Principal component analysis and arrhythmia recognition using Elman neural network

Cardiac arrhythmia refers to any abnormal electrical activity in the heart that causes irregular heartbeat. Under clinical settings, the arrhythmias can be monitored non-invasively using the electrocardiogram (ECG). Although reliable, the method is still prone to error due to its dependence on visual interpretation. This paper proposes a computerized method for recognition of cardiac arrhythmia using Elman neural network. 1600 ECG beat samples for healthy, cardiomyopathy, and bundle branch block arrhythmias were acquired from the PTB Diagnostic ECG database. Initially, de-noising and baseline wander rectification were performed using digital filters and polynomial fitting technique. 24 morphological features from Lead I, II and III were obtained through the median threshold method. Principal component analysis was then implemented for feature selection. The dataset were reduced to 15 features and is then used to train, test and validate the Elman neural network structure with four different learning algorithms. The overall network performance is then benchmarked with the original 24 dataset. Results show that both settings attained classification accuracies of more than 95%. In addition, PCA has successfully reduced the feature requirements while simultaneously maintaining the network performance.