Lucentia Research Group - Department of Software and Computing Systems, University of Alicante, Rd. San Vicente s/n, San Vicente del Raspeig, 03690, Spain; ValgrAI - Valencian Graduate School and Research Network of Artificial Intelligence, Camí de Vera s/n, Valencia, 46022, Spain.
Lucentia Research Group - Department of Software and Computing Systems, University of Alicante, Rd. San Vicente s/n, San Vicente del Raspeig, 03690, Spain.
Comput Biol Med. 2024 Dec;183:109305. doi: 10.1016/j.compbiomed.2024.109305. Epub 2024 Oct 31.
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.
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.
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.
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.
脑电图信号常用于 ADHD 的诊断，但它们经常受到噪声和伪影的影响。有效的预处理和分割方法可以显著提高 ADHD 分类的准确性和可靠性。
我们将滤波、ASR 和 ICA 预处理技术应用于 ADHD 儿童和神经典型对照组的 EEG 数据。对 EEG 记录进行分割，并根据统计显著性提取和选择特征。使用机器学习模型（SVM、KNN 和 XGBoost）对各种 EEG 片段和通道进行分类，以确定最有效的组合，从而实现准确的 ADHD 诊断。
我们的研究结果表明，在较晚的 EEG 片段上训练的模型实现了显著更高的准确性，这表明认知疲劳在区分 ADHD 方面可能发挥作用。使用 P3、P4 和 C3 通道的数据实现了最高的分类准确性（86.1%），Delta、Theta 和 Alpha 波段的峰度、Katz 分形维数和功率谱等关键特征对结果有贡献。
本研究强调了预处理和分割在通过 EEG 提高 ADHD 诊断可靠性方面的重要性。研究结果表明，进一步研究认知疲劳和分割可以提高 ADHD 患者的诊断准确性。
