Real-Time Quality Index to Control Data Loss in Real-Life Cardiac Monitoring Applications

Wearable cardiac sensors pave the way for advanced cardiac monitoring applications based on heart rate variability (HRV). In real-life settings, heart rate (HR) measurements are subject to motion artifacts that may lead to frequent data loss (missing samples in the HR signal), especially for commercial devices based on photoplethysmography (PPG). The current study had two main goals: (i) to provide a white-box quality index that estimates the amount of missing samples in any piece of HR signal; and (ii) to quantify the impact of data loss on feature extraction in a PPG-based HR signal. This was done by comparing real-life recordings from commercial sensors featuring both PPG (Empatica E4) and ECG (Zephyr BioHarness 3). After an outlier rejection process, our quality index was used to isolate portions of ECG-based HR signals that could be used as benchmark, to validate the output of Empatica E4 at the signal level and at the feature level. Our results showed high accuracy in estimating the mean HR (median error: 3.2%), poor accuracy for short-term HRV features (e.g., median error: 64% for high-frequency power), and mild accuracy for longer-term HRV features (e.g., median error: 25% for low-frequency power). These levels of errors could be reduced by using our quality index to identify time windows with few or no data loss (median errors: 0.0%, 27%, and 6.4% respectively, when no sample was missing). This quality index should be useful in future work to extract reliable cardiac features in real-life measurements, or to conduct a field validation study on wearable cardiac sensors.

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Signal Quality Index Based on Template Cross-Correlation in Multimodal Biosignal Chair for Smart Healthcare

Sensors ◽

10.3390/s21227564 ◽

2021 ◽

Vol 21 (22) ◽

pp. 7564

Author(s):

Seunghyeok Hong ◽

Jeong Heo ◽

Kwang Suk Park

Keyword(s):

Quality Index ◽

Screening Method ◽

Reference Signal ◽

Real Life ◽

Motion Artifacts ◽

Physiological Signals ◽

Signal Quality ◽

Body Parts ◽

Smart Healthcare ◽

Signal Quality Index

We investigated the effects of a quality screening method on unconstrained measured signals, including electrocardiogram (ECG), photoplethysmogram (PPG), and ballistocardiogram (BCG) signals, in our collective chair system for smart healthcare. Such an investigation is necessary because unattached or unbound sensors have weaker connections to body parts than do conventional methods. Using the biosignal chair, the physiological signals collected during sessions included a virtual driving task, a physically powered wheelchair drive, and three types of body motions. The signal quality index was defined by the similarity between the observed signals and noise-free signals from the perspective of the cross-correlations of coefficients with appropriate individual templates. The goal of the index was to qualify signals without a reference signal to assess the practical use of the chair in daily life. As expected, motion artifacts have adverse effects on the stability of physiological signals. However, we were able to observe a supplementary relationship between sensors depending on each movement trait. Except for extreme movements, the signal quality and estimated heart rate (HR) remained within the range of criteria usable for status monitoring. By investigating the signal reliability, we were able to confirm the suitability of using the unconstrained biosignal chair to collect real-life measurements to improve safety and healthcare.

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Benchmarking the Use of Immersive Virtual Bike Simulators for Understanding Cyclist Behaviors

10.31224/osf.io/mrxgh ◽

2021 ◽

Author(s):

Xiang Guo ◽

Erin Marie Robartes ◽

Austin Angulo ◽

T. Donna Chen ◽

Arsalan Heydarian

Keyword(s):

