A model-based monitor of human sleep stages

Stochastic models are proposed for sleep and for the sleep related electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG). The evolution of sleep through its various stages is described as a Markov chain. The EEG is modelled using Wiener processes. The EOG and EMG are modelled as combinations of Poisson point processes and Gaussian processes, respectively. The EEG models contain a feedback structure that is based on physiological data. The maximum likelihood sleep stage monitor, that uses the sleep-related observations, has been derived and implemented. The agreement between automatic and human stage classifications of six sleep recordings was 70.6%, which was 4.5% worse than the average agreement between six human classifiers. Monitoring of simulated sleep suggests that the difficulty in separating wakefulness from stage 1 is due to poor modelling. If one ignores this difference, which, from a diagnostic point of view is fairly unimportant, the above mentioned agreement reaches 81.8%, which is 0.5% better than the corresponding average human vs human agreement.     

Sleep staging with movement-related signals

Body movement related signals (i.e., activity due to postural changes and the ballistocardiac effort) were recorded from six normal volunteers using the static-charge-sensitive bed (SCSB). Visual sleep staging was performed on the basis of simultaneously recorded EEG, EMG and EOG signals. A statistical classification technique was used to determine if reliable sleep staging could be performed using only the SCSB signal. A classification rate of between 52% and 75% was obtained for sleep staging in the five conventional sleep stages and the awake state. These rates improved from 78% to 89% for classification between awake, REM and non-REM sleep and from 86% to 98% for awake versus asleep classification.     

A transition-constrained discrete hidden Markov model for automatic sleep staging

ABSTRACT: BACKGROUND: Approximately one-third of the human lifespan is spent sleeping. To diagnose sleep problems, all-night polysomnographic (PSG) recordings including electroencephalograms (EEGs), electrooculograms (EOGs) and electromyograms (EMGs), are usually acquired from the patient and scored by a well-trained expert according to Rechtschaffen & Kales (R&K) rules. Visual sleep scoring is a time-consuming and subjective process. Therefore, the development of an automatic sleep scoring method is desirable. METHOD: The EEG, EOG and EMG signals from twenty subjects were measured. In addition to selecting sleep characteristics based on the 1968 R&K rules, features utilized in other research were collected. Thirteen features were utilized including temporal and spectrum analyses of the EEG, EOG and EMG signals, and a total of 158 hours of sleep data were recorded. Ten subjects were used to train the Discrete Hidden Markov Model (DHMM), and the remaining ten were tested by the trained DHMM for recognition. Furthermore, the 2-fold cross validation was performed during this experiment. RESULTS: Overall agreement between the expert and the results presented is 85.29%. With the exception of S1, the sensitivities of each stage were more than 81%. The most accurate stage was SWS (94.9%), and the least-accurately classified stage was S1 (<34%). In the majority of cases, S1 was classified as Wake (21%), S2 (33%) or REM sleep (12%), consistent with previous studies. However, the total time of S1 in the 20 all-night sleep recordings was less than 4%. CONCLUSION: The results of the experiments demonstrate that the proposed method significantly enhances the recognition rate when compared with prior studies.     

Feature ranking and rank aggregation for automatic sleep stage classification: a comparative study

Background

Nowadays, sleep quality is one of the most important measures of healthy life, especially considering the huge number of sleep-related disorders. Identifying sleep stages using polysomnographic (PSG) signals is the traditional way of assessing sleep quality. However, the manual process of sleep stage classification is time-consuming, subjective and costly. Therefore, in order to improve the accuracy and efficiency of the sleep stage classification, researchers have been trying to develop automatic classification algorithms. Automatic sleep stage classification mainly consists of three steps: pre-processing, feature extraction and classification. Since classification accuracy is deeply affected by the extracted features, a poor feature vector will adversely affect the classifier and eventually lead to low classification accuracy. Therefore, special attention should be given to the feature extraction and selection process.

Methods

In this paper the performance of seven feature selection methods, as well as two feature rank aggregation methods, were compared. Pz-Oz EEG, horizontal EOG and submental chin EMG recordings of 22 healthy males and females were used. A comprehensive feature set including 49 features was extracted from these recordings. The extracted features are among the most common and effective features used in sleep stage classification from temporal, spectral, entropy-based and nonlinear categories. The feature selection methods were evaluated and compared using three criteria: classification accuracy, stability, and similarity.

