scholarly journals Comparison of two systems for long-term heart rate variability monitoring in free-living conditions - a pilot study

2011 ◽  
Vol 10 (1) ◽  
pp. 27 ◽  
Author(s):  
Jesper Kristiansen ◽  
Mette Korshøj ◽  
Jørgen H Skotte ◽  
Tobias Jespersen ◽  
Karen Søgaard ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Pilot Study ◽  
Living Conditions ◽  
Free Living ◽  
Two Systems ◽  
Free Living Conditions

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

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.

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

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 &gt;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 &gt;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 (<i>R</i><sup>2</sup>). 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. <i>R</i><sup>2</sup> 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, <i>R</i><sup>2</sup> values were higher at night (<i>P=</i>.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.

scholarly journals Critical evaluation of the factorial and heart-rate recording methods for the determination of energy expenditure of free-living elderly people

1997 ◽  
Vol 78 (5) ◽  
pp. 709-722 ◽  
Author(s):  
Beatrice Morio ◽  
Patrick Ritz ◽  
Elisabeth Verdier ◽  
Christophe Montaurier ◽  
Bernard Beaufrere ◽  
...  
Keyword(s):  
Heart Rate ◽  
Energy Expenditure ◽  
Elderly People ◽  
Living Conditions ◽  
Energy Costs ◽  
Free Living ◽  
Factorial Method ◽  
Recording Method ◽  
Mean Differences ◽  
Free Living Conditions

The aim of the present study was to validate against the doubly-labelled water (DLW) technique the factorial method and the heart rate (HR) recording method for determining daily energy expenditure (DEE) of elderly people in free-living conditions. The two methods were first calibrated and validated in twelve healthy subjects (six males and six females; 70·1 (sd 2·7) years) from opencircuit whole-body indirect calorimetry measurements during three consecutive days and during 1 d respectively. Mean energy costs of the various usual activities were determined for each subject using the factorial method, and individual relationships were set up between HR and energy expenditure for the HR recording method. In free-living conditions, DEE was determined over the same period of time by the DLW, the factorial and the HR recording methods during 17, 14 and 4 d respectively. Mean free-living DEE values for men estimated using the DLW, the factorial and the HR recording methods were 12·8 (sd 3·1), 12·7 (sd 2·2) and 13·5 (sd 2·7) MJ/d respectively. Mean free-living DEE values for women were 9·6 (sd 0·8), 8·8 (sd 1·2) and 10·2 (sd 1·5) MJ/d respectively. No significant differences were found between the three methods for either sex, using the Bland & Altman (1986) test. Mean differences in DEE of men were -0·9 (sd 11·8) % between the factorial and DLW methods, and +4·7 (sd 16·1) % between the HR recording and DLW methods. Similarly, in women, mean differences were -7·7 (sd 12·7) % between the factorial and DLW methods, and +5·9 (sd 8·8) % between the HR recording and DLW methods. It was concluded that the factorial and the HR recording methods are satisfactory alternatives to the DLW method when considering the mean DEE of a group of subjects. Furthermore, mean energy costs of activities calculated in the present study using the factorial method were shown to be suitable for determining free-living DEE of elderly people when the reference value (i.e. sleeping metabolic rate) is accurately measured.

scholarly journals Fitbit Charge HR Wireless Heart Rate Monitor: Validation Study Conducted Under Free-Living Conditions

2017 ◽  
Vol 5 (10) ◽  
pp. e157 ◽  
Author(s):  
Alexander Wilhelm Gorny ◽  
Seaw Jia Liew ◽  
Chuen Seng Tan ◽  
Falk Müller-Riemenschneider
Keyword(s):  
Heart Rate ◽  
Validation Study ◽  
Living Conditions ◽  
Free Living ◽  
Heart Rate Monitor ◽  
Free Living Conditions

Examining The Validity Of Fitbit Charge HR For Measuring Heart Rate In Free-living Conditions

