Multi-Label Classification on 12, 6, 4, 3 and 2 Lead Electrocardiography Signals Using Convolutional Recurrent Neural Networks

Automatic identification of cardiac abnormalities through the ECG with a reduced lead system (less than the standard 12-lead) can provide a valuable easy to use and lower cost diagnostic alternative to ordinary 12-lead ECG devices. This study investigates the use of Convolutional Recurrent Neural Networks (CRNNs) to identify cardiac abnormalities in 12, 6, 4, 3 and 2 lead ECG data. Multi-label classification with CRNNs relies on effective data pre-processing, model architecture and hyperparameter tuning. ECG signals were first pre-processed and then zero-padded or clipped to have an equal duration of 10 seconds). Additionally, a wavelet-based ECG segmentation algorithm was used to extract the characteristics and locations of the PQRST complexes (features), and both PQRST fiducial points and extracted features were used as inputs to two Convolutional Recurrent Neural Networks (CRNN), respectively, each one consisting of eight layers. The two CRNNs were subsequently concatenated. Final challenge results of the proposed method achieved an official score of −0.35 for the all-lead combination and a rank of 36 (team name: heartMAASters). In the discussion we provide some theoretical considerations on why we would expect the enhanced model to show a better performance.