Results

Simulation results show that MRMR-MID achieves the highest classification performance while Fisher method provides the most stable ranking. In our simulations, the performance of the aggregation methods was in the average level, although they are known to generate more stable results and better accuracy.

Conclusions

The Borda and RRA rank aggregation methods could not outperform significantly the conventional feature ranking methods. Among conventional methods, some of them slightly performed better than others, although the choice of a suitable technique is dependent on the computational complexity and accuracy requirements of the user.

     

Multivariate and multiorgan analysis of cardiorespiratory variability signals: the CAP sleep case.

Signals from different systems are analyzed during sleep on a beat-to-beat basis to provide a quantitative measure of synchronization with the heart rate variability (HRV) signal, oscillations of which reflect the action of the autonomic nervous system. Beat-to-beat variability signals synchronized to QRS occurrence on ECG signals were extracted from respiration, electroencephalogram (EEG) and electromyogram (EMG) traces. The analysis was restricted to sleep stage 2. Cyclic alternating pattern (CAP) periods were detected from EEG signals and the following conditions were identified: stage 2 non-CAP (2 NCAP), stage 2 CAP (2 CAP) and stage 2 CAP with myoclonus (2 CAP MC). The coupling relationships between pairs of variability signals were studied in both the time and frequency domains. Passing from 2 NCAP to 2 CAP, sympathetic activation is indicated by tachycardia and reduced respiratory arrhythmia in the heart rate signal. At the same time, we observed a marked link between EEG and HRV at the CAP frequency. During 2 CAP MC, the increased synchronization involved myoclonus and respiration. The underlying mechanism seems to be related to a global control system at the central level that involves the different systems.     

Sleep and EEG spectra in the pigeon (Columba livia) under baseline conditions and after sleep deprivation

Summary Sleep in adult domestic pigeons was studied by continuous 24-h recording of the EEG, EMG and EOG. Vigilance states were scored on the basis of behavioral observations, visual scoring of the polygraph records, and EEG power spectra.The animals showed a clear nocturnal preference for sleep. Throughout the dark period, EEG slow-wave activity was at a uniform level, whereas REM sleep (REMS) showed an increasing trend.EEG power density values differed significantly between the vigilance states. In general the values were highest in nonREM sleep (NREMS), intermediate in waking (W) and lowest in REMS.Twenty-four hour sleep deprivation reduced W and increased REMS, effects that are well documented in mammals. Unlike in mammals, EEG slow-wave activity remained unchanged, whereas EOG activity in W and NREMS was enhanced.Abbreviations EEG electroencephalogram - EMG electromyogram - EOG electrooculogram - SD sleep deprivation - L light - D dark - LD light dark - NREMS non rapid eye movement sleep - REMS REM sleep     

Novel whole body plethysmography system for the continuous characterization of sleep and breathing in a mouse

Sleep is associated with marked alterations in ventilatory control that lead to perturbations in respiratory timing, breathing pattern, ventilation, pharyngeal collapsibility, and sleep-related breathing disorders (SRBD). Mouse models offer powerful insight into the pathogenesis of SRBD; however, methods for obtaining the full complement of continuous, high-fidelity respiratory, electroencephalographic (EEG), and electromyographic (EMG) signals in unrestrained mice during sleep and wake have not been developed. We adapted whole body plethysmography to record EEG, EMG, and respiratory signals continuously in unrestrained, unanesthetized mice. Whole body plethysmography tidal volume and airflow signals and a novel noninvasive surrogate for respiratory effort (respiratory movement signal) were validated against simultaneously measured gold standard signals. Compared with the gold standard, we validated 1) tidal volume (correlation, R(2) = 0.87, P < 0.001; and agreement within 1%, P < 0.001); 2) inspiratory airflow (correlation, R(2) = 0.92, P < 0.001; agreement within 4%, P < 0.001); 3) expiratory airflow (correlation, R(2) = 0.83, P < 0.001); and 4) respiratory movement signal (correlation, R(2) = 0.79-0.84, P < 0.001). The expiratory airflow signal, however, demonstrated a decrease in amplitude compared with the gold standard. Integrating respiratory and EEG/EMG signals, we fully characterized sleep and breathing patterns in conscious, unrestrained mice and demonstrated inspiratory flow limitation in a New Zealand Obese mouse. Our approach will facilitate studies of SRBD mechanisms in inbred mouse strains and offer a powerful platform to investigate the effects of environmental and pharmacological exposures on breathing disturbances during sleep and wakefulness.     

