Comparative Neural Network Based on Template Cluster for Automated Abnormal Beat Detection in Electrocardiogram Signals

Automatic abnormal beat detection reduces the time and cost for signal analysis by a cardiologist because abnormal beats rarely occur in an electrocardiogram (ECG) signal. However, the characteristics of normal and abnormal beats vary by individuals, which leads to misdetection. In this study, instead of directly training the input beats, we combine the input beats with corresponding reference normal beats and train the difference within combined beats. We can classify normal and abnormal beats through this approach, even if various types of individual normal and abnormal beats are mixed. The proposed comparative learning has the advantage of being able to classify multiple records using only one neural network. In experiments with learning five records, including premature ventricular contraction beats, we achieved 99.47% sensitivity and 99.28% accuracy for 30 records, with only one comparatively trained neural network. In addition, we confirmed that real-time processing is possible with an average processing time of 20.87 ms per beat.