ECG feature extraction in temporal domain and detection of various heart conditions

Diagnosis of cardiac conditions is greatly dependent on ECG analysis. The current trend is to automate analysis and diagnosis through adopting various signal processing techniques. This paper presents a temporal feature extraction method for ECG feature extraction and detection of various heart conditions. This is achieved by extracting ECG features such as P, T wave, QRS complex, PR, QT, RR, ST intervals and ST segment deviations. The real time ECG data used for this study has been obtained from the MIT-BIH arrhythmia and European ST-T databases. The cardiac arrhythmias that this algorithm can successfully detect are Sinus Tachycardia, Sinus Bradycardia, Premature Atrial Contraction (PAC) and First-Degree Atrioventricular block. Target application for the proposed simple temporal extraction can be portable device for ECG signal acquisition and diagnosis for telemedicine applications in rural areas and also for personal cardio-care.