Real-Time Classification of EMG Myo Armband Data Using Support Vector Machine

ObjectivesThis study investigates the performance of the Support Vector Machine (SVM) to classify non-real-time and real-time EMG signals. The study also compares training performance using personalized and generalized data from all subjects. Thus, an idea about the data sets to be used in the training of the real-time classification model has been put forward. In addition, real-time classification results were obtained for ten days, and it was observed how training oneself would affect the classification results.Material and methods:EMG data were acquired for 7 hand gestures from 8 healthy subjects to create the data set: fist, fingers spread, wave-in, wave-out, pronation, supination, and rest. Subjects repeated each gesture 30 times. The Myo armband with 8 dry surface electrodes was used for data acquisition.Results14 features of the EMG signals have been extracted and non-real-time classification has been made for each feature; the highest accuracy of 96.38% was obtained using root mean square (RMS) and integrated EMG features. Three (3) kernel functions of SVM were tested in non-real-time classification and the highest accuracy was obtained with Cubic SVM using 3rd order polynomial. For this reason, Cubic SVM was used for real-time classification using the features that gave the best results in non-real-time classification. A subject repeated the gestures and real-time classification was performed. The highest accuracy of 99.05% was obtained with the mean absolute value (MAV) feature. The real-time classification was undertaken on eight subjects using the MAV feature's best performance with an average accuracy of 95.83% using the personalized data set and 91.79% using the generalized data set.ConclusionThe greatest accuracy is obtained by training the classifier with the subject's own data. Thus, it can be said that EMG signals are personal, just like fingerprints and retina. In addition, as a result, the tests repeated for 10 days showed the repeatability of the activation of the relevant muscle set and the training takes place and how this can be applied to those who will use prosthetic hands to obtain certain gestures.     

Robust Muscle Activity Onset Detection Using an Unsupervised Electromyogram Learning Framework

Accurate muscle activity onset detection is an essential prerequisite for many applications of surface electromyogram (EMG). This study presents an unsupervised EMG learning framework based on a sequential Gaussian mixture model (GMM) to detect muscle activity onsets. The distribution of the logarithmic power of EMG signal was characterized by a two-component GMM in each frequency band, in which the two components respectively correspond to the posterior distribution of EMG burst and non-burst logarithmic powers. The parameter set of the GMM was sequentially estimated based on maximum likelihood, subject to constraints derived from the relationship between EMG burst and non-burst distributions. An optimal threshold for EMG burst/non-burst classification was determined using the GMM at each frequency band, and the final decision was obtained by a voting procedure. The proposed novel framework was applied to simulated and experimental surface EMG signals for muscle activity onset detection. Compared with conventional approaches, it demonstrated robust performance for low and changing signal to noise ratios in a dynamic environment. The framework is applicable for real-time implementation, and does not require the assumption of non EMG burst in the initial stage. Such features facilitate its practical application.     

Analysis and automatic identification of sleep stages using higher order spectra

Electroencephalogram (EEG) signals are widely used to study the activity of the brain, such as to determine sleep stages. These EEG signals are nonlinear and non-stationary in nature. It is difficult to perform sleep staging by visual interpretation and linear techniques. Thus, we use a nonlinear technique, higher order spectra (HOS), to extract hidden information in the sleep EEG signal. In this study, unique bispectrum and bicoherence plots for various sleep stages were proposed. These can be used as visual aid for various diagnostics application. A number of HOS based features were extracted from these plots during the various sleep stages (Wakefulness, Rapid Eye Movement (REM), Stage 1-4 Non-REM) and they were found to be statistically significant with p-value lower than 0.001 using ANOVA test. These features were fed to a Gaussian mixture model (GMM) classifier for automatic identification. Our results indicate that the proposed system is able to identify sleep stages with an accuracy of 88.7%.     

Behavioral and electrophysiological patterns of wakefulness-sleep states in a lizard.

