Optimal Sparse Autoencoder Based Sleep Stage Classification Using Biomedical Signals

The recently developed machine learning(ML)models have the ability to obtain high detection rate using biomedical signals.Therefore,this article develops an Optimal Sparse Autoencoder based Sleep Stage Classification Model on Electroencephalography(EEG)Biomedical Signals,named OSAE-SSCEEG technique.The major intention of the OSAE-SSCEEG technique is tofind the sleep stage disorders using the EEG biomedical signals.The OSAE-SSCEEG technique primarily undergoes preprocessing using min-max data normalization approach.Moreover,the classification of sleep stages takes place using the Sparse Autoencoder with Smoothed Regularization(SAE-SR)with softmax(SM)approach.Finally,the parameter optimization of the SAE-SR technique is carried out by the use of Coyote Optimization Algorithm(COA)and it leads to boosted classification efficiency.In order to ensure the enhanced performance of the OSAE-SSCEEG technique,a wide ranging simulation analysis is performed and the obtained results demonstrate the betterment of the OSAE-SSCEEG tech-nique over the recent methods.

Competitive Multi-Verse Optimization with Deep Learning Based Sleep Stage Classification

Sleep plays a vital role in optimum working of the brain and the body.Numerous people suffer from sleep-oriented illnesses like apnea,insomnia,etc.Sleep stage classification is a primary process in the quantitative examination of polysomnographic recording.Sleep stage scoring is mainly based on experts’knowledge which is laborious and time consuming.Hence,it can be essential to design automated sleep stage classification model using machine learning(ML)and deep learning(DL)approaches.In this view,this study focuses on the design of Competitive Multi-verse Optimization with Deep Learning Based Sleep Stage Classification(CMVODL-SSC)model using Electroencephalogram(EEG)signals.The proposed CMVODL-SSC model intends to effectively categorize different sleep stages on EEG signals.Primarily,data pre-processing is performed to convert the actual data into useful format.Besides,a cascaded long short term memory(CLSTM)model is employed to perform classification process.At last,the CMVO algorithm is utilized for optimally tuning the hyperparameters involved in the CLSTM model.In order to report the enhancements of the CMVODL-SSC model,a wide range of simulations was carried out and the results ensured the better performance of the CMVODL-SSC model with average accuracy of 96.90%.

A Hybrid Deep Learning Scheme for Multi-Channel Sleep Stage Classification

Sleep stage classification plays a significant role in the accurate diagnosis and treatment of sleep-related diseases.This study aims to develop an efficient deep learning based scheme for correctly identifying sleep stages using multi-biological signals such as electroencephalography(EEG),electrocardiogram(ECG),electromyogram(EMG),and electrooculogram(EOG).Most of the prior studies in sleep stage classification focus on hand-crafted feature extraction methods.Traditional hand-crafted feature extraction methods choose features manually from raw data,which is tedious,and these features are limited in their ability to balance efficiency and accuracy.Moreover,most of the existing works on sleep staging are either single channel(a single-lead EEG may not contain enough information)or only EEG signal based which can not reveal more complicated physical features for reliable classification of various sleep stages.This study proposes an approach to combine Convolutional Neural Networks(CNNs)and Gated Recurrent Units(GRUs)that can discover hidden features from multi-biological signal data to recognize the different sleep stages efficiently.In the proposed scheme,the CNN is designed to extract concealed features from the multi-biological signals,and the GRU is employed to automatically learn the transition rules among different sleep stages.After that,the softmax layers are used to classify various sleep stages.The proposed method was tested on two publicly available databases:Sleep Heart Health Study(SHHS)and St.Vincent’s University Hospital/University College Dublin Sleep Apnoea(UCDDB).The experimental results reveal that the proposed model yields better performance compared to state-of-the-art works.Our proposed scheme will assist in building a new system to deal with multi-channel or multi-modal signal processing tasks in various applications.

The effect of Zhusha Anshen Wan decoction on states of the sleep-waking cycle in insomnic rats

