European Data Format Research Articles

Electroencephalographic Changes in Pregnant Women with Hyperemesis Gravidarum: A Case-Control Study.

Electroencephalogram (EEG), which is frequently used in the clinical practice of neurology, has also been investigated in eating disorders and some cortical dysfunctions have been reported. Based on this, we aimed to investigate EEG changes in pregnant women with hyperemesis gravidarum (HEG). This case-control study was conducted on 66 pregnant women who applied to the Umraniye Training and Research Hospital, Department of Obstetrics and Gynecology. The study group consisted of 34 pregnant women diagnosed with HEG. The control group consisted of 32 healthy pregnant women who were matched with the HEG group in terms of age and gestational week. EEGs of the participants were performed with a Micromed Brain Rapid EEG device in the Neurology Department of Umraniye Training and Research Hospital. In EEGs, all channels were selected as bipolar and samples of 18 channels (Fp2-F4, F4-C4, C4-P4, P4-O2, Fp2-F8, F8-T4, T4-T6, T6-O2, Fz-Cz, Cz-Pz, Fp1-F3, F3-C3, C3-P3, P3-O1, Fp1-F7, F7-T3, T3-T5, and T5-O1) were obtained. EEG signals were sampled with a sampling frequency of 200 Hz and digitized with 12-bit resolution. EEG signals were converted to EDF (European Data Format) extension files using the MATLAB software program and analyzed using statistical features on the time and frequency axis. HEG and control groups were compared in terms of signals obtained from these 18 selected channels. Both groups were similar in terms of mean age, gestational age, and parity (p>0.05). Among the 18 channels, significant changes were detected between the two groups only in the theta, beta, and gamma bands in the C4-P4 channel and the delta, beta, and gamma bands in the T4-T6 channel (p<0.05). No significant changes were detected in the channels and bands. Theta, beta, and gamma band abnormalities in the centro-parietal area of the right hemisphere and delta, beta, and gamma band abnormalities in the temporal area of the right hemisphere were observed on HEG. However, it is unclear whether abnormalities in EEG are primary changes responsible for the development of HEG or secondary to metabolic and hormonal changes resulting from HEG itself.

Automated Analysis of REM Sleep without Atonia in the Lewy Body Disease Spectrum

AbstractBackgroundQuantitative rapid eye movement (REM) sleep without atonia (RSWA) recorded during polysomnography (PSG) represents loss of normal REM sleep atonia. RSWA is the neurophysiologic signature of REM sleep behavior disorder (RBD). Visually scored RSWA is a candidate biomarker for synucleinopathies. However, visual RSWA scoring requires expert scoring, limiting widespread application. We comparatively analyzed RSWA in DLB and iRBD using the automated Ferri REM Atonia Index (RAI).MethodWe quantitatively analyzed the RAI in 12 DLB and 20 iRBD patients, compared to 20 controls using Hypnolab Software for Sleep Analysis (SWS Soft, Inc, Colognola ai Colli, Italy). PSG data were converted to European Data Format (.EDF), and the submentalis surface electromyogram (EMG) signal was notch‐filtered at 60 Hertz and rectified. Automatically calculated RAI scores were comparatively analyzed across groups (DLB, iRBD, controls) using Kruskal Wallis H tests for group comparisons, and regression analyses determined relationships between diagnosis, age, and sex. Logistic regression ROC determined optimal RAI for distinguishing iRBD from DLB. When interpreting RAI, lower values represent greater amounts of RSWA, and higher values represent more normal REM atonia.ResultMean age was older in DLB than in iRBD or controls (72.6 vs. 68.7 vs. 66.5 years). All 12 DLB participants were men, the iRBD group had 18 men and 2 women, and controls were comprised of 16 men and 4 women. Mean RAI was lowest in DLB (0.46), intermediate in iRBD (0.71) and highest in controls (0.96), indicating a gradient in RSWA amounts from DLB patients (highest) to controls (lowest) (Figure). Regression demonstrated diagnosis independently predicted RAI, controlling for age and sex (adjusted R2 = 0.49, p = 0.0001). The RAI that optimally distinguished iRBD from DLB was 0.46 (specificity 85%, sensitivity 58%, AUC = 0.75).ConclusionThe RAI demonstrated a clear gradient throughout the Lewy body spectrum; highest (indicating lowest RSWA amounts) in controls, intermediate in iRBD, and lowest (indicating greatest RSWA) in DLB. The RAI is a promising, time efficient and broadly applicable candidate biomarker for DLB and iRBD. Automated quantitative RSWA could be a valuable tool for deep phenotyping throughout the Lewy body disease spectrum.

