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Appl. Sci. 2017, 7(9), 954; doi:10.3390/app7090954

Computational Algorithms Underlying the Time-Based Detection of Sudden Cardiac Arrest via Electrocardiographic Markers

1
Faculty of Science, University of Technology Sydney, Ultimo, NSW 2007, Australia
2
Marcs Institute for Brain, Behaviour & Development, Western Sydney University, Penrith 2750, NSW, Australia
3
School of Biomedical Engineering, Centre for Health Technologies, Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia
*
Author to whom correspondence should be addressed.
Received: 17 August 2017 / Revised: 9 September 2017 / Accepted: 14 September 2017 / Published: 16 September 2017
(This article belongs to the Section Computer Science and Electrical Engineering)
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Abstract

Early detection of sudden cardiac arrest (SCA) is critical to prevent serious repercussion such as irreversible neurological damage and death. Currently, the most effective method involves analyzing electrocardiogram (ECG) features obtained during ventricular fibrillation. In this study, data from 10 normal patients and 10 SCA patients obtained from Physiobank were used to statistically compare features, such as heart rate, R-R interval duration, and heart rate variability (HRV) features from which the HRV features were then selected for classification via linear discriminant analysis (LDA) and linear and fine Gaussian support vector machines (SVM) in order to determine the ideal time-frame in which SCA can be accurately detected. The best accuracy was obtained at 2 and 8 min prior to SCA onset across all three classifiers. However, accuracy rates of 75–80% were also obtained at time-frames as early as 50 and 40 min prior to SCA onset. These results are clinically important in the field of SCA, as early detection improves overall patient survival. View Full-Text
Keywords: sudden cardiac arrest; detection; electrocardiogram; ventricular fibrillation; pattern classification; linear classification; support vector machine; machine learning sudden cardiac arrest; detection; electrocardiogram; ventricular fibrillation; pattern classification; linear classification; support vector machine; machine learning
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Raka, A.G.; Naik, G.R.; Chai, R. Computational Algorithms Underlying the Time-Based Detection of Sudden Cardiac Arrest via Electrocardiographic Markers. Appl. Sci. 2017, 7, 954.

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