Centre for Cyber Physical Systems, School of Electronics Engineering, Vellore Institute of Technology, Chennai, India.
School of Electronics Engineering, Vellore Institute of Technology, Chennai, India.
Technol Health Care. 2024;32(6):4485-4503. doi: 10.3233/THC-240644.
Autism Spectrum Disorder (ASD) is a condition with social interaction, communication, and behavioral difficulties. Diagnostic methods mostly rely on subjective evaluations and can lack objectivity. In this research Machine learning (ML) and deep learning (DL) techniques are used to enhance ASD classification.
This study focuses on improving ASD and TD classification accuracy with a minimal number of EEG channels. ML and DL models are used with EEG data, including Mu Rhythm from the Sensory Motor Cortex (SMC) for classification.
Non-linear features in time and frequency domains are extracted and ML models are applied for classification. The EEG 1D data is transformed into images using Independent Component Analysis-Second Order Blind Identification (ICA-SOBI), Spectrogram, and Continuous Wavelet Transform (CWT).
Stacking Classifier employed with non-linear features yields precision, recall, F1-score, and accuracy rates of 78%, 79%, 78%, and 78% respectively. Including entropy and fuzzy entropy features further improves accuracy to 81.4%. In addition, DL models, employing SOBI, CWT, and spectrogram plots, achieve precision, recall, F1-score, and accuracy of 75%, 75%, 74%, and 75% respectively. The hybrid model, which combined deep learning features from spectrogram and CWT with machine learning, exhibits prominent improvement, attained precision, recall, F1-score, and accuracy of 94%, 94%, 94%, and 94% respectively. Incorporating entropy and fuzzy entropy features further improved the accuracy to 96.9%.
This study underscores the potential of ML and DL techniques in improving the classification of ASD and TD individuals, particularly when utilizing a minimal set of EEG channels.
自闭症谱系障碍（ASD）是一种存在社交互动、沟通和行为困难的病症。诊断方法主要依赖于主观评估，可能缺乏客观性。在这项研究中，机器学习（ML）和深度学习（DL）技术被用于增强 ASD 的分类。
本研究旨在通过使用尽可能少的 EEG 通道来提高 ASD 和 TD 的分类准确性。使用 ML 和 DL 模型以及 EEG 数据，包括来自感觉运动皮层（SMC）的 Mu 节律进行分类。
提取时频域的非线性特征，并应用 ML 模型进行分类。使用独立成分分析二阶盲辨识（ICA-SOBI）、频谱图和连续小波变换（CWT）将 EEG 1D 数据转换为图像。
堆叠分类器结合非线性特征，得到的精度、召回率、F1 得分和准确率分别为 78%、79%、78%和 78%。进一步纳入熵和模糊熵特征可将准确性提高至 81.4%。此外，采用 SOBI、CWT 和频谱图的 DL 模型，其精度、召回率、F1 得分和准确率分别为 75%、75%、74%和 75%。将深度学习来自频谱图和 CWT 的特征与机器学习相结合的混合模型，在精度、召回率、F1 得分和准确率方面均有显著提高，分别为 94%、94%、94%和 94%。进一步纳入熵和模糊熵特征，可将准确性提高至 96.9%。
本研究强调了 ML 和 DL 技术在改善 ASD 和 TD 个体分类方面的潜力，特别是在使用最小数量的 EEG 通道时。
