A simple format for exchange of digitized polygraphic recordings

Pitfalls and possibilities of using Root SedLine for continuous assessment of EEG waveform-based metrics in intensive care research

Objective.The Root SedLine device is used for continuous electroencephalography (cEEG)-based sedation monitoring in intensive care patients. The cEEG traces can be collected for further processing and calculation of relevant metrics not already provided. Depending on the device settings during acquisition, the acquired traces may be distorted by max/min value cropping or high digitization errors. We aimed to systematically assess the impact of these distortions on metrics used for clinical research in the field of neuromonitoring.Approach.A 16 h cEEG acquired using the Root SedLine device at the optimal screen settings was analyzed. Cropping and digitization error effects were simulated by consecutive reduction of the maximum cEEG amplitude by 2µV or by reducing the vertical resolution. Metrics were calculated within ICM+ using minute-by-minute data, including the total power, alpha delta ratio (ADR), and 95% spectral edge frequency. Data were analyzed by creating violin- or box-plots.Main Results.Cropping led to a continuous reduction in total and band power, leading to corresponding changes in variability thereof. The relative power and ADR were less affected. Changes in resolution led to relevant changes. While the total power and power of low frequencies were rather stable, the power of higher frequencies increased with reducing resolution.Significance.Care must be taken when acquiring and analyzing cEEG waveforms from Root SedLine for clinical research. To retrieve good quality metrics, the screen settings must be kept within the central vertical scale, while pre-processing techniques must be applied to exclude unacceptable periods.

Interhemispheric coherence of EEG rhythms in children: Maturation and differentiation in corpus callosum dysgenesis

OBJECTIVES

To evaluate the evolution of interhemispheric coherences (ICo) in background and spindle frequency bands during childhood and use it to identify individuals with corpus callosum dysgenesis (CCd).

METHODS

A monocentric cohort of children aged from 0.25 to 15 years old, consisting of 13 children with CCd and 164 without, was analyzed. The ICo of background activity (ICOBckgrdA), sleep spindles (ICOspindles), and their sum (sICO) were calculated. The impact of age, gender, and CC status on the ICo was evaluated, and the sICO was used to discriminate children with or without CCd.

RESULTS

ICOBckgrdA, ICOspindles and sICO increased significantly with age without any effect of gender (p < 10-4), in both groups. The regression equations of the different ICo were stronger, with adjusted R2 values of 0.54, 0.35, and 0.57, respectively. The ICo was lower in children with CCd compared to those without CCd (p < 10-4 for all comparisons). The area under the precision recall curves for predicting CCd using sICO was 0.992 with 98.9 % sensitivity and 87.5 % specificity.

DISCUSSION

ICo of spindles and background activity evolve in parallel to brain maturation and depends on the integrity of the corpus callosum. sICO could be an effective diagnostic biomarker for screening children with interhemispheric dysfunction.

The National Sleep Research Resource: making data findable, accessible, interoperable, reusable and promoting sleep science

This paper presents a comprehensive overview of the National Sleep Research Resource (NSRR), a National Heart Lung and Blood Institute-supported repository developed to share data from clinical studies focused on the evaluation of sleep disorders. The NSRR addresses challenges presented by the heterogeneity of sleep-related data, leveraging innovative strategies to optimize the quality and accessibility of available datasets. It provides authorized users with secure centralized access to a large quantity of sleep-related data including polysomnography, actigraphy, demographics, patient-reported outcomes, and other data. In developing the NSRR, we have implemented data processing protocols that ensure de-identification and compliance with FAIR (Findable, Accessible, Interoperable, Reusable) principles. Heterogeneity stemming from intrinsic variation in the collection, annotation, definition, and interpretation of data has proven to be one of the primary obstacles to efficient sharing of datasets. Approaches employed by the NSRR to address this heterogeneity include (1) development of standardized sleep terminologies utilizing a compositional coding scheme, (2) specification of comprehensive metadata, (3) harmonization of commonly used variables, and (3) computational tools developed to standardize signal processing. We have also leveraged external resources to engineer a domain-specific approach to data harmonization. We describe the scope of data within the NSRR, its role in promoting sleep and circadian research through data sharing, and harmonization of large datasets and analytical tools. Finally, we identify opportunities for approaches for the field of sleep medicine to further support data standardization and sharing.

Deep-Learning-Based Automated Anomaly Detection of EEGs in Intensive Care Units

An intensive care unit (ICU) is a special ward in the hospital for patients who require intensive care. It is equipped with many instruments monitoring patients' vital signs and supported by the medical staff. However, continuous monitoring demands a massive workload of medical care. To ease the burden, we aim to develop an automatic detection model to monitor when brain anomalies occur. In this study, we focus on electroencephalography (EEG), which monitors the brain electroactivity of patients continuously. It is mainly for the diagnosis of brain malfunction. We propose the gated-recurrent-unit-based (GRU-based) model for detecting brain anomalies; it predicts whether the spike or sharp wave happens within a short time window. Based on the banana montage setting, the proposed model exploits characteristics of multiple channels simultaneously to detect anomalies. It is trained, validated, and tested on separated EEG data and achieves more than 90% testing performance on sensitivity, specificity, and balanced accuracy. The proposed anomaly detection model detects the existence of a spike or sharp wave precisely; it will notify the ICU medical staff, who can provide immediate follow-up treatment. Consequently, it can reduce the medical workload in the ICU significantly.

Pan-cortical electrophysiologic changes underlying attention

We previously reported that pan-cortical effects occur when cognitive tasks end afterdischarges. For this report, we analyzed wavelet cross-coherence changes during cognitive tasks used to terminate afterdischarges studying multiple time segments and multiple groups of inter-electrode-con distances. We studied 12 patients with intractable epilepsy, with 970 implanted electrode contacts, and 39,871 electrode contact combinations. When cognitive tasks ended afterdischarges, coherence varied similarly across the cortex throughout the tasks, but there were gradations with time, distance, and frequency: (1) They tended to progressively decrease relative to baseline with time and then to increase toward baseline when afterdischarges ended. (2) During most time segments, decreases from baseline were largest for the closest inter-contact distances, moderate for intermediate inter-contact distances, and smallest for the greatest inter-contact distances. With respect to our patients' intractable epilepsy, the changes found suggest that future therapies might treat regions beyond those closest to regions of seizure onset and treat later in a seizure's evolution. Similar considerations might apply to other disorders. Our findings also suggest that cognitive tasks can result in pan-cortical coherence changes that participate in underlying attention, perhaps complementing the better-known regional mechanisms.

