Contactless Monitoring of Heart Rate Variability During Respiratory Maneuvers

Shoushan, Monay Mokhtar; Reyes, Bersaín A.; Mejía-Rodríguez, Aldo R.; Chong, Jo Woon

doi:10.1109/jsen.2022.3174779

Cited by 10 publications

(7 citation statements)

References 53 publications

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“…This procedure turns out to be incomplete and misleading, since, in this way, the artifacts are averaged out, resulting in an overall better agreement. A recent study compared the PRV extracted from iPPG with the HRV obtained from ECG during a specific experimental protocol in a controlled setting, looking at the reliability of the PRV features [ 50 ]. In this study, we extracted the PRV signals from the UBFC-RPPG videos, and we investigated the reliability of PRV features by comparing them with the corresponding wearable PPG values.…”

Section: Discussionmentioning

confidence: 99%

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

Lopata

Scilingo

et al. 2023

Sensors

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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.

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Section: Discussionmentioning

confidence: 99%

“…Bland–Altman quantifies the bias between the mean differences labeled as

limits of agreement (LoA). The LoA was calculated as follows:

where

is the mean difference between the iPPG-based estimation and the finger clip PPG-based reference value, and SD is the standard deviation of the differences [ 50 ].…”

Section: Methodsmentioning

confidence: 99%

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

Lopata

Scilingo

et al. 2023

Sensors

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“…Widely used HRV time-domain indices [ 5 ] usually reflect the variations in inter-beat interval (IBI) between consecutive heartbeats in milliseconds (ms). A simple time-domain HRV metric, regularly used in comparative studies [ 6 ], is the root mean square (RMS) of the daily heartbeat intervals (beat-to-beat); it reflects the dynamic fluctuation range in heart rhythm [ 4 ].…”

Section: Methodsmentioning

confidence: 99%

“…Most patients with atrial fibrillation (AF) are unaware of its presence, i.e., asymptomatic. Additionally, as revealed by recent studies, defects in the autonomic nervous system (ANS), which involves the sympathetic, parasympathetic, and intrinsic neural networks, have been associated with the pathogenesis of AF [ 4 ], such abnormalities can be explored using heart rate variability (HRV) analysis on relatively long-term ECG recordings, by measuring the time intervals between heartbeats over a period of time, and it represents the equilibrium between the parasympathetic nervous system (PNS) and sympathetic nervous system (SNS) [ 5 , 6 ]. Early detection of AF onset could enable appropriate interventions, e.g., anticoagulation, aimed to reduce the risk of stroke.…”

Section: Introductionmentioning

confidence: 99%

Armband Sensors Location Assessment for Left Arm-ECG Bipolar Leads Waveform Components Discovery Tendencies around the MUAC Line

Escalona

Mukhtar

McEneaney

et al. 2022

Sensors

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Sudden cardiac death (SCD) risk can be reduced by early detection of short-lived and transient cardiac arrhythmias using long-term electrocardiographic (ECG) monitoring. Early detection of ventricular arrhythmias can reduce the risk of SCD by allowing appropriate interventions. Long-term continuous ECG monitoring, using a non-invasive armband-based wearable device is an appealing solution for detecting early heart rhythm abnormalities. However, there is a paucity of understanding on the number and best bipolar ECG electrode pairs axial orientation around the left mid-upper arm circumference (MUAC) for such devices. This study addresses the question on the best axial orientation of ECG bipolar electrode pairs around the left MUAC in non-invasive armband-based wearable devices, for the early detection of heart rhythm abnormalities. A total of 18 subjects with almost same BMI values in the WASTCArD arm-ECG database were selected to assess arm-ECG bipolar leads quality using proposed metrics of relative (normalized) signal strength measurement, arm-ECG detection performance of the main ECG waveform event component (QRS) and heart-rate variability (HRV) in six derived bipolar arm ECG-lead sensor pairs around the armband circumference, having regularly spaced axis angles (at 30° steps) orientation. The analysis revealed that the angular range from −30° to +30°of arm-lead sensors pair axis orientation around the arm, including the 0° axis (which is co-planar to chest plane), provided the best orientation on the arm for reasonably good QRS detection; presenting the highest sensitivity (Se) median value of 93.3%, precision PPV median value at 99.6%; HRV RMS correlation (p) of 0.97 and coefficient of determination (R2) of 0.95 with HRV gold standard values measured in the standard Lead-I ECG.

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“…For example, PPG can be integrated with the watch or the camera of a cell phone can measure the PPG signal as well. Furthermore, the non-contact PPG is also developing [32][33][34][35]. The PPG has the potential to be the health monitor in many situations.…”

Section: Fever Detection and Applicationmentioning

confidence: 99%

Very Short-Term Photoplethysmography-Based Heart Rate Variability for Continuous Autoregulation Assessment

Huang

Hsiao

2022

Applied Sciences

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Background: Heart rate variability (HRV) has been widely applied for disease diagnosis. However, the 5 min signal length for HRV analysis is needed. Method: A signal processing procedure for very short-term photoplethysmography (PPG) signal for fever detection and autoregulation assessment was proposed. The Time-Shift Multiscale Entropy Analysis (TSME) was applied to instantaneous pulse rate time series (iPR) and normalized by the cumulative distribution function (CDF) of all scales to calculate novel indices. A total of 33 subjects were recruited for the study. Fifteen participants whose body temperatures were higher than 37.9 °C were served as the fever group. Others were served as the non-fever group. The total 15 s PPG signal with 200 sampling rates was used for iPR calculation. Result: The CDF value of entropy on the scale k = 19 (CDF(E(k = 19))) of iPR had the lowest p-value calculated by the Weltch t-test between two groups (p < 0.001). The Spearman correlation r between CDF(E(k = 19)) and body temperature is −0.757, 0.287, and −0.830 in all subjects, the non-fever group and the Fever group, respectively. The area under the curve, calculated from the receiver operating characteristic of CDF(E(k = 19)) of iPR is 0.915. Conclusion: The entropy of iPR is useful for detecting fever. Moreover, a short-term PPG signal is suitable to develop real-time applications, and multiscale entropy provides different scales of information for daily healthcare.

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Contactless Monitoring of Heart Rate Variability During Respiratory Maneuvers

Cited by 10 publications

References 53 publications

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

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

Armband Sensors Location Assessment for Left Arm-ECG Bipolar Leads Waveform Components Discovery Tendencies around the MUAC Line

Very Short-Term Photoplethysmography-Based Heart Rate Variability for Continuous Autoregulation Assessment

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