Intelligent classification of heartbeats for automated real-time ECG monitoring

The automatic interpretation of electrocardiography (ECG) data can provide continuous analysis of heart activity, allowing the effective use of wireless devices such as the Holter monitor.

We propose an intelligent heartbeat monitoring system to detect the possibility of arrhythmia in real time. We detected heartbeats and extracted features such as the QRS complex and P wave from ECG signals using the Pan-Tompkins algorithm, and the heartbeats were then classified into 16 types using a decision tree.

We tested the sensitivity, specificity, and accuracy of our system against data from the MIT-BIH Arrhythmia Database. Our system achieved an average accuracy of 97% in heartbeat detection and an average heartbeat classification accuracy of above 96%, which is comparable with the best competing schemes.

This work provides a guide to the systematic design of an intelligent classification system for decision support in Holter ECG monitoring.