Emergent Cardiac Anomaly Classification Using Cascaded Auto- associative Multilayer Perceptrons for Bio-healthcare Systems

Proper indication of emergent cardiac anomalies is essential to saving human lives. Electrocardiogram (ECG) signals are mainly considered for indicating cardiac status. This work proposes a model that discriminates emergent cardiac anomalies (e.g. ventricular tachyarrhythmia, congestive heart failure, malignant ventricular ectopy, supraventricular arrhythmia) from normal cardiac status using an artificial neural network. The histogram of gradient (HOG) and principal component analysis (PCA) are applied to extract generic features of the ECG signals. Five auto-associative multilayer perceptrons (AAMLP) concatenated in a cascade manner are proposed for classification of four emergent cardiac ECG signals and normal ECG signals, which was developed for implementing a primitive prototype for a mobile bio-healthcare system. Experimental results show that the proposed model successfully classifies emergent cardiac anomalies.