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Ensemble Learning Algorithm Based on Multi-Parameters for Sleep Staging

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Ensemble Learning Algorithm Based on Multi-Parameters for Sleep Staging Medical & Biological Engineering & Computing https://doi.org/10.1007/s11517-019-01978-z ORIGINAL ARTICLE Ensemble learning algorithm based on multi-parameters for sleep staging Qiangqiang Wang1 & Dechun Zhao1 & Yi Wang1 & Xiaorong Hou2 Received: 21 May 2018 /Accepted: 4 April 2019 # International Federation for Medical and Biological Engineering 2019 Abstract The aim of this study is to propose a high-accuracy and high-efficiency sleep staging algorithm using single-channel electroen- cephalograms (EEGs). The process consists four parts: signal preprocessing, feature extraction, feature selection, and classifi- cation algorithms. In the preconditioning of EEG, wavelet function and IIR filter are used for noise reduction. In feature selection, 15 feature algorithms in time domain, time-frequency domain, and nonlinearity are selected to obtain 30 feature parameters. Feature selection is very important for eliminating irrelevant and redundant features. Feature selection algorithms as Fisher score, Sequential Forward Selection (SFS), Sequential Floating Forward Selection (SFFS), and Fast Correlation-Based Filter Solution (FCBF) were used. The paper establishes a new ensemble learning algorithm based on stacking model. The basic layers are k- Nearest Neighbor (KNN), Random Forest (RF), Extremely Randomized Trees (ERT), Multi-layer Perceptron (MLP), and Extreme Gradient Boosting (XGBoost) and the second layer is a Logistic regression. Comparing classification of RF, Gradient Boosting Decision Tree (GBDT), and XGBoost, the accuracies and kappa coefficients are 96.67% and 0.96 using the proposed method. It is higher than other classification algorithms.The results show that the proposed method can accurately sleep staging using single-channel EEG and has a high ability to predict sleep staging. Keywords EEG signal . Sleep stage . Feature selection . Ensemble learning algorithm . Stacking 1 Introduction sleep (REM). NREM can be divided into N-REM1, N-REM2, N-REM3, and N-REM4. Sleep is an important physiological phenomenon and a neces- The sleep stage classification is classically performed by sary physiological process. Sleep can be used to analyze the identifying the characteristics extracted from cerebral rhythms. quality of sleep for sleep conditions and to detect certain The awake stage contains alpha (more than 50%) wave activity sleep-based diseases such as neurasthenia and cardiovascular and low-amplitude mixed-frequency activity. N-REM1 is the disease. It has important clinical significance and broad appli- transitional phase of the brain from the awake phase to the sleep cation prospects. Sleep stage is an important prerequisite for phase. It is manifested by the transition from the alpha wave (8– understanding sleep status. In 1968, Rechtschaffen and Kates 13 Hz) to theta wave (4–7Hz).IntheN-REM2,theEEGbrain propose R&K staging rules [26] based on factors such as wave with low amplitude was mainly composed of sleep spin- EEG, electrooculogram (EOG), eye movements in electromy- dle, k complex wave, and delta (0.5–2Hz)wave(lessthan ography (EMG), and muscle size during sleep. According to 20%). The delta (0.5–2 Hz) wave is the dominant wave in N- the rules, sleep is divided into the awake stage, the non-rapid REM3. In the N-REM4, the ratio of delta (0.5–2Hz)wavesis eye movement sleep (NREM), and the rapid eye movement higher than 50% and saw-tooth waves appear. In REM, brainwave waveform is similar to the NREM1/2, but there is a period of rapid eye movements during this period. This period * Dechun Zhao is also the main period of dreaming. [email protected] Figure 1 shows the brain activity during different periods of sleep. NREM and REM sleep occur in alternating cycles, each 1 Chongqing University of Posts and Telecommunications, lasting approximately 90–110 min (min) in adults, with ap- Chongqing, China proximately 4–6 cycles during the course of a normal 6–8-h 2 Chongqing Medical University, Chongqing, China (h) sleep period. Med Biol Eng Comput Fig. 1 EEG signals at different sleep stages In clinical practice, sleep stages are widely classified using et al. [56] used Support Vector Machine classification algo- manual judgment. Manual staging is based upon visual in- rithm (SVM) to get an 87.5% accuracy rate. In order to obtain spection of the EEG as well as the EOG and EMG traces. It higher accuracy rate, Kaveh et al. [42] used Fpz-Cz EEG has a high sleep recognition rate. However, it needs to be signals and RDSTFT as a feature algorithm. Then they used completed by expert visual analysis. It is subjective and inef- a random forest to perform four-stage sleep classification. A ficient and can easily lead to misjudgment. Automated sleep 92.5% accuracy rate was obtained. analysis has been around for almost 30 years [10]. Automated The most important part of the sleep staging