A deep learning approach of heartbeat classification for the single-lead ECG signals and inter-patient paradigm

Abstract

The corresponding arrhythmia often occurs before the onset of cardiovascular disease (CVD), electrocardiogram (ECG) can more intuitively detect any abnormality in the heartbeat as a sign of arrhythmia. There are many traditional ECG classification methods, but these methods are constrained human costs and inaccuracy since they rely on manually extracting features, and cannot fully mine the deep pathological information hidden in the data. Consequently, a novel deep learning model based on single-lead ECG signals and inter-patient paradigm is developed to improve the shortcomings of traditional ECG heartbeat classification. The residual connection is adopted in the proposed method to improve classification accuracy and alleviate the gradient disappearance issue. Heartbeat complexes that included a targeted heartbeat and an adjacent heartbeat is selected as the input of the model. The MIT-BIH arrhythmia database is employed to valid proposed method. Besides, a focal loss function is used to address the classes imbalance of the database. The experimental results show that the positive predictive values of the proposed classification method for N, S, V, and F are 99.10%, 96.80%, 61.32%, and 95.36%, respectively. In addition, the sensitivity values are 95.83%, 95.54%, 90.43%, and 84.79%; the specificity values are 92.96%, 99.88%, 96.05%, and 99.97%, respectively. Compared with the art-of-state inter-patient ECG heartbeat classification approaches, our proposed approach achieved better results. Therefore, the proposed deep learning model of heartbeat classification is effective and feasible for the single-lead ECG signals and interpatient paradigm.