Preventing Long-Term Brain Damage by Nerve Agent-Induced Status Epilepticus in Rat Models Applicable to Infants: Significant Neuroprotection by Tezampanel Combined with Caramiphen but Not by Midazolam Treatment

Acute exposure to nerve agents induces a peripheral cholinergic crisis and prolonged status epilepticus (SE), causing death or long-term brain damage. To provide preclinical data pertinent to the protection of infants and newborns, we compared the antiseizure and neuroprotective effects of treating soman-induced SE with midazolam (MDZ) versus tezampanel (LY293558) in combination with caramiphen (CRM) in 12- and 7-day-old rats. The anticonvulsants were administered 1 hour after soman exposure; neuropathology data were collected up to 6 months postexposure. In both ages, the total duration of SE within 24 hours after soman exposure was significantly shorter in the LY293558 plus CRM groups compared with the MDZ groups. Neuronal degeneration was substantial in the MDZ-treated groups but absent or minimal in the groups treated with LY293558 plus CRM. Loss of neurons and interneurons in the basolateral amygdala and CA1 hippocampal area was significant in the MDZ-treated groups but virtually absent in the LY293558 plus CRM groups. Atrophy of the amygdala and hippocampus occurred only in MDZ-treated groups. Neuronal/interneuronal loss and atrophy of the amygdala and hippocampus deteriorated over time. Reduction of inhibitory activity in the basolateral amygdala and increased anxiety were found only in MDZ groups. Spontaneous recurrent seizures developed in the MDZ groups, deteriorating over time; a small percentage of rats from the LY293558 plus CRM groups also developed seizures. These results suggest that brain damage can be long lasting or permanent if nerve agent-induced SE in infant victims is treated with midazolam at a delayed timepoint after SE onset, whereas antiglutamatergic treatment with tezampanel and caramiphen provides significant neuroprotection. SIGNIFICANCE STATEMENT: To protect the brain and the lives of infants in a mass exposure to nerve agents, an anticonvulsant treatment must be administered that will effectively stop seizures and prevent neuropathology, even if offered with a relative delay after seizure onset. The present study shows that midazolam, which was recently approved by the Food and Drug Administration for the treatment of nerve agent-induced status epilepticus, is not an effective neuroprotectant, whereas brain damage can be prevented by targeting glutamate receptors.

Enhanced sleep staging with artificial intelligence: a validation study of new software for sleep scoring

Manual sleep staging (MSS) using polysomnography is a time-consuming task, requires significant training, and can lead to significant variability among scorers. STAGER is a software program based on machine learning algorithms that has been developed by Medibio Limited (Savage, MN, USA) to perform automatic sleep staging using only EEG signals from polysomnography. This study aimed to extensively investigate its agreement with MSS performed during clinical practice and by three additional expert sleep technicians. Forty consecutive polysomnographic recordings of patients referred to three US sleep clinics for sleep evaluation were retrospectively collected and analyzed. Three experienced technicians independently staged the recording using the electroencephalography, electromyography, and electrooculography signals according to the American Academy of Sleep Medicine guidelines. The staging initially performed during clinical practice was also considered. Several agreement statistics between the automatic sleep staging (ASS) and MSS, among the different MSSs, and their differences were calculated. Bootstrap resampling was used to calculate 95% confidence intervals and the statistical significance of the differences. STAGER's ASS was most comparable with, or statistically significantly better than the MSS, except for a partial reduction in the positive percent agreement in the wake stage. These promising results indicate that STAGER software can perform ASS of inpatient polysomnographic recordings accurately in comparison with MSS.

DICOM® integrated EEG data: A first clinical implementation of the new DICOM standard for neurophysiology data

OBJECTIVE

Demonstrating a pilot implementation of the Digital Imaging and Communication in Medicine (DICOM) neurophysiology standard published in 2020.

METHODS

An automated workflow for converting EEG data from a proprietary vendor EEG format to standardized and interoperable DICOM format was developed and tested.

RESULTS

Retrieval of proprietary EEG data, associated videos, annotations and metadata from the vendor EEG archive and their subsequent conversion to DICOM EEG was possible without changes to the departmental workflow. To transfer DICOM EEG data to the central radiology DICOM archive, only minor extensions in the parameterization of the archive's DICOM interfaces were necessary. Linkage with the electronic health record (EHR) and display in a DICOM EEG viewer could be demonstrated. A random sample of 88 DICOM EEG studies was compared to the original vendor files and EEG and video file sizes were comparable.

CONCLUSIONS

Storing and reviewing EEG data in standardized DICOM format is feasible, facilitated by existing DICOM infrastructure, and therefore allows for vendor independent access to EEG data.

SIGNIFICANCE

We report the first implementation of the DICOM neurophysiology standard, thus promoting standardization in the field of neurophysiology as well as data exchange and access to legacy recordings in an interoperable vendor independent format.

Somno-Art Software identifies pathology-induced changes in sleep parameters similarly to polysomnography

Polysomnographic sleep architecture parameters are commonly used to diagnose or evaluate treatment of sleep disorders. Polysomnography (PSG) having practical constraints, the development of wearable devices and algorithms to monitor and stage sleep is rising. Beside pure validation studies, it is necessary for a clinician to ensure that the conclusions drawn with a new generation wearable sleep scoring device are consistent to the ones of gold standard PSG, leading to similar interpretation and diagnosis. This paper reports on the performance of Somno-Art Software for the detection of differences in sleep parameters between patients suffering from obstructive sleep apnea (OSA), insomniac or major depressive disorder (MDD) compared to healthy subjects. On 244 subjects (n = 26 healthy, n = 28 OSA, n = 66 insomniacs, n = 124 MDD), sleep staging was obtained from PSG and Somno-Art analysis on synchronized electrocardiogram and actimetry signals. Mixed model analysis of variance was performed for each sleep parameter. Possible differences in sleep parameters were further assessed with Mann-Whitney U-test between the healthy subjects and each pathology group. All sleep parameters, except N1+N2, showed significant differences between the healthy and the pathology group. No significant differences were observed between Somno-Art Software and PSG, except a 3.6±2.2 min overestimation of REM sleep. No significant interaction 'group'*'technology' was observed, suggesting that the differences in pathologies are independent of the technology used. Overall, comparable differences between healthy subjects and pathology groups were observed when using Somno-Art Software or polysomnography. Somno-Art proposes an interesting valid tool as an aid for diagnosis and treatment follow-up in ambulatory settings.

