Comparative Analysis of Machine Learning Clustering Methods for Electroretinogram

Zhdanov A, Bulev D, Dolganov A, Kulyabin M (2023)

Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2023

Publisher: Springer

Series: Advances in Digital Health and Medical Bioengineering

Book Volume: Volume 1: Medical Devices, Measurements, and Artificial Intelligence Applications

Pages Range: 385–392

Conference Proceedings Title: Proceedings of the 11th International Conference on E-Health and Bioengineering, EHB-2023, November 9–10, 2023, Bucharest, Romania

Event location: Bucharest, Romania RO

URI: https://link.springer.com/chapter/10.1007/978-3-031-62502-2_45

DOI: 10.1007/978-3-031-62502-2_45

Open Access Link: https://doi.org/10.1007/978-3-031-62502-2_45

Abstract

Electroretinogram (ERG) signals are commonly used in electrophysiological research to measure the retina's electrical responses to light stimuli. Using publicly available signals from the IEEE DataPort repository, 11 clustering algorithms were assessed, including traditional and modern methods, to categorize the ERG signals into distinct groups. The clustering results were evaluated using metrics such as the Silhouette Coefficient, Calinski-Harabasz score, and Davies-Bouldin score. The study found that the Affinity Propagation algorithm was the most effective approach in classifying ERG signals. This method uses a message-passing framework to update the examples and responsibilities of each data point based on a damping factor and the affinities between the data points. The study's findings can contribute to enhancing the classification of ERG signals, ultimately improving the accuracy of electrophysiological research.

Authors with CRIS profile

Mikhail Kulyabin Lehrstuhl für Informatik 5 (Mustererkennung)

Involved external institutions

Ural Federal University B.N. Yeltsin RU Russian Federation (RU)

How to cite

APA:

Zhdanov, A., Bulev, D., Dolganov, A., & Kulyabin, M. (2023). Comparative Analysis of Machine Learning Clustering Methods for Electroretinogram. In Hariton-Nicolae Costin, Ratko Magjarević, Gladiola Gabriela Petroiu (Eds.), Proceedings of the 11th International Conference on E-Health and Bioengineering, EHB-2023, November 9–10, 2023, Bucharest, Romania (pp. 385–392). Bucharest, Romania, RO: Springer.

MLA:

Zhdanov, Aleksei, et al. "Comparative Analysis of Machine Learning Clustering Methods for Electroretinogram." Proceedings of the International Conference on e-Health and Bioengineering (EHB) 2023 - 11th Edition, Bucharest, Romania Ed. Hariton-Nicolae Costin, Ratko Magjarević, Gladiola Gabriela Petroiu, Springer, 2023. 385–392.

BibTeX: Download