Phase space and power spectral approaches for EEG-based automatic sleep-wake classification in humans: a comparative study using short and standard epoch lengths

Exploration of sleep function connection and classification strategies based on sub-period sleep stages

Background

As a medium for developing brain-computer interface systems, EEG signals are complex and difficult to identify due to their complexity, weakness, and differences between subjects. At present, most of the current research on sleep EEG signals are single-channel and dual-channel, ignoring the research on the relationship between different brain regions. Brain functional connectivity is considered to be closely related to brain activity and can be used to study the interaction relationship between brain areas.

Methods

Phase-locked value (PLV) is used to construct a functional connection network. The connection network is used to analyze the connection mechanism and brain interaction in different sleep stages. Firstly, the entire EEG signal is divided into multiple sub-periods. Secondly, Phase-locked value is used for feature extraction on the sub-periods. Thirdly, the PLV of multiple sub-periods is used for feature fusion. Fourthly, the classification performance optimization strategy is used to discuss the impact of different frequency bands on sleep stage classification performance and to find the optimal frequency band. Finally, the brain function network is constructed by using the average value of the fusion features to analyze the interaction of brain regions in different frequency bands during sleep stages.

Results

The experimental results have shown that when the number of sub-periods is 30, the α (8-13 Hz) frequency band has the best classification effect, The classification result after 10-fold cross-validation reaches 92.59%.

Conclusion

The proposed algorithm has good sleep staging performance, which can effectively promote the development and application of an EEG sleep staging system.

Poincaré Plot Nonextensive Distribution Entropy: A New Method for Electroencephalography (EEG) Time Series

As a novel form of visual analysis technique, the Poincaré plot has been used to identify correlation patterns in time series that cannot be detected using traditional analysis methods. In this work, based on the nonextensive of EEG, Poincaré plot nonextensive distribution entropy (NDE) is proposed to solve the problem of insufficient discrimination ability of Poincaré plot distribution entropy (DE) in analyzing fractional Brownian motion time series with different Hurst indices. More specifically, firstly, the reasons for the failure of Poincaré plot DE in the analysis of fractional Brownian motion are analyzed; secondly, in view of the nonextensive of EEG, a nonextensive parameter, the distance between sector ring subintervals from the original point, is introduced to highlight the different roles of each sector ring subinterval in the system. To demonstrate the usefulness of this method, the simulated time series of the fractional Brownian motion with different Hurst indices were analyzed using Poincaré plot NDE, and the process of determining the relevant parameters was further explained. Furthermore, the published sleep EEG dataset was analyzed, and the results showed that the Poincaré plot NDE can effectively reflect different sleep stages. The obtained results for the two classes of time series demonstrate that the Poincaré plot NDE provides a prospective tool for single-channel EEG time series analysis.

CEPS: An Open Access MATLAB Graphical User Interface (GUI) for the Analysis of Complexity and Entropy in Physiological Signals

BACKGROUND

We developed CEPS as an open access MATLAB^® GUI (graphical user interface) for the analysis of Complexity and Entropy in Physiological Signals (CEPS), and demonstrate its use with an example data set that shows the effects of paced breathing (PB) on variability of heart, pulse and respiration rates. CEPS is also sufficiently adaptable to be used for other time series physiological data such as EEG (electroencephalography), postural sway or temperature measurements.

METHODS

Data were collected from a convenience sample of nine healthy adults in a pilot for a larger study investigating the effects on vagal tone of breathing paced at various different rates, part of a development programme for a home training stress reduction system.

RESULTS

The current version of CEPS focuses on those complexity and entropy measures that appear most frequently in the literature, together with some recently introduced entropy measures which may have advantages over those that are more established. Ten methods of estimating data complexity are currently included, and some 28 entropy measures. The GUI also includes a section for data pre-processing and standard ancillary methods to enable parameter estimation of embedding dimension m and time delay τ ('tau') where required. The software is freely available under version 3 of the GNU Lesser General Public License (LGPLv3) for non-commercial users. CEPS can be downloaded from Bitbucket. In our illustration on PB, most complexity and entropy measures decreased significantly in response to breathing at 7 breaths per minute, differentiating more clearly than conventional linear, time- and frequency-domain measures between breathing states. In contrast, Higuchi fractal dimension increased during paced breathing.

