Automated ASD detection using hybrid deep lightweight features extracted from EEG signals
Article
Baygin, Mehmet, Dogan, Sengul, Tuncer, Turker, Barua, Prabal Datta, Faust, Oliver, Arunkumar, N., Abdulhay, Enas W., Palmer, Elizabeth Emma and Acharya, U. Rajendra. 2021. "Automated ASD detection using hybrid deep lightweight features extracted from EEG signals." Computers in Biology and Medicine. 134. https://doi.org/10.1016/j.compbiomed.2021.104548
| Article Title | Automated ASD detection using hybrid deep lightweight features extracted from EEG signals |
|---|---|
| ERA Journal ID | 5040 |
| Article Category | Article |
| Authors | Baygin, Mehmet, Dogan, Sengul, Tuncer, Turker, Barua, Prabal Datta, Faust, Oliver, Arunkumar, N., Abdulhay, Enas W., Palmer, Elizabeth Emma and Acharya, U. Rajendra |
| Journal Title | Computers in Biology and Medicine |
| Journal Citation | 134 |
| Article Number | 104548 |
| Number of Pages | 12 |
| Year | 2021 |
| Publisher | Elsevier |
| Place of Publication | United Kingdom |
| ISSN | 0010-4825 |
| 1879-0534 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1016/j.compbiomed.2021.104548 |
| Web Address (URL) | https://www.sciencedirect.com/science/article/abs/pii/S0010482521003425 |
| Abstract | Background Autism spectrum disorder is a common group of conditions affecting about one in 54 children. Electroencephalogram (EEG) signals from children with autism have a common morphological pattern which makes them distinguishable from normal EEG. We have used this type of signal to design and implement an automated autism detection model. Materials and method We propose a hybrid lightweight deep feature extractor to obtain high classification performance. The system was designed and tested with a big EEG dataset that contained signals from autism patients and normal controls. (i) A new signal to image conversion model is presented in this paper. In this work, features are extracted from EEG signal using one-dimensional local binary pattern (1D_LBP) and the generated features are utilized as input of the short time Fourier transform (STFT) to generate spectrogram images. (ii) The deep features of the generated spectrogram images are extracted using a combination of pre-trained MobileNetV2, ShuffleNet, and SqueezeNet models. This method is named hybrid deep lightweight feature generator. (iii) A two-layered ReliefF algorithm is used for feature ranking and feature selection. (iv) The most discriminative features are fed to various shallow classifiers, developed using a 10-fold cross-validation strategy for automated autism detection. Results A support vector machine (SVM) classifier reached 96.44% accuracy based on features from the proposed model. Conclusions The results strongly indicate that the proposed hybrid deep lightweight feature extractor is suitable for autism detection using EEG signals. The model is ready to serve as part of an adjunct tool that aids neurologists during autism diagnosis in medical centers. |
| Keywords | 1D_LBP-STFT; Hybrid lightweight deep feature generator ; ReliefF2; Autism classification ; Transfer learning |
| ANZSRC Field of Research 2020 | 400306. Computational physiology |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Byline Affiliations | Ardahan University, Turkiye |
| Firat University, Turkey | |
| School of Management and Enterprise | |
| Sheffield Hallam University, United Kingdom | |
| SASTRA University, India | |
| Jordan University of Science and Technology, Jordan | |
| Sydney Children's Hospital, Australia | |
| Ngee Ann Polytechnic, Singapore | |
| Singapore University of Social Sciences (SUSS), Singapore | |
| Asia University, Taiwan |
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