TY - GEN
T1 - Comparative utility of time and frequency HRV domains for automated classification of exercise exertion levels
AU - Jeong, In Cheol
AU - Finkelstein, Joseph
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/12/16
Y1 - 2015/12/16
N2 - Exercise exertion results in activation of sympathetic nervous system. Heart rate variability (HRV) has been used to analyze activity of sympathetic nervous system (ANS). However, approaches to use HRV for exercise exertion analysis were not explored systematically. The main goal of this study was to develop classification algorithms to determine level of exercise exertion in real time and to compare potential of HRV time domain parameters versus HRV frequency domain parameters versus combined time and frequency parameter set. Discriminant analysis was used to identify optimal parameter sets and to develop algorithms for classification of exercise exertion levels. Time-domain HRV parameters demonstrated higher classification accuracy (95.6%) as compared to frequency-domain parameters (82.2%). Combing HRV parameters from time and frequency domains improves classification accuracy (97.8%). Our results suggested that HRV analysis can be used to automatically classify exercise exertion levels. Future studies should focus on more granular approach in identifying different stages of exercise process. Evaluation of classification algorithms should be based on larger sample of diverse representatives of different age, sex and health condition groups.
AB - Exercise exertion results in activation of sympathetic nervous system. Heart rate variability (HRV) has been used to analyze activity of sympathetic nervous system (ANS). However, approaches to use HRV for exercise exertion analysis were not explored systematically. The main goal of this study was to develop classification algorithms to determine level of exercise exertion in real time and to compare potential of HRV time domain parameters versus HRV frequency domain parameters versus combined time and frequency parameter set. Discriminant analysis was used to identify optimal parameter sets and to develop algorithms for classification of exercise exertion levels. Time-domain HRV parameters demonstrated higher classification accuracy (95.6%) as compared to frequency-domain parameters (82.2%). Combing HRV parameters from time and frequency domains improves classification accuracy (97.8%). Our results suggested that HRV analysis can be used to automatically classify exercise exertion levels. Future studies should focus on more granular approach in identifying different stages of exercise process. Evaluation of classification algorithms should be based on larger sample of diverse representatives of different age, sex and health condition groups.
UR - http://www.scopus.com/inward/record.url?scp=84962419807&partnerID=8YFLogxK
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U2 - 10.1109/BIBM.2015.7359817
DO - 10.1109/BIBM.2015.7359817
M3 - Conference contribution
AN - SCOPUS:84962419807
T3 - Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015
SP - 983
EP - 989
BT - Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015
A2 - Schapranow, lng. Matthieu
A2 - Zhou, Jiayu
A2 - Hu, Xiaohua Tony
A2 - Ma, Bin
A2 - Rajasekaran, Sanguthevar
A2 - Miyano, Satoru
A2 - Yoo, Illhoi
A2 - Pierce, Brian
A2 - Shehu, Amarda
A2 - Gombar, Vijay K.
A2 - Chen, Brian
A2 - Pai, Vinay
A2 - Huan, Jun
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015
Y2 - 9 November 2015 through 12 November 2015
ER -