An Innovative Approach for the Prediction of Future Arrhythmia through T-wave Alternans on Surface Electrocardiogram (ECG)


  • Ali Farhan Department of Bioinformatics & Biotechnology, Government College University Faisalabad, Pakistan
  • Ijaz Rasul Department of Bioinformatics & Biotechnology, Government College University Faisalabad, Pakistan
  • Sahar Fazal Department of Bioinformatics & Biosciences, Capital University of Science and Technology, Islamabad, Pakistan
  • Azmat Hayat Department of Electrophysiology, Armed Forces Institute of Cardiology/National Institute of Heart Diseases/National University of Medical Sciences (NUMS) Rawalpindi Pakistan
  • Nayyer Masood Department of Bioinformatics & Biosciences, Capital University of Science and Technology, Islamabad, Pakistan
  • Alam Shah Department of Medicine, University of Health Sciences Lahore, Pakistan
  • Ali Hassan Department of Bioinformatics, Social Security Hospital Shahdara, Pakistan
  • Ghulam Ali Department of Computer Science, University of Okara, Okara Pakistan
  • Usama Munir Department of Electrical Engineering, Karadeniz Technical University, Turkey



Arrhythmia, Electrocardiogram, Python, MATLAB, T-wave


Objective: To explore the techniques for predicting risk-causing arrhythmia in cardiac patients.

Study Design: Prospective longitudinal Study

Place and Duration of Study: Armed Forces Institute of Cardiology, Rawalpindi Pakistan, and Government College
University, Faisalabad from Jul to Oct 2017.

Methodology: The Electrocardiograms of 24-hour Holter monitoring were collected from the Electrophysiology Department. Electrocardiogram data was collected in the portable document format that was further transformed into Image format for computational analysis. Administrative data were analysed in multiple episodes of cardiac arrhythmogenesis. Data were classified by using a Convolutional Neural Network (CNN) based on computing the results of selected T-waves in three consecutive peaks within each cardiac cycle of patients.

Results: One hundred twenty-six patients diagnosed with arrhythmia were selected. The mean episode of premature
ventricular contractions in participants was 21.5±30. The mean duration of significant ECG episodes was 3.33±9.65 (seconds). The accuracy and precision rate of the classifier was about 81% for the overall significance of data that exhibited the risk of causing future life-threatening arrhythmia.

Conclusion: This study introduces an innovative approach based on clinical paradigms that may help prevent the upcoming
cardiac arrhythmogenesis events.


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How to Cite

Farhan, A., Ijaz Rasul, Sahar Fazal, Hayat, A., Masood, N., Alam Shah, … Munir, U. (2023). An Innovative Approach for the Prediction of Future Arrhythmia through T-wave Alternans on Surface Electrocardiogram (ECG). Pakistan Armed Forces Medical Journal, 73(6), 1569–1572.



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