Four individuals of the lizard Ctenosaura pectinata were chronically implanted for electroencephalographic (EEG), electromyographic (EMG) and electro-oculographic (EOG) recordings. Four different vigilance states were observed throughout the nyctohemeral cycle. These states were: Active wakefulness (Aw), quiet wakefulness (Qw), quiet sleep (Qs) and active sleep (As). Each state displayed its own behavioral and electrophysiological characteristics. EEG waves were similar during Aw and Qw but they diminished in amplitude and frequency when passing from these states to Qs, and both parameters increased during As. Muscular activity was intense in Aw, it decreased during Qw and almost disappeared during Qs. This activity reappeared in a phasic way during As, coinciding with generalized motor manifestations. Ocular activity was intense during Aw but minimal during Qw, it disappeared in Qs and was present intermittently in As. Aw, Qw, Qs and As occupied 5.9%, 25.7%, 67.7% and 0.6% of the 24 hr period, respectively. The frequency and duration of As episodes showed great inter-animal variability and the mean duration was of 12.9 sec. Stimuli reaction threshold was highest during sleep. In conclusion, the lizard Ctenosaura pectinata exhibit two sleep phases (Qs and As) that may be assimilated to slow wave sleep (SWS) and paradoxical sleep (PS) of birds and mammals.     

Improving the separability of multiple EEG features for a BCI by neural-time-series-prediction-preprocessing

A recently proposed method for EEG preprocessing is extended and analyzed in this work via a range of different tests in combination with various other BCI components. Neural-time-series-prediction-processing (NTSPP) is a predictive approach to EEG preprocessing where prediction models (PMs) are trained to perform one-step-ahead prediction of the EEG times-series which reflect motor imagery induced alterations in neuronal activity. Due to the specialization of distinct PMs, the predicted signals (Ys) and error signals (Es) are distinctly different from the original (Os) signals. The PMs map the Os signals to a higher dimension which, in the majority of cases, produces features that are more separable than those produced by the Os signals. Four feature extraction procedures, ranging in complexity and in terms of the information which is extracted i.e., time domain, frequency domain and time–frequency (t–f) domain, are used to determine the separability enhancements which are verified by comparative statistical tests and brain–computer interface (BCI) tests on six subjects. It is shown that, in the majority of the tests, features extracted from the NTSPP signals are more separable than those extracted from the Os signals, in terms of increased Euclidean distance between class means, reduced inter-class correlations and intra-class variance, and higher classification accuracy (CA), information transfer (IT) rate and mutual information (MI).     

Time of night effects on heart rate variation in normal neonates

Circadian patterns have been observed in infants as early as the first few postnatal days. We hypothesized that, in each sleep-waking state, heart rate variation in several distinct frequency bands would show consistent variations across a night in newborn infants. Twelve-hour night-time recordings of EEG, ECG, EOG, digastric EMG, respiratory movements, and CO2 were obtained from 25 normal full-term infants at 2-7 days postnatal age. The extents of three types of heart rate variation were determined for all epochs identified as quiet sleep, rapid eye movement (REM) sleep, and waking during each 4-hr period of the night. In particular states, the extent of all three types of heart rate variation decreased from the evening (7-11pm) to the late night (11pm-3am). Heart rate variation at the respiratory frequency showed such a time-of-night effect in quiet sleep only, resulting in a significant sleep state effect on respiratory sinus arrhythmia during the evening that disappeared later in the night. Previous studies have indicated that respiratory sinus arrhythmia is enhanced during quiet sleep, relative to other states, after 3 mo of age; the present findings suggest that the tendency for enhancement during quiet sleep is present even in the neonate, although this tendency is only expressed during the evening. Results indicate that time-of-night effects on heart rate variation are not constant across physiological states in neonates, and heart rate variation during the waking state is particularly unresponsive to these time-of-night influences.     

