A patient-adaptive cardiac profiling scheme achieved 99.59% accuracy for beat detection and 97.42% accuracy for identifying abnormalities on the MIT-BIH arrhythmia database.
A novel patient-adaptive ECG profiling scheme demonstrated high accuracy in beat detection and abnormality classification on the MIT-BIH database, suggesting potential utility in automated arrhythmia monitoring.
Recent trends in clinical and telemedicine applications highly demand automation in electrocardiogram (ECG) signal processing and heart beat classification. A patient-adaptive cardiac profiling scheme using repetition-detection concept is proposed in this paper. We first employ an efficient wavelet-based beat-detection mechanism to extract precise fiducial ECG points. Then, we implement a novel local ECG beat classifier to profile each patient's normal cardiac behavior. ECG morphologies vary from person to person and even for each person, it can vary over time depending on the person's physical condition and/or environment. Having such profile is essential for various diagnosis (e.g., arrhythmia) purposes. One application of such profiling scheme is to automatically raise an early warning flag for the abnormal cardiac behavior of any individual. Our extensive experimental results on the MIT-BIH arrhythmia database show that our technique can detect the beats with 99.59% accuracy and can identify abnormalities with a high classification accuracy of 97.42%.
Faezipour et al. (Thu,) conducted a other in Arrhythmia. Patient-adaptive cardiac profiling scheme was evaluated on Beat detection accuracy and abnormality identification accuracy. A patient-adaptive cardiac profiling scheme achieved 99.59% accuracy for beat detection and 97.42% accuracy for identifying abnormalities on the MIT-BIH arrhythmia database.