Open AccessDiagnosis of autism spectrum disorder from EEG using a time–frequency spectrogram image‐based approach
Author(s) -
Tawhid M.N.A.,
Siuly S.,
Wang H.
Publication year - 2020
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2020.2646
Subject(s) - spectrogram , electroencephalography , autism , computer science , autism spectrum disorder , artificial intelligence , classifier (uml) , pattern recognition (psychology) , reliability (semiconductor) , speech recognition , psychology , developmental psychology , psychiatry , power (physics) , physics , quantum mechanics
Autism is a type of neurodevelopment disorder in which individuals often have difficulties in social relationship, communication, expressing and controlling emotions and poor eye contact, among other symptoms. Currently, electroencephalography (EEG) is the most popular tool to investigate the presence of autism biomarkers. Generally, EEG recordings generate huge volume data with dynamic behaviour. In current practice, the massive EEG data are visually analysed by specialist clinicians to identify autism, which is time consuming, costly, subject to human error, and reduces decision‐making reliability. Hence this Letter aims to develop an efficient autism diagnostic system that can automatically identify autism based on time–frequency spectrogram images from EEG signals. Firstly, the raw EEG data is pre‐processed using several techniques, such as re‐referencing, filtering and normalisation. After that, the pre‐processed EEG signals are converted to two‐dimensional images using a short‐time Fourier transform. Then, textural features are extracted, and significant features are selected using principal component analysis, and feed to support vector machine classifier for classification. The proposed system achieved an average of 95.25% accuracy in ten‐fold cross‐validation evaluation. The developed system's simplicity and performance indicates usefulness as a decision support tool for healthcare professionals in autism diagnosis.