Diagnosis of autism spectrum disorder from EEG using a time–frequency spectrogram image‐based approach

Autism is a type of neurodevelopment disorder in which individuals often have dif fi culties 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 ef fi cient 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, fi ltering 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 signi fi cant features are selected using principal component analysis, and feed to support vector machine classi fi er for classi fi cation. 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 health-care professionals in autism diagnosis.