Automated autism spectrum disorder detection using EEG signals and time-frequency visibility graphs

Early and objective screening of Autism Spectrum Disorder (ASD) remains challenging because conventional diagnosis primarily relies on behavioural assessment and clinical observation. To address this limitation, this study proposes a dual-domain computational framework for automated EEG-based ASD classification by integrating complementary time-frequency analysis with Horizontal Visibility Graph (HVG)-based network modelling. Four time-frequency decomposition techniques, namely the Short-Time Fourier Transform (STFT), Discrete Wavelet Transform (DWT), Wigner-Ville Distribution (WVD), and Superlet Transform (SLT), were employed to characterise the non-stationary dynamics of resting-state EEG signals. The resulting time-frequency representations were transformed into HVG networks, from which 17 graph-theoretic descriptors were extracted and evaluated using conventional machine learning classifiers, including a Soft Voting Ensemble. Among the investigated methods, the DWT-HVG framework combined with the Soft Voting Ensemble achieved the best performance, yielding an accuracy of 93.54%, sensitivity of 94.32%, specificity of 92.76%, F1-score of 93.62%, balanced accuracy of 93.54%, and an Area Under the Curve (AUC) of 98.17% using stratified 10-fold cross-validation. Statistical analysis using the Wilcoxon signed-rank test further confirmed the superiority of the DWT-based representation over the STFT, WVD, and SLT-based approaches. These findings demonstrate that integrating multiresolution time-frequency analysis with HVG-based graph-theoretic feature extraction provides an accurate, interpretable, and computationally efficient framework for objective EEG-based ASD screening.