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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| Vydáno v: | IEEE transactions on biomedical engineering Ročník 53; číslo 1; s. 133 - 139 |
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| Hlavní autoři: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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United States
IEEE
01.01.2006
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 0018-9294, 1558-2531 |
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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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| AbstractList | 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). 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-reiated 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). 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).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). A portable data recorder was developed to parallal measure the eleotrocardiogram 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-reiated 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-tow-frequency heartbeat fluctuations during low activity level (lying) were greater than during high activity level (nonlying). |
| Author | Lin, C.-H. Yu-Lin Ko Hsiao-Lung Chan Hui-Hsun Huang Shih-Chin Fang Ming-An Lin |
| Author_xml | – sequence: 1 surname: Hsiao-Lung Chan fullname: Hsiao-Lung Chan email: chanhl@mail.cgu.edu.tw;chanhlc@ms41.hinet.net organization: Dept. of Electr. Eng., Chang Gung Univ., Taoyuan, Taiwan – sequence: 2 surname: Shih-Chin Fang fullname: Shih-Chin Fang – sequence: 3 surname: Yu-Lin Ko fullname: Yu-Lin Ko – sequence: 4 surname: Ming-An Lin fullname: Ming-An Lin – sequence: 5 surname: Hui-Hsun Huang fullname: Hui-Hsun Huang – sequence: 6 givenname: C.-H. surname: Lin fullname: Lin, C.-H. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/16402613$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1007/978-1-4757-0450-1 10.1016/0002-9149(91)90355-O 10.1007/BF02513304 10.1152/ajpheart.1985.248.1.H151 10.1016/S0002-8703(99)70219-X 10.1161/01.CIR.85.1.164 10.1109/86.547939 10.21236/ADA106978 10.1161/01.CIR.81.2.537 10.1161/01.RES.59.2.178 10.1161/01.HYP.17.4_Suppl.III36 10.1016/0165-1838(94)00152-A 10.1016/0002-9149(90)90503-S 10.1067/mhj.2002.121928 |
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| References | Lin (ref5) 1999; 137 ref13 ref15 ref14 ref11 ref10 Kleiger (ref7) 1991; 68 ref2 ref1 ref16 ref8 ref9 ref4 Sekioka (ref12) 1997; 8 ref6 Novak (ref3) 1997 |
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| Snippet | A portable data recorder was developed to parallel measure the electrocardiogram and body accelerations. A multilayer fuzzy clustering algorithm was proposed... A portable data recorder was developed to parallal measure the eleotrocardiogram and body accelerations. A multilayer fuzzy clustering algorithm was proposed... |
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| SubjectTerms | 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 Heart Rate - physiology Heart rate variability Hospitals Humans Male Motor Activity - physiology Nonhomogeneous media Pattern Recognition, Automated physical activity Posture - physiology Wavelet analysis |
| Title | Heart rate variability characterization in daily physical activities using wavelet analysis and multilayer fuzzy activity clustering |
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