Heart Rate ◽

Real World ◽

Environmental Changes ◽

Low Cost ◽

Real Life ◽

Limited Information ◽

Perceived Safety ◽

Realistic Representation ◽

Human Sensing ◽

The Impact

Recent reports indicate that cyclist fatalities are rising. Unlike automobile driver crash and safety studies, there is very limited information and data on how different environmental or design features impact cyclists’ behaviors, attention, and awareness. Real world studies evaluating cyclist behavior are limited due to their inherent safety risk; therefore, there is a need for alternate data to better inform the planning and design of roadways for all users. Immersive virtual environments (IVE) have shown to provide a realistic representation of real-world conditions; however, these tools have not been evaluated and validated for vulnerable road users, such as cyclists. The purpose of this study is to assess the use of an IVE bike simulator to study the impact of design and environmental conditions on cyclists’ perceived safety and behavioral changes. By benchmarking cyclists' behaviors and perceived safety in real-life settings compared to its representative IVE bike simulation, we can validate whether these IVE simulators are realistic representations of real-world conditions. Furthermore, by connecting these environments with the latest low-cost human sensing devices, we have built a multimodal human sensing data collection system to track participants’ gaze, heart rate, and head movement. The preliminary results from a six-participant pilot study indicate that our simulators are capable of replicating cyclists’ speed profile, heart rate changes, and most of the head and gaze behaviors and that these measurements are sensitive to environmental changes.

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Heart Rate and Oxygen Saturation Monitoring With a New Wearable Wireless Device in the Intensive Care Unit: Pilot Comparison Trial (Preprint)

10.2196/preprints.18158 ◽

2020 ◽

Author(s):

Srinivasan Murali ◽

Francisco Rincon ◽

Tiziano Cassina ◽

Stephane Cook ◽

Jean-Jacques Goy

Keyword(s):

Intensive Care Unit ◽

Heart Rate ◽

Intensive Care ◽

Oxygen Saturation ◽

Cardiac Monitoring ◽

Data Loss ◽

Clinical Value ◽

Icu Patients ◽

Conduction Disturbances ◽

Wireless Biosensor

BACKGROUND Continuous cardiac monitoring with wireless sensors is an attractive option for early detection of arrhythmia and conduction disturbances and the prevention of adverse events leading to patient deterioration. We present a new sensor design (SmartCardia), a wearable wireless biosensor patch, for continuous cardiac and oxygen saturation (SpO2) monitoring. OBJECTIVE This study aimed to test the clinical value of a new wireless sensor device (SmartCardia) and its usefulness in monitoring the heart rate (HR) and SpO2 of patients. METHODS We performed an observational study and monitored the HR and SpO2 of patients admitted to the intensive care unit (ICU). We compared the device under test (SmartCardia) with the ICU-grade monitoring system (Dräger-Healthcare). We defined optimal correlation between the gold standard and the wireless system as <10% difference for HR and <4% difference for SpO2. Data loss and discrepancy between the two systems were critically analyzed. RESULTS A total of 58 ICU patients (42 men and 16 women), with a mean age of 71 years (SD 11), were included in this study. A total of 13.49 (SD 5.53) hours per patient were recorded. This represents a total recorded period of 782.3 hours. The mean difference between the HR detected by the SmartCardia patch and the ICU monitor was 5.87 (SD 16.01) beats per minute (bias=–5.66, SD 16.09). For SpO2, the average difference was 3.54% (SD 3.86; bias=2.9, SD 4.36) for interpretable values. SmartCardia’s patch measures SpO2 only under low-to-no activity conditions and otherwise does not report a value. Data loss and noninterpretable values of SpO2 represented 26% (SD 24) of total measurements. CONCLUSIONS The SmartCardia device demonstrated clinically acceptable accuracy for HR and SpO2 monitoring in ICU patients.

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Commercially-available heart rate monitor repurposed into a 9-gram standalone device for automatic arrhythmia detection with snapshot electrocardiographic capability: a pilot validation.

10.1101/2021.12.16.21267762 ◽

2021 ◽

Author(s):

Nicola Gaibazzi ◽

Claudio Reverberi ◽

Domenico Tuttolomondo ◽

Bernardo Di Maria

Keyword(s):

