ADHD classification using spherical phase space partitioning and symbolic time series analysis of multi-channel EEG signals

Abstract

Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common neurodevelopmental disorders in children, making early detection and intervention critically important. In this study, we propose a novel EEG based classification framework for ADHD that integrates advanced nonlinear signal analysis with deep learning methodologies. EEG recordings were collected from 61 children diagnosed with ADHD and 60 age-matched healthy controls during a visual attention task, utilizing 19 electrodes placed according to the international 10–20 system. The preprocessing pipeline involved artifact rejection, independent component analysis (ICA), and band-pass filtering. A key innovation of our method is the integration of spherical phase space partitioning with entropy-optimized symbolic time series analysis (SPSP-STSA), allowing for reliable and noise-resilient feature extraction across multiple EEG channels. The extracted symbolic sequences were then used for classification via cosine similarity and a bidirectional Long Short-Term Memory (LSTM) network, which effectively models temporal patterns to improve diagnostic accuracy. The proposed method achieved a classification accuracy of up to 98% using window-based analysis of 20-second EEG segments. Our findings highlight the potential of SPSP-STSA and deep learning for advancing EEG-based ADHD diagnosis, offering improved robustness and interpretability compared to conventional approaches.