Multimodal Classification of Arrhythmia and Ischemia Using QRS-ST Analysis
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-Tompkin
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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.
Keywords Electrocardiography Medical signal detection Linear discriminant analysis Decision trees Artificial neural networks
A.K. Bhoi (&) K.S. Sherpa Department of Electrical & Electronics Engineering, Sikkim Manipal Institute of Technology (SMIT), Sikkim Manipal University, Majitar, India e-mail: [email protected] K.S. Sherpa e-mail: [email protected] B. Khandelwal Department General Medicine, Central Referral Hospital and SMIMS, Sikkim Manipal University, Gangtok, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2018 A. Konkani et al. (eds.), Advances in Systems, Control and Automation, Lecture Notes in Electrical Engineering 442, https://doi.org/10.1007/978-981-10-4762-6_65
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1 Introduction Ventricular tachycardia (VT) is responsible for increasing the oxygen demand in heart muscle and also results in possible myocardial infarction [1]. Myocardial ischemia is responsible for morphological changes in ST-T wave. This will lead to bundle branch blocks and prolongation of QRS complex [2]. In addition to QRS slope particulars, the conventional ST segment analysis is an efficient method to monitor myocardial ischemia [3]. Various methods have been proposed, and with recent studies, it is found that (150–250 Hz) high-frequency content of the QRS complex is a better identification tool for ischemia than the traditional ST analysis [4–6]. Candil et al. discussed in their article that cute and transient myocardial ischemia detection may be initiated with QT interval analysis [7]. Xu et al. have presented an algorithm for QRS complex detection using slope vector waveform (SVW), and also, this method performed very well in case of noisy sig
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