Key result
A neural network with weighted fuzzy memberships using wavelet transforms and phase space reconstruction from ECG signals was developed to distinguish ventricular fibrillation from normal sinus rhythm.
A proposed algorithm combining wavelet transforms, phase space reconstruction, and a neural network classifier shows potential for detecting ventricular fibrillation in portable automated external defibrillators.
This study proposes the detection of ventricular fibrillation (VF) by wavelet transforms (WTs) and phase space reconstruction (PSR) from electrocardiogram (ECG) signals. A neural network with weighted fuzzy memberships (NEWFM) is used to detect VF as a classifier. In the first step, the WT was used to remove noise in ECG signals. In the second step, coordinates were mapped from the wavelet coefficients by the PSR. In the final step, NEWFM used the mapped coordinates-based features as inputs. The NEWFM has the bounded sum of weighted fuzzy memberships (BSWFM) that can easily appear the distinctness between the normal sinus rhythm (NSR) and VF in the graphical characteristics. The BSWFM can easily be set up in a portable automatic external defibrillator (AED) to detect VF in an emergency.
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Jang et al. (2021) studied Ventricular fibrillation. Wavelet transforms and phase space reconstruction with NEWFM classifier vs. Normal sinus rhythm was evaluated on Detection of ventricular fibrillation. A neural network with weighted fuzzy memberships using wavelet transforms and phase space reconstruction from ECG signals was developed to distinguish ventricular fibrillation from normal sinus rhythm.
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