A support vector machine approach for AF classification from a short single-lead ECG recording

摘要

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 F1 measure, and the final F1 score was defined as the average of F1n (the F1 score of normal rhythm), F1a (the F1 score of AF rhythm), and F1o (the F1 score of other rhythm). The results confirmed the high accuracy of our proposed method, which obtained 90.27%, 86.37%, and 75.08% for F1n, F1a, and F1n and the final F1 score of 84% on the training set. In the final test to assess the performance of all of the hidden data, the obtained F1n, F1a, F1o and the average F1 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.