Detection of ventricular fibrillation based on neuro-fuzzy system and phase space reconstruction

SANG-HONG, LEE (2016) Detection of ventricular fibrillation based on neuro-fuzzy system and phase space reconstruction. In: Fifth International Conference on Advances in Computing, Control and Networking - ACCN 2016, 25-26 September 2016, Bangkok, Thailand.

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This study proposes feature extraction using wavelet transform (WT), sequential increment method, and phase space reconstruction (PSR) to classify normal sinus rhythm (NSR) and ventricular fibrillation (VF) from ECG episodes. We implemented four pre-processing steps to extract features from ECG episodes. In the first step, we use the WT for multi-scale representation and analysis, and then we extract wavelet coefficients from ECG episodes. In the second step, we use sequential increment method to extract peaks from the wavelet coefficients. In the third step, we make a three-dimensional phase space reconstruction (PSR) using the successive peaks. In the final step, we calculate the Euclidean distance between the peaks that are plotted in a three-dimensional phase space diagram and origin (0, 0), and then extract 20 features from the Euclidean distances by using statistical methods, including frequency distributions and their variabilities. We apply the 20 features as inputs to a neural network with weighted fuzzy membership functions (NEWFM), and recorded sensitivity, specificity, and accuracy performances of 100%, 100%, and 100%, respectively.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: ventricular fibrillation, automated external defibrillator, neuro-fuzzy system, electrocardiogram, wavelet transform.
Depositing User: Mr. John Steve
Date Deposited: 22 Mar 2019 04:58
Last Modified: 22 Mar 2019 04:58

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