Identification of Cardiovascular Disease using ECG Images based on Deep Learning Procedure

When it comes to finding out what's wrong with your heart, cardiac imaging is crucial (CVD). Its previous purpose was restricted to qualitative and quantitative evaluations of the heart. The advent of big data and machine learning, however, has opened up new possibilities for developing AI tools that can aid the physician in the diagnosis of CVDs. Our study offers a unique approach to ECG illness prediction by combining cardiology, signal processing techniques, and a deep learning model. Then, using wavelet transformation and cardiology, we convert the ECG data into a time-frequency representation. Next, a deep convolutional network is used to categories the results of the time-frequency analysis. We find that by combining the benefits of signal processing with those of deep learning, we can significantly enhance the precision of categorization using our approach. The suggested methodology has the potential to enhance performance and reduces staffing costs for major general hospitals, all while lowering the rates at which primary care facilities make diagnostic errors.