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JSDP 2024, 21(2): 43-54 |
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mihandoost S. Atrial Fibrillation Classification Using PiCA-ESN Algorithm and Stockwell Transform. JSDP 2024; 21 (2) : 4
URL: http://jsdp.rcisp.ac.ir/article-1-1374-en.html
URL: http://jsdp.rcisp.ac.ir/article-1-1374-en.html
Urmia University of Technology
Abstract: (764 Views)
Atrial fibrillation (AF) is a prevalent cardiac arrhythmia characterized by irregular heartbeats, often without noticeable symptoms in patients. Diagnosing AF is challenging for cardiologists, requiring advanced methods for accurate identification using electrocardiogram (ECG) signals. Automated AF diagnosis can significantly aid cardiologists in prompt identification, potentially reducing the risks associated with acute heart disease and stroke. Various non-invasive techniques based on ECG signal processing have been suggested to better understand the mechanisms by analyzing the atrial fibrillatory waves (f-waves). Different signal processing methods for f-wave extraction have been explored, which may be classified as follows: average beat subtraction and its advanced variants, QT-interval interpolation, principal and independent component analysis, nonlinear adaptive filtering using an echo state network, diffusion geometry, and extended Kalman filtering. This study aims to extract the f-wave from the ECG signal using the PiCA-ESN algorithm, which yields better results compared to other methods. Additionally, the f-wave's time-frequency behavior was analyzed using the Stockwell transform to differentiate between terminated and non-terminated AF states for the first time in this study. First, the PiCA-ESN algorithm facilitated the extraction of the f-wave from the ECG signal. Subsequently, the Stockwell transform was used to compute the time-frequency maps of the extracted f-wave. Various features were derived from the amplitude of the Stockwell transform and utilized in conjunction with three classifiers: MLP, SVM, and AdaBoost. The findings reveal that the proposed method outperforms selected methodologies from the Physionet Challenge 2004, achieving an impressive 100% accuracy in both tasks. Additionally, an experiment was conducted to assess the robustness of the proposed features across consecutive signal segments, validating their stability during signal analysis.
Article number: 4
Keywords: Electroencephalogram (ECG), Atrial fibrilation (AF), f-wave, PiCA-ESN algorithm, Stockwell transform (S transform)
Type of Study: Research |
Subject:
Paper
Received: 2023/04/7 | Accepted: 2024/08/22 | Published: 2024/11/4 | ePublished: 2024/11/4
Received: 2023/04/7 | Accepted: 2024/08/22 | Published: 2024/11/4 | ePublished: 2024/11/4
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