Signal Processing Methods for Identification of Sudden Cardiac Death

  • Reeta DeviEmail author
  • Hitender Kumar Tyagi
  • Dinesh Kumar
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 958)


Sudden cardiac death (SCD) is defined as sudden natural death occurring within few minutes to an hour from the onset of symptoms due to known or unknown cardiac cause. An early stage prediction or identification of SCD has become a major challenge among the medical fraternity to save the life of SCD affected person. For prediction of sudden cardiac death, three distinct kinds of markers viz. markers of structural heart disease, markers of electrical instability and markers of abnormal autonomic balance have been devised. Based on these markers, many signal processing techniques like signal averaged electrocardiography, extraction of longer QRS duration, identification of QT-dispersion, and feature extraction from T-wave alternans, heart rate variability (HRV), and heart rate turbulence (HRT) with data mining, statistical and machine learning algorithms are fused together to validate the SCD prediction accuracy. But despite significant advances in the engineering research and medical science, there is no standard technique adopted to identify the SCD at an early stage which limits the fusion of any method into a medical product due to its own limitations. This paper is therefore, designed to discuss different signal processing methods based on these three markers in order to predict sudden cardiac death at an early and alarming stage. The contents embodied in this paper would benefit the community of the research groups designing signal processing algorithms for early prediction of SCD which will help the clinicians to save the precious life of the SCD affected patients.


SCD Signal processing Structural heart disease Electrical instability Autonomic system 



This present work is carried out with the help of library and research resource facilities provided by Kurukshetra University, Kurukshetra, Haryana, India.


The authors do not have any conflict of interest to declare.


Ethical approval: This article does not contain any studies with human participants or animals performed by any of the authors.


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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Reeta Devi
    • 1
    Email author
  • Hitender Kumar Tyagi
    • 2
  • Dinesh Kumar
    • 3
  1. 1.Department of Electronics and Communication Engineering, University Institute of Engineering and TechnologyKurukshetra UniversityKurukshetraIndia
  2. 2.Department of Electronics, University CollegeKurukshetra UniversityKurukshetraIndia
  3. 3.YMCA University of Science and TechnologyFaridabadIndia

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