algorithm is the sleep analysis uses modern signal processing technology to accuracy of identification [41]. Features are important factors that achieve effective and accurate staging. Arthur et al. [15]ex- affect recognition. How to select the feature parameters with high tracted the complexity and correlation coefficients and used accuracy is the key to the algorithm. Most papers do not specify the hidden Markov model (HMM) by C3 and C4 EEG data. this point in detail, and they are all based on their respective char- The accuracy rate of the algorithm was 80%. Luay et al. [18] acteristic parameters for the staging algorithm. In this paper, 30 extracted wavelet coefficients and used the regression tree feature parameters are derived from 15 feature algorithm groups, classification algorithm to classify sleep for six periods by which are highly reliable feature sets, are synthesized. The inte- Pz-Oz EEG signals. In this regard, they obtain a 75% accuracy grated feature set will inevitably have problems of redundancy rate. By using the C4-A1 EEG signal and a decision tree and time-consuming. Then, feature selection algorithm is used to classification algorithm (DT), Salih et al. [19]extractedthe obtain the optimal feature set. For sleep staging, most of the early Welch coefficients and divided EEG waves into six periods. classification algorithms are SVM and NN [1]. Currently, random The authors obtained an 82.15% accuracy rate finally. In an- forests are widely used in this field and get good results [18]. The other study, Thiago et al. [11] used Pz-Oz EEG signals to integration algorithm has many advantages. Random forest is a extract variance, skewness, and kurtosis. Then they obtained prominent representative. In this paper, 30 feature parameters are a 90.5% accuracy rate by using a random forest classification derived from 15 feature algorithms (Stacking) is proposed to in- algorithm (RF) to classify sleep into six periods. Farideh et al. tegrate random forest and other algorithms. Compared with sev- [13] who extracted wavelet packet coefficients by using Pz-Oz eral integration algorithms as RF, GBDT, and XGBoost, the sleep EEG signal, used Artificial Neural Network classification al- staging results are greatly improved. gorithm (ANN) to get a result a 93% accuracy rate when The paper is organized in the following manner: The first divided it into six periods. By Pz-Oz EEG signal and thinking part introduces current research status and the main research Different Visibility Graph (DVG) as a feature algorithm, Zhu results in the automatic stage of sleep. The second part Med Biol Eng Comput introduces the datum and the detailed algorithm including data preprocessing, feature extraction, feature selection, and clas- sification algorithms. The third part provides the results of feature selection algorithm and integrated classification algo- rithm through the simulation of MATLAB and python. The fourth part compares and discusses the accuracy. In the fifth part, it describes and summarizes the results of this paper and look forward to the direction of future research in the automat- ic staging of sleep. 2Methods 2.1 Data description and preprocessing Fig. 2 The raw signal and noise-reduced signal consist of the awake The data set used in the study was provided by the Sleep-EDF period in Pz-Oz, with a total of 3000 epochs. The raw EEG signal is database [23, 28]. It was obtained from Caucasian males and shown in the first plot, then the following is noise-reduced signal females (21–35 years old) without any medication. They con- tain horizontal EOG, Fpz-Cz, and Pz-Oz EEG, each sampled as features of sleep staging. Hjorth activity, Hjorth complexity, at 100 Hz. Hypnograms are manually scored according to Hjorth mobility, Energy (δ/α), LZ complexity, and the energy Rechtschaffen & Kales based on Fpz-Cz / Pz-Oz EEG instead of the four rhythmic waves (α, β, θ,andδ) are taken from of C4-A1 / C3-A2 EEG [50]. In this study, the Pz-Oz channel [29]. Fractal dimension, largest Lyapunov exponent, and EEG signal was selected to analyze and identify the sleep Hurst exponent are taken from [37]. Kurtosis and skewness stages because it can provide better automatic classification are taken from [45]. Tsallis entropy and permutation entropy accuracy than the Fpz-Cz channel [56]. The interval of each are taken from [43]. Fuzzy entropy is taken from [8]. Sample segment (or epoch) in this study is defined as 30 s and contains entropy is taken from [22]. Based on the important influence 3000 data points. The data composition is shown in Table 1. of four rhythmic waves on sleep staging in sleep staging, this The study uses an adaptive threshold discrete wavelet func- paper statistically makes the following feature selection: the tion and IIR filter function filtering method to reduce the noise standard deviation of four rhythm waves (α, β, θ,andδ)and of EEG signals, which can effectively improve the signal-to- maximum value of four rhythm waves (α, β, θ,andδ). All the noise ratio of the signal.
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