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Non-stationary neural signal to image conversion framework for image-based deep learning algorithms
This paper presents a time-efficient preprocessing framework that converts any given 1D physiological signal recordings into a 2D image representation for training image-based deep learning models. The non-stationary signal is rasterized into the 2D image using Bresenham’s line algorithm with time c...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10079945/ https://www.ncbi.nlm.nih.gov/pubmed/37035716 http://dx.doi.org/10.3389/fninf.2023.1081160 |
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author | Patel, Sahaj Anilbhai Yildirim, Abidin |
author_facet | Patel, Sahaj Anilbhai Yildirim, Abidin |
author_sort | Patel, Sahaj Anilbhai |
collection | PubMed |
description | This paper presents a time-efficient preprocessing framework that converts any given 1D physiological signal recordings into a 2D image representation for training image-based deep learning models. The non-stationary signal is rasterized into the 2D image using Bresenham’s line algorithm with time complexity O(n). The robustness of the proposed approach is evaluated based on two publicly available datasets. This study classified three different neural spikes (multi-class) and EEG epileptic seizure and non-seizure (binary class) based on shapes using a modified 2D Convolution Neural Network (2D CNN). The multi-class dataset consists of artificially simulated neural recordings with different Signal-to-Noise Ratios (SNR). The 2D CNN architecture showed significant performance for all individual SNRs scores with (SNR/ACC): 0.5/99.69, 0.75/99.69, 1.0/99.49, 1.25/98.85, 1.5/97.43, 1.75/95.20 and 2.0/91.98. Additionally, the binary class dataset also achieved 97.52% accuracy by outperforming several others proposed algorithms. Likewise, this approach could be employed on other biomedical signals such as Electrocardiograph (EKG) and Electromyography (EMG). |
format | Online Article Text |
id | pubmed-10079945 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-100799452023-04-08 Non-stationary neural signal to image conversion framework for image-based deep learning algorithms Patel, Sahaj Anilbhai Yildirim, Abidin Front Neuroinform Neuroscience This paper presents a time-efficient preprocessing framework that converts any given 1D physiological signal recordings into a 2D image representation for training image-based deep learning models. The non-stationary signal is rasterized into the 2D image using Bresenham’s line algorithm with time complexity O(n). The robustness of the proposed approach is evaluated based on two publicly available datasets. This study classified three different neural spikes (multi-class) and EEG epileptic seizure and non-seizure (binary class) based on shapes using a modified 2D Convolution Neural Network (2D CNN). The multi-class dataset consists of artificially simulated neural recordings with different Signal-to-Noise Ratios (SNR). The 2D CNN architecture showed significant performance for all individual SNRs scores with (SNR/ACC): 0.5/99.69, 0.75/99.69, 1.0/99.49, 1.25/98.85, 1.5/97.43, 1.75/95.20 and 2.0/91.98. Additionally, the binary class dataset also achieved 97.52% accuracy by outperforming several others proposed algorithms. Likewise, this approach could be employed on other biomedical signals such as Electrocardiograph (EKG) and Electromyography (EMG). Frontiers Media S.A. 2023-03-24 /pmc/articles/PMC10079945/ /pubmed/37035716 http://dx.doi.org/10.3389/fninf.2023.1081160 Text en Copyright © 2023 Patel and Yildirim. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Patel, Sahaj Anilbhai Yildirim, Abidin Non-stationary neural signal to image conversion framework for image-based deep learning algorithms |
title | Non-stationary neural signal to image conversion framework for image-based deep learning algorithms |
title_full | Non-stationary neural signal to image conversion framework for image-based deep learning algorithms |
title_fullStr | Non-stationary neural signal to image conversion framework for image-based deep learning algorithms |
title_full_unstemmed | Non-stationary neural signal to image conversion framework for image-based deep learning algorithms |
title_short | Non-stationary neural signal to image conversion framework for image-based deep learning algorithms |
title_sort | non-stationary neural signal to image conversion framework for image-based deep learning algorithms |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10079945/ https://www.ncbi.nlm.nih.gov/pubmed/37035716 http://dx.doi.org/10.3389/fninf.2023.1081160 |
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