A brief review of computation techniques for ECG signal analysis

Salleh Sh Hussain, Fuad Noman, Hadri Hussain, Chee-Ming Ting, Syed Rasul G.Syed bin Hamid, Hadrina Sh-Hussain, M. A. Jalil, Ahmad Zubaidi Ahmad, Syed Zuhaib Haider Rizvi, Kuryati Kipli, Kavikumar Jacob, Kanad Ray, M. Shamim Kaiser, Mufti Mahmud, Jalil Ali

Research output: Chapter in Book/Report/Conference proceedingConference PaperOtherpeer-review


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.

Original languageEnglish
Title of host publicationProceedings of the Third International Conference on Trends in Computational and Cognitive Engineering, TCCE 2021
EditorsM. Shamim Kaiser, Kanad Ray, Anirban Bandyopadhyay, Kavikumar Jacob, Kek Sie Long
Place of PublicationSingapore Singapore
Number of pages12
ISBN (Electronic)9789811675973
ISBN (Print)9789811675966
Publication statusPublished - 28 Feb 2022
EventInternational Conference on Trends in Computational and Cognitive Engineering 2021 - Parit Raja, Malaysia
Duration: 21 Oct 202122 Oct 2021
Conference number: 3rd
https://link.springer.com/book/10.1007/978-981-16-7597-3 (Proceedings)

Publication series

NameLecture Notes in Networks and Systems
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389


ConferenceInternational Conference on Trends in Computational and Cognitive Engineering 2021
Abbreviated titleTCCE 2021
CityParit Raja
Internet address


  • Classification
  • ECG
  • Feature extraction
  • Segmentation

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