PyDapsys: an open-source library for accessing electrophysiology data recorded with DAPSYS

In the field of neuroscience, a considerable number of commercial data acquisition and processing solutions rely on proprietary formats for data storage. This often leads to data being locked up in formats that are only accessible by using the original software, which may lead to interoperability problems. In fact, even the loss of data access is possible if the software becomes unsupported, changed, or otherwise unavailable. To ensure FAIR data management, strategies should be established to enable long-term, independent, and unified access to data in proprietary formats. In this work, we demonstrate PyDapsys, a solution to gain open access to data that was acquired using the proprietary recording system DAPSYS. PyDapsys enables us to open the recorded files directly in Python and saves them as NIX files, commonly used for open research in the electrophysiology domain. Thus, PyDapsys secures efficient and open access to existing and prospective data. The manuscript demonstrates the complete process of reverse engineering a proprietary electrophysiological format on the example of microneurography data collected for studies on pain and itch signaling in peripheral neural fibers.

Timing of cognitive effects on afterdischarge termination

OBJECTIVE

We previously studied efficacy of cognitive tasks on afterdischarge termination in patients undergoing cortical stimulation and found that diffuse wavelet cross-coherence changes on electrocorticography were associated with termination efficacy. We now report wavelet cross-coherence findings during different time segments of trials during which afterdischarges ended.

METHODS

For 12 patients with implanted subdural electrodes, we compared wavelet cross-coherence findings among several 1-second portions of cognitive tasks, reflecting task presentation, patient replies, and afterdischarge termination.

RESULTS

Coherence decreased significantly and progressively over time for 16.89, 22.53, and 30.03 Hz frequency ranges, but increased with afterdischarge termination. Coherence first increased, and then decreased for the 7.13 Hz frequency range.

CONCLUSIONS

The findings suggest that cumulative but non-specific factors, likely related primarily to attention, influence the coherence results throughout the task, with a separate effect due to resolution of the afterdischarges at the end.

SIGNIFICANCE

Task performance is well known to localize to specific brain regions and to be restricted in timing. In contrast, attention and overall mental activation might be due to emergent properties of brain as a whole and that are less circumscribed in space or time. Cognitive tasks might modify seizures and other neurological disorders.

naplib-python: Neural acoustic data processing and analysis tools in python

Recently, the computational neuroscience community has pushed for more transparent and reproducible methods across the field. In the interest of unifying the domain of auditory neuroscience, naplib-python provides an intuitive and general data structure for handling all neural recordings and stimuli, as well as extensive preprocessing, feature extraction, and analysis tools which operate on that data structure. The package removes many of the complications associated with this domain, such as varying trial durations and multi-modal stimuli, and provides a general-purpose analysis framework that interfaces easily with existing toolboxes used in the field.

Ikelos-RWA. Validation of an Automatic Tool to Quantify REM Sleep Without Atonia

Study Objectives. To present and evaluate an automatic scoring algorithm for quantification of REM-sleep without atonia (RWA) in patients with REM-sleep behaviour disorder (RBD) based on a generally accepted, well-validated visual scoring method, ("Montreal" phasic and tonic) and a recently developed, concise scoring method (Ikelos-RWA). Methods. Video-polysomnographies of 20 RBD patients (68.2 ± 7.2 years) and 20 control patients with periodic limb movement disorder (65.9 ± 6.7 years) were retrospectively analysed. RWA was estimated from chin electromyogram during REM-sleep. Visual and automated RWA scorings were correlated, and agreement (a) and Cohen's Kappa (k) calculated for 1735 minutes of REM-sleep of the RBD patients. Discrimination performance was evaluated with receiver operating characteristic (ROC) analysis. The algorithm was then applied on the polysomnographies of a cohort of 232 RBD patients (total analysed REM-sleep: 17,219 minutes) and evaluated, while correlating the different output parameters. Results. Visual and computer-derived RWA scorings correlated significantly (tonic Montreal: rTM = 0.77; phasic Montreal: rPM = 0.78; Ikelos-RWA: rI = 0.97; all p < 0.001) and showed good to excellent Kappa coefficients (kTM = 0.71; kPM = 0.79; kI = 0.77). The ROC analysis showed high sensitivities (95%-100%) and specificities (84%-95%) at the optimal operation points, with area under the curve (AUC) of 0.98, indicating high discriminating capacity. The automatic RWA scorings of 232 patients correlated significantly (rTM{I} = 0.95; rPM{I} = 0.91, p < 0.0001). Conclusions. The presented algorithm is an easy-to-use and valid tool for automatic RWA scoring in patients with RBD and may prove effective for general use being publicly available.

Neonatal EEG graded for severity of background abnormalities in hypoxic-ischaemic encephalopathy

This report describes a set of neonatal electroencephalogram (EEG) recordings graded according to the severity of abnormalities in the background pattern. The dataset consists of 169 hours of multichannel EEG from 53 neonates recorded in a neonatal intensive care unit. All neonates received a diagnosis of hypoxic-ischaemic encephalopathy (HIE), the most common cause of brain injury in full term infants. For each neonate, multiple 1-hour epochs of good quality EEG were selected and then graded for background abnormalities. The grading system assesses EEG attributes such as amplitude, continuity, sleep-wake cycling, symmetry and synchrony, and abnormal waveforms. Background severity was then categorised into 4 grades: normal or mildly abnormal EEG, moderately abnormal EEG, majorly abnormal EEG, and inactive EEG. The data can be used as a reference set of multi-channel EEG for neonates with HIE, for EEG training purposes, or for developing and evaluating automated grading algorithms.

The Future of Sleep Staging, Revisited

In 1996, I published a paper entitled "The Future of Sleep Staging". At this time, paper and ink records were the standard way of recording sleep records. Computerised systems had only recently become commercially available. The original article was a response to those initial computer-based systems, pointing out the potential limitations of the systems. Now, digital sleep recording is ubiquitous and software and hardware capabilities have improved immeasurably. However, I will argue that despite 50 years of progress, there has not been an increase in the accuracy of sleep staging. I will propose that this is due to the limitations of the task that we have set the automatic analysis methods.

