MULTI-LEVEL CLASSIFICATION BASED ON DEEP LEARNING FOR ACCURATE RISK STRATIFICATION OF ARRHYTHMIAS

Evin Şahin Sadık

doi:10.18038/estubtda.1466349

EN

MULTI-LEVEL CLASSIFICATION BASED ON DEEP LEARNING FOR ACCURATE RISK STRATIFICATION OF ARRHYTHMIAS

Abstract

Arrhythmias, also known as irregular heartbeats, are important health problems that must be accurately identified to diagnose and treat cardiovascular disease. Within the scope of this study, a network for classifying arrhythmias, which are important in the diagnosis and treatment of cardiovascular diseases, was proposed by using one-dimensional convolutional neural network (1D CNN), one of the deep learning techniques. With the proposed 1D-CNN architecture, arrhythmia types and normal rhythm ECGs were subjected to a more detailed examination from general to specific according to urgency situations. In the classifications made, first of all, a binary classification was made and an evaluation was made as whether there was a life risk or not. In triple, quadruple and six-fold classification, the detection of arrhythmia status is detailed. More complex classifications have helped to define different types of arrhythmias in more detail. This study proposes a deep learning network for automatic identification and classification of arrhythmias and shows that different arrhythmia conditions can be diagnosed with a single network model by applying the proposed network structure to multi-class arrhythmia disorders.

Keywords

Cardiac Arrhythmias, CNN, Classification, ECG

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Details

Primary Language

English

Subjects

Electronics

Journal Section

Research Article

Authors

Evin Şahin Sadık ^*
0000-0002-2212-4210
Türkiye

Publication Date

September 30, 2024

Submission Date

April 7, 2024

Acceptance Date

August 20, 2024

Published in Issue

Year 2024 Volume: 25 Number: 3

DOI

https://doi.org/10.18038/estubtda.1466349

IZ

https://izlik.org/JA85ZS47SG

APA

Şahin Sadık, E. (2024). MULTI-LEVEL CLASSIFICATION BASED ON DEEP LEARNING FOR ACCURATE RISK STRATIFICATION OF ARRHYTHMIAS. Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering, 25(3), 442-455. https://doi.org/10.18038/estubtda.1466349

AMA

1.Şahin Sadık E. MULTI-LEVEL CLASSIFICATION BASED ON DEEP LEARNING FOR ACCURATE RISK STRATIFICATION OF ARRHYTHMIAS. Estuscience - Se. 2024;25(3):442-455. doi:10.18038/estubtda.1466349

Chicago

Şahin Sadık, Evin. 2024. “MULTI-LEVEL CLASSIFICATION BASED ON DEEP LEARNING FOR ACCURATE RISK STRATIFICATION OF ARRHYTHMIAS”. Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering 25 (3): 442-55. https://doi.org/10.18038/estubtda.1466349.

EndNote

Şahin Sadık E (September 1, 2024) MULTI-LEVEL CLASSIFICATION BASED ON DEEP LEARNING FOR ACCURATE RISK STRATIFICATION OF ARRHYTHMIAS. Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering 25 3 442–455.

IEEE

[1]E. Şahin Sadık, “MULTI-LEVEL CLASSIFICATION BASED ON DEEP LEARNING FOR ACCURATE RISK STRATIFICATION OF ARRHYTHMIAS”, Estuscience - Se, vol. 25, no. 3, pp. 442–455, Sept. 2024, doi: 10.18038/estubtda.1466349.

ISNAD

Şahin Sadık, Evin. “MULTI-LEVEL CLASSIFICATION BASED ON DEEP LEARNING FOR ACCURATE RISK STRATIFICATION OF ARRHYTHMIAS”. Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering 25/3 (September 1, 2024): 442-455. https://doi.org/10.18038/estubtda.1466349.

JAMA

1.Şahin Sadık E. MULTI-LEVEL CLASSIFICATION BASED ON DEEP LEARNING FOR ACCURATE RISK STRATIFICATION OF ARRHYTHMIAS. Estuscience - Se. 2024;25:442–455.

MLA

Şahin Sadık, Evin. “MULTI-LEVEL CLASSIFICATION BASED ON DEEP LEARNING FOR ACCURATE RISK STRATIFICATION OF ARRHYTHMIAS”. Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering, vol. 25, no. 3, Sept. 2024, pp. 442-55, doi:10.18038/estubtda.1466349.

Vancouver

1.Evin Şahin Sadık. MULTI-LEVEL CLASSIFICATION BASED ON DEEP LEARNING FOR ACCURATE RISK STRATIFICATION OF ARRHYTHMIAS. Estuscience - Se. 2024 Sep. 1;25(3):442-55. doi:10.18038/estubtda.1466349

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