Enhancing heart disease diagnosis through ECG image vectorization-based classification.

Heart disease is a major issue, and the severity of its effects can be reduced through early detection and prevention. ECG is an effective diagnostic tool. Automating ECG analysis increases the possibility of timely analysis and prediction of heart conditions, improving patient outcomes. The extraction of heart-related features further enhances the accuracy of ECG-based classification models, paving the way for more effective and efficient online detection and prevention of heart diseases. The image-vectorization technique suggested in this study produces a vector representation that precisely captures the distinctive features of the heart signal. It involves image cropping, erasing the ECG grid lines, and assigning pixels to distinguish the heart signal from the background. Compared to the feature vector produced by VGG16, the extracted feature vector is 589 times shorter than the feature vector produced by VGG16, which significantly decreased the amount of memory required, increased algorithm convergence, and required less computing power. The feature vector extracted using image-vectorization is used to create the training dataset, which is used to train artificial neural networks (ANNs). The results demonstrate that using image-vectorization techniques improved the performance of machine learning algorithms compared to using conventional feature extraction algorithms like CNNs and VGG16.