L-Tetrolet Pattern-Based Sleep Stage Classification Model Using Balanced EEG Datasets

Background: Sleep stage classification is a crucial process for the diagnosis of sleep or sleep-related diseases. Currently, this process is based on manual electroencephalogram (EEG) analysis, which is resource-intensive and error-prone. Various machine learning models have been recommended to standardize and automate the analysis process to address these problems. Materials and methods: The well-known cyclic alternating pattern (CAP) sleep dataset is used to train and test an L-tetrolet pattern-based sleep stage classification model in this research. By using this dataset, the following three cases are created, and they are: Insomnia, Normal, and Fused cases. For each of these cases, the machine learning model is tasked with identifying six sleep stages. The model is structured in terms of feature generation, feature selection, and classification. Feature generation is established with a new L-tetrolet (Tetris letter) function and multiple pooling decomposition for level creation. We fuse ReliefF and iterative neighborhood component analysis (INCA) feature selection using a threshold value. The hybrid and iterative feature selectors are named threshold selection-based ReliefF and INCA (TSRFINCA). The selected features are classified using a cubic support vector machine. Results: The presented L-tetrolet pattern and TSRFINCA-based sleep stage classification model yield 95.43%, 91.05%, and 92.31% accuracies for Insomnia, Normal dataset, and Fused cases, respectively. Conclusion: The recommended L-tetrolet pattern and TSRFINCA-based model push the envelope of current knowledge engineering by accurately classifying sleep stages even in the presence of sleep disorders.

Math Skills: a New Look from Functional Data Analysis

Mental calculations involve various areas of the brain. The frontal, parietal and temporal lobes of the left hemisphere have a principal role in the completion of this typology of tasks. Their level of activation varies based on the mathematical competence and attentiveness of the subject under examination and the perceived difficulty of the task. Recent literature often investigates patterns of cerebral activity through fMRI, which is an expensive technique. In this scenario, EEGs represent a more straightforward and cheaper way to collect information regarding brain activity. In this work, we propose an EEG based method to detect differences in the cerebral activation level of people characterized by different abilities in carrying out the same arithmetical task. Our approach consists in the extraction of the activation level of a given region starting from the EEG acquired during resting state and during the completion of a subtraction task. We then analyze these data through Functional Data Analysis, a statistical technique that allows operating on biomedical signals as if they were functions. The application of this technique allowed for the detection of distinct cerebral patterns among the two groups and, more specifically, highlighted the presence of higher levels of activation in the parietal lobe in the population characterized by a lower performance.

OSASUD: A dataset of stroke unit recordings for the detection of Obstructive Sleep Apnea Syndrome

Polysomnography (PSG) is a fundamental diagnostical method for the detection of Obstructive Sleep Apnea Syndrome (OSAS). Historically, trained physicians have been manually identifying OSAS episodes in individuals based on PSG recordings. Such a task is highly important for stroke patients, since in such cases OSAS is linked to higher mortality and worse neurological deficits. Unfortunately, the number of strokes per day vastly outnumbers the availability of polysomnographs and dedicated healthcare professionals. The data in this work pertains to 30 patients that were admitted to the stroke unit of the Udine University Hospital, Italy. Unlike previous studies, exclusion criteria are minimal. As a result, data are strongly affected by noise, and individuals may suffer from several comorbidities. Each patient instance is composed of overnight vital signs data deriving from multi-channel ECG, photoplethysmography and polysomnography, and related domain expert's OSAS annotations. The dataset aims to support the development of automated methods for the detection of OSAS events based on just routinely monitored vital signs, and capable of working in a real-world scenario.

Spatial and temporal profile of high-frequency oscillations in posttraumatic epileptogenesis

We studied the role of temporal and spatial changes in high-frequency oscillation (HFO, 80–500 Hz) generation in epileptogenesis following traumatic brain injury (TBI).

Experiments were conducted on adult male Sprague Dawley rats. For the TBI group, fluid percussion injury (FPI) on the left sensorimotor area was performed to induce posttraumatic epileptogenesis. For the sham control group, only the craniotomy was performed. After TBI, 8 bipolar micro-electrodes were implanted bilaterally in the prefrontal cortex, perilesional area and homotopic contralateral site, striatum, and hippocampus. Long-term video/local field potential (LFP) recordings were performed for up to 21 weeks to identify and characterize seizures and capture HFOs. The electrode tip locations and the volume of post TBI brain lesions were further estimated by ex-vivo MRI scans. HFOs were detected during slow-wave sleep and categorized as ripple (80–200 Hz) and fast ripple (FR, 250–500 Hz) events. HFO rates and the HFO peak frequencies were compared in the 8 recording locations and across 8-weeks following TBI.

Data from 48 rats (8 sham controls and 40 TBI rats) were analyzed. Within the TBI group, 22 rats (55%) developed recurrent spontaneous seizures (E+ group), at an average of 62.2 (+17.1) days, while 18 rats (45%) did not (E− group). We observed that the HFOs in the E+ group had a higher mean peak frequency than the E− group and the sham group (P < 0.05). Furthermore, the FR rate of the E+ group showed a significant increase compared to the E−group (P < 0.01) and sham control group (P < 0.01), specifically in the perilesional area, homotopic contralateral site, bilateral hippocampus, and to a lesser degree bilateral striatum. When compared across time, the increased FR rate in the E+ group occurred immediately after the insult and remained stable across the duration of the experiment. In addition, lesion size was not statistically different in the E+ and E− group and was not correlated with HFO rates.

Our results suggest that TBI results in the formation of a widespread epileptogenic network. FR rates serve as a biomarker of network formation and predict the future development of epilepsy, however FR are not a temporally specific biomarker of TBI sequelae responsible for epileptogenesis. These results suggest that in patients, future risk of post-TBI epilepsy can be predicted early using FR.

