Classification of Multi-Label Electrocardiograms Utilizing the EfficientNet CNN Model - Akkuzu, Nida; Ucan, Murat; Kaya, Mehmet (Institute of Electrical and Electronics Engineers Inc., 2023)
Using electrocardiogram (ECG) signal images, the status of Covid-19, Abnormal heartbeat, Myocardial infarction, Myocardial Infarction History and Normal findings can be detected. Disease detections made with traditional methods by specialist doctors in the field can lead to mistreatment due to human error. The successes obtained from the classification studies using ECG images in the literature do not have an acceptable success rate yet. The aim of this study is to propose a new approach with high success rate for the detection of diseases using ECG images and to analyze detailed test results. A publicly available dataset containing 5-class ECG images was used in this study. Training and testing processes were carried out using the EfficientNetB0 convolutional neural network architecture. Afterwards, the results were analyzed in detail, graphs were drawn and the results were compared with other studies in the literature. The proposed multi-class classification architecture offers 99.13% accuracy. With the success achieved, it was superior to other studies in the literature. This study will contribute to the rapid and reliable detection of 5 different findings that can be detected from ECG images and to more accurate treatment of patients. © 2023 IEEE.
4th International Conference on Data Analytics for Business and Industry, ICDABI 2023 -- 25 October 2023 through 27 October 2023 -- Virtual, Online -- 201891
Keywords: Classification; Convolutional Neural Network; Deep Learning; ECG; EfficientNet; Electrocardiogram