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

• Published in: Computers in biology and medicine Vol. 162; p. 107082
• Main Authors: Liu, Qingshan, Gao, Cuiyun, Zhao, Yang, Huang, Songqun, Zhang, Yuqing, Dong, Xiaoyu, Lu, Zhonghai
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.08.2023; Elsevier Limited
• Subjects: 3R ECG sample; 3R-TSH-L; Abnormalities; Aging; Algorithms; Cardiac arrhythmia; Classification; Datasets; Disease; ECG abnormality Detection; EKG; Electrocardiography; Fourier transforms; Frequency domain analysis; Health warning; Heart rate; Internal Medicine; Internet of Things; Kurtosis; Methods; Model testing; Morphology; Neural networks; Other; Population; Support vector machines; Time domain analysis; Transfer learning; Warning; Wavelet transforms; China; 3R-TSH-L; ECG abnormality Detection; Health warning; 3R ECG sample; Transfer learning
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2023.107082

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. •High-quality ECG signal selection based on dual threshold and fluctuation value.•Variable-length 3R ECG sample extraction adaptive to different heart rates.•Multi-domain combination features including sub-band spectrum and harmonic ratio.•Health warning model based on weighted ECG abnormality rate and heart rate variability coefficient.