Power spectrum and spectrogram of EEG analysis during general anesthesia: Python-based computer programming analysis

The commonly used principle for measuring the depth of anesthesia involves changes in the frequency components of the electroencephalogram (EEG) under general anesthesia. Therefore, it is essential to construct an effective spectrum and spectrogram to analyze the relationship between the depth of anesthesia and the EEG frequency during general anesthesia. This paper reviews the computer programming techniques for analyzing the spectrum and spectrogram derived from a single-channel EEG recorded during general anesthesia. A periodogram is obtained by repeating a Fourier transform on EEG segments separated by short time intervals, but spectral leakage (i.e., dissociation from the true spectrum) occurs as a consequence of unnatural segmentation and noise. While offsetting the securing of the dynamic range, practical analyses of the adaptation of the window function are explained. Finally, the multitaper method, which can suppress artifacts caused by the edges of the analysis segments, suppress noise, and probabilistically infer values that are close to the real power spectral density, is explained using practical examples of the analysis. All analyses were performed and all graphs plotted using Python under Jupyter Notebook. The analyses demonstrated the effectiveness of Python-based programming under the integrated development environment Jupyter Notebook for constructing an effective spectrum and spectrogram for analyzing the relationship between the depth of anesthesia and EEG frequency analysis in general anesthesia.

Neural fragility as an EEG marker of the seizure onset zone

Over 15 million patients with epilepsy worldwide do not respond to drugs. Successful surgical treatment requires complete removal or disconnection of the seizure onset zone (SOZ), brain region(s) where seizures originate. Unfortunately, surgical success rates vary between 30 and 70% because no clinically validated biological marker of the SOZ exists. We develop and retrospectively validate a new electroencephalogram (EEG) marker-neural fragility-in a retrospective analysis of 91 patients by using neural fragility of the annotated SOZ as a metric to predict surgical outcomes. Fragility predicts 43 out of 47 surgical failures, with an overall prediction accuracy of 76% compared with the accuracy of clinicians at 48% (successful outcomes). In failed outcomes, we identify fragile regions that were untreated. When compared to 20 EEG features proposed as SOZ markers, fragility outperformed in predictive power and interpretability, which suggests neural fragility as an EEG biomarker of the SOZ.

Inter-database validation of a deep learning approach for automatic sleep scoring

STUDY OBJECTIVES

Development of inter-database generalizable sleep staging algorithms represents a challenge due to increased data variability across different datasets. Sharing data between different centers is also a problem due to potential restrictions due to patient privacy protection. In this work, we describe a new deep learning approach for automatic sleep staging, and address its generalization capabilities on a wide range of public sleep staging databases. We also examine the suitability of a novel approach that uses an ensemble of individual local models and evaluate its impact on the resulting inter-database generalization performance.

METHODS

A general deep learning network architecture for automatic sleep staging is presented. Different preprocessing and architectural variant options are tested. The resulting prediction capabilities are evaluated and compared on a heterogeneous collection of six public sleep staging datasets. Validation is carried out in the context of independent local and external dataset generalization scenarios.

RESULTS

Best results were achieved using the CNN_LSTM_5 neural network variant. Average prediction capabilities on independent local testing sets achieved 0.80 kappa score. When individual local models predict data from external datasets, average kappa score decreases to 0.54. Using the proposed ensemble-based approach, average kappa performance on the external dataset prediction scenario increases to 0.62. To our knowledge this is the largest study by the number of datasets so far on validating the generalization capabilities of an automatic sleep staging algorithm using external databases.

CONCLUSIONS

Validation results show good general performance of our method, as compared with the expected levels of human agreement, as well as to state-of-the-art automatic sleep staging methods. The proposed ensemble-based approach enables flexible and scalable design, allowing dynamic integration of local models into the final ensemble, preserving data locality, and increasing generalization capabilities of the resulting system at the same time.

PSG-Audio, a scored polysomnography dataset with simultaneous audio recordings for sleep apnea studies

The sleep apnea syndrome is a chronic condition that affects the quality of life and increases the risk of severe health conditions such as cardiovascular diseases. However, the prevalence of the syndrome in the general population is considered to be heavily underestimated due to the restricted number of people seeking diagnosis, with the leading cause for this being the inconvenience of the current reference standard for apnea diagnosis: Polysomnography. To enhance patients' awareness of the syndrome, a great endeavour is conducted in the literature. Various home-based apnea detection systems are being developed, profiting from information in a restricted set of polysomnography signals. In particular, breathing sound has been proven highly effective in detecting apneic events during sleep. The development of accurate systems requires multitudinous datasets of audio recordings and polysomnograms. In this work, we provide the first open access dataset, comprising 212 polysomnograms along with synchronized high-quality tracheal and ambient microphone recordings. We envision this dataset to be widely used for the development of home-based apnea detection techniques and frameworks.

The critical care data exchange format: a proposed flexible data standard for combining clinical and high-frequency physiologic data in critical care

Objective.To develop a standardized format for exchanging clinical and physiologic data generated in the intensive care unit. Our goal was to develop a format that would accommodate the data collection pipelines of various sites but would not require dataset-specific schemas or ad-hoc tools for decoding and analysis.Approach.A number of centers had independently developed solutions for storing clinical and physiologic data using Hierarchical Data Format-Version 5 (HDF5), a well-supported standard already in use in multiple other fields. These individual solutions involved design choices that made the data difficult to share despite the underlying common framework. A collaborative process was used to form the basis of a proposed standard that would allow for interoperability and data sharing with common analysis tools.Main Results.We developed the HDF5-based critical care data exchange format which stores multiparametric data in an efficient, self-describing, hierarchical structure and supports real-time streaming and compression. In addition to cardiorespiratory and laboratory data, the format can, in future, accommodate other large datasets such as imaging and genomics. We demonstated the feasibility of a standardized format by converting data from three sites as well as the MIMIC III dataset.Significance.Individual approaches to the storage of multiparametric clinical data are proliferating, representing both a duplication of effort and a missed opportunity for collaboration. Adoption of a standardized format for clinical data exchange will enable the development of a digital biobank, facilitate the external validation of machine learning models and be a powerful tool for sharing multiparametric, high frequency patient level data in multisite clinical trials. Our proposed solution focuses on supporting standardized ontologies such as LOINC allowing easy reading of data regardless of the source and in so doing provides a useful method to integrate large amounts of existing data.

Age-related morphological differences in the spike-and-wave complexes of absence epilepsy

OBJECTIVE

Absence epilepsy shows age-related clinical features, as is observed in childhood and juvenile absence epilepsy. Electroencephalogram (EEG) is characterized by bursts of 3 Hz spike-and-wave complex (SWC). We noticed a morphological variation of the slow-wave component of SWCs between patients. This study investigated whether the waveform of SWC might be associated with the child's age of this epilepsy.

