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Optimized time–frequency features and semi-supervised SVM to heartbeat classification

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Abstract

One of the most significant indicator of heart disease is arrhythmia showing heartbeat patterns. Thus, early and accurate detection of arrythmia types by categorization of heartbeats is important. In this paper, we introduce an ECG beat classifier system integrating two main parts: feature extraction and classification. For the first part, we consider the features observed in the time–frequency (t, f) plane where the ECG is projected using a variant of Stockwell transform. For the second part, the framework of semi-supervised SVM with asymmetric costs (AS3VM) has been applied for assessment of the obtained feature sets performance. Notice that four heartbeat types have been considered: normal beats (N), left and right bundle branch blocks (L and R) and premature ventricular contractions (V). The proposed method has been evaluated on PhysionNet’s MIT-BIT arrythmia database. The obtained results show that the suggested approach achieves significant separability of the classes and thus, able to make prediction accuracies of \(99.35\%\), \(98.73\%\), \(98.57\%\) and \(99.44\%\) for, respectively, N, L, R and V beats.

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Acknowledgements

This work was partly supported by the Algerian Ministry of Higher Education and research under the CNEPRU Project: A10N01UN150120150001.

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Authors and Affiliations

  1. Laboratoire LAMPA, Départment d’Electronique, Université Mouloud Mammeri, Tizi-Ouzou, Algeria

    Redouane Lekhal & Zahia Zidelmal

  2. Laboratoire IRIMAS, Université de Haute Alsace, Mulhouse, France

    Djaffar Ould-Abdesslam

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  1. Redouane Lekhal
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  2. Zahia Zidelmal
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  3. Djaffar Ould-Abdesslam
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Correspondence to Djaffar Ould-Abdesslam.

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Lekhal, R., Zidelmal, Z. & Ould-Abdesslam, D. Optimized time–frequency features and semi-supervised SVM to heartbeat classification. SIViP 14, 1471–1478 (2020). https://doi.org/10.1007/s11760-020-01681-9

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  • DOI: https://doi.org/10.1007/s11760-020-01681-9

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