Standardized Image-Based Polysomnography Database and Deep Learning Algorithm for Sleep Stage Classification.

Polysomnography (PSG) scoring is labor intensive, subjective, and often ambiguous. Recently several deep learning (DL) models for automated sleep scoring have been developed, they are tied to a fixed amount of input channels and resolution. In this study, we constructed a standardized image-based PSG dataset in order to overcome the heterogeneity of raw signal data obtained from various PSG devices and various sleep laboratory environments. All individually exported European data format files containing raw signals were converted into images with an annotation file, which contained the demographics, diagnoses, and sleep statistics. An image-based DL model for automatic sleep staging was developed, compared with a signal-based model and validated in an external dataset. We constructed 10,253 image-based PSG datasets using a standardized format. Among these, 7,745 diagnostic PSG data were used to develop our DL model. The DL model using the image dataset showed similar performance to the signal-based dataset for the same subject. The overall DL accuracy was greater than 80%, even with severe obstructive sleep apnea. Moreover, for the first time, we showed explainable DL in the field of sleep medicine as visualized key inference regions using Eigen-class activation maps. Furthermore, when a DL model for sleep scoring performs external validation, we achieved a relatively good performance. Our main contribution demonstrates the availability of a standardized image-based dataset, and highlights that changing the data sampling rate or number of sensors may not require retraining, although performance decreases slightly as the number of sensors decreases.

Automated Seizure Detection in Neonatal EEG using Signal Processing Algorithms

The crucial challenge of identifying seizures in newborn electroencephalogram (EEG) data is the subject of this research. (ElectroEncephaloGram) EEG analysis has traditionally been labor-intensive and subjective due to the heavy reliance on human inspection by skilled electrophysiologists and neurologists. The authors suggest a novel online automatic detection approach to get over these restrictions. This approach intends to open the door for effective and reliable early seizure detection, delivering a potential development in neonatal care and neurological research. It does this by utilizing the capabilities of signal processing and brain-inspired modeling’s suggested machine-learning method makes effective use of real-time EEG signal processing methods. Its processing of incoming EEG data streams makes it appropriate for neonates to be closely tracked in critical care situations. Automation of the detection method minimizes the need for manual inspection by highly specialized experts, allowing for more rapid responses and possibly increasing seizure management's overall results. Using metal electrodes, the electroencephalogram (EEG) analyses the electrical activity of the brain. European Data Format (EDF) BROWSER and EEG STUDIO are used to analyses EEG signals. Root Mean Square (RMS) and signal samples are performed by EDF BROWSER, as well as mean, frequency, and frequency from filtered output using a band-pass filter. The standard deviation and average frequency are examined by EEG STUDIO. Spike detection, frequency domain analysis, and nonlinear techniques are used in seizure prediction. EOG, EKG, and EMG artifacts produced by the eyes, the heart, and the muscles, respectively may be present in EEG readings.

Automatic sleep staging algorithm for stochastic depth residual networks based on transfer learning

The existing automatic sleep staging algorithms have the problems of too many model parameters and long training time, which in turn results in poor sleep staging efficiency. Using a single channel electroencephalogram (EEG) signal, this paper proposed an automatic sleep staging algorithm for stochastic depth residual networks based on transfer learning (TL-SDResNet). Firstly, a total of 30 single-channel (Fpz-Cz) EEG signals from 16 individuals were selected, and after preserving the effective sleep segments, the raw EEG signals were pre-processed using Butterworth filter and continuous wavelet transform to obtain two-dimensional images containing its time-frequency joint features as the input data for the staging model. Then, a ResNet50 pre-trained model trained on a publicly available dataset, the sleep database extension stored in European data format (Sleep-EDFx) was constructed, using a stochastic depth strategy and modifying the output layer to optimize the model structure. Finally, transfer learning was applied to the human sleep process throughout the night. The algorithm in this paper achieved a model staging accuracy of 87.95% after conducting several experiments. Experiments show that TL-SDResNet50 can accomplish fast training of a small amount of EEG data, and the overall effect is better than other staging algorithms and classical algorithms in recent years, which has certain practical value.

