OBJECTIVE: In this paper, a support vector machine (SVM) approach using statistical features, P wave absence, spectrum features, and length-adaptive entropy are presented to classify ECG rhythms as four types: normal rhythm, atrial fibrillation (AF), other rhythm, and too noisy to classify. APPROACH: The proposed algorithm consisted of three steps: (1) signal pre-processing based on the wavelet method; (2) feature extraction, the extracted features including one power feature, two spectrum features, two entropy features, 17 RR interval-related features, and 11 P wave features; and (3) classification using the SVM classifier. MAIN RESULTS: The algorithm was trained by 8528 single-lead ECG recordings lasting from 9 s to just over 60 s and then tested on a hidden test set consisting of 3658 recordings of similar lengths, which were all provided by the PhysioNet/Computing in Cardiology Challenge 2017. The scoring for this challenge used an F 1 measure, and the final F 1 score was defined as the average of F 1n (the F 1 score of normal rhythm), F 1a (the F 1 score of AF rhythm), and F 1o (the F 1 score of other rhythm). The results confirmed the high accuracy of our proposed method, which obtained 90.27%, 86.37%, and 75.08% for F 1n , F 1a , and F 1n and the final F 1 score of 84% on the training set. In the final test to assess the performance of all of the hidden data, the obtained F 1n , F 1a , F 1o and the average F 1 were 90.82%, 78.56%, 71.77% and 80%, respectively. SIGNIFICANCE: The proposed algorithm targets a large number of raw, short single ECG data rather than a small number of carefully selected, often clean ECG records, which have been studied in most of the previous literature. It breaks through the limitation in applicability and provides reliable AF detection from a short single-lead ECG.
OBJECTIVE: In this paper, a support vector machine (SVM) approach using statistical features, P wave absence, spectrum features, and length-adaptive entropy are presented to classify ECG rhythms as four types: normal rhythm, atrial fibrillation (AF), other rhythm, and too noisy to classify. APPROACH: The proposed algorithm consisted of three steps: (1) signal pre-processing based on the wavelet method; (2) feature extraction, the extracted features including one power feature, two spectrum features, two entropy features, 17 RR interval-related features, and 11 P wave features; and (3) classification using the SVM classifier. MAIN RESULTS: The algorithm was trained by 8528 single-lead ECG recordings lasting from 9 s to just over 60 s and then tested on a hidden test set consisting of 3658 recordings of similar lengths, which were all provided by the PhysioNet/Computing in Cardiology Challenge 2017. The scoring for this challenge used an F 1 measure, and the final F 1 score was defined as the average of F 1n (the F 1 score of normal rhythm), F 1a (the F 1 score of AF rhythm), and F 1o (the F 1 score of other rhythm). The results confirmed the high accuracy of our proposed method, which obtained 90.27%, 86.37%, and 75.08% for F 1n , F 1a , and F 1n and the final F 1 score of 84% on the training set. In the final test to assess the performance of all of the hidden data, the obtained F 1n , F 1a , F 1o and the average F 1 were 90.82%, 78.56%, 71.77% and 80%, respectively. SIGNIFICANCE: The proposed algorithm targets a large number of raw, short single ECG data rather than a small number of carefully selected, often clean ECG records, which have been studied in most of the previous literature. It breaks through the limitation in applicability and provides reliable AF detection from a short single-lead ECG.
Authors: Ali Bahrami Rad; Conner Galloway; Daniel Treiman; Joel Xue; Qiao Li; Reza Sameni; Dave Albert; Gari D Clifford Journal: PLoS One Date: 2021-11-16 Impact factor: 3.240