BACKGROUND： Sedative and hypnotic chemical drugs prolong the total-sleep time （TST） by a decrease in slow-wave sleep 2 （SWS2） and rapid-eye-movement sleep （REMS） and a relative increase in slow-wave sleep 1 （SWS1）. OBJECTIVE： To investigate the effect of the Chinese medicine Zhusha Anshen Wan at different doses on each sleeping state in insomnic rats, and to identify its mode of action in improving sleep. DESIGN, TIME AND SETTING： A randomized controlled study in rats. This study was performed in the Department of Pharmacology of Chinese Materia Medica, Heilongjiang University of Traditional Chinese Medicine during the period from January 2005 to July 2006. MATERIALS： Twenty-four male Wistar rats, weighing （220±5） g, were selected. The main components in Zhusha Anshen Wan, Cinnabaris, Rhizoma Coptidis, Radix Glycyrrhixae, Prepared Radix Glycyrhizae Radix Angelicae Sinensis, and Rehmannia Pride Rhizome, were authenticated by Dr Xiaowei Du, Professor of Pharmacology. ND-97 Digital Polysomnography was purchased from the Shanghai Medical Instrument High Technology Company and Footplate Electrical Stimulator from the Harbin Research Institute of Electrical Instruments. METHODS： Rats were deprived of sleep by using the Footplate Electrical Stimulator. Insomnic rats were randomized into high-, mid- and low-dose Zhusha Anshen Wan groups, eight rats in each group. Animals were administrated with different doses of Zhusha Anshen Wan （equal to crude drug 7.2, 3.6, 1.8 g/kg） consecutively for seven days. MAIN OUTCOME MEASURES： 30 minutes after the last administration, rats of each group suffered 8 hours foot-shocks while electroencephalography signals were recorded using Digital Polysomnography. Total time of waking （W）, SWS1, SWS2, REMS and TST were calculated for pre- and post-administration, respectively. RESULTS： All 24 rats were included in the statistical analysis of the results without any loss. In the low-dose Zhusha Anshen Wan group, SWS2 was increased significantly compared with pre-administration. In the middle-dose Zhusha Anshen Wan group, W was decreased significantly, but SWS1, SWS2 and TST were increased markedly compared with pre-administration, and there were significant differences between pre- and post-administration （P 〈 0.05-0.01）. In the high-dose Zhusha Anshen Wan group, the duration of W was significantly decreased after administration, but SWS1, SWS2, REMS and TST were significantly longer than pre-administration （P 〈 0.05-0.01）. CONCLUSION： The effect of Zhusha Anshen Wan on sleeping states is dose-dependent. Zhusha Anshen Wan acts by extending SWS1 and SWS2 to increase the total sleeoing time.

Automatic Sleep Staging Algorithm Based on Random Forest and Hidden Markov Model

In the field of medical informatics,sleep staging is a challenging and timeconsuming task undertaken by sleep experts.According to the new standard of the American Academy of Sleep Medicine(AASM),the stages of sleep are divided into wakefulness(W),rapid eye movement(REM)and non-rapid eye movement(NREM)which includes three sleep stages(N1,N2 and N3)that describe the depth of sleep.This study aims to establish an automatic sleep staging algorithm based on the improved weighted random forest(WRF)and Hidden Markov Model(HMM)using only the features extracted from double-channel EEG signals.The WRF classification model focuses on reducing the bias of the imbalance data,while the HMM model focuses on improving the detection rate of sleep staging through the relationship between adjacent sleep stages.In particular,the improved weighted RF classification model can increase the recognition rate of the N1 stage.In addition,the method of removing features with low variance is used to select meaningful and contributing feature parameters for model training.This is an innovative content of this paper.The sleep EEG data are first segmented into 30 s epochs,and the feature parameters of the epoch data are extracted from the double-channel by applying continuous wavelet packet transform(WPT).Each epoch is then segmented into 29 subepochs of 2 s long with 1 s overlap,and the frequency domain features and statistical features of each subepoch are extracted.The performance of the proposed method is tested by evaluating the accuracy(AC),Kappa coefficient,Recall(R),Precision(P)and F1-score(F1).In the Sleep-EDF database,the overall AC and Kappa coefficient obtained by WRF are 93.20%and 0.890,respectively using the subject-non-independent test.In the 10 sc*and 10 st*Sleep-EDF Expanded database,the overall AC and Kappa coefficient obtained by proposed method are 91.97%and 0.874,respectively using the subject-independent test.The best AC and Kappa coefficient of single subject can reach 96.3%and 0.912,respectively.Experimental results show that the performance of the proposed method is competitive with the most current methods and results,and the recognition rate of N1 stage is significantly improved.

A sleep staging model for the sleep environment control based on machine learning

To date,dynamic sleep environment has been attracted the focus of researchers.Owing to the individual difference on sleep phase and thermal comfort,changes in sleep environment should be occupant-centered,and precise regulation of the environment required current sleep stages.However,few studies connected occupants and the environment through physiological signal-based model of sleep staging.Therefore,this study tried to develop a data driven sleep staging model with higher accuracy through sleep experiments collecting information.Raw database was processed and selected efficiently according to the characteristics of physiological signals.Finally,the sleep staging model with an average accuracy of 93.9%was built,and other mean indicators(precision:82.5%,recall:83.1%,F1 score:82.8%)performed well.The features adopted by model were found to come from different brain regions,and the global brain signals were suggested to play an important role in the construction of sleep staging model.Moreover,the computational processing of physiology signals should consider their characteristics,i.e.,time domain,frequency domain,time-frequency domain and nonlinear characteristics.The model obtained in this study may deliver a credible reference to advance the research on control of sleep environment.