Automated ASD detection using hybrid deep lightweight features extracted from EEG signals. (July 2021)

• Record Type: Journal Article
• Authors: Baygin, Mehmet; Dogan, Sengul; Tuncer, Turker; Datta Barua, Prabal; Faust, Oliver; Arunkumar, N.; Abdulhay, Enas W.; Emma Palmer, Elizabeth; Rajendra Acharya, U.
• Is Part Of: Computers in biology and medicine. Volume 134(2021)
• Publication Date: 2021-07
• Subjects: Hybrid lightweight deep feature generator -- 1D_LBP-STFT -- ReliefF2 -- Autism classification -- Transfer learning; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2021.104548
• Languages: English
• ISSNs: 0010-4825

Abstract

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. Graphical abstract: Image 1 Highlights: Automated classification of normal and ASD using EEG spectrograms. Hybrid lightweight deep feature generator is presented. Big dataset is used for ASD detection. Accuracy of 96.44% is achieved with SVM classifier.