A deep learning framework for epileptic seizure detection based on neonatal EEG signals

Electroencephalogram (EEG) is one of the main diagnostic tests for epilepsy. The detection of epileptic activity is usually performed by a human expert and is based on finding specific patterns in the multi-channel electroencephalogram. This is a difficult and time-consuming task, therefore various attempts are made to automate it using both conventional and Deep Learning (DL) techniques. Unfortunately, authors do not often provide sufficiently detailed and complete information to be able to reproduce their results. Our work is intended to fill this gap. Using a carefully selected 79 neonatal EEG recordings we developed a complete framework for seizure detection using DL approch. We share a ready to use R and Python codes which allow: (a) read raw European Data Format files, (b) read data files containing the seizure annotations made by human experts, (c) extract train, validation and test data, (d) create an appropriate Convolutional Neural Network (CNN) model, (e) train the model, (f) check the quality of the neural classifier, (g) save all learning results.

Deep learning for sleep stages classification: modified rectified linear unit activation function and modified orthogonal weight initialisation

Background and Aim: Each stage of sleep can affect human health, and not getting enough sleep at any stage may lead to sleep disorder like parasomnia, apnea, insomnia, etc. Sleep-related diseases could be diagnosed using Convolutional Neural Network Classifier. However, this classifier has not been successfully implemented into sleep stage classification systems due to high complexity and low accuracy of classification. The aim of this research is to increase the accuracy and reduce the learning time of Convolutional Neural Network Classifier. Methodology: The proposed system used a modified Orthogonal Convolutional Neural Network and a modified Adam optimisation technique to improve the sleep stage classification accuracy and reduce the gradient saturation problem that occurs due to sigmoid activation function. The proposed system uses Leaky Rectified Linear Unit (ReLU) instead of sigmoid activation function as an activation function. Results: The proposed system called Enhanced Sleep Stage Classification system (ESSC) used six different databases for training and testing the proposed model on the different sleep stages. These databases are University College Dublin database (UCD), Beth Israel Deaconess Medical Center MIT database (MIT-BIH), Sleep European Data Format (EDF), Sleep EDF Extended, Montreal Archive of Sleep Studies (MASS), and Sleep Heart Health Study (SHHS). Our results show that the gradient saturation problem does not exist anymore. The modified Adam optimiser helps to reduce the noise which in turn result in faster convergence time. Conclusion: The convergence speed of ESSC is increased along with better classification accuracy compared to the state of art solution.

Deep learning analysis for estimating sleep syndromedetection utilizing the twin convolutional model FTC2

Manual sleep stage scoring is frequently performed by sleep specialists by visually evaluating the patient’sneurophysiological signals acquired in sleep laboratories. This is a difficult, time-consuming, and laborious process.Because of the limits of human sleep stage scoring, there is a greater need for creating automatic sleep stageclassification (ASSC) systems. Sleep stage categorization is the process of distinguishing the distinct stagesof sleep and is an important step in assisting physicians in the diagnosis and treatment of associated sleepdisorders. In this research, we offer a unique method and a practical strategy to predict early onset of sleepdisorders, such as restless leg syndrome and insomnia, using the twin convolutional model FTC2, based on analgorithm composed of two modules. To provide localized time-frequency information, 30-second-long epochs ofelectroencephalogram (EEG) recordings are subjected to a fast Fourier transform, and a deep convolutional longshort-term networks neural network is trained for sleep stage categorization. Automating sleep stage detectionfrom EEG data offers a great potential to tackle sleep irregularities on a daily basis. Thereby, a novel approach forsleep stage classification is proposed, which combines the best of signal processing and statistics. In this study,we used the PhysioNet Sleep European Data Format (EDF) database. The code evaluation showed impressiveresults, reaching an accuracy of 90.43, precision of 77.76, recall of 93,32, F1 score of 89.12, and the final meanfalse error loss of 0.09. All the source code is available athttps://github.com/timothy102/eeg..

Classification of Sleep Apnea Based on Sub-Band Decomposition of EEG Signals.

