Статья

Localization and Classification of Myocardial Infarction Based on Artificial Neural Network

Bahodir MuminovFundamentals of Informatics, Tashkent University of Information Technologies named after Muhammad Al-Khwarizmi, Tashkent, UzbekistanRashid NasimovComputer Systems Tashkent University of Information Technologies named after Muhammad Al-Khwarizmi, Tashkent, UzbekistanSanjar MirzahalilovComputer Systems Tashkent University of Information Technologies named after Muhammad Al-Khwarizmi, Tashkent, UzbekistanNargiza SayfullaevaComputer Systems Tashkent University of Information Technologies named after Muhammad Al-Khwarizmi, Tashkent, UzbekistanNigora GadoyboyevaComputer Systems Tashkent University of Information Technologies named after Muhammad Al-Khwarizmi, Tashkent, Uzbekistan

2020en

ABI

Аннотация

In this article, has been developed Convolutional Neural Network (CNN) architecture for classifying and determining the localization of myocardial infarction based on electrocardiogram (ECG). Training process is carried out on deep learning toolbox of MatLab. For the training, the database based on a 12-lead ECG device is developed. Database consisted of 11 classes: 10 classes belong to patients of myocardial infarction and one class's data taken from healthy persons. All data of total classes saved on. mat file form, and each class consisted of different amount of .mat files. For each class, network accuracy calculated separately and average result determined. The training result achieved at 98.47%. The results also compared to the obtained results of other researchers. The specifics of each taken results are also discussed.

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Темы

ECG Monitoring and Analysis Non-Invasive Vital Sign Monitoring EEG and Brain-Computer Interfaces

Идентификаторы

DOI: 10.1109/ictc49638.2020.9123300

Цитирования и источники

Цитирований: 12 Использованных источников: 35

Показатели — AkademScholar