Iranian Journal of Biomedical Engineering (IJBME)
نشریه علمی مهندسی پزشکی زیستی

Arrhythmia Classification Improvement by Individually Mapping the Feature Space of each patient

Document Type : Full Research Paper

Authors

Hamid Shafaatfar 1
Mehdi Taghizadeh 2
Morteza Valizadeh 3
Mohamad Hossein Fatehi 2

1 Ph.D. Student, Department of Electrical Engineering, Kazerun Branch, Islamic Azad University, Kazerun, Iran

2 Assistant Professor, Department of Electrical Engineering, Kazerun Branch, Islamic Azad University, Kazerun, Iran

3 Assistant Professor, Department of Electrical Engineering, Urmia University, Urmia, Iran

https://doi.org/10.22041/ijbme.2022.554427.1776
Abstract
Automatic detection of cardiac arrhythmias is very important for the successful treatment of heart disease and machine learning is used for this purpose. To correctly classify arrhythmic classes, it is important to extract the appropriate features to distinguish between different classes. In this paper, a deep convolutional neural network is used to extract the feature. Due to the fact that the heart rates of different patients are very different, arrhythmia classes will have many intra-class changes. To reduce intra-class changes, each patient’s heart rate is mapped with a dedicated function to increase its resemblance to the heart rate of one of the training patient data’s. The proposed specific mapping reduces intra-class changes and significantly increases the classification accuracy of cardiac arrhythmias. To prove the effectiveness of the proposed method, its results were compared with several new studies based on three criteria for accuracy, sensitivity and specificity and on the same data set. The accuracy obtained is about 96.24%, which shows the better performance of the proposed method compared to other works.

Keywords

Cardiac Arrhythmia
Classification
Feature Extraction
Feature Space Mapping

Subjects

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Iranian Journal of Biomedical Engineering (IJBME)
Volume 16, Issue 2
Summer 2022
Pages 147-158

  • Receive Date 06 June 2022
  • Revise Date 28 November 2022
  • Accept Date 21 December 2022

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