12-Lead ECG arrhythmia classification using cascaded convolutional neural network and expert feature.

Owing to widely available digital ECG data and recent advances in deep learning techniques, automatic ECG arrhythmia classification based on deep neural network has gained growing attention. However, existing neural networks are mainly validated on single‑lead ECG, not involving the correlation and difference between multiple leads, while multiple leads ECG provides more complete description of the cardiac activity in different directions. This paper proposes a 12‑lead ECG arrhythmia classification method using a cascaded convolutional neural network (CCNN) and expert features. The one-dimensional (1-D) CNN is firstly designed to extract features from each single‑lead signal. Subsequently, considering the temporal correlation and spatial variability between multiple leads, features are cascaded as input to two-dimensional (2-D) densely connected ResNet blocks to classify the arrhythmia. Furthermore, features based on expert knowledge are extracted and a random forest is applied to get a classification probability. Results from both CCNN and expert features are combined using the stacking technique as the final classification result. The method has been validated against the first China ECG Intelligence Challenge, obtaining a final score of 86.5% for classifying 12‑lead ECG data with multiple labels into 9 categories.