To classify between normal and sleep apnea subjects based on sub-band decomposition of electroencephalogram (EEG) signals. This study comprised 159 subjects obtained from the ISRUC (Institute of System and Robotics—University of Coimbra), Sleep-EDF (European Data Format), and CAP (Cyclic Alternating Pattern) Sleep database, which consists of normal and sleep apnea subjects. The wavelet packet decomposition method was incorporated to categorize the EEG signals into five frequency bands, namely, alpha, beta, delta, gamma, and theta. Entropy and energy (non-linear) for all bands was calculated and as a result, 10 features were obtained for each EEG signal. The ratio of EEG bands included four parameters, including heart rate, brain perfusion, neural activity, and synchronization. In this study, a support vector machine with kernels and random forest classifiers was used for classification. The performance measures demonstrated that the improved results were obtained from the support vector machine classifier with a kernel polynomial order 2. The accuracy (90%), sensitivity (100%), and specificity (83%) with 14 features were estimated using the data obtained from ISRUC database. The proposed study is feasible and seems to be accurate in classifying the subjects with sleep apnea based on the extracted features from EEG signals using a support vector machine classifier.

528 REM Sleep Without Atonia in Idiopathic REM Sleep Behavior Disorder in the North American Prodromal Synucleinopathy Cohort

Abstract Introduction Idiopathic/isolated REM sleep behavior disorder (iRBD) is a prodromal alpha-synucleinopathy characterized by dream enactment behavior and REM sleep without atonia (RSWA). We sought to define quantitative RSWA diagnostic thresholds in the North American Prodromal Synucleinopathy (NAPS) Consortium cohort. We analyzed RSWA between iRBD patients across participating NAPS sleep centers, compared to normative controls, and hypothesized that previous diagnostic RSWA thresholds were overestimates. Methods All digital polysomnography files were converted to European Data Format and scored at a central laboratory (Mayo Clinic) which standardized display scoring montages, channel sensitivities, and filtering, and scripted computational analyses for visual scoring. RSWA was quantitatively analyzed in the submentalis (SM) and anterior tibialis (AT) muscles in iRBD (n=86) patients and controls (n=118) utilizing well validated visual (Mayo) and automated (RAI) methods. Parametric statistics were used to compare RSWA metrics, and RSWA thresholds were developed using receiver operating characteristic curves. Results RSWA was significantly higher for the RAI and all visual individual and combined muscle activity metrics in iRBD compared to controls (all p&amp;lt;0.001). Average SM phasic measures were: 14.2% (Mayo), 17.9% (McGill), 18.5% (UCLA), and 9.4% (Washington University). Average AT phasic measures at each site were: 26.7% (Mayo), 17.1% (McGill), 23.3% (UCLA), and 17.4% (Washington University). Average SM/AT ‘any’ measures at each site were: 45.4% (Mayo), 35.9% (McGill), 53.4% (UCLA), and 23.5% (Washington University). Overall cohort RBD diagnostic thresholds (AUC, specificity/sensitivity) were: SM phasic 4.9% (90.0, 82.2%/83.7%); AT phasic 7.6% (88.7%, 82.2%/81.4%) and combined SM/AT ‘any’ 13% (94.6, 83.9%/96.5%). Conclusion RSWA thresholds in the NAPS cohort were substantially lower than previously reported, suggesting previously overestimated diagnostic RSWA thresholds due to smaller, enriched patient samples and overfit statistical modeling. Confirmation of these findings in the complete NAPS cohort (n=300 iRBD patients across all 10 NAPS centers) is planned. Support (if any):

A Sleep Stage Classification Algorithm of Wearable System Based on Multiscale Residual Convolutional Neural Network

Sleep disorder is a serious public health problem. Unobtrusive home sleep quality monitoring system can better open the way of sleep disorder‐related diseases screening and health monitoring. In this work, a sleep stage classification algorithm based on multiscale residual convolutional neural network (MRCNN) was proposed to detect the characteristics of electroencephalogram (EEG) signals detected by wearable systems and classify sleep stages. EEG signals were analyzed in each epoch of every 30 seconds, and then 5‐class sleep stage classification, wake (W), rapid eye movement sleep (REM), and nonrapid eye movement sleep (NREM) including N1, N2, and N3 stages was outputted. Good results (accuracy rate of 92.06% and 91.13%, Cohen’s kappa of 0.7360 and 0.7001) were achieved with 5‐fold cross‐validation and independent subject cross‐validation, respectively, which performed on European Data Format (EDF) dataset containing 197 whole‐night polysomnographic sleep recordings. Compared with several representative deep learning methods, this method can easily obtain sleep stage information from single‐channel EEG signals without specialized feature extraction, which is closer to clinical application. Experiments based on CinC2018 dataset also proved that the method has a good performance on large dataset and can provide support for sleep disorder‐related diseases screening and health surveillance based on automatic sleep staging.