CONCLUSIONS

We have developed CEPS software as a physiological data visualiser able to integrate state of the art techniques. The interface is designed for clinical research and has a structure designed for integrating new tools. The aim is to strengthen collaboration between clinicians and the biomedical community, as demonstrated here by using CEPS to analyse various physiological responses to paced breathing.

Patient-Specific Seizure Detection Method using Hybrid Classifier with Optimized Electrodes

In this paper the EEG signal is analyzed by reconstructing the time series EEG signal in High dimensional Phase Space. The computational complexity in higher dimension is reduced by Principal Component Analysis for the High dimensional Phase Space output. Poincare sectioning is done for the first and second Principal Components (PCs). The intersection points of PCs and the Poincare section are collected and used for features calculation. Two layer of classification is done using SVM as first layer and Naive Bayes as second layer. The proposed methodology is evaluated using the CHB-MIT database for 23 subjects. The results are obtained using different channel combinations of EEG signal and highest of 95.63% accuracy, 95.7% sensitivity and 96.55% specificity is obtained for 12 electrode combinations which include electrodes from parietal and occipital lobes. This infers that most of the subjects have dysfunction in hearing (controlled by parietal) and vision (controlled by occipital) during the time of seizure. This GUI has channel selection option and seizure detection for every channel (23) for every 1 s.

Buprenorphine Depresses Respiratory Variability in Obese Mice with Altered Leptin Signaling

BACKGROUND

Opiate-induced respiratory depression is sexually dimorphic and associated with increased risk among the obese. The mechanisms underlying these associations are unknown. The present study evaluated the two-tailed hypothesis that sex, leptin status, and obesity modulate buprenorphine-induced changes in breathing.

METHODS

Mice (n = 40 male and 40 female) comprising four congenic lines that differ in leptin signaling and body weight were injected with saline and buprenorphine (0.3 mg/kg). Whole-body plethysmography was used to quantify the effects on minute ventilation. The data were evaluated using three-way analysis of variance, regression, and Poincaré analyses.

RESULTS

Relative to B6 mice with normal leptin, buprenorphine decreased minute ventilation in mice with diet-induced obesity (37.2%; P < 0.0001), ob/ob mice that lack leptin (62.6%; P < 0.0001), and db/db mice with dysfunctional leptin receptors (65.9%; P < 0.0001). Poincaré analyses showed that buprenorphine caused a significant (P < 0.0001) collapse in minute ventilation variability that was greatest in mice with leptin dysfunction. There was no significant effect of sex or body weight on minute ventilation.

CONCLUSIONS

The results support the interpretation that leptin status but not body weight or sex contributed to the buprenorphine-induced decrease in minute ventilation. Poincaré plots illustrate that the buprenorphine-induced decrease in minute ventilation variability was greatest in mice with impaired leptin signaling. This is relevant because normal respiratory variability is essential for martialing a compensatory response to ventilatory challenges imposed by disease, obesity, and surgical stress.

A New Method for Automatic Sleep Stage Classification

Traditionally, automatic sleep stage classification is quite a challenging task because of the difficulty in translating open-textured standards to mathematical models and the limitations of handcrafted features. In this paper, a new system for automatic sleep stage classification is presented. Compared with existing sleep stage methods, our method can capture the sleep information hidden inside electroencephalography (EEG) signals and automatically extract features from raw data. To translate open sleep stage standards into machine rules recognized by computers, a new model named fast discriminative complex-valued convolutional neural network (FDCCNN) is proposed to extract features from raw EEG data and classify sleep stages. The new model combines complex-valued backpropagation and the Fisher criterion. It can learn discriminative features and overcome the negative effect of imbalance dataset. More importantly, the orthogonal decision boundaries for the real and imaginary parts of a complex-valued convolutional neuron are proven. A speed-up algorithm is proposed to reduce computational workload and yield improvements of over an order of magnitude compared to the normal convolution algorithm. The classification performances of handcrafted features and different convolutional neural networks are compared with that of the FDCCNN. The total accuracy and kappa coefficient of the proposed method are 92% and 0.84, respectively. Experiment results demonstrated that the performance of our system is comparable to those of human experts.

A comparative review on sleep stage classification methods in patients and healthy individuals

BACKGROUND AND OBJECTIVE

Proper scoring of sleep stages can give clinical information on diagnosing patients with sleep disorders. Since traditional visual scoring of the entire sleep is highly time-consuming and dependent to experts' experience, automatic schemes based on electroencephalogram (EEG) analysis are broadly developed to solve these problems. This review presents an overview on the most suitable methods in terms of preprocessing, feature extraction, feature selection and classifier adopted to precisely discriminate the sleep stages.

