Li Yabing, Dong Xinglong
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 University of Posts and Telecommunications, Xi'an, Shaanxi, China.
Front Neurosci. 2023 Mar 14;17:1108059. doi: 10.3389/fnins.2023.1108059. eCollection 2023.
K-complex detection traditionally relied on expert clinicians, which is time-consuming and onerous. Various automatic k-complex detection-based machine learning methods are presented. However, these methods always suffered from imbalanced datasets, which impede the subsequent processing steps.
In this study, an efficient method for k-complex detection using electroencephalogram (EEG)-based multi-domain features extraction and selection method coupled with a RUSBoosted tree model is presented. EEG signals are first decomposed using a tunable Q-factor wavelet transform (TQWT). Then, multi-domain features based on TQWT are pulled out from TQWT sub-bands, and a self-adaptive feature set is obtained from a feature selection based on the consistency-based filter for the detection of k-complexes. Finally, the RUSBoosted tree model is used to perform k-complex detection.
Experimental outcomes manifest the efficacy of our proposed scheme in terms of the average performance of recall measure, AUC, and F-score. The proposed method yields 92.41 ± 7.47%, 95.4 ± 4.32%, and 83.13 ± 8.59% for k-complex detection in Scenario 1 and also achieves similar results in Scenario 2.
COMPARISON TO STATE-OF-THE-ART METHODS: The RUSBoosted tree model was compared with three other machine learning classifiers [i.e., linear discriminant analysis (LDA), logistic regression, and linear support vector machine (SVM)]. The performance based on the kappa coefficient, recall measure, and F-score provided evidence that the proposed model surpassed other algorithms in the detection of the k-complexes, especially for the recall measure.
In summary, the RUSBoosted tree model presents a promising performance in dealing with highly imbalanced data. It can be an effective tool for doctors and neurologists to diagnose and treat sleep disorders.
传统上，K 复合波的检测依赖于临床专家，这既耗时又费力。目前已提出了各种基于自动 K 复合波检测的机器学习方法。然而，这些方法总是受到数据集不平衡的困扰，这阻碍了后续的处理步骤。
在本研究中，提出了一种高效的 K 复合波检测方法，该方法使用基于脑电图（EEG）的多域特征提取和选择方法，并结合 RUSBoosted 树模型。首先使用可调 Q 因子小波变换（TQWT）对 EEG 信号进行分解。然后，从 TQWT 子带中提取基于 TQWT 的多域特征，并通过基于一致性滤波器的特征选择获得用于检测 K 复合波的自适应特征集。最后，使用 RUSBoosted 树模型进行 K 复合波检测。
实验结果表明，我们提出的方案在召回率、AUC 和 F 分数的平均性能方面是有效的。在场景 1 中，所提出的方法在 K 复合波检测方面的准确率分别为 92.41 ± 7.47%、95.4 ± 4.32% 和 83.13 ± 8.59%，在场景 2 中也取得了类似的结果。
将 RUSBoosted 树模型与其他三种机器学习分类器[即线性判别分析（LDA）、逻辑回归和线性支持向量机（SVM）]进行了比较。基于卡帕系数、召回率和 F 分数的性能提供了证据，表明所提出的模型在 K 复合波检测方面优于其他算法，尤其是在召回率方面。
总之，RUSBoosted 树模型在处理高度不平衡数据方面表现出了良好的性能。它可以成为医生和神经科医生诊断和治疗睡眠障碍的有效工具。
