Multi-parameter detection of ectopic heart beats

Abstract

We analyse features from R waveform of electrocardiogram (ECG) and blood pressure (BP) signals for intelligent detection of ectopic (premature) heart beats. Detection of these beats are important are they could be pre-cursor for serious arrhythmias. The combination of ECG and blood pressure signals to detect ectopic heart beats are relatively new as most methods use only ECG signals. Two new features, mobility and complexity factor (used in electroencephalogram analysis) derived from R waveform of ECG signal are studied in addition to the conventional ECG and BP features. Three cases of beats are considered: 2 ectopic - premature ventricular contraction (PVC) and premature supraventricular contraction (PSC) and normal (N). All the features are normalized with some factor inherent in the signal to reduce the inter-subject variance of the features. Data from 50 subjects totaling 3000 beats (1000 N, 1000 PSC and 1000 PVC) from Massachusetts General Hospital/Marquette Foundation database are used. The 13 features were classified by the multilayer perceptron - backpropagation neural network into the 3 classes. The results gave classification performance up to 96.47%. It is concluded that ECG and BP features could be used to detect ectopic beats successfully.