Authors :
SHAIK CHANDINI; DONELLI BABY SRILAKSHMI; KONDAMURU SRI AKHILA
Volume/Issue :
Volume 7 - 2022, Issue 5 - May
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3xZmBnE
DOI :
https://doi.org/10.5281/zenodo.6662978
Abstract :
Recently, deep learning models have arrived as
assuring methods for the diagnosis of various diseases.
Cardiac disease is one of the leading life-threatening diseases
on global scale. The aim of this paper is to propose a
heartbeat classifier from the electrocardiogram by using
CNN. The proposed Electrocardiogram classification model
is designed with a CNN configuration to classify heartbeat
arrhythmias in less time. Already existing designs like
machine learning techniques are time-consuming and needs
extensive experimentation. To overcome this problems, we
are using CNN model for ECG Classification.
Keywords :
ECG analysis, cardiac arrhythmias, ECG classification, deep learning and convolutional neural networks.
Recently, deep learning models have arrived as
assuring methods for the diagnosis of various diseases.
Cardiac disease is one of the leading life-threatening diseases
on global scale. The aim of this paper is to propose a
heartbeat classifier from the electrocardiogram by using
CNN. The proposed Electrocardiogram classification model
is designed with a CNN configuration to classify heartbeat
arrhythmias in less time. Already existing designs like
machine learning techniques are time-consuming and needs
extensive experimentation. To overcome this problems, we
are using CNN model for ECG Classification.
Keywords :
ECG analysis, cardiac arrhythmias, ECG classification, deep learning and convolutional neural networks.
