Chen Wenjing, Kang Tongzhou, Heyat Md Belal Bin, Fatima Jamal E, Xu Yuanning, Lai Dakun
West China Hospital, Sichuan University, Chengdu, China.
Biomedical Imaging and Electrophysiology Laboratory, School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China.
Front Neurol. 2025 Mar 26;16:1455613. doi: 10.3389/fneur.2025.1455613. eCollection 2025.
This study aims to develop an unsupervised automated method for detecting high-frequency oscillations (HFOs) in intracranial electroencephalogram (iEEG) signals, addressing the limitations of manual detection processes.
The proposed method utilizes an unsupervised convolutional variational autoencoder (CVAE) model in conjunction with the short-term energy method (STE) to analyze two-dimensional time-frequency representations of iEEG signals. Candidate HFOs are identified using STE and transformed into time-frequency maps using the continuous wavelet transform (CWT). The CVAE model is trained for dimensionality reduction and feature reconstruction, followed by clustering of the reconstructed maps using the K-means algorithm for automated HFOs detection.
Evaluation of the proposed unsupervised method on clinical iEEG data demonstrates its superior performance compared to traditional supervised models. The automated approach achieves an accuracy of 93.02%, sensitivity of 94.48%, and specificity of 92.06%, highlighting its efficacy in detecting HFOs with high accuracy.
The unsupervised automated method developed in this study offers a reliable and efficient solution for detecting HFOs in iEEG signals, overcoming the limitations of manual detection processes of traditional supervised models. By providing clinicians with a clinically useful diagnostic tool, this approach holds promise for enhancing surgical resection planning in epilepsy patients and improving patient outcomes.
本研究旨在开发一种无监督自动方法，用于检测颅内脑电图（iEEG）信号中的高频振荡（HFOs），以解决手动检测过程的局限性。
所提出的方法利用无监督卷积变分自编码器（CVAE）模型结合短期能量法（STE）来分析iEEG信号的二维时频表示。使用STE识别候选HFOs，并使用连续小波变换（CWT）将其转换为时频图。对CVAE模型进行训练以进行降维和特征重建，然后使用K均值算法对重建图进行聚类，以自动检测HFOs。
在临床iEEG数据上对所提出的无监督方法进行评估，结果表明其性能优于传统的监督模型。该自动方法的准确率达到93.02%，灵敏度为94.48%，特异性为92.06%，突出了其在高精度检测HFOs方面的有效性。
本研究中开发的无监督自动方法为检测iEEG信号中的HFOs提供了一种可靠且高效的解决方案，克服了传统监督模型手动检测过程的局限性。通过为临床医生提供一种临床有用的诊断工具，这种方法有望加强癫痫患者的手术切除规划并改善患者预后。
