International Research Journal of Education and Technology

Cardiac arrhythmias is a serious health issue that many people have. The classification of cardiac arrhythmias using a variety of deep learning approaches, including Convolutional Neural Networks (CNNs), is presented in this article in a novel way. The ECG data from the MIT-BIH Arrhythmia Database were used in this investigation. Sorting ECG data into normal and pathological categories is the main goal of this study. The twenty-four features used to extract ECG signals are taken from both normal and problematic clinical clusters. The P, Q, R, S, and T voltage-time properties from the MIT-BIH database are analyzed for these signals. To accurately detect arrhythmias and other heart rhythm abnormalities, the suggested CNN-based system automates crucial processes including signal preprocessing and feature extraction. To ensure a clear depiction ofthe electrical activity of the heart, signal preprocessing involves filtering and cleaning the ECG data to remove noise and artifacts. The CNN system can produce accurate and consistent assessments by automating this procedure, which helps it to successfully identify even mild arrhythmias. Finding particular characteristics in the ECG signals that are suggestive of dif erent arrhythmias, like atrial fibrillation and ventricular tachycardia, isthe goal of feature extraction. The ability ofthe CNN to carry out these duties with high accuracy is anticipated to improve diagnostic ef iciency and precision, assisting medical practitioners in making well informed decisions about patient treatment. In ECG analysis, the potential decrease in diagnostic errors is one of the main advantages of using deep learning methods like CNNs. As a precaution against any errors made by human examiners, the CNN system adds another level of analysis. This integrated strategy increases healthcare practitioners' confidence while also strengthening the accuracy of diagnosis. Faster diagnosis and prompt therapeutic interventions are also made possible by CNNs'speedy processing and analytical capabilities, which are essential in emergencies. An important development in medicine is the application of CNN-based deep learning techniquesto 6 the diagnosis of cardiac arrhythmias. Through the optimization of ECG signal evaluation, thisstudy leverages deep learning to improve treatment approaches and patient care. Improved diagnosis ef iciency and accuracy, as well as the anticipated decline in diagnostic errors, will ultimately lead to better patient outcomes and more ef icient healthcaredelivery. By transforming the diagnosis andtreatmentofcardiac arrhythmias, this research could set a newstandardinthe medical industry. Key Words: Deep Learning Methods, Convolutional NeuralNetworks (CNNs), Electrocardiogram(ECG) Analysis, SignalPreprocessing, Feature Extraction, HealthcareEfficiency,Real-time Analysis Error Reduction, Real-timePredictionetc…