Fusing deep metric learning with KNN for 12-lead multi-labelled ECG classification

Abstract

Electrocardiograms (ECG) provides essential information of the electrical activity of the heart which is important for the auto-diagnosis of cardiovascular arrhythmia. Although several state-of-art models based on deep learning have been proposed for auto-classification of cardiac arrhythmia, their performance efficiency still require further improvement. This paper presented an auto-detection algorithm based on the encoder-decoder model, where the encoder and decoder are responsible for feature extraction and classification respectively. The advantage of the developed model is at combining metric learning with residual neural networks, extracting discriminative features whilst reducing the complexity of network, therefore, increasing the computational efficiency of the model by reducing its computing demand. Moreover, it incorporates temporal features of RR intervals (i.e., the time interval between two successive R-wave peaks of ECG signals) into feature embeddings for feature fusion. Through evaluating the distance between the feature embeddings produced by the deep metric model, the K-Nearest Neighbours algorithm was implemented as a decoder to process the classification based on the feature embeddings. Additionally, the frame blocking pre-treats the raw ECG records, unifying the size of all records. By testing the developed model over 12-lead ECG recordings from the CPSC dataset, the proposed algorithm achieved an average F1 score of 0.874, outperforming other contemporary classification models with an increased F1 score by 0.09 in average. It also showed a promising efficiency with a small model size and GFLOPs. In conclusion, an efficient deep metric-based model with feature infusion for classifying multiple rhythmic states of the heart has been developed, showing promising potentials for future practical applications.