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AF episodes recognition using optimized time-frequency features and cost-sensitive SVM

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Abstract

Although atrial fibrillation (AF) Arrhythmia is highly prevalent within a wide range of populations with major associated risks and due to its episodic occurrence, its recognition remains a challenge for doctors. This paper aims to present and experimentally validate a new efficient approach for the detection and classification of this cardiac anomaly using multiple Electrocardiogram (ECG) signals. This work consists of applying Stockwell transform (ST) with compact support kernel (ST-CSK) for ECG time-frequency analysis. The estimation of the atrial activity (AA) is then achieved after analyzing P-waves of the ECG signals for each heartbeat. ECG signals segmentation allows characterizing the AA by making use of its (t, f) flatness, (t, f) flux, energy concentration and heart rate variability. The features matrix is employed as an input of the support vector machines (SVM) working in binary and asymmetrical mode with an embedded reject option. The proposed algorithm is trained and then tested using different ECG sources namely two databases provided by PhysionNet (MIT-BIH Arrhythmia, MIT-BIH Atrial Fibrillation) and recorded ECG signals using MySignals HW development platform with raspberry Pi 3 model B\(+\). The used method has achieved \(98.46\%\) and \(97.81\%\) as sensitivity and specificity, respectively. The obtained results confirm that the proposed approach represents a promising tool for Atrial Fibrillation Episodes (AFE) recognition with significant separability between Normal atrial activity and atrial activity with AF even under real and clinical conditions.

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Authors and Affiliations

  1. Laboratoire Analyse et Modélisation des Phénomènes Aléatoires (LAMPA), Faculté de Génie Electrique et Informatique, Département d’Electronique, Université Mouloud Mammeri de Tizi-Ouzou, BP 17 RP, 15000, Tizi-Ouzou, Algeria

    Hocine Hamil & Zahia Zidelmal

  2. Laboratoire Systèmes Electroniques et Numériques, École Militaire Polytechnique, BP 17, 16111, Bordj El-Bahri, Algeria

    Mohamed Salah Azzaz & Redouane Kaibou

  3. Laboratoire SE, École Militaire Polytechnique, BP 17, 16111, Bordj El-Bahri, Algeria

    Samir Sakhi

  4. Institut IRIMAS, Université de Haute Alsace, 68093, Mulhouse, France

    Djaffar Ould Abdeslam

Authors
  1. Hocine Hamil
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  2. Zahia Zidelmal
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  3. Mohamed Salah Azzaz
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  4. Samir Sakhi
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  5. Redouane Kaibou
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  6. Djaffar Ould Abdeslam
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Correspondence to Mohamed Salah Azzaz.

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Hamil, H., Zidelmal, Z., Azzaz, M.S. et al. AF episodes recognition using optimized time-frequency features and cost-sensitive SVM. Phys Eng Sci Med 44, 613–624 (2021). https://doi.org/10.1007/s13246-021-01005-2

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  • DOI: https://doi.org/10.1007/s13246-021-01005-2

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