Sleep/wake estimation using only anterior tibialis electromyography data

ABSTRACT: BACKGROUND: In sleep efficiency monitoring system, actigraphy is the simplest and most commonly used device. However, low specificity to wakefulness of actigraphy was revealed in previous studies. In this study, we assumed that sleep/wake estimation using actigraphy and electromyography (EMG) signals would show different patterns. Furthermore, each EMG pattern in two states (sleep, wake during sleep) was analysed. Finally, we proposed two types of method for the estimation of sleep/wake patterns using only EMG signals from anterior tibialis muscles and the results were compared with PSG data. METHODS: Seven healthy subjects and five patients (2 obstructive sleep apnea, 3 periodic limb movement disorder) participated in this study. Night time polysomnography (PSG) recordings were conducted, and electrooculogram, EMG, electroencephalogram, electrocardiogram, and respiration data were collected. Time domain analysis and frequency domain analysis were applied to estimate the sleep/wake patterns. Each method was based on changes in amplitude or spectrum (total power) of anterior tibialis electromyography signals during the transition from the sleep state to the wake state. To obtain the results, leave-one-out-cross-validation technique was adopted. RESULTS: Total sleep time of the each group was about 8 hours. For healthy subjects, the mean epoch-by-epoch results between time domain analysis and PSG data were 99%, 71%, 80% and 0.64 (sensitivity, specificity, accuracy and kappa value), respectively. For frequency domain analysis, the corresponding values were 99%, 73%, 81% and 0.67, respectively. Absolute and relative differences between sleep efficiency index from PSG and our methods were 0.8 and 0.8% (for frequency domain analysis). In patients with sleep-related disorder, our proposed methods revealed the substantial agreement (kappa > 0.61) for OSA patients and moderate or fair agreement for PLMD patients. CONCLUSIONS: The results of our proposed methods were comparable to those of PSG. The time and frequency domain analyses showed the similar sleep/wake estimation performance.     

Classification of Motor Imagery EEG Based on Time-Domain and Frequency-Domain Dual-Stream Convolutional Neural Network

Background and objectiveAn important task of the brain-computer interface (BCI) of motor imagery is to extract effective time-domain features, frequency-domain features or time-frequency domain features from the raw electroencephalogram (EEG) signals for classification of motor imagery. However, choosing an appropriate method to combine time domain and frequency domain features to improve the performance of motor imagery recognition is still a research hotspot.MethodsIn order to fully extract and utilize the time-domain and frequency-domain features of EEG in classification tasks, this paper proposed a novel dual-stream convolutional neural network (DCNN), which can use time domain signal and frequency domain signal as the inputs, and the extracted time-domain features and frequency-domain features are fused by linear weighting for classification training. Furthermore, the weight can be learned by the DCNN automatically.ResultsThe experiments based on BCI competition II dataset III and BCI competition IV dataset 2a showed that the model proposed by this study has better performance than other conventional methods. The model used time-frequency signal as the inputs had better performance than the model only used time-domain signals or frequency-domain signals. The accuracy of classification was improved for each subject compared with the models only used one signals as the inputs.ConclusionsFurther analysis shown that the fusion weight of different subject is specifically, adjusting the weight coefficient automatically is helpful to improve the classification accuracy.     

Multimodal information improves the rapid detection of mental fatigue

One of the major challenge in the detection of mental states is improving the accuracy of brain activity-based detectors with additional information from extracranial signals. We assessed the suitability, for real-time mental fatigue detection, of EEG, EOG and ECG measurements, taken separately or together. Thirteen subjects performed six blocks of switching tasks. For each participant, the block with the lowest error rate from the first two blocks and the block with the highest error rate from the last three blocks were discriminated with a machine learning algorithm (support vector machine). The classification scores obtained with ECG or EOG were greater than would be expected by chance (>50%) for time windows of at least 8 s. EEG was the best single mode of detection, with classification scores ranging from 80 ± 3% with a 4 s time window to 94 ± 2% with a 30 s time window. The addition of ECG and EOG features to EEG features significantly increased classification scores for short time windows (e.g., to 86 ± 3% with a 4 s time window, p < 0.001). For short time windows (up to 12 s), ECG significantly increased the discriminatory power of EEG, whereas EOG did not. These results demonstrate that mental state detection on the basis of extracerebral measurements is feasible and that a combination of EEG and ECG is particularly appropriate for the rapid detection of mental fatigue.     

