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

This research introduces a novel technique for early prediction of cardiac affliction in ECG imagery. The initial phase involves pre-processing using an Adaptive Wiener Filter. Subsequently, segmentation is performed utilizing a Modified Fuzzy C-Mean to isolate distinct components of the ECG signal. The next crucial step involves feature extraction deploying Gray Level Co-occurrence Matrix, enabling the capture of texture and spatial dependencies. The classification stage, utilizes Hybrid Sparrow-Chicken Swarm Optimized Deep Convolutional Neural Network, tailored to optimize the deep learning model. The execution with the Python software, achieves 98.5% precision in the detection of cardiac afflictions from ECG imagery.