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ECG Data Analysis with Denoising Approach and Customized CNNs
Abhinav Mishra1, Ganapathiraju Dharahas1, Shilpa Gite1
1Symbiosis Institute of Technology, Pune 412115, India.
Insights
Artificial intelligence enhances arrhythmia detection in cardiovascular diseases. Denoising electrocardiography (ECG) signals with custom convolutional neural networks (CCNNs) significantly improves diagnostic accuracy compared to raw data.
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.
Abstract:
In the last decade, the proactive diagnosis of diseases with artificial intelligence and its aligned technologies has been an exciting and fruitful area. One of the areas in medical care where constant monitoring is required is cardiovascular diseases. Arrhythmia, one of the cardiovascular diseases, is generally diagnosed by doctors using Electrocardiography (ECG), which records the heart's rhythm and electrical activity. The use of neural networks has been extensively adopted to identify abnormalities in the last few years. It is found that the probability of detecting arrhythmia increases if the denoised signal is used rather than the raw input signal. This paper compares six filters implemented on ECG signals to improve classification accuracy. Custom convolutional neural networks (CCNNs) are designed to filter ECG data. Extensive experiments are drawn by considering the six ECG filters and the proposed custom CCNN models. Comparative analysis reveals that the proposed models outperform the competitive models in various performance metrics.
