Prediction and Analysis of Heart Diseases Using Heterogeneous Computing Platform

GiriBabu Sinnapolu, Shadi Alawneh () and Simon R. Dixon
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GiriBabu Sinnapolu: Electrical and Computer Engineering Department, Oakland University, Rochester, MI 48309, USA
Shadi Alawneh: Electrical and Computer Engineering Department, Oakland University, Rochester, MI 48309, USA
Simon R. Dixon: Department of Cardiovascular Medicine, Beaumont Hospitals, Royal Oak, MI 48073, USA

Mathematics, 2023, vol. 11, issue 8, 1-22

Abstract: The work in this paper helps study cardiac rhythms and the electrical activity of the heart for two of the most critical cardiac arrhythmias. Various consumer devices exist, but implementation of an appropriate device at a certain position on the body at a certain pressure point containing an enormous number of blood vessels and developing filtering techniques for the most accurate signal extraction from the heart is a challenging task. In this paper, we provide evidence of prediction and analysis of Atrial Fibrillation (AF) and Ventricular Fibrillation (VF). Long-term monitoring of diseases such as AF and VF occurrences is very important, as these will lead to occurrence of ischemic stroke, cardiac arrest and complete heart failure. The AF and VF signal classification accuracy are much higher when processed on a Graphics Processor Unit (GPU) than Central Processing Unit (CPU) or traditional Holter machines. The classifier COMMA-Z filter is applied to the highly-sensitive industry certified Bio PPG sensor placed at the earlobe and computed on GPU.

Keywords: Graphics Processing Unit (GPU); proximity sensors; heart diseases; Atrial Fibrillation; Ventricular Fibrillation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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