Preprint Article Version 1 This version is not peer-reviewed

Event Driven ECG Processing for Diagnosis of Cardiac Diseases

Version 1 : Received: 27 October 2018 / Approved: 30 October 2018 / Online: 30 October 2018 (09:15:43 CET)

How to cite: Qaisar, S.M.; Subasi, A.; Balakhsher, W.S.; Zamzami, D.; Alsharbini, G.; Bahri, M.G.; Arab, H. Event Driven ECG Processing for Diagnosis of Cardiac Diseases. Preprints 2018, 2018100718 (doi: 10.20944/preprints201810.0718.v1). Qaisar, S.M.; Subasi, A.; Balakhsher, W.S.; Zamzami, D.; Alsharbini, G.; Bahri, M.G.; Arab, H. Event Driven ECG Processing for Diagnosis of Cardiac Diseases. Preprints 2018, 2018100718 (doi: 10.20944/preprints201810.0718.v1).

Abstract

The aim of this paper is to develop an intelligent event-driven Electrocardiogram (ECG) processing module in order to achieve an efficient solution for diagnosis of the cardiac diseases. The suggested method acquires the signal with an event-driven A/D converter (EDADC). The output of EDADC is passed through the activity selection and interpolation blocks. It allows focusing only on the important signal parts and resampling it uniformly. Later on, the signal is de-noised. The autoregressive (AR) method is used to extract the classifiable features of the de-noised signal. Afterwards, the output is classified by employing different robust classification techniques such as support vector machines (SVMs), K- Nearest Neighbor (KNN) and Artificial Neural Network (ANN). The event-driven feature enables to adapt the system processing load according to the signal temporal variations. This interesting feature of the devised system aptitudes a drastic reduction in its processing activity and therefore in the power consumption as compared to the traditional ones. A comparison of the performance of different classifiers is also made in terms of accuracy. Results show that the proposed system is a potential candidate for an automatic diagnosis of the cardiac diseases.

Subject Areas

event-driven processing; ECG; cardiac diseases; features extraction; classification

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