Wavelet-Based Heart-Rate Detection And Ecg Classification Of Arrhythmia Using Alexnet Deep Cnn
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Date
2021-09
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Publisher
Department of Electrical and Electronic Engineering
Abstract
Effective monitoring of heart patients based on heart signals has the potential to save a large
number of lives. Classification and prediction of heart diseases based on ECG signals have
become increasingly important for patients and doctors over the last decade. The majority
of contemporary techniques are based on custom-designed features for automatic heart rate
detection and ECG signal classification. The fundamental objective of this thesis work is to
calculation of heart-rate and create a comprehensive learning-based technique that can
classify ECG signals more accurately. We collected all data from PhysioNet for doing this
work. We calculated heart rate using the Discrete Wavelet Transform (DWT) and trained a
pre-trained Convolutional Neural Network (CNN), specifically AlexNet, to classify ECG
signals. To begin, we extracted a spectrogram for each of the signals and transformed them
to RGB images using the Continuous Wavelet Transform (CWT); these images were then
put into AlexNet and trained with minor specification adjustments. The study's findings
indicate that our technique achieves an accuracy of 97.14 %
Description
submitted by S.M. Abdulla Al Hasan Tasim,
bearing Matric ID. ET163078 and Masum Hosen Sajjad, bearing Matric ID. ET163061 of
session Autumn 2020