features from 35,036 (98.7%) of 35,502 finger PPG cycles and 36307 (99.1%) of 36,652 wrist PPG cycles can be successfully identified. The extracted features became more stable and the standard deviations of their distributions were reduced. Furthermore, significant correlations up to 0.92 were shown between the finger and wrist PPG waveforms regarding the positions and widths of the third to fifth component waves.

C o n c l u s i o n

: The proposed missing-feature imputation and ambiguous-feature resolution solve the problems encountered during PPG feature extraction and expand the feature availability for further processing. More intrinsic properties of finger and wrist PPG are revealed. The coherence between the finger and wrist PPG waveforms enhances the applicability of the wrist PPG. Full article

(This article belongs to the Special Issue Advances in Wearable Photoplethysmography Applications in Health Monitoring)

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15 pages, 3230 KiB

Open AccessArticle

An Examination System to Detect Deep Vein Thrombosis of a Lower Limb Using Light Reflection Rheography

by Shing-Hong Liu, Jia-Jung Wang, Wenxi Chen, Kuo-Li Pan and Chun-Hung Su

Sensors 2021, 21(7), 2446; https://doi.org/10.3390/s21072446 - 02 Apr 2021

Cited by 12 | Viewed by 3613

Abstract

Deep vein thrombosis (DVT) of lower limbs can easily arise from prolonged sitting or standing. Elders and pregnant women are most likely to have this disease. When the embolus of DVT comes to pass the lung, it will become a life-threatening disease. Thus, for DVT disease, early detection and the early treatment are needed. The goal of this study was to develop an examination system to be used at non-medical places to detect the DVT of lower limbs with light reflection rheography (LRR). Consisting of a wearable device and a mobile application (APP), the system is operated in a wireless manner to control the actions of sensors and display and store the LRR signals on the APP. Then, the recorded LRR signals are processed to find the parameters of DVT examination. Twenty subjects were recruited to perform experiments. The veins of lower limbs were occluded by pressuring the cuff up to 100 mmHg and 150 mmHg to simulate the slight and serious DVT scenarios, respectively. Six characteristic parameters were defined to classify whether there was positive or negative DVT using the receiver operating characteristic curves, including the slopes of emptying and refilling curves in the LRR signal, and the changes of venous pump volume. Under the slight DVT scenario (0 mmHg vs. 100 mmHg), the first three parameters, m₁₀, m₄₀, and m₅₀, had accuracies of 72%, 69%, and 69%, respectively. Under the serious DVT scenario (0 mmHg vs. 150 mmHg), m₁₀, m₄₀, and m₅₀ achieved accuracies of 73%, 76%, and 73%, respectively. The experimental results show that this proposed examination system may be practical as an auxiliary tool to screen DVT in homecare settings. Full article

(This article belongs to the Special Issue Advances in Wearable Photoplethysmography Applications in Health Monitoring)

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