METHODS

This study round up a wide range of research findings concerning the application of the sleep stage classification. The fundamental qualitative methods along with the state-of-the-art quantitative techniques for sleep stage scoring are comprehensively introduced. Moreover, according to the results of the investigated studies, five research papers are chosen and practically implemented on a well-known public available sleep EEG dataset. They are applied to single-channel EEG of 40 subjects containing equal number of healthy and patient individuals. Feature extraction and classification schemes are assessed in terms of accuracy and robustness against noise. Furthermore, an additional implementation phase is added to this research in which all combinations of the implemented features and classifiers are considered to find the best combination for sleep analysis.

RESULTS

According to our achieved results on both groups, entropy of wavelet coefficients along with random forest classifier are chosen as the best feature and classifier, respectively. The mentioned feature and classifier provide 87.06% accuracy on healthy subjects and 69.05% on patient group.

CONCLUSIONS

In this paper, the road map of EEG-base sleep stage scoring methods is clearly sketched. Implementing the state-of-the-art methods and even their combination on both healthy and patient datasets indicates that although the accuracy on healthy subjects are remarkable, the results for the main community (patient group) by the quantitative methods are not promising yet. The reasons rise from adopting non-matched sleep EEG features from other signal processing fields such as communication. As a conclusion, developing sleep pattern-related features deem necessary to enhance the performance of this process.

An algorithm for automatic detection of drowsiness for use in wearable EEG systems

Lack of proper restorative sleep can induce sleepiness at odd hours making a person drowsy. This onset of drowsiness can be detrimental for the individual in a number of ways if it happens at an unwanted time. For example, drowsiness while driving a vehicle or operating heavy machinery poses a threat to the safety and wellbeing of individuals as well as those around them. Timely detection of drowsiness can prevent the occurrence of unfortunate accidents thereby improving road and work environment safety. In this paper, by analyzing the electroencephalographic (EEG) signals of human subjects in the frequency domain, several features across different EEG channels are explored. Of these, three features are identified to have a strong correlation with drowsiness. A weighted sum of these defining features, extracted from a single EEG channel, is then used with a simple classifier to automatically separate the state of wakefulness from drowsiness. The proposed algorithm resulted in drowsiness detection sensitivity of 85% and specificity of 93%.

ISRUC-Sleep: A comprehensive public dataset for sleep researchers

To facilitate the performance comparison of new methods for sleep patterns analysis, datasets with quality content, publicly-available, are very important and useful. We introduce an open-access comprehensive sleep dataset, called ISRUC-Sleep. The data were obtained from human adults, including healthy subjects, subjects with sleep disorders, and subjects under the effect of sleep medication. Each recording was randomly selected between PSG recordings that were acquired by the Sleep Medicine Centre of the Hospital of Coimbra University (CHUC). The dataset comprises three groups of data: (1) data concerning 100 subjects, with one recording session per subject; (2) data gathered from 8 subjects; two recording sessions were performed per subject, and (3) data collected from one recording session related to 10 healthy subjects. The polysomnography (PSG) recordings, associated with each subject, were visually scored by two human experts. Comparing the existing sleep-related public datasets, ISRUC-Sleep provides data of a reasonable number of subjects with different characteristics such as: data useful for studies involving changes in the PSG signals over time; and data of healthy subjects useful for studies involving comparison of healthy subjects with the patients, suffering from sleep disorders. This dataset was created aiming to complement existing datasets by providing easy-to-apply data collection with some characteristics not covered yet. ISRUC-Sleep can be useful for analysis of new contributions: (i) in biomedical signal processing; (ii) in development of ASSC methods; and (iii) on sleep physiology studies. To evaluate and compare new contributions, which use this dataset as a benchmark, results of applying a subject-independent automatic sleep stage classification (ASSC) method on ISRUC-Sleep dataset are presented.

Classification of normal and epileptic seizure EEG signals using wavelet transform, phase-space reconstruction, and Euclidean distance

This paper proposes new combined methods to classify normal and epileptic seizure EEG signals using wavelet transform (WT), phase-space reconstruction (PSR), and Euclidean distance (ED) based on a neural network with weighted fuzzy membership functions (NEWFM). WT, PSR, ED, and statistical methods that include frequency distributions and variation, were implemented to extract 24 initial features to use as inputs. Of the 24 initial features, 4 minimum features with the highest accuracy were selected using a non-overlap area distribution measurement method supported by the NEWFM. These 4 minimum features were used as inputs for the NEWFM and this resulted in performance sensitivity, specificity, and accuracy of 96.33%, 100%, and 98.17%, respectively. In addition, the area under Receiver Operating Characteristic (ROC) curve was used to measure the performances of NEWFM both without and with feature selections.

