چكيده لاتين
Heart disease is one of the leading causes of death in the world, accounting for more than 50% of heart disease and second only to cancer as the leading cause of death. This phenomenon is the consequences of intense activity on the operating system, which causes the destruction of the heart systems in people, and if not resuscitated in time, it leads to the loss of pulse, blood pressure, breathing, and then brain death and finally biological death. Due to the importance of the subject, practical tools and methods have been invented to investigate the field of heart function in modern medicine, which I can mention methods such as recording heart signals, electrocardiogram, angiography, etc. Due to the fact that the analysis of the images recorded on the hearts of experts and because severe changes in the function occur in a way, can occur in a moment, the probability of error in diagnosis is high and, in most cases, the lives of patients are at risk of death. Therefore, the rapid restoration of regular heart function and blood supply can be very beneficial. Despite public access to defibrillators in the 21st century, the optimal and important solution is to predict the health of that health in advance. For this reason, many studies have been done in this field to make it smarter and increase accuracy and diagnosis. In this project, heart health is predicted by using electrocardiogram processing.
At first, the preprocessing and application steps were performed on the input ECG signals, the RCN algorithm was used to extract key features from the raw ECG cases, enabling the CNN model to learn and classify patterns related to cardiac dynamics. But subsequent iterations showed that the deletion of these steps is correctly predicted. evaluation of the race training model showed it in SCD predictions with an accuracy of 98% 20 minutes, which showed an accuracy of and a score of F1.
The results of this study highlight the proposed method in predicting SCD events.
