A novel lightweight computerized ECG interpretation approach based on clinical 12-lead data

Although 12-lead electrocardiograms (ECGs) provide a wide range of spatiotemporal characteristics, interpreting them for arrhythmia detection is difficult due to a lack of reliable large-scale clinical datasets. Herein, we proposed an innovative lightweight computerized ECG interpretation approach based on 12-lead data. Our model was trained, validated, and tested on 53845 standard 12-lead ECG records collected at Shanghai First People’s Hospital in affiliation with Shanghai Jiao Tong University. The experiments revealed that our approach had a classification accuracy of 94.41% in the classification task of seven types of rhythms, which was markedly superior to related single-lead and 12-lead ECG classification methods. Moreover, the average receiver operating characteristic area under the curve reached a value of 0.940, and the precision values for sinus tachycardia and sinus bradycardia were 0.945 and 0.91, respectively, with specificity values of 0.996 and 0.994. By employing our boosting method, we were able to improve the accuracy to 94.85%. To investigate the performance degradation of the proposed neural network in some classes, an ECG cardiologist was enlisted to review questionable ECGs; this process provides a promising direction for network performance improvement. Therefore, the proposed computerized ECG interpretation approach has practical significance because it could help professional physicians analyze patients’ heart conditions based on real-time 12-lead ECG or grade their disease severity in advance.