Abstract：This study proposed a three-type classification method for wearable ECG signal quality assessment. Rationale for three-type classification is from the clinical requirement for diagnosis,and the three types for signal quality are:1) clinically useful signals with good signal quality;2) clinically useful signals with poor signal quality;3) clinically useless signals. The method firstly extracted 12 signal quality features from time-domain,frequency-domain and nonlinear-domain,and constructed the feature matrix. Then a support vector machine (SVM) classifier based on a radial basis kernel function was trained on the collected wearable ECG signals with manual annotations. Results from 375 independent test ECG samples with clinical labels showed that the proposed method achieved an F measure of 0.909,0.827 and 0.973 for the three-type quality signals respectively,and the overall classification accuracy was 92.3%,which was 2.2% and 6.4% higher than the comparable methods,i.e.,the CNN-based model and the traditional SVM model. This study demonstrated that the new three-type classification model of signal quality had application potentials in wearable dynamic ECG signal quality assessment.
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