ECG Data Analysis with Denoising Approach and Customized CNNs.

Area of Science:

• Medical Artificial Intelligence
• Cardiovascular Disease Diagnosis
• Signal Processing

Background:

• Cardiovascular diseases necessitate continuous patient monitoring.
• Arrhythmia diagnosis relies on Electrocardiography (ECG) interpretation.
• Artificial intelligence (AI) shows promise in proactive disease detection.

Purpose of the Study:

• To compare the efficacy of six different filters for improving ECG signal quality.
• To develop and evaluate custom convolutional neural networks (CCNNs) for ECG denoising.
• To enhance the accuracy of arrhythmia detection using AI-driven signal processing techniques.

Main Methods:

• Implementation and comparison of six distinct filtering techniques on ECG signals.
• Design and application of custom convolutional neural networks (CCNNs) for ECG data preprocessing.
• Extensive experimental evaluation of filtering methods and CCNN models on ECG datasets.

Main Results:

• Denoised ECG signals lead to a higher probability of accurate arrhythmia detection compared to raw signals.
• The proposed custom CCNN models demonstrated superior performance over existing competitive models.
• Comparative analysis showed significant improvements in various performance metrics for the CCNN approach.

Conclusions:

• AI-powered signal denoising, particularly with CCNNs, is crucial for accurate arrhythmia diagnosis.
• The developed CCNN models offer a robust solution for enhancing ECG analysis in clinical settings.
• This research highlights the potential of advanced AI techniques in improving cardiovascular healthcare outcomes.