Sleep apnoea detection from ECG using features extracted from reconstructed phase space and frequency domain

Sleep apnoea is a very common sleep disorder which is able to cause symptoms such as daytime sleepiness, irritability and poor concentration. This paper presents a combinational feature extraction approach based on some nonlinear features extracted from Electro Cardio Graph (ECG) Reconstructed Phase Space (RPS) and usually used frequency domain features for detection of sleep apnoea. Here 6 nonlinear features extracted from ECG RPS are combined with 3 frequency based features to reconstruct final feature set. The nonlinear features consist of Detrended Fluctuation Analysis (DFA), Correlation Dimensions (CD), 3 Large Lyapunov Exponents (LLEs) and Spectral Entropy (SE). The final proposed feature set show about 94.8% accuracy over the Physionet sleep apnoea dataset using a kernel based SVM classifier. This research also proves that using non-linear analysis to detect sleep apnoea can potentially improve the classification accuracy of apnoea detection system.     

Automated detection of schizophrenia using optimal wavelet-based [... formula ...] norm features extracted from single-channel EEG

Schizophrenia (SZ) is a mental disorder, which affects the ability of human thinking, memory, and way of living. Manual screening of SZ patients is tedious, laborious and prone to human errors. Hence, we developed a computer-aided diagnosis (CAD) system to diagnose SZ patients accurately using single-channel electroencephalogram (EEG) signals. The EEG signals are nonlinear and non-stationary. Hence, we have used wavelet-based features to capture the hidden non-stationary nature present in the signal. First, the EEG signals are subjected to the the wavelet decomposition through six iterations, which yields seven sub-bands. The l1 norm is computed for each sub-band. The extracted norm features are disseminated to various classification algorithms. We have obtained the highest accuracy of 99.21% and 97.2% using K-nearest neighbor classifiers with ten-fold and leave-one-subject-out cross-validations. The developed single-channel EEG wavelet-based CAD model can help the clinicians to confirm the outcome of their manual screening and obtain an accurate diagnosis.     

An automatic EEG-based sleep staging system with introducing NAoSP and NAoGP as new metrics for sleep staging systems

Different biological signals are recorded in sleep labs during sleep for the diagnosis and treatment of human sleep problems. Classification of sleep stages with electroencephalography (EEG) is preferred to other biological signals due to its advantages such as providing clinical information, cost-effectiveness, comfort, and ease of use. The evaluation of EEG signals taken during sleep by clinicians is a tiring, time-consuming, and error-prone method. Therefore, it is clinically mandatory to determine sleep stages by using software-supported systems. Like all classification problems, the accuracy rate is used to compare the performance of studies in this domain, but this metric can be accurate when the number of observations is equal in classes. However, since there is not an equal number of observations in sleep stages, this metric is insufficient in the evaluation of such systems. For this purpose, in recent years, Cohen’s kappa coefficient and even the sensitivity of NREM1 have been used for comparing the performance of these systems. Still, none of them examine the system from all dimensions. Therefore, in this study, two new metrics based on the polygon area metric, called the normalized area of sensitivity polygon and normalized area of the general polygon, are proposed for the performance evaluation of sleep staging systems. In addition, a new sleep staging system is introduced using the applications offered by the MATLAB program. The existing systems discussed in the literature were examined with the proposed metrics, and the best systems were compared with the proposed sleep staging system. According to the results, the proposed system excels in comparison with the most advanced machine learning methods. The single-channel method introduced based on the proposed metrics can be used for robust and reliable sleep stage classification from all dimensions required for real-time applications.Electronic supplementary materialThe online version of this article (10.1007/s11571-020-09641-2) contains supplementary material, which is available to authorized users.     

Electrographic and behavioural correlations of the rest-activity cycle in the sea turtle, Caretta caretta L. (Chelonia)

Behavioural and electrographic (EEG, EOG, EMG, EKG) observations were carried out during the activity-inactivity cycle of the sea turtle, Caretta caretta L. Observations were made on turtles kept in a tank with flowing sea water and under natural lighting. There were no clearcut changes in the amount of activity associated with the dark-light phase, although the peak of activity occurred in the early afternoon hours. During periods of behavioural inactivity, EEG changes were minimum and were not accompanied by consistent changes in the EMG of the neck muscles. Eye movements were absent at that time. Although much information is lacking, we tentatively conclude that the sea turtle does not exhibit signs of sleep, but alternates between states of activity and inactivity that are simultaneous with a non-altered level of responsiveness. Such association of low but responsive activity might be of survival value.