Vol. 184, Issue 1, January 2015, pp. 92-100

Abstract: Current healthcare mobile monitoring solutions do not offer the ability to autonomously recognize cardiac arrhythmias. The proposed electrocardiogram detection and classification software is designed to run on a mobile cardiovascular disease detection sensor suite alleviating the need for human interpretation. The electrocardiogram is filtered using the Wavelet Transform; the principally important wave points detected using a modified version of the Pan Tompkins rule set and the cardiac rhythm is classified using an N-ary tree. Implemented on a custom designed printed circuit board, testing results show autonomous classifications are possible using a three lead electrocardiogram system while the patient is at rest. The proposed solution serves as a stepping stone towards a fully reliable patient disease management teaching tool with the potential to serve as an aid to the cardiovascular healthcare industry.

Keywords: Embedded ECG sensor, Real-time algorithm, ECG classification.

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