Identifying individuals using ECG beats

In this paper, we propose a technique to identify individuals using features extracted from QRS segment of electrocardiogram (ECG) signals. A total of 2000 samples from 10 subjects from the Arrhythmia Laboratory at Boston's Beth Israel Hospital (now the Beth Israel Deaconess Medical Center) database were used. These data are available as MIT-BH Normal Sinus Rhythm database and consist of 18 hour long-term recordings with 2 ECG signals. The commonly used features like R-R interval, R amplitude, QRS interval, QR amplitude and RS amplitude were used. In addition to these features, we propose the use of form factor of the QRS segment. Form factor has been used previously in electroencephalogram analysis and it is a measure of the complexity of the signal. These six features were then used by two neural network classifiers: multilayer perceptron-backpropagation (MLP-BP) and simplified fuzzy ARTMAP (SFA). The data were split equally for MLP-BP and SFA training and testing. The results gave classification performance up to 97.6%. This indicates that ECG has the potential to be used as a biometric tool.