Deep Learning Analysis for Estimating Sleep Syndrome Detection Utilizing the Twin Convolutional Model FTC2

Manual sleep stage scoring is frequently performed by sleep specialists by visually evaluating the patient's neurophysiological signals acquired in sleep laboratories. This is a difficult, time-consuming, laborious process. Because of the limits of human sleep stage scoring, there is a greater need for creating Automatic Sleep Stage Classification (ASSC) systems. Sleep stage categorization is the process of distinguishing the distinct stages of sleep is an important step in assisting physicians in the diagnosis treatment of associated sleep disorders. In this research, we offer a unique method a practical strategy to predicting early onsets of sleep disorders, such as restless leg syndrome insomnia, using the Twin Convolutional Model FTC2, based on an algorithm composed of two modules. To provide localised time-frequency information, 30 second long epochs of EEG recordings are subjected to a Fast Fourier Transform, a deep convolutional LSTM neural network is trained for sleep stage categorization. Automating sleep stages detection from EEG data offers a great potential to tackling sleep irregularities on a daily basis. Thereby, a novel approach for sleep stage classification is pro- posed which combines the best of signal processing statistics. In this study, we used the PhysioNet Sleep European Data Format (EDF) Database. The code evaluation showed impressive results, reaching accuracy of 90.43, precision of 77.76, recall of 93,32, F1-score of 89.12 with the final mean false error loss 0.09. All the source code is availlable at https://github.com/timothy102/eeg.

Deep Learning Analysis for Estimating Sleep Syndrome Detection Utilizing the Twin Convolutional Model FTC2

Sleep specialists often conduct manual sleep stage scoring by visually inspecting the patient's neurophysiological signals collected at sleep labs. This is a difficult, tedious and a time-consuming task. The limitations of manual sleep stage scor- ing have escalated the demand for developing Automatic Sleep Stage Classification (ASSC) systems. Sleep stage classification refers to identifying the various stages of sleep and is a critical step in an effort to assist physicians in the diag- nosis and treatment of related sleep disorders. In this paper, we propose a novel method and a practical approach to predicting early onsets of sleep syndromes utilizing the Twin Convolutional Model FTC2, including restless leg syndrome, insomnia, based on an algorithm which is comprised of two modules. A Fast Fourier Transform is applied to 30 seconds long epochs of EEG recordings to provide localized time-frequency information, and a deep convolutional LSTM neural network is trained for sleep stage classification. Automating sleep stages detection from EEG data offers a great potential to tackling sleep irregularities on a daily basis. Thereby, a novel approach for sleep stage classification is pro- posed which combines the best of signal processing and statistics. In this study, we used the PhysioNet Sleep European Data Format (EDF) Database. The code evaluation showed impressive results, reaching accuracy of 90.43, precision of 77.76, recall of 93,32, F1-score of 89.12 with the final mean false error loss 0.09. All the source code is availlable at https://github.com/timothy102/eeg.

Epilepsy analysis using open source EDF tools for information science and data analytics

SummaryElectroencephalogram (EEG) is the signals that measure the electrical variances of brain using metal electrodes. We observe the EEG signals by using European Data Format (EDF) BROWSER and EEG STUDIO. By using EDF BROWSER, we can get the mean and frequency from the filtered output signal using band‐pass filter. Using EDF BROWSER, we can also perform Root Mean Square (RMS) and signal samples. Using EEG STUDIO, we can analyze the average frequency and standard deviation. Epileptic seizure prediction and detection are done by spike detection, frequency domain analysis, and nonlinear methods. EEG signal contains different artifacts like electrooculography (EOG), EKG, and electrocardiogram (ECG). ECG signals are produced by heart. EOG signals are produced by eyes. EMG signals are produced by muscle coordination.

The National Sleep Research Resource: towards a sleep data commons.

