Detection of ectopic heart beats using ECG and blood pressure signals

In this paper, we propose using a combination of ECG and blood pressure signals to detect ectopic heart beats, specifically premature supraventricular and ventricular contractions (PSC and PVC). Detection of these beats are important are they could be pre-cursor for serious arrhythmias. Common detection methods of these beats use only ECG signals. However, the stroke volume changes after the occurrence of these beats, which results in blood pressure variations. Following this fact, we combined features extracted from QRS complex of ECG signal Lead I with systolic and diastolic arterial blood pressure values to classify normal, PSC and PVC beats. Data from 5 subjects totaling 750 beats (250 normal, 250 PSC and 250 PVC) from Massachusetts General Hospital/Marquette Foundation (MGH/MF) database were used. The data were split equally for Multilayer Perceptron - Backpropagation (MLP-BP) neural network training and testing. The combined features were classified by the MLP-BP neural network into the 3 classes. The features were normalized using some parameter inherent in the signals. This was to normalize the features across different subjects. The results gave classification performance up to 92.00%. It is concluded that ECG and blood pressure features could detect PSC and PVC.