Sleep scoring is one of the primary tasks for the classification of sleep stages using electroencephalogram (EEG) signals. It is one of the most important diagnostic methods in sleep research and must be carried out with a high degree of accuracy because any errors in the scoring in the patient’s sleep EEG recordings can cause serious problems. The aim of this research is to develop a new automatic method for detecting the most important characteristics in sleep stage 2 such as k-complexes based on multi-domain features. In this study, each EEG signal is divided into a set of segments using a sliding window technique. Based on extensive experiments during the training phase, the size of the sliding window is set to 0.5 s (s). Then a set of statistical, fractal, frequency and non-linear features are extracted from each epoch based on the time domain, Katz's algorithm, power spectrum density (PSD) and tunable Q-factor wavelet transform (TQWT). As a result, a vector of twenty-two features is obtained to represent each EEG segment. In order to detect k-complexes, the extracted features were analysed for their ability to detect the k-complex waveforms. Based on the analysis of the features, twelve out of twenty-two features are selected and forwarded to a least square support vector machine (LS-SVM) classifier to identify k-complexes in EEG signals. A set of various classification techniques of K-means and extreme learning machine classifiers are used to compare the obtained results and to evaluate the performance of the proposed method.The experimental results showed that the proposed method, based on multi-domain features, achieved better recognition results than other methods and classifiers. An average accuracy, sensitivity and specificity of 97.7 %, 97 %, and 94.2 % were obtained, respectively, with the CZ-A1 channel according to the R&K standard. The experimental results with high classification performance demonstrated that the technique can help doctors optimize the diagnosis and treatment of sleep disorders.

睡眠评分是使用脑电图 (EEG) 信号对睡眠阶段进行分类的主要任务之一。它是睡眠研究中最重要的诊断方法之一，必须以高度的准确性进行，因为患者睡眠脑电图记录中的任何评分错误都可能导致严重问题。本研究的目的是开发一种新的自动方法，用于检测睡眠阶段 2 中最重要的特征，例如基于多域特征的 k 复合体。在这项研究中，使用滑动窗口技术将每个 EEG 信号分成一组段。根据训练阶段的大量实验，滑动窗口的大小设置为 0.5 s (s)。那么一组统计的，分形的，基于时域、Katz 算法、功率谱密度 (PSD) 和可调 Q 因子小波变换 (TQWT)，从每个 epoch 中提取频率和非线性特征。结果，获得了一个包含 22 个特征的向量来表示每个 EEG 段。为了检测k-复数，分析了提取的特征检测k-复数波形的能力。基于对特征的分析，从 22 个特征中选择 12 个并将其转发到最小二乘支持向量机 (LS-SVM) 分类器以识别 EEG 信号中的 k 复合体。使用K-means和极限学习机分类器的一组各种分类技术来比较所得结果并评估所提出方法的性能。 实验结果表明，所提出的方法，基于多域特征，取得了比其他方法和分类器更好的识别效果。根据 R&K 标准，使用 CZ-A1 通道分别获得了 97.7%、97% 和 94.2% 的平均准确度、灵敏度和特异性。高分类性能的实验结果表明，该技术可以帮助医生优化睡眠障碍的诊断和治疗。