Cost-Sensitive Learning for Anomaly Detection in Imbalanced ECG Data Using Convolutional Neural Networks.

Arrhythmia detection algorithms based on deep learning are attracting considerable interest due to their vital role in the diagnosis of cardiac abnormalities. Despite this interest, deep feature representation for ECG is still challenging and intriguing due to the inter-patient variability of the ECG's morphological characteristics. The aim of this study was to learn a balanced deep feature representation that incorporates both the short-term and long-term morphological characteristics of ECG beats. For efficient feature extraction, we designed a temporal transition module that uses convolutional layers with different kernel sizes to capture a wide range of morphological patterns. Imbalanced data are a key issue in developing an efficient and generalized model for arrhythmia detection as they cause over-fitting to minority class samples (abnormal beats) of primary interest. To mitigate the imbalanced data issue, we proposed a novel, cost-sensitive loss function that ensures a balanced deep representation of class samples by assigning effective weights to each class. The cost-sensitive loss function dynamically alters class weights for every batch based on class distribution and model performance. The proposed method acquired an overall accuracy of 99.81% for intra-patient classification and 96.36% for the inter-patient classification of heartbeats. The experimental results reveal that the proposed approach learned a balanced representation of ECG beats by mitigating the issue of imbalanced data and achieved an improved classification performance as compared to other studies.