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 characteri...

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Bibliographic Details
Published in:IEEE transactions on biomedical engineering 2006-01, Vol.53 (1), p.133-139
Main Authors: Hsiao-Lung Chan, Shih-Chin Fang, Yu-Lin Ko, Ming-An Lin, Hui-Hsun Huang, Lin, C.-H.
Format: Article
Language:English
Subjects:
Acceleration
Accelerometers
Activities of Daily Living
Adult
Algorithms
Cluster Analysis
Diagnosis, Computer-Assisted - methods
Discrete wavelet transform
Discrete wavelet transforms
Electrocardiography
Electrocardiography, Ambulatory - methods
Fluctuations
fuzzy clustering
Fuzzy Logic
Heart beat
Heart Rate - physiology
Heart rate variability
Hospitals
Humans
Male
Motor Activity - physiology
Nonhomogeneous media
Pattern Recognition, Automated
physical activity
Posture - physiology
Wavelet analysis
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Summary: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).
ISSN:0018-9294
1558-2531
DOI:10.1109/TBME.2005.859811