IEICE TRANSACTIONS on Fundamentals
Deformable Part Model Based Arrhythmia Detection Using Time Domain Features
Summary :
Heart disease is one of the major causes of death in many advanced countries. For prevention or treatment of heart disease, getting an early diagnosis from a long time period of electrocardiogram (ECG) examination is necessary. However, it could be a large burden on medical experts to analyze this large amount of data. To reduce the burden and support the analysis, this paper proposes an arrhythmia detection method based on a deformable part model, which absorbs individual variation of ECG waveform and enables the detection of various arrhythmias. Moreover, to detect the arrhythmia in low processing delay, the proposed method only utilizes time domain features. In an experimental result, the proposed method achieved 0.91 F-measure for arrhythmia detection.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E100-A No.11 pp.2221-2229
- Publication Date
- 2017/11/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E100.A.2221
- Type of Manuscript
- Special Section PAPER (Special Section on Smart Multimedia & Communication Systems)
- Category
- Digital Signal Processing
Authors
Yuuka HIRAO
Osaka University
Yoshinori TAKEUCHI
Osaka University
Masaharu IMAI
Osaka University
Jaehoon YU
Osaka University
Keyword
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Yuuka HIRAO, Yoshinori TAKEUCHI, Masaharu IMAI, Jaehoon YU, "Deformable Part Model Based Arrhythmia Detection Using Time Domain Features" in IEICE TRANSACTIONS on Fundamentals,
vol. E100-A, no. 11, pp. 2221-2229, November 2017, doi: 10.1587/transfun.E100.A.2221.
Abstract: Heart disease is one of the major causes of death in many advanced countries. For prevention or treatment of heart disease, getting an early diagnosis from a long time period of electrocardiogram (ECG) examination is necessary. However, it could be a large burden on medical experts to analyze this large amount of data. To reduce the burden and support the analysis, this paper proposes an arrhythmia detection method based on a deformable part model, which absorbs individual variation of ECG waveform and enables the detection of various arrhythmias. Moreover, to detect the arrhythmia in low processing delay, the proposed method only utilizes time domain features. In an experimental result, the proposed method achieved 0.91 F-measure for arrhythmia detection.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E100.A.2221/_p
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@ARTICLE{e100-a_11_2221,
author={Yuuka HIRAO, Yoshinori TAKEUCHI, Masaharu IMAI, Jaehoon YU, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Deformable Part Model Based Arrhythmia Detection Using Time Domain Features},
year={2017},
volume={E100-A},
number={11},
pages={2221-2229},
abstract={Heart disease is one of the major causes of death in many advanced countries. For prevention or treatment of heart disease, getting an early diagnosis from a long time period of electrocardiogram (ECG) examination is necessary. However, it could be a large burden on medical experts to analyze this large amount of data. To reduce the burden and support the analysis, this paper proposes an arrhythmia detection method based on a deformable part model, which absorbs individual variation of ECG waveform and enables the detection of various arrhythmias. Moreover, to detect the arrhythmia in low processing delay, the proposed method only utilizes time domain features. In an experimental result, the proposed method achieved 0.91 F-measure for arrhythmia detection.},
keywords={},
doi={10.1587/transfun.E100.A.2221},
ISSN={1745-1337},
month={November},}
Copy
TY - JOUR
TI - Deformable Part Model Based Arrhythmia Detection Using Time Domain Features
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2221
EP - 2229
AU - Yuuka HIRAO
AU - Yoshinori TAKEUCHI
AU - Masaharu IMAI
AU - Jaehoon YU
PY - 2017
DO - 10.1587/transfun.E100.A.2221
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E100-A
IS - 11
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - November 2017
AB - Heart disease is one of the major causes of death in many advanced countries. For prevention or treatment of heart disease, getting an early diagnosis from a long time period of electrocardiogram (ECG) examination is necessary. However, it could be a large burden on medical experts to analyze this large amount of data. To reduce the burden and support the analysis, this paper proposes an arrhythmia detection method based on a deformable part model, which absorbs individual variation of ECG waveform and enables the detection of various arrhythmias. Moreover, to detect the arrhythmia in low processing delay, the proposed method only utilizes time domain features. In an experimental result, the proposed method achieved 0.91 F-measure for arrhythmia detection.
ER -
English
