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

Year 2024, , 442 - 455, 30.09.2024

Evin Şahin Sadık

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

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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