Cargando…
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...
Autores principales: | , , , , , |
---|---|
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 |
_version_ | 1783713631059312640 |
---|---|
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. |
format | Online Article Text |
id | pubmed-8232415 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT tawhidmdnurulahad aspectrogramimagebasedintelligenttechniqueforautomaticdetectionofautismspectrumdisorderfromeeg AT siulysiuly aspectrogramimagebasedintelligenttechniqueforautomaticdetectionofautismspectrumdisorderfromeeg AT wanghua aspectrogramimagebasedintelligenttechniqueforautomaticdetectionofautismspectrumdisorderfromeeg AT whittakerfrank aspectrogramimagebasedintelligenttechniqueforautomaticdetectionofautismspectrumdisorderfromeeg AT wangkate aspectrogramimagebasedintelligenttechniqueforautomaticdetectionofautismspectrumdisorderfromeeg AT zhangyanchun aspectrogramimagebasedintelligenttechniqueforautomaticdetectionofautismspectrumdisorderfromeeg AT tawhidmdnurulahad spectrogramimagebasedintelligenttechniqueforautomaticdetectionofautismspectrumdisorderfromeeg AT siulysiuly spectrogramimagebasedintelligenttechniqueforautomaticdetectionofautismspectrumdisorderfromeeg AT wanghua spectrogramimagebasedintelligenttechniqueforautomaticdetectionofautismspectrumdisorderfromeeg AT whittakerfrank spectrogramimagebasedintelligenttechniqueforautomaticdetectionofautismspectrumdisorderfromeeg AT wangkate spectrogramimagebasedintelligenttechniqueforautomaticdetectionofautismspectrumdisorderfromeeg AT zhangyanchun spectrogramimagebasedintelligenttechniqueforautomaticdetectionofautismspectrumdisorderfromeeg |