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Erschienen in:

18.12.2023

Improving the Efficiency of Automatic Cardiac Arrhythmias Classification by a Novel Patient-Specific Feature Space Mapping

verfasst von: Hamid Shafaatfar, Mehdi Taghizadeh, Morteza Valizadeh, Mohammad Hossein Fatehi

Erschienen in: Circuits, Systems, and Signal Processing | Ausgabe 4/2024

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Abstract

In this paper, a novel method is introduced to diagnose the cardiac arrhythmias. Automatic diagnosis of cardiac arrhythmias is crucial for successful treatment of heart diseases, and nowadays, machine learning is used to facilitate this purpose. For accurate classification of arrhythmias, it is vital to extract some appropriate features which distinguish different classes. In this research, a deep convolutional neural network is used to extract the features. Since the heart rates of various patients are very different, arrhythmias of a specific class will be placed in different locations of feature space. To reduce the intra-class variations, each patient’s heart rate is mapped with a dedicated function to increase its similarity to the heart rate of a training patient. The proposed patient-specific mapping decreases the intra-class variation and significantly increases the classification accuracy of cardiac arrhythmias. The calculated accuracy is about 91.08%, which shows the better performance compared to other similar works.

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Metadaten
Titel
Improving the Efficiency of Automatic Cardiac Arrhythmias Classification by a Novel Patient-Specific Feature Space Mapping
verfasst von
Hamid Shafaatfar
Mehdi Taghizadeh
Morteza Valizadeh
Mohammad Hossein Fatehi
Publikationsdatum
18.12.2023
Verlag
Springer US
Erschienen in
Circuits, Systems, and Signal Processing / Ausgabe 4/2024
Print ISSN: 0278-081X
Elektronische ISSN: 1531-5878
DOI
https://doi.org/10.1007/s00034-023-02550-9