METHODS

Digitally-recorded EEGs under medication-free conditions were collected from 25 children who received the diagnosis of childhood or juvenile absence epilepsy. The morphology of slow wave in SWC in the frontal midline region was quantitatively compared between younger and older children using a cluster-based permutation test.

RESULTS

At <7 years of age (2.9-6.5 years of age, n = 6), the electrical potential of the descending slope in the slow wave was positively correlated with age whereas this correlation was not observed in patients of ≥7 years of age (7.1-12.9 years, n = 19). A cluster-based permutation test confirmed the results-among the entire slow wave period (0-285 msec), the period of the descending slope (195-260 msec) showed significantly lower potential in patients of <7 years of age in comparison to patients of ≥7 years of age (sum of t-values: 46.57, p-value: 0.011).

CONCLUSIONS

The current study demonstrated an age-dependent morphological difference in the slow-wave components of SWCs in EEGs of patients with pediatric absence epilepsy. This finding may provide a clue to understanding the age-related clinical manifestations of this epilepsy.

Snoring: a source of noise pollution and sleep apnea predictor

Snoring is a highly prevalent condition associated with obstructive sleep apnea (OSA) and sleep disturbance in bed partners. Objective measurements of snoring in the community, however, are limited. The present study was designed to measure sound levels produced by self-reported habitual snorers in a single night. Snorers were excluded if they reported nocturnal gasping or had severe obesity (BMI > 35 kg/m2). Sound was measured by a monitor mounted 65 cm over the head of the bed on an overnight sleep study. Snoring was defined as sound ≥40 dB(A) during flow limited inspirations. The apnea hypopnea index (AHI) and breath-by-breath peak decibel levels were measured. Snore breaths were tallied to determine the frequency and intensity of snoring. Regression models were used to determine the relationship between objective measures of snoring and OSA (AHI ≥ 5 events/h). The area under the curve (AUC) for the receiver operating characteristic (ROC) was used to predict OSA. Snoring intensity exceeded 45 dB(A) in 66% of the 162 participants studied, with 14% surpassing the 53 dB(A) threshold for noise pollution. Snoring intensity and frequency were independent predictors of OSA. AUCs for snoring intensity and frequency were 77% and 81%, respectively, and increased to 87% and 89%, respectively, with the addition of age and sex as predictors. Snoring represents a source of noise pollution in the bedroom and constitutes an important target for mitigating sound and its adverse effects on bed partners. Precise breath-by-breath identification and quantification of snoring also offers a way to risk stratify otherwise healthy snorers for OSA.

Fetal heart rate during maternal sleep

Despite prolonged and cumulative exposure during gestation, little is known about the fetal response to maternal sleep. Eighty-four pregnant women with obesity (based on pre-pregnancy BMI) participated in laboratory-based polysomnography (PSG) with continuous fetal electrocardiogram monitoring at 36 weeks gestation. Multilevel modeling revealed both correspondence and lack of it in maternal and fetal heart rate patterns. Fetal heart rate (fHR) and variability (fHRV), and maternal heart rate (mHR) and variability (mHRV), all declined during the night, with steeper rates of decline prior to 01:00. fHR declined upon maternal sleep onset but was not otherwise associated with maternal sleep stage; fHRV differed during maternal REM and NREM. There was frequent maternal waking after sleep onset (WASO) and fHRV and mHRV were elevated during these episodes. Cross-correlation analyses revealed little temporal coupling between maternal and fetal heart rate, except during WASO, suggesting that any observed associations in maternal and fetal heart rates during sleep are the result of other physiological processes. Implications of the maternal sleep context for the developing fetus are discussed, including the potential consequences of the typical sleep fragmentation that accompanies pregnancy.

A Long-Term, Real-Life Parkinson Monitoring Database Combining Unscripted Objective and Subjective Recordings

Accurate real-life monitoring of motor and non-motor symptoms is a challenge in Parkinson’s disease (PD). The unobtrusive capturing of symptoms and their naturalistic fluctuations within or between days can improve evaluation and titration of therapy. First-generation commercial PD motion sensors are promising to augment clinical decision-making in general neurological consultation, but concerns remain regarding their short-term validity, and long-term real-life usability. In addition, tools monitoring real-life subjective experiences of motor and non-motor symptoms are lacking. The dataset presented in this paper constitutes a combination of objective kinematic data and subjective experiential data, recorded parallel to each other in a naturalistic, long-term real-life setting. The objective data consists of accelerometer and gyroscope data, and the subjective data consists of data from ecological momentary assessments. Twenty PD patients were monitored without daily life restrictions for fourteen consecutive days. The two types of data can be used to address hypotheses on naturalistic motor and/or non-motor symptomatology in PD.

An Integrative Approach to Study Structural and Functional Network Connectivity in Epilepsy Using Imaging and Signal Data

A key area of research in epilepsy neurological disorder is the characterization of epileptic networks as they form and evolve during seizure events. In this paper, we describe the development and application of an integrative workflow to analyze functional and structural connectivity measures during seizure events using stereotactic electroencephalogram (SEEG) and diffusion weighted imaging data (DWI). We computed structural connectivity measures using electrode locations involved in recording SEEG signal data as reference points to filter fiber tracts. We used a new workflow-based tool to compute functional connectivity measures based on non-linear correlation coefficient, which allows the derivation of directed graph structures to represent coupling between signal data. We applied a hierarchical clustering based network analysis method over the functional connectivity data to characterize the organization of brain network into modules using data from 27 events across 8 seizures in a patient with refractory left insula epilepsy. The visualization of hierarchical clustering values as dendrograms shows the formation of connected clusters first within each insulae followed by merging of clusters across the two insula; however, there are clear differences between the network structures and clusters formed across the 8 seizures of the patient. The analysis of structural connectivity measures showed strong connections between contacts of certain electrodes within the same brain hemisphere with higher prevalence in the perisylvian/opercular areas. The combination of imaging and signal modalities for connectivity analysis provides information about a patient-specific dynamical functional network and examines the underlying structural connections that potentially influences the properties of the epileptic network. We also performed statistical analysis of the absolute changes in correlation values across all 8 seizures during a baseline normative time period and different seizure events, which showed decreased correlation values during seizure onset; however, the changes during ictal phases were varied.

