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Prediction of the Successful Defibrillation using Hilbert-Huang Transform  

Jang, Yong-Gu (Department of Biomedical Engineering, Yonsei University)
Jang, Seung-Jin (Department of Biomedical Engineering, Yonsei University)
Hwang, Sung-Oh (Department of Emergency Medicine, Wonju College of Medicine, Yonsei University)
Yoon, Young-Ro (Department of Biomedical Engineering, Yonsei University)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SC / v.44, no.5, 2007 , pp. 45-54 More about this Journal
Abstract
Time/frequency analysis has been extensively used in biomedical signal processing. By extracting some essential features from the electro-physiological signals, these methods are able to determine the clinical pathology mechanisms of some diseases. However, this method assumes that the signal should be stationary, which limits its application in non-stationary system. In this paper, we develop a new signal processing method using Hilbert-Huang Transform to perform analysis of the nonlinear and non-stationary ventricular fibrillation(VF). Hilbert-Huang Transform combines two major analytical theories: Empirical Mode Decomposition(EMD) and the Hilbert Transform. Hilbert-Huang Transform can be used to decompose natural data into independent Intrinsic Mode Functions using the theories of EMD. Furthermore, Hilbert-Huang Transform employs Hilbert Transform to determine instantaneous frequency and amplitude, and therefore can be used to accurately describe the local behavior of signals. This paper studied for Return Of Spontaneous Circulation(ROSC) and non-ROSC prediction performance by Support Vector Machine and three parameters(EMD-IF, EMD-FFT) extracted from ventricular fibrillation ECG waveform using Hilbert-Huang transform. On the average results of sensitivity and specificity were 87.35% and 76.88% respectively. Hilbert-Huang Transform shows that it enables us to predict the ROSC of VF more precisely.
Keywords
Empirical Mode Decomposition(EMD); Intrinsic Mode Function(IMF); Support Vector Machine(SVM);
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