Abstract
An irregular heartbeat is referred to as cardiac arrhythmia. It’s a condition in which the pulse is either too slow or too quick. When electrical impulses in the heart do not operate properly, cardiac arrhythmia ensues. This is referred to as a disturbance in the usual electrical system that controls your heart rate and rhythm. We use Electrocardiography (ECG) data to categorize individuals with cardiac arrhythmias in this study. We're working on a machine learning system that categorizes patients into 16 different types of cardiac arrhythmias. To identify patients, several machine learning methods such as SVM, Logistic Regression, KNN, Decision Trees, and others can be utilized. This system has a great potential to serve the medical industry. This work can be very useful for detecting and predicting different cardiac diseases before it is too late. This work can be useful for proper and accurate diagnosis of cardio diseases which is very essential nowadays because these are the most common diseases all over the world. This proposed work overcome the limitations of the manual analysis of ECG signals.
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Rama Santosh Naidu, P., Lavanaya Devi, G., Kondapalli, V., Neelapu, R. (2022). A Novel and Self Adapting Machine Learning Approach of ECG Signal Classification in Association with Cardiac Arrhythmia. In: Satyanarayana, C., Gao, XZ., Ting, CY., Muppalaneni, N.B. (eds) Proceedings of the International Conference on Computer Vision, High Performance Computing, Smart Devices and Networks. Advanced Technologies and Societal Change. Springer, Singapore. https://doi.org/10.1007/978-981-19-4044-6_21
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DOI: https://doi.org/10.1007/978-981-19-4044-6_21
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