Heart Rate ◽

Real Life ◽

Smartphone Application ◽

High Rate ◽

Cardiac Monitoring ◽

Arrhythmia Detection ◽

Low Weight ◽

Screening Strategies ◽

Missed Diagnosis ◽

Electrocardiogram Ecg

Background: The usefulness of opportunistic arrhythmia screening strategies, using an electrocardiogram (ECG) or other methods for random snapshot assessments is limited by the unexpected and occasional nature of arrhythmias, leading to a high rate of missed-diagnosis. We have previously validated a cardiac monitoring system for AF detection pairing simple consumer-grade Bluetooth low-energy (BLE) heart rate (HR) sensors with a smartphone application (RITMIA, Heart Sentinel srl, Italy). In the current study we test a significant upgrade to the abovementioned system, thanks to the technical capability of new HR sensors to run algorithms on the sensor itself and to acquire (and store on-board) single-lead ECG strips, if asked to do so. Methods and Results We have reprogrammed a HR monitor intended for sports use (Movensense HR+) to run our proprietary RITMIA algorithm code in real-time, based on RR analysis, so that if any type of arrhythmia is detected it triggers a brief retrospective recording of a single-lead ECG, providing tracings of the specific arrhythmia for later consultation. We report the initial data on the behavior, feasibility and high diagnostic accuracy of this ultra-low weight customized device for standalone automatic arrhythmia detection and ECG recording, when several types of arrhythmias were simulated, under different baseline conditions. Conclusions This customized device was capable to detect all types of simulated arrhythmias and correctly triggered an visually interpretable ECG tracing. Future human studies are needed to address real-life accuracy of this device.

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Measuring Heart Rate Variability in Free-Living Conditions Using Consumer-Grade Photoplethysmography: Validation Study

JMIR Biomedical Engineering ◽

10.2196/17355 ◽

2020 ◽

Vol 5 (1) ◽

pp. e17355

Author(s):

Emily Lam ◽

Shahrose Aratia ◽

Julian Wang ◽

James Tung

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Root Mean Square ◽

Living Conditions ◽

Motion Artifacts ◽

Data Sets ◽

Mean Square ◽

Free Living ◽

Frequency Power ◽

Free Living Conditions

Background Heart rate variability (HRV) is used to assess cardiac health and autonomic nervous system capabilities. With the growing popularity of commercially available wearable technologies, the opportunity to unobtrusively measure HRV via photoplethysmography (PPG) is an attractive alternative to electrocardiogram (ECG), which serves as the gold standard. PPG measures blood flow within the vasculature using color intensity. However, PPG does not directly measure HRV; it measures pulse rate variability (PRV). Previous studies comparing consumer-grade PRV with HRV have demonstrated mixed results in short durations of activity under controlled conditions. Further research is required to determine the efficacy of PRV to estimate HRV under free-living conditions. Objective This study aims to compare PRV estimates obtained from a consumer-grade PPG sensor with HRV measurements from a portable ECG during unsupervised free-living conditions, including sleep, and examine factors influencing estimation, including measurement conditions and simple editing methods to limit motion artifacts. Methods A total of 10 healthy adults were recruited. Data from a Microsoft Band 2 and a Shimmer3 ECG unit were recorded simultaneously using a smartphone. Participants wore the devices for >90 min during typical day-to-day activities and while sleeping. After filtering, ECG data were processed using a combination of discrete wavelet transforms and peak-finding methods to identify R-R intervals. P-P intervals were edited for deletion using methods based on outlier detection and by removing sections affected by motion artifacts. Common HRV metrics were compared, including mean N-N, SD of N-N intervals, percentage of subsequent differences >50 ms (pNN50), root mean square of successive differences, low-frequency power (LF), and high-frequency power. Validity was assessed using root mean square error (RMSE) and Pearson correlation coefficient (R2). Results Data sets for 10 days and 9 corresponding nights were acquired. The mean RMSE was 182 ms (SD 48) during the day and 158 ms (SD 67) at night. R2 ranged from 0.00 to 0.66, with 2 of 19 (2 nights) trials considered moderate, 7 of 19 (2 days, 5 nights) fair, and 10 of 19 (8 days, 2 nights) poor. Deleting sections thought to be affected by motion artifacts had a minimal impact on the accuracy of PRV measures. Significant HRV and PRV differences were found for LF during the day and R-R, SDNN, pNN50, and LF at night. For 8 of the 9 matched day and night data sets, R2 values were higher at night (P=.08). P-P intervals were less sensitive to rapid R-R interval changes. Conclusions Owing to overall poor concurrent validity and inconsistency among participant data, PRV was found to be a poor surrogate for HRV under free-living conditions. These findings suggest that free-living HRV measurements would benefit from examining alternate sensing methods, such as multiwavelength PPG and wearable ECG.

