Unreadable Segment Recognition of Single-lead Dynamic Electrocardiogram Signals Based on Morphological Algorithm and Random Forest Classifier

Recognizing unreadable electrocardiogram (ECG) signals could reduce the error rate of automatic software analysis and improve the interpretation efficiency of doctors, especially for single-lead dynamic ECGs. In this paper, we propose an unreadable ECG segment recognition method based on morphological algorithm and random forest classifier (RFC). The single-lead ECG signals are first filtered and normalized for morphological opening and closing operation, to generate detection sequences with more obvious QRS waves, since the large amplitudes introduced by motion interference could be suppressed during this procedure. Then features such as Shannon entropy and kurtosis are extracted and the RFC is used for unreadable segment classification. A total of 3354 readable segments and 2199 unreadable segments with a length of 4 seconds are obtained from 37 patients for method evaluation. The accuracy of our method (92.94 ± 0.93%) is significantly higher than that of the method without morphological algorithm (85.68 ± 1.30%). Moreover, we also used the “N” and “~” categories of the database from PhysioNet/CinC Challenge 2017 for further verification, and the accuracy of the proposed method (93.75 ± 0.69%) is significantly higher than that of the model without morphological processing (82.25 ± 1.06%) as well.