A NOVEL DEEP LEARNING-BASED CLASSIFICATION APPROACH FOR THE DETECTION OF HEART ARRHYTHMIAS FROM THE ELECTROCARDIOGRAPHY SIGNAL

Cardiovascular disease causes more deaths than any other cause in the globe. The present method of illness identification involves electrocardiogram (ECG) analysis, a medical monitoring gadget that captures heart activity. Regrettably, a great deal of medical resources is required to locate specialists in ECG data. Consequently, ML feature detection in ECG is rapidly gaining popularity. Human intervention is required for "feature recognition, complex models, and lengthy training timeframes"- limitations that are inherent to these traditional approaches. Using the "MIT-BIH Arrhythmia" database, this study presents five distinct categories of heartbeats and the efficient and effective deep-learning (DL) classification algorithms that go along with them. The five types of pulse features are classified experimentally using the wavelet self-adaptive threshold denoising method. Models such as AlexNet and CNN are employed in this dataset. For model evaluation use some performance metrics, like recall, accuracy, precision, and f1-score. The suggested Alex Net model achieves an overall classification accuracy of 99.68%, while the recommended CNN model achieves an accuracy of 99.89%. The end findings demonstrate that the suggested models outperform the current model on several performance criteria and are more efficient overall. With its accurate categorization, important medical resources are better preserved, which has a positive effect on the practice of medicine.