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Analysis of the Impact of Interpolation Methods of Missing RR-intervals Caused by Motion Artifacts on HRV Features Estimations

Sensors ◽

10.3390/s19143163 ◽

2019 ◽

Vol 19 (14) ◽

pp. 3163 ◽

Cited By ~ 8

Author(s):

Davide Morelli ◽

Alessio Rossi ◽

Massimo Cairo ◽

David A. Clifton

Keyword(s):

Heart Rate ◽

Missing Data ◽

Missing Values ◽

Time Windows ◽

Motion Artifacts ◽

Linear Quadratic ◽

Optimal Method ◽

Rr Intervals ◽

Interpolation Methods ◽

The Impact

Wearable physiological monitors have become increasingly popular, often worn during people’s daily life, collecting data 24 hours a day, 7 days a week. In the last decade, these devices have attracted the attention of the scientific community as they allow us to automatically extract information about user physiology (e.g., heart rate, sleep quality and physical activity) enabling inference on their health. However, the biggest issue about the data recorded by wearable devices is the missing values due to motion and mechanical artifacts induced by external stimuli during data acquisition. This missing data could negatively affect the assessment of heart rate (HR) response and estimation of heart rate variability (HRV), that could in turn provide misleading insights concerning the health status of the individual. In this study, we focus on healthy subjects with normal heart activity and investigate the effects of missing variation of the timing between beats (RR-intervals) caused by motion artifacts on HRV features estimation by randomly introducing missing values within a five min time windows of RR-intervals obtained from the nsr2db PhysioNet dataset by using Gilbert burst method. We then evaluate several strategies for estimating HRV in the presence of missing values by interpolating periods of missing values, covering the range of techniques often deployed in the literature, via linear, quadratic, cubic, and cubic spline functions. We thereby compare the HRV features obtained by handling missing data in RR-interval time series against HRV features obtained from the same data without missing values. Finally, we assess the difference between the use of interpolation methods on time (i.e., the timestamp when the heartbeats happen) and on duration (i.e., the duration of the heartbeats), in order to identify the best methodology to handle the missing RR-intervals. The main novel finding of this study is that the interpolation of missing data on time produces more reliable HRV estimations when compared to interpolation on duration. Hence, we can conclude that interpolation on duration modifies the power spectrum of the RR signal, negatively affecting the estimation of the HRV features as the amount of missing values increases. We can conclude that interpolation in time is the optimal method among those considered for handling data with large amounts of missing values, such as data from wearable sensors.

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Acoustic Sensing as a Novel Wearable Approach for Cardiac Monitoring at the Wrist

Scientific Reports ◽

10.1038/s41598-019-55599-5 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 3

Author(s):

Piyush Sharma ◽

Syed Anas Imtiaz ◽

Esther Rodriguez-Villegas

Keyword(s):

Heart Rate ◽

Acoustic Pulse ◽

Power Spectral Analysis ◽

Pulse Signal ◽

Absolute Error ◽

Motion Artifacts ◽

Cardiac Monitoring ◽

Acoustic Sensing ◽

Power Spectral ◽

Commercial Device

AbstractThis paper introduces the concept of using acoustic sensing over the radial artery to extract cardiac parameters for continuous vital sign monitoring. It proposes a novel measurement principle that allows detection of the heart sounds together with the pulse wave, an attribute not possible with existing photoplethysmography (PPG)-based methods for monitoring at the wrist. The validity of the proposed principle is demonstrated using a new miniature, battery-operated wearable device to sense the acoustic signals and a novel algorithm to extract the heart rate from these signals. The algorithm utilizes the power spectral analysis of the acoustic pulse signal to detect the S1 sounds and additionally, the K-means method to remove motion artifacts for an accurate heartbeat detection. It has been validated on a dataset consisting of 12 subjects with a data length of 6 hours. The results demonstrate an accuracy of 98.78%, mean absolute error of 0.28 bpm, limits of agreement between −1.68 and 1.69 bpm, and a correlation coefficient of 0.998 with reference to a state-of-the-art PPG-based commercial device. The results in this proof of concept study demonstrate the potential of this new sensing modality to be used as an alternative, or to complement existing methods, for continuous monitoring of heart rate at the wrist.

