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Health warning based on 3R ECG Sample's combined features and LSTM
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- Liu, Qingshan (author)
- Power Quality Analysis and Load Detection Technology Laboratory, Anhui Jianzhu University, Hefei, 230601, China
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- Gao, Cuiyun (author)
- Power Quality Analysis and Load Detection Technology Laboratory, Anhui Jianzhu University, Hefei, 230601, China
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- Zhao, Yang (author)
- Power Quality Analysis and Load Detection Technology Laboratory, Anhui Jianzhu University, Hefei, 230601, China
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- Huang, Songqun (author)
- Department of Cardiovasology Changhai Hospital, Second Military Medical University, Shanghai, 200433, China
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- Zhang, Yuqing (author)
- Power Quality Analysis and Load Detection Technology Laboratory, Anhui Jianzhu University, Hefei, 230601, China
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- Dong, Xiaoyu (author)
- Power Quality Analysis and Load Detection Technology Laboratory, Anhui Jianzhu University, Hefei, 230601, China
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- Lu, Zhonghai (author)
- KTH,Elektronik och inbyggda system
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(creator_code:org_t)
- Elsevier BV, 2023
- 2023
- English.
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In: Computers in Biology and Medicine. - : Elsevier BV. - 0010-4825 .- 1879-0534. ; 162
- Related links:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Subject headings
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- Most researches use the fixed-length sample to identify ECG abnormalities based on MIT ECG dataset, which leads to information loss. To address this problem, this paper proposes a method for ECG abnormality detection and health warning based on ECG Holter of PHIA and 3R-TSH-L method. The 3R-TSH-L method is implemented by:(1) getting 3R ECG samples using Pan-Tompkins method and using volatility to obtain high-quality raw ECG data; (2) extracting combination features including time-domain features, frequency domain features and time-frequency domain features; (3) using LSTM for classification, training and testing the algorithm based on the MIT-BIH dataset, and obtaining relatively optimal features as spliced normalized fusion features including kurtosis, skewness and RR interval time domain features, STFT-based sub-band spectrum features, and harmonic ratio features. The ECG data were collected using the self-developed ECG Holter (PHIA) on 14 subjects, aged between 24 and 75 including both male and female, to build the ECG dataset (ECG-H). The algorithm was transferred to the ECG-H dataset, and a health warning assessment model based on abnormal ECG rate and heart rate variability weighting was proposed. Experiments show that 3R-TSH-L method proposed in the paper has a high accuracy of 98.28% for the detection of ECG abnormalities of MIT-BIH dataset and a good transfer learning ability of 95.66% accuracy for ECG-H. The health warning model was also testified to be reasonable. The key technique of the ECG Holter of PHIA and the method 3R-TSH-L proposed in this paper is expected to be widely used in family-oriented healthcare.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Hälsovetenskap -- Annan hälsovetenskap (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Health Sciences -- Other Health Sciences (hsv//eng)
Keyword
- 3R ECG sample
- 3R-TSH-L
- ECG abnormality Detection
- Health warning
- Transfer learning
Publication and Content Type
- ref (subject category)
- art (subject category)
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