K-complexes identification is a challenging task in sleep research. The detection of k-complexes in electroencephalogram (EEG) signals based on visual inspection is time consuming, prone to errors, and requires well-trained knowledge. Many existing methods for k-complexes detection rely mainly on analyzing EEG signals in time and frequency domains. In this study, an efficient method is proposed to detect k-complexes from EEG signals based on fractal dimension (FD) of time frequency (T-F) images coupled with undirected graph features. Firstly, an EEG signal is partitioned into smaller segments using a sliding window technique. Each EEG segment is passed through a spectrogram of short time Fourier transform (STFT) to obtain the T-F images. Secondly, the box counting method is applied to each T-F image to discover the FDs in EEG signals. A vector of FD features are extracted from each T-F image and then mapped into an undirected graph. The structural properties of the graphs are used as the representative features of the original EEG signals for the input of a least square support vector machine (LS-SVM) classifier. Key graphic features are extracted from the undirected graphs. The extracted graph features are forwarded to the LS-SVM for classification. To investigate the classification ability of the proposed feature extraction combined with the LS-SVM classifier, the extracted features are also forwarded to a k-means classifier for comparison. The proposed method is compared with several existing k-complexes detection methods in which the same datasets were used. The findings of this study shows that the proposed method yields better classification results than other existing methods in the literature. An average accuracy of 97% for the detection of the k-complexes is obtained using the proposed method. The proposed method could lead to an efficient tool for the scoring of automatic sleep stages which could be useful for doctors and neurologists in the diagnosis and treatment of sleep disorders and for sleep research.

中文翻译：

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使用时频图像的分形维数技术结合无向图特征检测 EEG K 复合物

K-复合物识别是睡眠研究中的一项具有挑战性的任务。基于视觉检查的脑电图 (EEG) 信号中 k 复合体的检测耗时、容易出错，并且需要训练有素的知识。许多现有的 k 复合物检测方法主要依赖于在时域和频域中分析 EEG 信号。在这项研究中，提出了一种基于时频 (TF) 图像的分形维数 (FD) 结合无向图特征从 EEG 信号中检测 k 复合体的有效方法。首先，使用滑动窗口技术将 EEG 信号划分为更小的段。每个脑电图段都通过短时傅立叶变换 (STFT) 的频谱图以获得 TF 图像。其次，将框计数方法应用于每个 TF 图像以发现 EEG 信号中的 FD。从每个 TF 图像中提取一个 FD 特征向量，然后映射到一个无向图。图的结构特性被用作原始 EEG 信号的代表特征，用于最小二乘支持向量机 (LS-SVM) 分类器的输入。从无向图中提取关键图形特征。提取的图特征被转发到 LS-SVM 进行分类。为了研究所提出的特征提取结合 LS-SVM 分类器的分类能力，提取的特征也被转发到 k-means 分类器进行比较。将所提出的方法与使用相同数据集的几种现有 k-复合物检测方法进行比较。本研究的结果表明，所提出的方法比文献中的其他现有方法产生更好的分类结果。使用所提出的方法获得了 97% 的检测 k 复合体的平均准确度。所提出的方法可以为自动睡眠阶段的评分提供一种有效的工具，这对于医生和神经学家在睡眠障碍的诊断和治疗以及睡眠研究中可能有用。