Integrating Advanced Combined Numerical Filters for ECG Denoising and Cardiovascular Disease Classification Using Deep Learning

In this paper, a novel method for ECG signal processing for cardiovascular disease classification using deep learning is proposed. The central element of the study is a new combined numerical filter specifically designed for ECG signal denoising. This filter, a unique fusion of Chebyshev, Butterworth, and Daubechies filters, has been optimized to effectively denoise the ECG signal while preserving its relevant features. This improvement in signal quality is critical for the successful classification of heart disease using advanced artificial intelligence (AI) techniques. The method has been specifically applied to the case of arrhythmias, where a significant improvement in signal quality has been demonstrated. The application of this filter by deep neural networks significantly improves the accuracy and reliability of cardiac abnormality detection and classification, especially in arrhythmia cases. The potential of combining advanced filtering techniques with deep learning for more accurate medical diagnosis is demonstrated, opening up new perspectives in the diagnosis and monitoring of cardiovascular diseases.