DocumentCode :
630457
Title :
Detection of Ventricular Fibrillation Based on Time Domain Analysis
Author :
Sang-Hong Lee ; Lim, J.S.
Author_Institution :
Dept. of Comput. Sci. & Eng., Anyang Univ., Anyang, South Korea
fYear :
2013
fDate :
24-26 June 2013
Firstpage :
1
Lastpage :
3
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.
Keywords :
Hilbert transforms; computational geometry; electrocardiography; fuzzy set theory; medical signal processing; neural nets; signal reconstruction; statistical analysis; 2D phase space diagram; ECG episodes; Euclidean distance; Hilbert transforms; NEWFM; feature extraction; neural network; normal sinus rhythm; peak extraction; phase space reconstruction; statistical methods; time domain analysis; ventricular fibrillation detection; weighted fuzzy membership functions; Accuracy; Electrocardiography; Feature extraction; Fibrillation; Standards; Statistical analysis; Transforms;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Science and Applications (ICISA), 2013 International Conference on
Conference_Location :
Suwon
Print_ISBN :
978-1-4799-0602-4
Type :
conf
DOI :
10.1109/ICISA.2013.6579507
Filename :
6579507
Link To Document :
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