Heart rate variability characterization in daily physical activities using wavelet analysis and multilayer fuzzy activity clustering.
A portable data recorder was developed to parallel measure the electrocardiogram and body accelerations. A multilayer fuzzy clustering algorithm was proposed to classify the physical activity based on body accelerations. Discrete wavelet transform was incorporated to retrieve time-varying characteristics of heart rate variability under different physical activities. Nine healthy subjects were included to investigate activity-related heart rate variability during 24 h. The results showed that the heartbeat fluctuations in high frequencies were the greatest during lying and the smallest during standing. Moreover, very-low-frequency heartbeat fluctuations during low activity level (lying) were greater than during high activity level (nonlying).
Chan
Hsiao-Lung
HL
Department of Electrical Engineering, Chang Gung University, Taoyuan, Taiwan. chanhl@mail.cgu.edu.tw
Fang
Shih-Chin
SC
Ko
Yu-Lin
YL
Lin
Ming-An
MA
Huang
Hui-Hsun
HH
Lin
Chun-Hsien
CH
D000203:Activities of Daily Living; D000328:Adult; D000465:Algorithms; D016000:Cluster Analysis; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D015716:Electrocardiography, Ambulatory / Q000379:methods*; D017143:Fuzzy Logic; D006339:Heart Rate / Q000502:physiology*; D006801:Humans; D008297:Male; D009043:Motor Activity / Q000502:physiology*; D010363:Pattern Recognition, Automated; D011187:Posture / Q000502:physiology*