Refining ADHD diagnosis with EEG: The impact of preprocessing and temporal segmentation on classification accuracy

TL;DR

This study demonstrates that careful preprocessing and segmentation of EEG signals significantly improve the accuracy of ADHD diagnosis, with the best results achieved using specific channels and features.

Contribution

It introduces an optimized EEG preprocessing and segmentation pipeline that enhances machine learning classification accuracy for ADHD diagnosis.

Findings

Highest accuracy of 86.1% with specific channels and features

Later EEG segments improve classification performance

Preprocessing techniques like filtering, ASR, and ICA are effective

Abstract

Background: EEG signals are commonly used in ADHD diagnosis, but they are often affected by noise and artifacts. Effective preprocessing and segmentation methods can significantly enhance the accuracy and reliability of ADHD classification. Methods: We applied filtering, ASR, and ICA preprocessing techniques to EEG data from children with ADHD and neurotypical controls. The EEG recordings were segmented, and features were extracted and selected based on statistical significance. Classification was performed using various EEG segments and channels with Machine Learning models (SVM, KNN, and XGBoost) to identify the most effective combinations for accurate ADHD diagnosis. Results: Our findings show that models trained on later EEG segments achieved significantly higher accuracy, indicating the potential role of cognitive fatigue in distinguishing ADHD. The highest classification accuracy (86.1%) was achieved using data from the P3, P4, and C3 channels, with key features such as Kurtosis, Katz fractal dimension, and power spectrums in the Delta, Theta, and Alpha bands contributing to the results. Conclusion: This study highlights the importance of preprocessing and segmentation in improving the reliability of ADHD diagnosis through EEG. The results suggest that further research on cognitive fatigue and segmentation could enhance diagnostic accuracy in ADHD patients.