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A brief review of computation techniques for ECG signal analysis

Sh. Hussain, Salleh and Noman, Fuad and Hussain, Hadri and Ting, Chee Ming and Syed Hamid, Syed Rasul G. and Sh. Hussain, Hadrina and Jalil, Muhammad Arif and Ahmad Zubaidi, A. L. and Rizvi, Syed Zuhaib Haider and Kipli, Kuryati and Jacob, Kavikumar and Ray, Kanad and Kaiser, M. Shamim and Mahmud, Mufti and Ali, Jalil (2022) A brief review of computation techniques for ECG signal analysis. In: 3rd International Conference on Trends in Computational and Cognitive Engineering, TCCE 2021, 21 October 2021 - 22 October 2021, Parit Raja, Batu Pahat, Johor, Malaysia.

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Official URL: http://dx.doi.org/10.1007/978-981-16-7597-3_18


Automatic detection of life-threatening cardiac arrhythmias has been a subject of interest for many decades. The automatic ECG signal analysis methods are mainly aiming for the interpretation of long-term ECG recordings. In fact, the experienced cardiologists perform the ECG analysis using a strip of ECG graph paper in an event-by-event manner. This manual interpretation becomes more difficult, time-consuming, and more tedious when dealing with long-term ECG recordings. Rather, an automatic computerized ECG analysis system will provide valuable assistance to the cardiologists to deliver fast or remote medical advice and diagnosis to the patient. However, achieving accurate automated arrhythmia diagnosis is a challenging task that has to account for all the ECG characteristics and processing steps. Detecting the P wave, QRS complex, and T wave is crucial to perform automatic analysis of EEG signals. Most of the research in this area uses the QRS complex as it is the easiest symbol to detect in the first stage. The QRS complex represents ventricular depolarization and consists of three consequences waves. However, the main challenge in any algorithm design is the large variation of QRS, P, and T waveform, leading to failure for each method. The QRS complex may only occupy R waves QR (no R), QR (no S), S (no Q), or RSR, depending on the ECG lead. Variations from the normal electrical patterns can indicate damage to the heart, and these variations are manifested as heart attack or heart disease. This paper will discuss the most recent and relevant methods related to each sub-stage, maintaining the related literature to the scope of ECG research.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:classification, ECG, feature extraction, segmentation
Subjects:Q Science > QC Physics
ID Code:98707
Deposited By: Yanti Mohd Shah
Deposited On:02 Feb 2023 14:04
Last Modified:02 Feb 2023 14:04

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