Complex-valued unsupervised convolutional neural networks for sleep stage classification
Introduction
Sleep is fundamental physiological activity of the human body, and its quality can seriously affect the quality of life of patients. Sleep diseases, such as insomnias, hyper-somnias, sleep-related breathing disorders, cardiovascular diseases, parasomnias, sleep movement disorders, disturb sleep quality and thus threaten human health. Effective diagnosis and treatment of sleep-related diseases mainly rely on accurate classification of sleep stages [1]. Therefore, sleep stage classification is the most important step for sleep analysis.
Sleep stage is scored by many rhythms in the electroencephalogram (EEG), electromyogram (EMG), electrooculogram (EOG) and electrocardiogram (ECG). Because different sleep stages correspond to different brain rhythms, we use EEG to score sleep stage in this paper. Table 1 shows different EEG rhythms. Sleep is scored based on the Rechtschaffen and Kales (R&K) rules [2]. First, EEG is divided into non-overlapping 30-s epochs. Second, clinicians and experts classify each of these epochs into one sleep stage. According to the R&K rules [2], sleep is categorized into six categories. They are rapid eye movement (REM), stage 1 (S1), stage 2 (S2), stage 3 (S3), stage 4 (S4) and wake (W). Usually, S3 and S4 were combined as slow wave sleep (SWS) stage [3], [4], [5]. According to our investigation, an experienced clinician should take about 40 min to score a recording. Because there are many patients every night, the manual sleep scoring will be a time-consuming task. At the same time, the quality of the scoring depends on the experience and fatigue of the experts. Therefore, many methods for automatic sleep stage classification have been developed [6]. For example, the literature provides several examples involving different techniques: artificial neural network (ANN) [7], [8], [9], [10], support vector machine (SVM) [11], [12], [13], spectral analysis [14], [15], hidden markov models [16], [17], [18], nonlinear feature analysis [19], [20], [21], and other methods [22], [23], [24]. However, all these methods need handcrafted features. The effectiveness of these features is overly dependent on the knowledge and expertise, which makes these features not robust and flexible enough to adapt different circumstances.
Convolutional neural network (CNN) [25], which is able to fuse the two major blocks (feature extraction and classification) into a single learning process, will be helpful to solve the problem. CNN allows the extraction of automatic features from recorded EEG signals within its layers [26], [27], [28]. Because complex-valued neural network (CVNN) has better classification ability and more efficient classification performance than real-valued neural network (RVNN) [29], [30], various complex-valued convolutional models have been proposed [31], [32], [33], [34], [35], [36], [37], [38]. [31], [32], [33] used complex-valued convolutional neural network (CCNN) to solve complex-valued input problems. [34] used CCNN to solve real-valued image classification. Tygert et al. [35] gave a mathematical motivation for CCNN. However, few studies have applied CCNN to classify sleep stages. Our previous work [37] has proved that the decision boundaries for the real and imaginary parts of a complex-valued convolutional neuron intersect orthogonally. The orthogonal decision boundaries help to improve the generalization ability of the complex-valued convolutional neural network (CCNN). Our another work [38] combined complex-valued backpropagation and the Fisher criterion. It can learn discriminative features and overcome the negative effect of imbalance dataset. The two works [37], [38] have shown that the CCNN can obtain state-of-the-art classification performance based on deep learning models. However, supervised training algorithm was used in the works [37], [38]. It means lots of labeled data are needed. Through literatures analysis [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], apart from [40], [41], all the deep learning methods need labeled data. There are two problems for obtaining labeled data by visual inspection: quality control and throughput. Obtaining labeled data depends on specialist domain knowledge and will differ between two specialists (inter-rater variance). At the same time, even an experienced specialist requires hours to annotate the sleep stage patterns. This makes it difficult to carry out quantitative and comprehensive sleep research. According to [40], [41], the EEG, EOG and EMG were used to classify sleep stage. It needs that the polysomnography (PSG) should be used to collect these signals. PSG measurements have many disadvantages. First, patients must wear many electrodes on different parts of their body. Because of these electrodes, patients are difficult to sleep and thus produce discrepant results which are not able to reflect the actual sleep condition. Second, the operation of PSG is very complicate. Patients can't monitor sleep at home. Because of the limitation of monitoring rooms, many patients can't be diagnosed in time. In this case, an affordable, portable, and unobtrusive sleep monitoring system for at-home use would be required. Wearable sleep devices will provide a good solution for long-term monitoring at home. Lots of EEG and ECG data can be collected from these devices. Compared with EEG, ECG is easy to obtain and interpret. Although many studies have used ECG to classify sleep stages [47], the best performance is still EEG-based method. Therefore, we choose EEG to classify sleep stages. Because obtaining labeled sleep data that is derived from wearable sleep devices is impossible, unsupervised learning is important for wearable sleep monitoring system. Hence, we develop a new unsupervised method to classify sleep stages based on the single channel EEG.
The present paper proposes a novel deep learning model called complex-valued unsupervised convolutional neural network (CUCNN) to classify sleep stages. To the best of our knowledge, this study is the first to apply a CUCNN model in sleep stage classification. The paper is organized as follows. The complex-valued K-means, CCNN and CUCNN are presented in Section 2. The experiments, results, and performance comparison are presented in Section 3. In Section 4, the experimental results are discussed. Finally, Section 5 concludes the paper.
Section snippets
Sleep stage method
In the paper, the sleep stage method resembles that of [38]. It includes three parts: 1) preprocessing, 2) feature extraction, and 3) classification. The difference is that CUCNN is used to extract features.
Data preprocessing
In order to cancel out power line disturbances of the EEG signal, a notch filtering at 50/60 Hz is used. After downsampling the EEG signal to 120 Hz, an eighth-order butterworth band-pass filter of 0.5–30 Hz for EEG is used to filter the downsampled signals [4]. According to [48], [49],
Datasets
In order to show the robustness of the proposed approach, two common datasets are used in this paper. The first dataset is obtained from the UCD database [65], which is available online at http://physionet.org/physiobank/database/ucddb. The dataset consisted of the full overnight PSG recordings of 25 subjects with sleep-disordered breathing. Each subject has a unique study number. Each study number corresponded to three data files which can be downloaded from the website. The details are shown
Discussion
This paper proposes a novel deep learning model named CUCNN. To the best of our knowledge, this study is the first to apply a complex-valued unsupervised model to automated sleep stage classification. The proposed method integrated complex-valued network and unsupervised training component. The TAC of the 4-layer CUCNN applied to the single-channel EEG from UCD dataset reached 87%, and the sensitivities of each stage reached 88%, 80.2%, 94.05%, 90.07%, and 85.02%, respectively.
Tables 3 and 4
Conclusions
This paper proposes a new unsupervised sleep stage method. It can learn useful features in an unsupervised fashion, and complex-valued classifiers can demonstrate excellent classification performance. The proposed approach is robust and fully automated, and the method can be easily adapted to other physiological signal analysis and prediction problems. Our approach, which learned features from raw sleep data, is a real automatic sleep stage method. Almost all the other methods [1], [5], [7],
Conflict of interest
None
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