UGC Approved Journal no 63975(19)

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Published in:

Volume 8 Issue 12
December-2021
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR2112221


Registration ID:
317881

Page Number

c168-c171

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Title

ECG Anomaly Detection Toward Cardiac Monitoring using Machine Learning

Abstract

According to WHO, an estimated 17.9 million people died from CVDs each year representing 31% of all global deaths. The annual number of deaths from heart related diseases is projected to reach 4.77 million in India. This obtained data tell us monitoring and taking care of the heart is very essential part. This paper revolves around Electrocardiography (ECG) Signal readings which are further cleaned and feature extraction is done to obtain useful information. Support Vector Machine (SVM) is mainly used in for feature extraction for the dataset that is trained and tested followed by passing the updated dataset to a combination of different Machine Learning Techniques by use of K-Means in order to provide accurate training and testing accuracy results from the dataset received. This model further has the potential to be used in hospitals where huge amount of dataset is present, with training and testing of the model with the right Machine Learning algorithm the accuracy and the time results are obtained will be greatly reduced, this will further help save many people in shorter period of time

Key Words

SVM, ECG Prediction, K-means, Arrhythmia Classification

Cite This Article

"ECG Anomaly Detection Toward Cardiac Monitoring using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 12, page no.c168-c171, December-2021, Available :http://www.jetir.org/papers/JETIR2112221.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"ECG Anomaly Detection Toward Cardiac Monitoring using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 12, page no. ppc168-c171, December-2021, Available at : http://www.jetir.org/papers/JETIR2112221.pdf

Publication Details

Published Paper ID: JETIR2112221
Registration ID: 317881
Published In: Volume 8 | Issue 12 | Year December-2021
DOI (Digital Object Identifier):
Page No: c168-c171
Country: Pune, Maharashtra, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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