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Development of Ventricular Fibrillation Diagnosis Method Based on Neuro-fuzzy Systems for Automated External Defibrillators

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Abstract

The aim of this study is to develop an embedded method for automatic diagnosis of ventricular fibrillation (VF) using a neuro-fuzzy system embedded in an automated external defibrillator (AED). To diagnose VF using AEDs, we use the neural network with weighted fuzzy membership functions (NEWFM), a wavelet transform (WT), a sequential increment method (SIM), and phase-space reconstruction (PSR) in order to classify normal sinus rhythm (NSR) and VF of electrocardiogram (ECG) episodes. This study has the following key points. The first contribution is the extraction of peaks from ECG episodes by the use of the WT and SIM by a time–frequency technique. The second contribution is that NSR and VF are distinguished by means of three-dimensional (3D) PSR based on a 3D graphic model. The third contribution is the identification of feature differences between NSR and VF by the use of graphical characteristics of weighted fuzzy membership functions (WFMs) supported by the NEWFM. The final contribution is the development of a neuro-fuzzy system for automatic diagnosis of VF using the WFMs embedded in the AED. The following four preprocessing steps are implemented to extract features from ECG episodes. In the first step, the WT is used for multi-scale representation and analysis and wavelet coefficients are then generated from the ECG episodes. In the second step, the SIM is used to extract peaks from the wavelet coefficients. In the third step, successive peaks are plotted in a 3D phase-space diagram by performing 3D PSR. In the final step, the distance between the origin (0, 0, 0) and the successive peaks plotted in a 3D phase-space diagram is calculated; then, 20 features are extracted from the calculated distances using statistical methods, including frequency distributions and their variability. The 20 extracted features are applied as inputs to the NEWFM, and the result is that the classification accuracy of the NEWFM is 100 %.

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Acknowledgments

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2014R1A1A2054293).

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  1. Department of Computer Science & Engineering, Anyang University, Anyang-si, Republic of Korea

    Sang-Hong Lee

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Lee, SH. Development of Ventricular Fibrillation Diagnosis Method Based on Neuro-fuzzy Systems for Automated External Defibrillators. Int. J. Fuzzy Syst. 19, 440–451 (2017). https://doi.org/10.1007/s40815-016-0174-0

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  • DOI: https://doi.org/10.1007/s40815-016-0174-0

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