Journal
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
Volume 49, Issue -, Pages 349-359Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.bspc.2018.12.016
Keywords
Electrocardiogram (ECG); Empirical Mode Decomposition; Heart arrhythmia; Support vector machine; Ventricular Fibrillation (VF)
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Funding
- ICT Fellowship
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Ventricular Fibrillation (VF), one of the most dangerous arrhythmias, is responsible for sudden cardiac arrests. Thus, various algorithms have been developed to predict VF from electrocardiogram (ECG), which is a binary classification problem. In the literature, we find a number of algorithms based on signal processing, where, after some robust mathematical operations the decision is given based on a predefined threshold over a single value. On the other hand, some machine learning based algorithms are also reported in the literature; however, these algorithms merely combine some parameters and make a prediction using those as features. Both the approaches have their perks and pitfalls; thus our motivation was to coalesce them to get the best out of the both worlds. Hence we have developed, VFPred that, in addition to employing a signal processing pipeline, namely, Empirical Mode Decomposition and Discrete Fourier Transform for useful feature extraction, uses a Support Vector Machine for efficient classification. VFPred turns out to be a robust algorithm as it is able to successfully segregate the two classes with equal confidence (sensitivity= 99.99%, specificity = 98.40%) even from a short signal of 5 s long, whereas existing works though requires longer signals, flourishes in one but fails in the other. (C) 2018 Elsevier Ltd. All rights reserved.
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