Detection of ventricular fibrillation based on time domain analysis
- Authors
- Lee, S.-H.; Lim, J.S.
- Issue Date
- 2013
- Keywords
- Hilbert transforms; NEWFM; Phase space reconstruction; Ventricular fibrillation
- Citation
- 2013 International Conference on Information Science and Applications, ICISA 2013
- Journal Title
- 2013 International Conference on Information Science and Applications, ICISA 2013
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/14948
- DOI
- 10.1109/ICISA.2013.6579507
- ISSN
- 0000-0000
- Abstract
- This study proposes feature extraction using Hilbert transforms and phase space reconstruction to detect ventricular fibrillation (VF) and normal sinus rhythm (NSR) from ECG episodes. We implemented three pre-processing steps to extract features from ECG episodes. In the first step, we use Hilbert transforms to extract peaks. In the second step, we use statistical methods and extract 4 features from the peaks. In the final step, we extract 4 features using statistical methods based on the Euclidean distance between the origin (0, 0) and the peaks after the peaks are plotted in a two dimensional phase space diagram. We applied the 8 features as inputs to a neural network with weighted fuzzy membership functions (NEWFM), and recorded sensitivity, specificity, and accuracy performances of 76.37%, 89.18%, and 86.63%, respectively. © 2013 IEEE.
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Collections - IT융합대학 > 컴퓨터공학과 > 1. Journal Articles
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