Identification of temporal variations in mental workload using locally-linear-embedding-based EEG feature reduction and support-vector-machine-based clustering and classification techniques

Identifying the abnormal changes of mental workload (MWL) over time is quite crucial for preventing the accidents due to cognitive overload and inattention of human operators in safety-critical human-machine systems. It is known that various neuroimaging technologies can be used to identify the MWL variations. In order to classify MWL into a few discrete levels using representative MWL indicators and small-sized training samples, a novel EEG-based approach by combining locally linear embedding (LLE), support vector clustering (SVC) and support vector data description (SVDD) techniques is proposed and evaluated by using the experimentally measured data. The MWL indicators from different cortical regions are first elicited by using the LLE technique. Then, the SVC approach is used to find the clusters of these MWL indicators and thereby to detect MWL variations. It is shown that the clusters can be interpreted as the binary class MWL. Furthermore, a trained binary SVDD classifier is shown to be capable of detecting slight variations of those indicators. By combining the two schemes, a SVC-SVDD framework is proposed, where the clear-cut (smaller) cluster is detected by SVC first and then a subsequent SVDD model is utilized to divide the overlapped (larger) cluster into two classes. Finally, three-class MWL levels (low, normal and high) can be identified automatically. The experimental data analysis results are compared with those of several existing methods. It has been demonstrated that the proposed framework can lead to acceptable computational accuracy and has the advantages of both unsupervised and supervised training strategies.

Intensive care unit depth of sleep: proof of concept of a simple electroencephalography index in the non-sedated

INTRODUCTION

Intensive care unit (ICU) patients are known to experience severely disturbed sleep, with possible detrimental effects on short- and long- term outcomes. Investigation into the exact causes and effects of disturbed sleep has been hampered by cumbersome and time consuming methods of measuring and staging sleep. We introduce a novel method for ICU depth of sleep analysis, the ICU depth of sleep index (IDOS index), using single channel electroencephalography (EEG) and apply it to outpatient recordings. A proof of concept is shown in non-sedated ICU patients.

METHODS

Polysomnographic (PSG) recordings of five ICU patients and 15 healthy outpatients were analyzed using the IDOS index, based on the ratio between gamma and delta band power. Manual selection of thresholds was used to classify data as either wake, sleep or slow wave sleep (SWS). This classification was compared to visual sleep scoring by Rechtschaffen & Kales criteria in normal outpatient recordings and ICU recordings to illustrate face validity of the IDOS index.

RESULTS

When reduced to two or three classes, the scoring of sleep by IDOS index and manual scoring show high agreement for normal sleep recordings. The obtained overall agreements, as quantified by the kappa coefficient, were 0.84 for sleep/wake classification and 0.82 for classification into three classes (wake, non-SWS and SWS). Sensitivity and specificity were highest for the wake state (93% and 93%, respectively) and lowest for SWS (82% and 76%, respectively). For ICU recordings, agreement was similar to agreement between visual scorers previously reported in literature.

CONCLUSIONS

Besides the most satisfying visual resemblance with manually scored normal PSG recordings, the established face-validity of the IDOS index as an estimator of depth of sleep was excellent. This technique enables real-time, automated, single channel visualization of depth of sleep, facilitating the monitoring of sleep in the ICU.

An EEGLAB plugin to analyze individual EEG alpha rhythms using the "channel reactivity-based method"

A recent paper [1] proposed a new technique, termed the channel reactivity-based method (CRB), for characterizing EEG alpha rhythms using individual (IAFs) and channel (CAFs) alpha frequencies. These frequencies were obtained by identifying the frequencies at which the power of the alpha rhythms decreases. In the present study, we present a graphical interactive toolbox that can be plugged into the popular open source environment EEGLAB, making it easy to use CRB. In particular, we illustrate the major functionalities of the software and discuss the advantages of this toolbox for common EEG investigations. The CRB analysis plugin, along with extended documentation and the sample dataset utilized in this study, is freely available on the web at http://bio.dei.unipd.it/crb/.