2016 ◽  
Vol 48 ◽  
pp. 786-787 ◽  
Author(s):  
Jung-Min M. Lee ◽  
Hyunsung An ◽  
Seoung-ki Kang ◽  
Youngdeok Kim ◽  
Danae Dinkel
Keyword(s):  
Heart Rate ◽  
Living Conditions ◽  
Free Living ◽  
Free Living Conditions

scholarly journals Detecting sleep in free-living conditions without sleep-diaries: a device-agnostic, wearable heart rate sensing approach

2020 ◽  
Author(s):  
Ignacio Perez-Pozuelo ◽  
Marius Posa ◽  
Dimitris Spathis ◽  
Kate Westgate ◽  
Nicholas Wareham ◽  
...  
Keyword(s):  
Heart Rate ◽  
Mean Squared Error ◽  
Sleep Time ◽  
Living Conditions ◽  
Free Living ◽  
Sleep Diaries ◽  
Detection Algorithms ◽  
Angular Change ◽  
Large Populations ◽  
Free Living Conditions

Study Objectives: The rise of multisensor wearable devices offers a unique opportunity for the objective inference of sleep outside laboratories, enabling longitudinal monitoring in large populations. To enhance objectivity and facilitate cross-cohort comparisons, sleep detection algorithms in free-living conditions should rely on personalized but device-agnostic features, which can be applied without laborious human annotations or sleep diaries. We developed and validated a heart rate-based algorithm that captures inter- and intra-individual sleep differences, does not require human input and can be applied in free-living conditions. Methods: The algorithm was evaluated across four study cohorts using different research- and consumer-grade devices for over 2,000 nights. Recording periods included both 24-hour free-living and conventional lab-based night-only data. Our method was systematically optimized and validated against polysomnography and sleep diaries and compared to sleep periods produced by accelerometry-based angular change algorithms. Results: We evaluated our approach in four cohorts comprising two free-living studies with detailed sleep diaries and two PSG studies. In the free-living studies, the algorithm yielded a mean squared error (MSE) of 0.06 to 0.07 and a total sleep time deviation of -0.60 to -14.08 minutes. In the laboratory studies, the MSE ranged between 0.06 and 0.10 yielding a time deviation between -23.23 and -33.15 minutes. Conclusions: Our results suggest that our heart rate-based algorithm can reliably and objectively infer sleep under longitudinal, free-living conditions, independent of the wearable device used. This represents the first open-source algorithm to leverage heart rate data for inferring sleep without requiring sleep diaries or annotations.

scholarly journals Comparative Assessment of Heart Rate Variability Obtained via Ambulatory ECG and Polar Heart Rate Monitors in Healthy Cats: A Pilot Study

2021 ◽  
Vol 8 ◽  
Author(s):  
Emma K. Grigg ◽  
Yu Ueda ◽  
Ashley L. Walker ◽  
Lynette A. Hart ◽  
Samany Simas ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Pilot Study ◽  
Cardiac Activity ◽  
Cardiac Monitoring ◽  
Heart Rate Monitors ◽  
Ambulatory Ecg ◽  
Two Systems ◽  
Handling Practices ◽  
Adult Cats

Chronic exposure to stressful environments can negatively impact cats' health and welfare, affecting behavioral, autonomic, endocrine, and immune function, as with cats in shelters. Low-stress handling practices likely improve shelter cat welfare, but data supporting improved outcomes remain limited. Cardiac activity, particularly heart rate variability (HRV), is an indicator of stress and emotional state in humans and non-human animals, tracking important body functions associated with stress responsiveness, environmental adaptability, mental, and physical health. HRV studies in cats are limited, involving mainly anesthetized or restrained cats. This pilot study tested the feasibility of obtaining HRV data from unrestrained cats, using a commercially available cardiac monitoring system (Polar H10 with chest strap), compared with data from a traditional ambulatory electrocardiogram. Simultaneous data for the two systems were obtained for five adult cats. Overall, the Polar H10 monitor assessments of HRV were lower than the true HRV assessment by ambulatory ECG, except for SDNN. Correlation between the two systems was weak. Possible reasons for the lack of agreement between the two methods are discussed. At this time, our results do not support the use of Polar H10 heart rate monitors for studies of HRV in cats.

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