Li Yabing, Dong Xinglong, Song Kun, Bai Xiangyun, Li Hongye, Karray Fakhreddine
School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an, Shaanxi, China.
Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi'an, Shaanxi, China.
Front Neurosci. 2023 Sep 8;17:1224784. doi: 10.3389/fnins.2023.1224784. eCollection 2023.
K-complex detection plays a significant role in the field of sleep research. However, manual annotation for electroencephalography (EEG) recordings by visual inspection from experts is time-consuming and subjective. Therefore, there is a necessity to implement automatic detection methods based on classical machine learning algorithms. However, due to the complexity of EEG signal, current feature extraction methods always produce low relevance to k-complex detection, which leads to a great performance loss for the detection. Hence, finding compact yet effective integrated feature vectors becomes a crucially core task in k-complex detection.
In this paper, we first extract multi-domain features based on time, spectral analysis, and chaotic theory. Those features are extracted from a 0.5-s EEG segment, which is obtained using the sliding window technique. As a result, a vector containing twenty-two features is obtained to represent each segment. Next, we explore several feature selection methods and compare their performance in detecting k-complex. Based on the analysis of the selected features, we identify compact features which are fewer than twenty-two features and deemed as relevant and proceeded to the next step. Additionally, three classical classifiers are employed to evaluate the performance of the feature selection models.
The results demonstrate that combining different features significantly improved the k-complex detection performance. The best performance is achieved by applying the feature selection method, which results in an accuracy of 93.03%7.34, sensitivity of 93.81%5.62%, and specificity 94.135.81, respectively, using a smaller number of the combined feature sets.
The proposed method in this study can serve as an efficient tool for the automatic detection of k-complex, which is useful for neurologists or doctors in the diagnosis of sleep research.
K 复合波检测在睡眠研究领域发挥着重要作用。然而，由专家通过目视检查对脑电图（EEG）记录进行手动标注既耗时又主观。因此，有必要基于经典机器学习算法实现自动检测方法。然而，由于 EEG 信号的复杂性，当前的特征提取方法与 K 复合波检测的相关性总是较低，这导致检测性能大幅下降。因此，找到紧凑而有效的综合特征向量成为 K 复合波检测中至关重要的核心任务。
在本文中，我们首先基于时间、频谱分析和混沌理论提取多域特征。这些特征是从使用滑动窗口技术获得的 0.5 秒 EEG 片段中提取的。结果，获得了一个包含 22 个特征的向量来表示每个片段。接下来，我们探索了几种特征选择方法，并比较了它们在检测 K 复合波方面的性能。基于对所选特征的分析，我们确定了少于 22 个且被认为相关的紧凑特征，并进入下一步。此外，使用三个经典分类器来评估特征选择模型的性能。
结果表明，组合不同特征显著提高了 K 复合波检测性能。通过应用特征选择方法实现了最佳性能，使用较少数量的组合特征集时，准确率分别为 93.03%±7.34、灵敏度为 93.81%±5.62%、特异性为 94.13±5.81%。
本研究中提出的方法可作为 K 复合波自动检测的有效工具，对神经科医生或医生在睡眠研究诊断中很有用。
