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Machine Intelligence and Signal Analysis pp 597–608Cite as

Automated CAD Identification System Using Time–Frequency Representation Based on Eigenvalue Decomposition of ECG Signals

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 748))

Abstract

Coronary artery disease (CAD) is a condition where coronary arteries become narrow due to the deposition of plaque inside them. It may result in heart failure and heart attack which are life-threatening conditions. Therefore, human life can be saved by detection of CAD at an early stage. Electrocardiogram (ECG) signals can be used to detect CAD. Manual inspection of ECG recordings is not reliable as the accuracy of the outcome depends on the skills and experience of clinicians. Therefore, an automated detection method for CAD based on a time–frequency representation (TFR) known as improved eigenvalue decomposition of Hankel matrix and Hilbert transform (IEVDHM-HT) using ECG beats is proposed in the present work. Time–frequency flux (TFF), coefficient of variation (COV), and energy concentration measure (ECM) are computed from the TFR matrix and fed to the random forest classifier. The proposed method has yielded 93.77% classification accuracy.

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Authors and Affiliations

  1. Discipline of Electrical Engineering, Indian Institute of Technology Indore, Indore, 453552, India

    Rishi Raj Sharma, Mohit Kumar & Ram Bilas Pachori

Authors
  1. Rishi Raj Sharma
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  2. Mohit Kumar
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  3. Ram Bilas Pachori
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Correspondence to Rishi Raj Sharma .

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Editors and Affiliations

  1. Discipline of Mathematics, Indian Institute of Technology Indore, Simrol, Madhya Pradesh, India

    M. Tanveer

  2. Discipline of Electrical Engineering, Indian Institute of Technology Indore, Simrol, Madhya Pradesh, India

    Ram Bilas Pachori

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Sharma, R.R., Kumar, M., Pachori, R.B. (2019). Automated CAD Identification System Using Time–Frequency Representation Based on Eigenvalue Decomposition of ECG Signals. In: Tanveer, M., Pachori, R. (eds) Machine Intelligence and Signal Analysis. Advances in Intelligent Systems and Computing, vol 748. Springer, Singapore. https://doi.org/10.1007/978-981-13-0923-6_51

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