Automated ECG profiling and beat classification

Recent trends in clinical and telemedicine applications highly demand automation in (electrocardiogram) ECG signal processing and heart beat classification. A real-time patient-adaptive cardiac profiling scheme using repetition detection is proposed in this paper. We introduce a novel local ECG beat classifier to profile each patient's normal cardiac behavior. As ECG morphologies vary from person to person, and even for each person, it can vary depending on the person's physical condition, having such profile is essential for various diagnosis (e.g. arrhythmia) purposes, and can successfully raise an early warning flag for the abnormal cardiac behavior of any individual. Experimental results show that our technique follows the MIT/BIH arrhythmia database annotations with high accuracy.