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Measuring Heart Rate Variability in Free-Living Conditions Using Consumer-Grade Photoplethysmography: Validation Study (Preprint)

10.2196/preprints.17355 ◽

2019 ◽

Author(s):

Emily Lam ◽

Shahrose Aratia ◽

Julian Wang ◽

James Tung

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Root Mean Square ◽

Living Conditions ◽

Motion Artifacts ◽

Data Sets ◽

Mean Square ◽

Free Living ◽

Frequency Power ◽

Free Living Conditions

BACKGROUND Heart rate variability (HRV) is used to assess cardiac health and autonomic nervous system capabilities. With the growing popularity of commercially available wearable technologies, the opportunity to unobtrusively measure HRV via photoplethysmography (PPG) is an attractive alternative to electrocardiogram (ECG), which serves as the gold standard. PPG measures blood flow within the vasculature using color intensity. However, PPG does not directly measure HRV; it measures pulse rate variability (PRV). Previous studies comparing consumer-grade PRV with HRV have demonstrated mixed results in short durations of activity under controlled conditions. Further research is required to determine the efficacy of PRV to estimate HRV under free-living conditions. OBJECTIVE This study aims to compare PRV estimates obtained from a consumer-grade PPG sensor with HRV measurements from a portable ECG during unsupervised free-living conditions, including sleep, and examine factors influencing estimation, including measurement conditions and simple editing methods to limit motion artifacts. METHODS A total of 10 healthy adults were recruited. Data from a Microsoft Band 2 and a Shimmer3 ECG unit were recorded simultaneously using a smartphone. Participants wore the devices for >90 min during typical day-to-day activities and while sleeping. After filtering, ECG data were processed using a combination of discrete wavelet transforms and peak-finding methods to identify R-R intervals. P-P intervals were edited for deletion using methods based on outlier detection and by removing sections affected by motion artifacts. Common HRV metrics were compared, including mean N-N, SD of N-N intervals, percentage of subsequent differences >50 ms (pNN50), root mean square of successive differences, low-frequency power (LF), and high-frequency power. Validity was assessed using root mean square error (RMSE) and Pearson correlation coefficient (R2). RESULTS Data sets for 10 days and 9 corresponding nights were acquired. The mean RMSE was 182 ms (SD 48) during the day and 158 ms (SD 67) at night. R2 ranged from 0.00 to 0.66, with 2 of 19 (2 nights) trials considered moderate, 7 of 19 (2 days, 5 nights) fair, and 10 of 19 (8 days, 2 nights) poor. Deleting sections thought to be affected by motion artifacts had a minimal impact on the accuracy of PRV measures. Significant HRV and PRV differences were found for LF during the day and R-R, SDNN, pNN50, and LF at night. For 8 of the 9 matched day and night data sets, R2 values were higher at night (P=.08). P-P intervals were less sensitive to rapid R-R interval changes. CONCLUSIONS Owing to overall poor concurrent validity and inconsistency among participant data, PRV was found to be a poor surrogate for HRV under free-living conditions. These findings suggest that free-living HRV measurements would benefit from examining alternate sensing methods, such as multiwavelength PPG and wearable ECG.

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Gender Differences in Cardiac Chronotropic Control: Implications for Heart Rate Variability Research

Applied Psychophysiology and Biofeedback ◽

10.1007/s10484-021-09528-w ◽

2021 ◽

Author(s):

DeWayne P. Williams ◽

Nicholas Joseph ◽

Gina M. Gerardo ◽

LaBarron K. Hill ◽

Julian Koenig ◽

...

