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Sökning: onr:"swepub:oai:DiVA.org:kth-331486" > Health warning base...

Health warning based on 3R ECG Sample's combined features and LSTM

Liu, Qingshan (författare)
Power Quality Analysis and Load Detection Technology Laboratory, Anhui Jianzhu University, Hefei, 230601, China
Gao, Cuiyun (författare)
Power Quality Analysis and Load Detection Technology Laboratory, Anhui Jianzhu University, Hefei, 230601, China
Zhao, Yang (författare)
Power Quality Analysis and Load Detection Technology Laboratory, Anhui Jianzhu University, Hefei, 230601, China
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Huang, Songqun (författare)
Department of Cardiovasology Changhai Hospital, Second Military Medical University, Shanghai, 200433, China
Zhang, Yuqing (författare)
Power Quality Analysis and Load Detection Technology Laboratory, Anhui Jianzhu University, Hefei, 230601, China
Dong, Xiaoyu (författare)
Power Quality Analysis and Load Detection Technology Laboratory, Anhui Jianzhu University, Hefei, 230601, China
Lu, Zhonghai (författare)
KTH,Elektronik och inbyggda system
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 (creator_code:org_t)
Elsevier BV, 2023
2023
Engelska.
Ingår i: Computers in Biology and Medicine. - : Elsevier BV. - 0010-4825 .- 1879-0534. ; 162
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
Stäng  
  • 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.

Ämnesord

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)

Nyckelord

3R ECG sample
3R-TSH-L
ECG abnormality Detection
Health warning
Transfer learning

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