Electroretinogram Analysis Using a Short-Time Fourier Transform and Machine Learning Techniques

Albasu FB, Kulyabin M, Zhdanov A, Dolganov A, Ronkin M, Borisov V, Dorosinsky L, Constable P, Al-masni M, Maier A (2024)

Publication Language: English

Publication Type: Journal article, Original article

Publication year: 2024

Journal

Bioengineering MDPI

Book Volume: 11

Article Number: 866

Journal Issue: 9

DOI: 10.3390/bioengineering11090866

Open Access Link: https://www.mdpi.com/2306-5354/11/9/866

Abstract

Electroretinography (ERG) is a non-invasive method of assessing retinal function by recording the retina’s response to a brief flash of light. This study focused on optimizing the ERG waveform signal classification by utilizing Short-Time Fourier Transform (STFT) spectrogram preprocessing with a machine learning (ML) decision system. Several window functions of different sizes and window overlaps were compared to enhance feature extraction concerning specific ML algorithms. The obtained spectrograms were employed to train deep learning models alongside manual feature extraction for more classical ML models. Our findings demonstrated the superiority of utilizing the Visual Transformer architecture with a Hamming window function, showcasing its advantage in ERG signal classification. Also, as a result, we recommend the RF algorithm for scenarios necessitating manual feature extraction, particularly with the Boxcar (rectangular) or Bartlett window functions. By elucidating the optimal methodologies for feature extraction and classification, this study contributes to advancing the diagnostic capabilities of ERG analysis in clinical settings.

Authors with CRIS profile

Mikhail Kulyabin Lehrstuhl für Informatik 5 (Mustererkennung) Andreas Maier Lehrstuhl für Informatik 5 (Mustererkennung)

Involved external institutions

Flinders University AU Australia (AU) Ural Federal University B.N. Yeltsin RU Russian Federation (RU) Sejong University (SJU) / 세종대학교 KR Korea, Republic of (KR)

How to cite

APA:

Albasu, F.B., Kulyabin, M., Zhdanov, A., Dolganov, A., Ronkin, M., Borisov, V.,... Maier, A. (2024). Electroretinogram Analysis Using a Short-Time Fourier Transform and Machine Learning Techniques. Bioengineering, 11(9). https://doi.org/10.3390/bioengineering11090866

MLA:

Albasu, Faisal B., et al. "Electroretinogram Analysis Using a Short-Time Fourier Transform and Machine Learning Techniques." Bioengineering 11.9 (2024).

BibTeX: Download