Arrhythmia Disease Diagnosis Based on ECG Time-Frequency Domain Fusion and Convolutional Neural Network
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Arrhythmia Disease Diagnosis Based on ECG Time-Frequency Domain Fusion and Convolutional Neural Network
K. Swetha Shailaja1, Karravula Satyakanth2, Mamidi Sathyanarayana 3, Maddala Raj Kumar4
Assistant Professor of Department of CSE(AI&ML) of ACE Engineering College 1 Students of Department CSE(AI&ML) of ACE Engineering College 2,3,4
Abstract
Electrocardiogram (ECG) signals are crucial in diagnosing cardiac arrhythmias, which can lead to severe health issues if not detected early. This paper presents an advanced approach to arrhythmia diagnosis by integrating ECG time-frequency domain fusion with a Convolutional Neural Network (CNN). Time-frequency domain analysis enhances the feature extraction process by capturing temporal and spectral patterns in ECG signals. The CNN model effectively processes these complex features, enabling accurate and automated arrhythmia detection. The proposed method improves diagnostic accuracy and robustness compared to traditional machine learning models.
Keyword: Arrhythmia Detection, Time-Frequency Analysis, Wavelet Transform (WT), Convolutional Neural Network (CNN), Deep Learning, Automated Diagnosis.
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