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Demographic Variable ◽

Meta Analysis ◽

Heart Period ◽

High Frequency Power ◽

Potential Impact ◽

The Impact ◽

Resting Period ◽

Frequency Power

AbstractThere is a continuing debate concerning “adjustments” to heart period variability [i.e., heart rate variability (HRV)] for the heart period [i.e., increases inter-beat-intervals (IBI)]. To date, such arguments have not seriously considered the impact a demographic variable, such as gender, can have on the association between HRV and the heart period. A prior meta-analysis showed women to have greater HRV compared to men despite having shorter IBI and higher heart rate (HR). Thus, it is plausible that men and women differ in the association between HRV and HR/IBI. Thus, the present study investigates the potential moderating effect of gender on the association between HRV and indices of cardiac chronotropy, including both HR and IBI. Data from 633 participants (339 women) were available for analysis. Cardiac measures were assessed during a 5-min baseline-resting period. HRV measures included the standard deviation of inter-beat-intervals, root mean square of successive differences, and autoregressive high frequency power. Moderation analyses showed gender significantly moderated the association between all HRV variables and both HR and IBI (each p < 0.05). However, results were not consistent when using recently recommended HRV variables “adjusted” for IBI. Overall, the current investigation provides data illustrating a differential association between HRV and the heart period based on gender. Substantial neurophysiological evidence support the current findings; women show greater sensitivity to acetylcholine compared to men. If women show greater sensitivity to acetylcholine, and acetylcholine increases HRV and the heart period, then the association between HRV and the heart period indeed should be stronger in women compared to men. Taken together, these data suggest that routine “adjustments” to HRV for the heart period are unjustified and problematic at best. As it relates to the application of future HRV research, it is imperative that researchers continue to consider the potential impact of gender.

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Heart Rate and Oxygen Saturation Monitoring With a New Wearable Wireless Device in the Intensive Care Unit: Pilot Comparison Trial

JMIR Biomedical Engineering ◽

10.2196/18158 ◽

2020 ◽

Vol 5 (1) ◽

pp. e18158

Author(s):

Srinivasan Murali ◽

Francisco Rincon ◽

Tiziano Cassina ◽

Stephane Cook ◽

Jean-Jacques Goy

Keyword(s):

Intensive Care Unit ◽

Heart Rate ◽

Intensive Care ◽

Oxygen Saturation ◽

Cardiac Monitoring ◽

Data Loss ◽

Clinical Value ◽

Icu Patients ◽

Conduction Disturbances ◽

Wireless Biosensor

Background Continuous cardiac monitoring with wireless sensors is an attractive option for early detection of arrhythmia and conduction disturbances and the prevention of adverse events leading to patient deterioration. We present a new sensor design (SmartCardia), a wearable wireless biosensor patch, for continuous cardiac and oxygen saturation (SpO2) monitoring. Objective This study aimed to test the clinical value of a new wireless sensor device (SmartCardia) and its usefulness in monitoring the heart rate (HR) and SpO2 of patients. Methods We performed an observational study and monitored the HR and SpO2 of patients admitted to the intensive care unit (ICU). We compared the device under test (SmartCardia) with the ICU-grade monitoring system (Dräger-Healthcare). We defined optimal correlation between the gold standard and the wireless system as <10% difference for HR and <4% difference for SpO2. Data loss and discrepancy between the two systems were critically analyzed. Results A total of 58 ICU patients (42 men and 16 women), with a mean age of 71 years (SD 11), were included in this study. A total of 13.49 (SD 5.53) hours per patient were recorded. This represents a total recorded period of 782.3 hours. The mean difference between the HR detected by the SmartCardia patch and the ICU monitor was 5.87 (SD 16.01) beats per minute (bias=–5.66, SD 16.09). For SpO2, the average difference was 3.54% (SD 3.86; bias=2.9, SD 4.36) for interpretable values. SmartCardia’s patch measures SpO2 only under low-to-no activity conditions and otherwise does not report a value. Data loss and noninterpretable values of SpO2 represented 26% (SD 24) of total measurements. Conclusions The SmartCardia device demonstrated clinically acceptable accuracy for HR and SpO2 monitoring in ICU patients.

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