ObjectiveThe gold standard for diagnosing sleep disorders is polysomnography, which generates extensive data about biophysical changes occurring during sleep. We developed the National Sleep Research Resource (NSRR), a comprehensive system for sharing sleep data. The NSRR embodies elements of a data commons aimed at accelerating research to address critical questions about the impact of sleep disorders on important health outcomes.ApproachWe used a metadata-guided approach, with a set of common sleep-specific terms enforcing uniform semantic interpretation of data elements across three main components: (1) annotated datasets; (2) user interfaces for accessing data; and (3) computational tools for the analysis of polysomnography recordings. We incorporated the process for managing dataset-specific data use agreements, evidence of Institutional Review Board review, and the corresponding access control in the NSRR web portal. The metadata-guided approach facilitates structural and semantic interoperability, ultimately leading to enhanced data reusability and scientific rigor.ResultsThe authors curated and deposited retrospective data from 10 large, NIH-funded sleep cohort studies, including several from the Trans-Omics for Precision Medicine (TOPMed) program, into the NSRR. The NSRR currently contains data on 26 808 subjects and 31 166 signal files in European Data Format. Launched in April 2014, over 3000 registered users have downloaded over 130 terabytes of data.ConclusionsThe NSRR offers a use case and an example for creating a full-fledged data commons. It provides a single point of access to analysis-ready physiological signals from polysomnography obtained from multiple sources, and a wide variety of clinical data to facilitate sleep research.

A novel, fast and efficient single-sensor automatic sleep-stage classification based on complementary cross-frequency coupling estimates

ObjectiveLimitations of the manual scoring of polysomnograms, which include data from electroencephalogram (EEG), electro-oculogram (EOG), electrocardiogram (ECG) and electromyogram (EMG) channels have long been recognized. Manual staging is resource intensive and time consuming, and thus considerable effort must be spent to ensure inter-rater reliability. As a result, there is a great interest in techniques based on signal processing and machine learning for a completely Automatic Sleep Stage Classification (ASSC). MethodsIn this paper, we present a single-EEG-sensor ASSC technique based on the dynamic reconfiguration of different aspects of cross-frequency coupling (CFC) estimated between predefined frequency pairs over 5 s epoch lengths. The proposed analytic scheme is demonstrated using the PhysioNet Sleep European Data Format (EDF) Database with repeat recordings from 20 healthy young adults. We validate our methodology in a second sleep dataset. ResultsWe achieved very high classification sensitivity, specificity and accuracy of 96.2 ± 2.2%, 94.2 ± 2.3%, and 94.4 ± 2.2% across 20 folds, respectively, and also a high mean F1 score (92%, range 90–94%) when a multi-class Naive Bayes classifier was applied. High classification performance has been achieved also in the second sleep dataset. ConclusionsOur method outperformed the accuracy of previous studies not only on different datasets but also on the same database. SignificanceSingle-sensor ASSC makes the entire methodology appropriate for longitudinal monitoring using wearable EEG in real-world and laboratory-oriented environments.

IoT-Based Wireless Polysomnography Intelligent System for Sleep Monitoring

Polysomnography (PSG) is considered the gold standard in the diagnosis of obstructive sleep apnea (OSA). The diagnosis of OSA requires an overnight sleep experiment in a laboratory. However, due to limitations in relation to the number of labs and beds available, patients often need to wait a long time before being diagnosed and eventually treated. In addition, the unfamiliar environment and restricted mobility when a patient is being tested with a polysomnogram may disturb their sleep, resulting in an incomplete or corrupted test. Therefore, it is posed that a PSG conducted in the patient’s home would be more reliable and convenient. The Internet of Things (IoT) plays a vital role in the e-Health system. In this paper, we implement an IoT-based wireless polysomnography system for sleep monitoring, which utilizes a battery-powered, miniature, wireless, portable, and multipurpose recorder. A Java-based PSG recording program in the personal computer is designed to save several bio-signals and transfer them into the European data format. These PSG records can be used to determine a patient’s sleep stages and diagnose OSA. This system is portable, lightweight, and has low power-consumption. To demonstrate the feasibility of the proposed PSG system, a comparison was made between the standard PSG-Alice 5 Diagnostic Sleep System and the proposed system. Several healthy volunteer patients participated in the PSG experiment and were monitored by both the standard PSG-Alice 5 Diagnostic Sleep System and the proposed system simultaneously, under the supervision of specialists at the Sleep Laboratory in Taipei Veteran General Hospital. A comparison of the results of the time-domain waveform and sleep stage of the two systems shows that the proposed system is reliable and can be applied in practice. The proposed system can facilitate the long-term tracing and research of personal sleep monitoring at home.

