Chen X, Cheng Z, Wang S, Lu G, Xv G, Liu Q, Zhu X. Atrial fibrillation detection based on multi-feature extraction and convolutional neural network for processing ECG signals.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021;
202:106009. [PMID:
33631641 DOI:
10.1016/j.cmpb.2021.106009]
[Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/14/2020] [Accepted: 02/14/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND AND OBJECTIVE
The incidence of atrial fibrillation is increasing annually. We develop an automatic detection system, which is of great significance for the early detection and treatment of atrial fibrillation. This can lead to the reduction of the incidence of critical illnesses and mortality.
METHODS
We propose an atrial fibrillation detection algorithm based on multi-feature extraction and convolutional neural network of atrial activity via electrocardiograph signals, and compare its detection based on cluster analysis, one-versus-one rule and support vector machine, using accuracy, specificity, sensitivity and true positive rate as evaluation criteria.
RESULTS
The atrial fibrillation detection algorithm proposed in this paper has an accuracy rate of 98.92%, a specificity of 97.04%, a sensitivity of 97.19%, and a true positive rate of 96.47%. The average accuracy of the algorithms we compared is 80.26%, and the accuracy of our algorithm is 23.25% higher than this average pertaining to the other algorithms.
CONCLUSION
We implemented an atrial fibrillation detection algorithm that meets the requirements of high accuracy, robustness and generalization ability. It has important clinical and social significance for early detection of atrial fibrillation, improvement of patient treatment plans and improvement of medical diagnosis.
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