Classification of heart diseases using time-frequency representations of electrocardiogram signals by transfer learning networks
Year: 2025
Type: article
Abstract: It is crucial to monitor and diagnose cardiac function early to prevent the development of future, more severe issues. This study categorized 193 male and female subjects into three groups based on their ECG signals obtained during an exercise test: healthy, myocardial infarction, and left bundle branch block. The data were then processed and converted into images representing three time-frequency representations: a spectrogram, a scalogram, and a spectrum. These images were used as input for two pre-trained networks through transfer learning. The ResNet-18 and GoogLeNet networks were utilized in this study. The ResNet-18 network achieved an accuracy of 88.64% for the spectrogram, 98.41% for the scalogram, and 83.33% of the spectrum. The results for the GoogLeNet network were as follows: 77.27% for the spectrogram, 97.62% for the scalogram, and 78.57% of the spectrum.
Authors: Jalalzadeh, Amir Hossein, Ebrahimi, Hamid, Jahangiri Moghadam, Maryam
Institutions: Shahed University, Kermanshah University of Medical Sciences
Language: English
Topic: ECG Monitoring and Analysis
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W6917308585
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