Automatic detection of epileptic seizure based on one dimensional cascaded convolutional autoencoder with adaptive window-thresholding.

Sunday Timothy Aboyeji, Xin Wang, Yan Chen, Ijaz Ahmad, Lin Li, Zhenzhen Liu, Chen Yao, Guoru Zhao, Yu Zhang, Guanglin Li, Shixiong Chen
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Abstract

Objective. Identifying the seizure occurrence period (SOP) in extended EEG recordings is crucial for neurologists to diagnose seizures effectively. However, many existing computer-aided diagnosis systems for epileptic seizure detection (ESD) primarily focus on distinguishing between ictal and interictal states in EEG recordings. This focus has limited their application in clinical settings, as these systems typically rely on supervised learning approaches that require labeled data.Approach. To address this, our study introduces an unsupervised learning framework for ESD using a 1D- cascaded convolutional autoencoder (1D-CasCAE). In this approach, EEG recordings from selected patients in the CHB-MIT datasets are first segmented into 5 s epochs. Eight informative channels are chosen based on the correlation coefficient and Shannon entropy. The 1D-CasCAE is designed to autonomously learn the characteristic patterns of interictal (non-seizure) segments through downsampling and upsampling processes. The integration of adaptive thresholding and a moving window significantly enhances the model's robustness, enabling it to accurately identify ictal segments in long EEG recordings.Main results. Experimental results demonstrate that the proposed 1D-CasCAE effectively learns normal EEG signal patterns and efficiently detects anomalies (ictal segments) using reconstruction errors. When compared with other leading methods in anomaly detection, our model exhibits superior performance, as evidenced by its average Gmean, sensitivity, specificity, precision, and false positive rate scores of 98.00% ± 3.51%, 94.94% ± 6.92%, 99.60% ± 0.30%, 79.92% ± 13.56% and 0.0044 ± 0.0030 h-1respectively for a typical patient in CHB-MIT datasets.Significance. The developed model framework can be employed in clinical settings, replacing the manual inspection process of EEG signals by neurologists. Furthermore, the proposed automated system can adapt to each patient's SOP through the use of variable time windows for seizure detection.

基于一维级联卷积自动编码器和自适应窗口阈值的癫痫发作自动检测。
目的:在扩展脑电图记录中识别癫痫发作期(SOP)对于神经科医生有效诊断癫痫发作至关重要。然而,现有的许多用于癫痫发作检测(ESD)的计算机辅助诊断系统(CAD)主要侧重于区分脑电图记录中的发作期和发作间期状态。这一重点限制了它们在临床环境中的应用,因为这些系统通常依赖于需要标记数据的超级可视化学习方法:为解决这一问题,我们的研究采用一维级联卷积自动编码器(1D-CasCAE)为 ESD 引入了一种无监督学习框架。在这种方法中,首先将 CHB-MIT 数据集中选定患者的脑电图记录分割成 5 秒的历时。根据相关系数和香农熵选择八个信息通道。1D-CasCAE 的设计目的是通过下采样和上采样过程自主学习发作间期(非发作)片段的特征模式。自适应阈值和移动窗口的整合大大增强了模型的鲁棒性,使其能够在长时间的脑电图记录中准确识别发作节段:实验结果表明,所提出的 1D-CasCAE 能有效学习正常的脑电信号模式,并利用重建误差高效检测异常(发作节段)。与异常检测领域的其他主要方法相比,我们的模型表现出更优越的性能,其在 CHB-MIT 数据集中典型患者的平均平均值、灵敏度、特异性、预判和假阳性率分别为 98.00±3.51%、94.94±6.92%、99.60±0.30%、79.92±13.56% 和 0.0044±0.0030/h:最后,所开发的模型框架可用于临床环境,取代神经科医生对脑电图信号的人工检查过程。通过使用可变时间窗检测癫痫发作,该自动化系统可适应每位患者的 SOP。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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