Sleep: An Open-Source Python Software for Visualization, Analysis, and Staging of Sleep Data.

We introduce Sleep, a new Python open-source graphical user interface (GUI) dedicated to visualization, scoring and analyses of sleep data. Among its most prominent features are: (1) Dynamic display of polysomnographic data, spectrogram, hypnogram and topographic maps with several customizable parameters, (2) Implementation of several automatic detection of sleep features such as spindles, K-complexes, slow waves, and rapid eye movements (REM), (3) Implementation of practical signal processing tools such as re-referencing or filtering, and (4) Display of main descriptive statistics including publication-ready tables and figures. The software package supports loading and reading raw EEG data from standard file formats such as European Data Format, in addition to a range of commercial data formats. Most importantly, Sleep is built on top of the VisPy library, which provides GPU-based fast and high-level visualization. As a result, it is capable of efficiently handling and displaying large sleep datasets. Sleep is freely available (http://visbrain.org/sleep) and comes with sample datasets and an extensive documentation. Novel functionalities will continue to be added and open-science community efforts are expected to enhance the capacities of this module.

New algorithms for processing time-series big EEG data within mobile health monitoring systems

Background and objectivesRecent advances in miniature biomedical sensors, mobile smartphones, wireless communications, and distributed computing technologies provide promising techniques for developing mobile health systems. Such systems are capable of monitoring epileptic seizures reliably, which are classified as chronic diseases. Three challenging issues raised in this context with regard to the transformation, compression, storage, and visualization of big data, which results from a continuous recording of epileptic seizures using mobile devices. MethodsIn this paper, we address the above challenges by developing three new algorithms to process and analyze big electroencephalography data in a rigorous and efficient manner. The first algorithm is responsible for transforming the standard European Data Format (EDF) into the standard JavaScript Object Notation (JSON) and compressing the transformed JSON data to decrease the size and time through the transfer process and to increase the network transfer rate. The second algorithm focuses on collecting and storing the compressed files generated by the transformation and compression algorithm. The collection process is performed with respect to the on-the-fly technique after decompressing files. The third algorithm provides relevant real-time interaction with signal data by prospective users. It particularly features the following capabilities: visualization of single or multiple signal channels on a smartphone device and query data segments. ResultsWe tested and evaluated the effectiveness of our approach through a software architecture model implementing a mobile health system to monitor epileptic seizures. The experimental findings from 45 experiments are promising and efficiently satisfy the approach's objectives in a price of linearity. Moreover, the size of compressed JSON files and transfer times are reduced by 10% and 20%, respectively, while the average total time is remarkably reduced by 67% through all performed experiments. ConclusionsOur approach successfully develops efficient algorithms in terms of processing time, memory usage, and energy consumption while maintaining a high scalability of the proposed solution. Our approach efficiently supports data partitioning and parallelism relying on the MapReduce platform, which can help in monitoring and automatic detection of epileptic seizures.

Sleep Stage Classification Using EEG Signal Analysis: A Comprehensive Survey and New Investigation

Sleep specialists often conduct manual sleep stage scoring by visually inspecting the patient’s neurophysiological signals collected at sleep labs. This is, generally, a very difficult, tedious and time-consuming task. The limitations of manual sleep stage scoring have escalated the demand for developing Automatic Sleep Stage Classification (ASSC) systems. Sleep stage classification refers to identifying the various stages of sleep and is a critical step in an effort to assist physicians in the diagnosis and treatment of related sleep disorders. The aim of this paper is to survey the progress and challenges in various existing Electroencephalogram (EEG) signal-based methods used for sleep stage identification at each phase; including pre-processing, feature extraction and classification; in an attempt to find the research gaps and possibly introduce a reasonable solution. Many of the prior and current related studies use multiple EEG channels, and are based on 30 s or 20 s epoch lengths which affect the feasibility and speed of ASSC for real-time applications. Thus, in this paper, we also present a novel and efficient technique that can be implemented in an embedded hardware device to identify sleep stages using new statistical features applied to 10 s epochs of single-channel EEG signals. In this study, the PhysioNet Sleep European Data Format (EDF) Database was used. The proposed methodology achieves an average classification sensitivity, specificity and accuracy of 89.06%, 98.61% and 93.13%, respectively, when the decision tree classifier is applied. Finally, our new method is compared with those in recently published studies, which reiterates the high classification accuracy performance.