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Home > Archives > Volume 20, No 5 (2022) > Article

DOI: 10.14704/nq.2022.20.5.NQ22197

Machine Learning Techniques Applied for Exploring Heart Disease and Classifying Stages through ECG Signal

K. Babu and Dr.P. Marikkannu

Abstract

Electrocardiogram (ECG) has given crucial data concerning various heart attack criterion of the human heart this examination can be the key goal of the research for the detection and prevention of cardiac circumstances frighten. The proposed method using machine learning techniques for classifying and analyzing ECG signal processing and this research mainly developed for early discovery of heart diseases and also the prediction of stages level. The dataset was utilized as a person ECG signal of Heart Database which was taken from the UCI repository of Machine learning dataset vault. In this paper, a simple algorithm is presented for to discover R-peaks automatically from a single lead digital ECG data. The proposed method detecting the time interval of the ECG signal from the R-peaks level next level with the double squared difference signal is used to localize the region of QRS which is the time interval between the binary data. this method consists of different stages of sorting from the raw data for reducing nosier signal, threshold a difference signal of ECG by analyzing the time interval of QRS, and finally a comparison of relative magnitude to detect the region of interval processing to analyze accuracy result. The proposed research novel machine learning techniques of the multi-module neural network system (MMNNS) is used to analyze the imbalance problem form the ECG signal classification if the wave was abnormal then the user of dataset patients will be affected by heart diseases. Using the time interval varies from the range of QRS then it analyzes the abnormal of ECG by MMNNS algorithm to define the classification result and finally analyze the stages. If the patients were presented an abnormal ECG signal compared with a normal ECG signal wave graph then they were affected by heart diseases if whether they patients were affected then finally classify the stages of heart diseases whether they are predictable/unpredictable stages. The examination result is completed among two strategies on the better accuracy premise and effective training time of the process.

Keywords

Machine Learning, ECG Signal, MMNNS Algorithm, Interval Processing, Detecting QRS Region, Stages Classification, Heart Disease Prediction.

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