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:	Md Nurul Ahad Tawhid, Siuly Siuly, Hua Wang, Frank Whittaker, Kate Wang, Yanchun Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/91a6faa56c3240f9a3a067ee9e622690
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id	oai:doaj.org-article:91a6faa56c3240f9a3a067ee9e622690
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spelling	oai:doaj.org-article:91a6faa56c3240f9a3a067ee9e6226902021-12-02T20:15:46ZA spectrogram image based intelligent technique for automatic detection of autism spectrum disorder from EEG.1932-620310.1371/journal.pone.0253094https://doaj.org/article/91a6faa56c3240f9a3a067ee9e6226902021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0253094https://doaj.org/toc/1932-6203Autism 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.Md Nurul Ahad TawhidSiuly SiulyHua WangFrank WhittakerKate WangYanchun ZhangPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 6, p e0253094 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Md Nurul Ahad Tawhid Siuly Siuly Hua Wang Frank Whittaker Kate Wang Yanchun Zhang A spectrogram image based intelligent technique for automatic detection of autism spectrum disorder from EEG.
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.
format	article
author	Md Nurul Ahad Tawhid Siuly Siuly Hua Wang Frank Whittaker Kate Wang Yanchun Zhang
author_facet	Md Nurul Ahad Tawhid Siuly Siuly Hua Wang Frank Whittaker Kate Wang Yanchun Zhang
author_sort	Md Nurul Ahad Tawhid
title	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_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_sort	spectrogram image based intelligent technique for automatic detection of autism spectrum disorder from eeg.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/91a6faa56c3240f9a3a067ee9e622690
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A spectrogram image based intelligent technique for automatic detection of autism spectrum disorder from EEG.

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