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Multimodal Classification of Arrhythmia and Ischemia Using QRS-ST Analysis

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Advances in Systems, Control and Automation

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 442))

Abstract

Probabilistic classification approaches have been presented for arrhythmic and ischemic data using QRS-ST evaluation. The proposed methodology is segregated into two major parts, i.e., (a) detection of QRS complex and ST segments by improvised Pan-Tompkins and difference operation method, respectively, and (b) classification of healthy, arrhythmic, and ischemic classes using linear discriminant analysis (LDA), decision tree (DT), and artificial neural network (ANN), respectively. Two correlative classification features (frequency and time domain) of QRS-ST, i.e., (1) ratio of power spectrum (PS) and power spectral density (PSD) and (2) area under the curve (AUC), are introduced to these classifiers. The algorithm is evaluated and validated with standard databases such as FANTASIA (healthy), MIT-BIH Arrhythmia (arrhythmic), and long-term ST database (ischemic), respectively. For uniform probability classification, ECG episodes with 100% sensitivity (Se) and the specificity (Sp) are included in this analytical modeling. As the experimentation is performed to validate the possibility of these features for classification, the percentage of classification certainly could be improved by considering other vital features. We conclude that correlative analysis of QRS-ST may be evoked as significant marker for arrhythmia and ischemia.

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Authors and Affiliations

  1. Department of Electrical & Electronics Engineering, Sikkim Manipal Institute of Technology (SMIT), Sikkim Manipal University, Majitar, India

    Akash Kumar Bhoi & Karma Sonam Sherpa

  2. Department General Medicine, Central Referral Hospital and SMIMS, Sikkim Manipal University, Gangtok, India

    Bidita Khandelwal

Authors
  1. Akash Kumar Bhoi
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  2. Karma Sonam Sherpa
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  3. Bidita Khandelwal
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Correspondence to Akash Kumar Bhoi .

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Editors and Affiliations

  1. Clinical Engineering, University of Virginia Health System, Charlottesville, Virginia, USA

    Avinash Konkani

  2. Department of Electronics and Communication Engineering, Sikkim Manipal Institute of Technology(SMIT), Rangpo, Sikkim, India

    Rabindranath Bera

  3. Department of Energy Engineering, North-Eastern Hill University, Shillong, Megalaya, India

    Samrat Paul

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Bhoi, A.K., Sherpa, K.S., Khandelwal, B. (2018). Multimodal Classification of Arrhythmia and Ischemia Using QRS-ST Analysis. In: Konkani, A., Bera, R., Paul, S. (eds) Advances in Systems, Control and Automation. Lecture Notes in Electrical Engineering, vol 442. Springer, Singapore. https://doi.org/10.1007/978-981-10-4762-6_65

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  • DOI: https://doi.org/10.1007/978-981-10-4762-6_65

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-4761-9

  • Online ISBN: 978-981-10-4762-6

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