ECG-based Detection and Prediction Models of Sudden Cardiac Death: Current Performances and New Perspectives on Signal Processing Techniques

Authors

  • Mohd Zubir Suboh Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 Bangi, Malaysia. https://orcid.org/0000-0001-8338-1010
  • Rosmina Jaafar Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 Bangi, Malaysia.
  • Nazrul Anuar Nayan Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 Bangi, Malaysia.
  • Noor Hasmiza Harun Medical Engineering Technology Section, Universiti Kuala Lumpur, 53100 Gombak, Ma-laysia.

DOI:

https://doi.org/10.3991/ijoe.v15i15.11688

Keywords:

electrocardiography, sudden cardiac death, sudden cardiac arrest, photoplethysmography

Abstract


Heart disease remains the main leading cause of death globally and around 50% of the patients died due to sudden cardiac death (SCD). Early detection and prediction of SCD have become an important topic of research and it is crucial for cardiac patient’s survival. Electrocardiography (ECG) has always been the first screening method for patient with cardiac complaints and it is proven as an important predictor of SCD. ECG parameters such as RR interval, QT duration, QRS complex curve, J-point elevation and T-wave alternan are found effective in differentiating normal and SCD subjects. The objectives of this paper are to give an overview of SCD and to analyze multiple important ECG-based SCD detection and prediction models in terms of processing techniques and performance wise. Detail discussions are made in four major stages of the models developed including ECG data, signal pre-processing and processing techniques as well as classification methods. Heart rate variability (HRV) is found as an important SCD predictor as it is widely used in detecting or predicting SCD. Studies showed the possibility of SCD to be detected as early as one hour prior to the event using linear and non-linear features of HRV. Currently, up to 3 hours of analysis has been carried out. However, the best prediction models are only able to detect SCD at 6 minutes before the event with acceptable accuracy of 92.77%. A few arguments and recommendation in terms of data preparation, processing and classification techniques, as well as utilizing photoplethysmography with ECG are pointed out in this paper so that future analysis can be done with better accuracy of SCD detection accuracy.

Author Biographies

Mohd Zubir Suboh, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 Bangi, Malaysia.

Mohd Zubir Suboh is a lecturer at Universiti Kuala Lumpur, 53100 Gombak, Malaysia. He currently doing his Ph.D in Faculty of Engineering & Built Environment, Universiti Kebangsaan Malaysia (UKM). His main research interest is in biomedical engineering that includes medical instrumentation, signal processing and artificial intelligence.

Rosmina Jaafar, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 Bangi, Malaysia.

Dr. Rosmina Jaafar is senior lecturer at the Dept. Electrical, Electronics & Systems Engineering, Faculty of Engineering & Built Environment, Universiti Kebangsaan Malaysia (UKM), 43600 Bangi, Malaysia. She has attained her Ph.D in Electrical, Electronic & Systems Engineering UKM in 2009. Her main research interest is biomedical engineering that includes signal processing, imaging and medical informatics as well as medical electronics & instrumentation. 

Nazrul Anuar Nayan, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 Bangi, Malaysia.

Ir. Dr. Nazrul Anuar Nayan is a professional engineer and a senior lecturer at the Faculty of Engineering & Built Environment, Universiti Kebangsaan Malaysia (UKM), 43600 Bangi, Malaysia. He obtained his Ph.D in Electronics and Information Systems Engineering at Gifu University, Japan in 2011. He has also gone for a-two year post-doctoral research programme at The Institute of Biomedical Engineering, Univ. of Oxford, United Kingdom. His research interests lie in the field of Big Data in Healthcare, Digital Integrated Circuit Design and Computational Thinking.

Noor Hasmiza Harun, Medical Engineering Technology Section, Universiti Kuala Lumpur, 53100 Gombak, Ma-laysia.

Dr. Noor Hasmiza Harun is senior lecturer at the Medical Engineering Technology Section, Universiti Kuala Lumpur (UniKL), 53100 Gombak, Malaysia. She has attained her Ph.D in Instrumentation Engineering from Universiti Putra Malaysia in 2015. Her main research interest is biomedical engineering that includes medical electronics & instrumentation, rehabilitation engineering and inductive sensor.

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Published

2019-12-17

How to Cite

Suboh, M. Z., Jaafar, R., Nayan, N. A., & Harun, N. H. (2019). ECG-based Detection and Prediction Models of Sudden Cardiac Death: Current Performances and New Perspectives on Signal Processing Techniques. International Journal of Online and Biomedical Engineering (iJOE), 15(15), pp. 110–126. https://doi.org/10.3991/ijoe.v15i15.11688

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Papers