Patient Adaptive ECG Beat Classifier Using Repetition Detection Approach Enhanced by Neural Networks

Automated electrocardiogram (ECG) signal processing and accurate beat classification is of high need in clinical applications. A repetition detection approach is employed to create an adaptive profile for each person according to his cardiac behaviour. Heart arrhythmia are characterised by variations in the heart rate and irregularity. The key novelty of this approach is twofold. A technique using wavelet analysis with adaptive thresholding is employed to accurately extract the QRS complexes of an ECG signal. Next the patient adaptive profiling scheme is implemented to derive the cardiac profile specific to an individual. As ECG morphologies vary from person to person and from conditions to conditions an adaptive ECG profile is very much needed. This technique clearly identifies a normal region for a person and can thus identify abnormal beats that fall outside this region. The multilayer perceptron back propagation neural network is then combined which acts as a global classifier for enhanced classification performance.