  This paper presents a suitable and efficient implementation of a feature
extraction algorithm (Pan Tompkins algorithm) on electrocardiography (ECG)
signals, for detection and classification of four cardiac diseases: Sleep
Apnea, Arrhythmia, Supraventricular Arrhythmia and Long Term Atrial
Fibrillation (AF) and differentiating them from the normal heart beat by using
pan Tompkins RR detection followed by feature extraction for classification
purpose .The paper also presents a new approach towards signal classification
using the existing neural networks classifiers.