Classification of Stereo-EEG Contacts in White Matter vs. Gray Matter Using Recorded Activity

For epileptic patients requiring resective surgery, a modality called stereo-electroencephalography (SEEG) may be used to monitor the patient's brain signals to help identify epileptogenic regions that generate and propagate seizures. SEEG involves the insertion of multiple depth electrodes into the patient's brain, each with 10 or more recording contacts along its length. However, a significant fraction (≈ 30% or more) of the contacts typically reside in white matter or other areas of the brain which can not be epileptogenic themselves. Thus, an important step in the analysis of SEEG recordings is distinguishing between electrode contacts which reside in gray matter vs. those that do not. MRI images overlaid with CT scans are currently used for this task, but they take significant amounts of time to manually annotate, and even then it may be difficult to determine the status of some contacts. In this paper we present a fast, automated method for classifying contacts in gray vs. white matter based only on the recorded signal and relative contact depth. We observe that bipolar referenced contacts in white matter have less power in all frequencies below 150 Hz than contacts in gray matter, which we use in a Bayesian classifier to attain an average area under the receiver operating characteristic curve of 0.85 ± 0.079 (SD) across 29 patients. Because our method gives a probability for each contact rather than a hard labeling, and uses a feature of the recorded signal that has direct clinical relevance, it can be useful to supplement decision-making on difficult to classify contacts or as a rapid, first-pass filter when choosing subsets of contacts from which to save recordings.

Advances of ECG Sensors from Hardware, Software and Format Interoperability Perspectives

It is well-known that cardiovascular disease is one of the major causes of death worldwide nowadays. Electrocardiogram (ECG) sensor is one of the tools commonly used by cardiologists to diagnose and detect signs of heart disease with their patients. Since fast, prompt and accurate interpretation and decision is important in saving the life of patients from sudden heart attack or cardiac arrest, many innovations have been made to ECG sensors. However, the use of traditional ECG sensors is still prevalent in the clinical settings of many medical institutions. This article provides a comprehensive survey on ECG sensors from hardware, software and data format interoperability perspectives. The hardware perspective outlines a general hardware architecture of an ECG sensor along with the description of its hardware components. The software perspective describes various techniques (denoising, machine learning, deep learning, and privacy preservation) and other computer paradigms used in the software development and deployment for ECG sensors. Finally, the format interoperability perspective offers a detailed taxonomy of current ECG formats and the relationship among these formats. The intention is to help researchers towards the development of modern ECG sensors that are suitable and approved for adoption in real clinical settings.

In search for the most optimal EEG method: A practical evaluation of a water-based electrode EEG system

The study assessed a mobile electroencephalography system with water-based electrodes for its applicability in cognitive and behavioural neuroscience. It was compared to a standard gel-based wired system. Electroencephalography was recorded on two occasions (first with gel-based, then water-based system) as participants completed the flanker task. Technical and practical considerations for the application of the water-based system are reported based on participant and experimenter experiences. Empirical comparisons focused on electroencephalography data noise levels, frequency power across four bands (theta, alpha, low beta and high beta) and event-related components (P300 and ERN). The water-based system registered more noise compared to the gel-based system which resulted in increased loss of data during artefact rejection. Signal-to-noise ratio was significantly lower for the water-based system in the parietal channels which affected the observed parietal beta power. It also led to a shift in topography of the maximal P300 activity from parietal to frontal regions. The water-based system may be prone to slow drift noise which may affect the reliability and consistency of low-frequency band analyses. Practical considerations for the use of water-based electrode electroencephalography systems are provided.

Technical considerations when using the EEG export of the SEDLine Root device

Electroencephalographic (EEG) patient monitoring during general anesthesia can help to assess the real-time neurophysiology of unconscious states. Some monitoring systems like the SEDLine Root allow export of the EEG to be used for retrospective analysis. We show that changes made to the SEDLine display during recording affected the recorded EEG. These changes can strongly impact retrospective analysis of EEG signals. Real-time changes of the feed speed in the SEDLine Root device display modifies the sampling rate of the exported EEG. We used a patient as well as a simulated EEG recording to highlight the effects of the display settings on the extracted EEG. Therefore, we changed EEG feed and amplitude resolution on the display in a systematic manner. To visualize the effects of these changes, we present raw EEG segments and the density spectral array of the recording. Changing the display’s amplitude resolution affects the amplitudes. If the amplitude resolution is too fine, the exported EEG contains clipped amplitudes. If the resolution is too coarse, the EEG resolution becomes too low leading to a low-quality signal making frequency analysis impossible. The proportion of clipped or zero-line data caused by the amplitude setting was > 60% in our sedated patient. Changing the display settings results in undocumented changes in EEG amplitude, sampling rate, and signal quality. The occult nature of these changes could make the analysis of data sets difficult if not invalid. We strongly suggest researchers adequately define and keep the EEG display settings to export good quality EEG and to ensure comparability among patients.

Towards standardization guidelines for in silico approaches in personalized medicine

Despite the ever-progressing technological advances in producing data in health and clinical research, the generation of new knowledge for medical benefits through advanced analytics still lags behind its full potential. Reasons for this obstacle are the inherent heterogeneity of data sources and the lack of broadly accepted standards. Further hurdles are associated with legal and ethical issues surrounding the use of personal/patient data across disciplines and borders. Consequently, there is a need for broadly applicable standards compliant with legal and ethical regulations that allow interpretation of heterogeneous health data through in silico methodologies to advance personalized medicine. To tackle these standardization challenges, the Horizon2020 Coordinating and Support Action EU-STANDS4PM initiated an EU-wide mapping process to evaluate strategies for data integration and data-driven in silico modelling approaches to develop standards, recommendations and guidelines for personalized medicine. A first step towards this goal is a broad stakeholder consultation process initiated by an EU-STANDS4PM workshop at the annual COMBINE meeting (COMBINE 2019 workshop report in same issue). This forum analysed the status quo of data and model standards and reflected on possibilities as well as challenges for cross-domain data integration to facilitate in silico modelling approaches for personalized medicine.

