Long Short-Term Memory based Electrocardiogram diagnosis for Premature Ventricular Contraction in children

Detection of premature ventricular contraction (PVC) in children is an important step in the diagnosis of arrhythmia. It not only requires professional knowledge, but also occupies a large amount of repetitive work of clinicians. Deep learning based computer model has recently been applied into the clinical field for disease diagnosis. In this study, we built a Long Short-Term Memory (LSTM) recurrent neural networks (RNN) model to detect PVC with children's electrocardiogram (ECG). 1019 children with and 1198 without PVC were selected for this study. The lead II of the 12 leads ECG signal for each child was used for diagnosis. In total, 220 studies were selected randomly as validation set, 222 studies as testing set, and the rest as training set. The best LSTM model achieved a testing F1 score 0.94 on PVC classification task. With 10-folds validation, the area under receiver operating characteristic curve (AUC) achieved 0.97 +/- 0.01. To conclude, this is a meaningful step towards large scale and efficient PVC diagnosis.