Electrocardiogram (ECG) Signal and Interference Filtering for Clinical Diagnostic Support
- October 28, 2020
- Posted by: RSIS Team
- Categories: Computer Science and Engineering, IJRSI
International Journal of Research and Scientific Innovation (IJRSI) | Volume VII, Issue X, October 2020 | ISSN 2321–2705
Electrocardiogram (ECG) Signal and Interference Filtering for Clinical Diagnostic Support
Gabriel Ayobami Ayeni1, Taofik Tola Ajagbe2, Adewale W. Yekinni3
1Researcher, Department of Computer Science, Rivers State University, Port Harcourt, Nigeria.
2Department of Computer Science, Lagos State University, Ojo, Lagos State, Nigeria
3Department of Computer Science, School of Tech, Lagos State Polytechnic, Ikorodu, Lagos State, Nigeria
Abstract – Signal processing for electrocardiogram (ECG) records cardiac activity to unveil any abnormality in the heart through electrocardiograph. The pictorial representation comes in graph to indicate electric potential changes occurring between electrodes when patients’ cardiovascular state is being examined. The electrical functioning of the heart is translated into a waveform, being utilized to find the heart condition. An ECG signal tracks heart diseases, such as poor blood flow to the heart and structural abnormalities. Analysis of ECG signal and removal of interference for clinical diagnosis in presented in this paper.
Keywords: Electrocardiogram (ECG), Graph, Signal, Cardiac, Diagnosis, Interference
I. INTRODUCTION
Cardiac ailment is the worst of all health related diseases because it is associated with the heart of every living organism, and if not taken serious could lead to death [1]. Electrocardiographic signals may be recorded on a long timescale (i.e., several days) for the purpose of identifying intermittently occurring disturbances in heart rhythm [2]. Therefore, the produced ECG recording amounts to huge data sizes that quickly fill up available storage space. Data compression is an essential operation and, consequently, represents yet another objective of ECG signal processing [3]. Signal processing has significant contribution to a new understanding of the ECG and its dynamic properties as expressed by changes in rhythm and beat morphology [5]. Techniques have been developed that characterize oscillations related to the cardiovascular system and reflected by subtle variations in heart rate. The detection of low-level, alternating changes in T wave amplitude is another instance of oscillatory behavior that has been established as an indicator of increased risk for sudden, life-threatening arrhythmias [4]. The electrocardiogram (ECG) signal is one of the diagnosing approaches to detect heart disease. ECG signals provide evident information about heart functional conditions and circulation system. By placing the electrodes on body surface, the electrical activity of the heart muscles is measured [6]. The electrical activity of the signal is represented by electrocardiogram (ECG) [7].
