Advancing cardiac health assessment using deep convolutional neural networks for ECG image analysis

Area of Science:

• Biomedical Engineering
• Medical Imaging Analysis
• Artificial Intelligence in Healthcare

Background:

• Early detection of cardiac afflictions is crucial for timely intervention and improved patient outcomes.
• Electrocardiogram (ECG) imagery provides valuable diagnostic information but requires sophisticated analysis techniques.
• Current methods for ECG analysis may lack the precision and efficiency needed for widespread early screening.

Purpose of the Study:

• To develop and validate a novel, high-precision technique for the early prediction of cardiac afflictions from ECG imagery.
• To integrate advanced signal processing and deep learning for robust ECG analysis.
• To establish a new benchmark in automated cardiac condition detection.

Main Methods:

• Pre-processing of ECG imagery using an Adaptive Wiener Filter.
• Segmentation of ECG signals with Modified Fuzzy C-Mean.
• Feature extraction employing Gray Level Co-occurrence Matrix for texture and spatial information.
• Classification using a Hybrid Sparrow-Chicken Swarm Optimized Deep Convolutional Neural Network.

Main Results:

• The proposed technique demonstrated a high precision rate of 98.5% in detecting cardiac afflictions.
• The combination of Adaptive Wiener Filter, Modified Fuzzy C-Mean, GLCM, and the optimized CNN yielded superior performance.
• The method effectively captures subtle features indicative of cardiac conditions.

Conclusions:

• The developed technique offers a promising solution for accurate and early detection of cardiac afflictions.
• The optimized deep learning model significantly enhances the diagnostic capability of ECG imagery analysis.
• This approach has the potential to improve patient care through earlier and more reliable cardiac diagnoses.