Topological Feature Search Method for Multichannel EEG: Application in ADHD classification

• URL: http://arxiv.org/abs/2404.06676v2
• Date: Tue, 05 Nov 2024 03:37:22 GMT
• Title: Topological Feature Search Method for Multichannel EEG: Application in ADHD classification
• Authors: Tianming Cai, Guoying Zhao, Junbin Zang, Chen Zong, Zhidong Zhang, Chenyang Xue,
• Abstract summary: Topological Data Analysis offers a novel perspective for ADHD classification. This paper presents an enhanced TDA approach applicable to multi-channel EEG in ADHD. Results demonstrate that the accuracy, sensitivity, and specificity reach 78.27%, 80.62%, and 75.63%, respectively.
• Score: 13.381770446807016
• License:
• Abstract: In recent years, the preliminary diagnosis of ADHD using EEG has attracted the attention from researchers. EEG, known for its expediency and efficiency, plays a pivotal role in the diagnosis and treatment of ADHD. However, the non-stationarity of EEG signals and inter-subject variability pose challenges to the diagnostic and classification processes. Topological Data Analysis offers a novel perspective for ADHD classification, diverging from traditional time-frequency domain features. However, conventional TDA models are restricted to single-channel time series and are susceptible to noise, leading to the loss of topological features in persistence diagrams.This paper presents an enhanced TDA approach applicable to multi-channel EEG in ADHD. Initially, optimal input parameters for multi-channel EEG are determined. Subsequently, each channel's EEG undergoes phase space reconstruction (PSR) followed by the utilization of k-Power Distance to Measure for approximating ideal point clouds. Then, multi-dimensional time series are re-embedded, and TDA is applied to obtain topological feature information. Gaussian function-based Multivariate Kernel Density Estimation is employed in the merger persistence diagram to filter out desired topological feature mappings. Finally, the persistence image method is employed to extract topological features, and the influence of various weighting functions on the results is discussed.The effectiveness of our method is evaluated using the IEEE ADHD dataset. Results demonstrate that the accuracy, sensitivity, and specificity reach 78.27%, 80.62%, and 75.63%, respectively. Compared to traditional TDA methods, our method was effectively improved and outperforms typical nonlinear descriptors. These findings indicate that our method exhibits higher precision and robustness.

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