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A Neoteric Parametric Representation and Classification of ECG Signal

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Abstract

In the last 20 years, most of the human demise have been due to improper functioning of the heart. ECG is a very essential, initial clinical test for diagnosing dangerous cardiac electrical disturbances. Many techniques have been proposed to analyze and process ECG signals. It is difficult to check the performance conspicuously because the actual ECG is corrupted by several sources of noise and artifacts. In view of this, the generation of an artificial ECG signal that can reflect all the characteristics of a real ECG signal is a very challenging task in biomedical signal processing. Classification of ECG data aids early detection and prediction of any heart-related disease so that timely treatment can be suggested by the clinicians. This paper presents a method of modeling an ECG signal based on the parametric cubic splines and derives new data set therefrom, followed by ECG classification, with three different classification techniques with the aid of Orange software. The support vector machine (SVM), CN2 rule Induction and tree classifiers have been tested on the model and the Tree algorithm has shown the best performance metrics. This approach is applied for normal and abnormal sinus rhythms. Analysis and processing of these modeled ECG signals eliminate the requirement of pre-processing and conventional feature extraction techniques.

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Data Availability Statement

The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.

Notes

  1. The program for developing spline model of ECG is available through the following link upon reader’s request: https://drive.google.com/file/d/10-VGZYjYvMeBoN8QEuEw5HfRvC-BrU75/view?usp=sharing

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Authors and Affiliations

  1. Electrical and Electronics Engineering, Bhilai Institute of Technology, Bhilai House, Durg, Chhattisgarh, 491001, India

    Alka Mishra & Surekha Bhusnur

  2. Mechanical Engineering, Bhilai Institute of Technology, Bhilai House, Durg, Chhattisgarh, 491001, India

    Santosh Mishra

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  1. Alka Mishra
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  2. Surekha Bhusnur
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  3. Santosh Mishra
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Mishra, A., Bhusnur, S. & Mishra, S. A Neoteric Parametric Representation and Classification of ECG Signal. Circuits Syst Signal Process 42, 5725–5738 (2023). https://doi.org/10.1007/s00034-023-02359-6

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  • DOI: https://doi.org/10.1007/s00034-023-02359-6

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