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A spectrogram image based intelligent technique for automatic detection of autism spectrum disorder from EEG

Autism spectrum disorder (ASD) is a developmental disability characterized by persistent impairments in social interaction, speech and nonverbal communication, and restricted or repetitive behaviors. Currently Electroencephalography (EEG) is the most popular tool to inspect the existence of neurolog...

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Autores principales: Tawhid, Md. Nurul Ahad, Siuly, Siuly, Wang, Hua, Whittaker, Frank, Wang, Kate, Zhang, Yanchun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8232415/
https://www.ncbi.nlm.nih.gov/pubmed/34170979
http://dx.doi.org/10.1371/journal.pone.0253094
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author Tawhid, Md. Nurul Ahad
Siuly, Siuly
Wang, Hua
Whittaker, Frank
Wang, Kate
Zhang, Yanchun
author_facet Tawhid, Md. Nurul Ahad
Siuly, Siuly
Wang, Hua
Whittaker, Frank
Wang, Kate
Zhang, Yanchun
author_sort Tawhid, Md. Nurul Ahad
collection PubMed
description Autism spectrum disorder (ASD) is a developmental disability characterized by persistent impairments in social interaction, speech and nonverbal communication, and restricted or repetitive behaviors. Currently Electroencephalography (EEG) is the most popular tool to inspect the existence of neurological disorders like autism biomarkers due to its low setup cost, high temporal resolution and wide availability. Generally, EEG recordings produce vast amount of data with dynamic behavior, which are visually analyzed by professional clinician to detect autism. It is laborious, expensive, subjective, error prone and has reliability issue. Therefor this study intends to develop an efficient diagnostic framework based on time-frequency spectrogram images of EEG signals to automatically identify ASD. In the proposed system, primarily, the raw EEG signals are pre-processed using re-referencing, filtering and normalization. Then, Short-Time Fourier Transform is used to transform the pre-processed signals into two-dimensional spectrogram images. Afterward those images are evaluated by machine learning (ML) and deep learning (DL) models, separately. In the ML process, textural features are extracted, and significant features are selected using principal component analysis, and feed them to six different ML classifiers for classification. In the DL process, three different convolutional neural network models are tested. The proposed DL based model achieves higher accuracy (99.15%) compared to the ML based model (95.25%) on an ASD EEG dataset and also outperforms existing methods. The findings of this study suggest that the DL based structure could discover important biomarkers for efficient and automatic diagnosis of ASD from EEG and may assist to develop computer-aided diagnosis system.
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spelling pubmed-82324152021-07-07 A spectrogram image based intelligent technique for automatic detection of autism spectrum disorder from EEG Tawhid, Md. Nurul Ahad Siuly, Siuly Wang, Hua Whittaker, Frank Wang, Kate Zhang, Yanchun PLoS One Research Article Autism spectrum disorder (ASD) is a developmental disability characterized by persistent impairments in social interaction, speech and nonverbal communication, and restricted or repetitive behaviors. Currently Electroencephalography (EEG) is the most popular tool to inspect the existence of neurological disorders like autism biomarkers due to its low setup cost, high temporal resolution and wide availability. Generally, EEG recordings produce vast amount of data with dynamic behavior, which are visually analyzed by professional clinician to detect autism. It is laborious, expensive, subjective, error prone and has reliability issue. Therefor this study intends to develop an efficient diagnostic framework based on time-frequency spectrogram images of EEG signals to automatically identify ASD. In the proposed system, primarily, the raw EEG signals are pre-processed using re-referencing, filtering and normalization. Then, Short-Time Fourier Transform is used to transform the pre-processed signals into two-dimensional spectrogram images. Afterward those images are evaluated by machine learning (ML) and deep learning (DL) models, separately. In the ML process, textural features are extracted, and significant features are selected using principal component analysis, and feed them to six different ML classifiers for classification. In the DL process, three different convolutional neural network models are tested. The proposed DL based model achieves higher accuracy (99.15%) compared to the ML based model (95.25%) on an ASD EEG dataset and also outperforms existing methods. The findings of this study suggest that the DL based structure could discover important biomarkers for efficient and automatic diagnosis of ASD from EEG and may assist to develop computer-aided diagnosis system. Public Library of Science 2021-06-25 /pmc/articles/PMC8232415/ /pubmed/34170979 http://dx.doi.org/10.1371/journal.pone.0253094 Text en © 2021 Tawhid et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Tawhid, Md. Nurul Ahad
Siuly, Siuly
Wang, Hua
Whittaker, Frank
Wang, Kate
Zhang, Yanchun
A spectrogram image based intelligent technique for automatic detection of autism spectrum disorder from EEG
title A spectrogram image based intelligent technique for automatic detection of autism spectrum disorder from EEG
title_full A spectrogram image based intelligent technique for automatic detection of autism spectrum disorder from EEG
title_fullStr A spectrogram image based intelligent technique for automatic detection of autism spectrum disorder from EEG
title_full_unstemmed A spectrogram image based intelligent technique for automatic detection of autism spectrum disorder from EEG
title_short A spectrogram image based intelligent technique for automatic detection of autism spectrum disorder from EEG
title_sort spectrogram image based intelligent technique for automatic detection of autism spectrum disorder from eeg
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8232415/
https://www.ncbi.nlm.nih.gov/pubmed/34170979
http://dx.doi.org/10.1371/journal.pone.0253094
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