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Heart rate variability characterization in daily physical activities using wavelet analysis and multilayer fuzzy activity clustering.

Hsiao-Lung Chan·Shih-Chin Fang·Yu-Lin Ko·Ming-An Lin·Hui-Hsun Huang·Chun-Hsien Lin
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Abstract

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).

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Activities of Daily LivingAdultAlgorithmsCluster AnalysisDiagnosis, Computer-AssistedElectrocardiography, AmbulatoryFuzzy LogicHeart RateHumansMaleMotor ActivityPattern Recognition, AutomatedPostureClinical TrialJournal ArticleResearch Support, Non-U.S. Gov't

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Hsiao-Lung Chan, Shih-Chin Fang, Yu-Lin Ko, Ming-An Lin, Hui-Hsun Huang, Chun-Hsien Lin. 2006. Heart rate variability characterization in daily physical activities using wavelet analysis and multilayer fuzzy activity clustering.. https://doi.org/10.1109/tbme.2005.859811

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