Machine-learning-based diagnostics of EEG pathology

Machine learning (ML) methods have the potential to automate clinical EEG analysis. They can be categorized into feature-based (with handcrafted features), and end-to-end approaches (with learned features). Previous studies on EEG pathology decoding have typically analyzed a limited number of features, decoders, or both. For a I) more elaborate feature-based EEG analysis, and II) in-depth comparisons of both approaches, here we first develop a comprehensive feature-based framework, and then compare this framework to state-of-the-art end-to-end methods. To this aim, we apply the proposed feature-based framework and deep neural networks including an EEG-optimized temporal convolutional network (TCN) to the task of pathological versus non-pathological EEG classification. For a robust comparison, we chose the Temple University Hospital (TUH) Abnormal EEG Corpus (v2.0.0), which contains approximately 3000 EEG recordings. The results demonstrate that the proposed feature-based decoding framework can achieve accuracies on the same level as state-of-the-art deep neural networks. We find accuracies across both approaches in an astonishingly narrow range from 81 to 86%. Moreover, visualizations and analyses indicated that both approaches used similar aspects of the data, e.g., delta and theta band power at temporal electrode locations. We argue that the accuracies of current binary EEG pathology decoders could saturate near 90% due to the imperfect inter-rater agreement of the clinical labels, and that such decoders are already clinically useful, such as in areas where clinical EEG experts are rare. We make the proposed feature-based framework available open source and thus offer a new tool for EEG machine learning research.

Real-Time Monitoring of Blood Pressure Using Digitalized Pulse Arrival Time Calculation Technology for Prompt Detection of Sudden Hypertensive Episodes During Laryngeal Microsurgery: Retrospective Observational Study

Background

Laryngeal microsurgery (LMS) is often accompanied by a sudden increase in blood pressure (BP) during surgery because of stimulation around the larynx. This sudden change in the hemodynamic status is not immediately reflected in a casual cuff-type measurement that takes intermittent readings every 3 to 5 min.

Objective

This study aimed to investigate the potential of pulse arrival time (PAT) as a marker for a BP surge, which usually occurs in patients undergoing LMS.

Methods

Intermittent measurements of BP and electrocardiogram (ECG) and photoplethysmogram (PPG) signals were recorded during LMS. PAT was defined as the interval between the R-peak on the ECG and the maximum slope on the PPG. Mean PAT values before and after BP increase were compared. PPG-related parameters and the correlations between changes in these variables were calculated.

Results

BP surged because of laryngoscopic manipulation (mean systolic BP [SBP] from 115.3, SD 21.4 mmHg, to 159.9, SD 25.2 mmHg; P<.001), whereas PAT decreased significantly (from mean 460.6, SD 51.9 ms, to 405.8, SD 50.1 ms; P<.001) in most of the cases. The change in SBP showed a significant correlation with the inverse of the PAT (r=0.582; P<.001). Receiver-operating characteristic curve analysis indicated that an increase of 11.5% in the inverse of the PAT could detect a 40% increase in SBP, and the area under the curve was 0.814.

Conclusions

During LMS, where invasive arterial catheterization is not always possible, PAT shows good correlation with SBP and may, therefore, have the potential to identify abrupt BP surges during laryngoscopic manipulations in a noninvasive manner.

Electroencephalographic analysis in soman-exposed 21-day-old rats and the effects of midazolam or LY293558 with caramiphen

Acute nerve agent exposure induces status epilepticus (SE), which can cause brain damage or death. Research aiming at developing effective therapies for controlling nerve agent-induced SE is commonly performed in adult rats. The characteristics of nerve agent-induced SE in young rats are less clear; relevant knowledge is necessary for developing effective pediatric therapies. Here, we have used electroencephalographic (EEG) recordings and analysis to study seizures in postnatal day 21 rats exposed to 1.2 × LD₅₀ of soman, and compared the antiseizure efficacy of midazolam (MDZ)-currently considered by the Food and Drug Administration to replace diazepam for treating SE in victims of nerve agent exposure-with that of LY293558, an AMPA/GluK1 receptor antagonist, administered in combination with caramiphen, an antimuscarinic with N-methyl-d-aspartate receptor antagonistic properties. Prolonged SE developed in 80% of the rats and was reflected in behavioral seizures/convulsions. Both MDZ and LY293558 + caramiphen stopped the SE induced by soman, but there was a significant recurrence of seizures within 24 h postexposure only in the MDZ-treated group, as revealed in the raw EEG data and their representation in the frequency domain using a fast Fourier transform and in spectral analysis over 24 hours. In contrast to the high efficacy of LY293558 + caramiphen, MDZ is not an effective treatment for SE induced by soman in young animals.

EEG sleep stages identification based on weighted undirected complex networks

BACKGROUND AND OBJECTIVE

Sleep scoring is important in sleep research because any errors in the scoring of the patient's sleep electroencephalography (EEG) recordings can cause serious problems such as incorrect diagnosis, medication errors, and misinterpretations of patient's EEG recordings. The aim of this research is to develop a new automatic method for EEG sleep stages classification based on a statistical model and weighted brain networks.

METHODS

Each EEG segment is partitioned into a number of blocks using a sliding window technique. A set of statistical features are extracted from each block. As a result, a vector of features is obtained to represent each EEG segment. Then, the vector of features is mapped into a weighted undirected network. Different structural and spectral attributes of the networks are extracted and forwarded to a least square support vector machine (LS-SVM) classifier. At the same time the network's attributes are also thoroughly investigated. It is found that the network's characteristics vary with their sleep stages. Each sleep stage is best represented using the key features of their networks.

RESULTS

In this paper, the proposed method is evaluated using two datasets acquired from different channels of EEG (Pz-Oz and C3-A2) according to the R&K and the AASM without pre-processing the original EEG data. The obtained results by the LS-SVM are compared with those by Naïve, k-nearest and a multi-class-SVM. The proposed method is also compared with other benchmark sleep stages classification methods. The comparison results demonstrate that the proposed method has an advantage in scoring sleep stages based on single channel EEG signals.

CONCLUSIONS

An average accuracy of 96.74% is obtained with the C3-A2 channel according to the AASM standard, and 96% with the Pz-Oz channel based on the R&K standard.

An emerging technology for the identification and characterization of postural-dependent obstructive sleep apnea

STUDY OBJECTIVES

Body posture has a significant impact on the presence and severity of obstructive sleep apnea (OSA). The majority of polysomnography (PSG) systems have the capacity to categorize body (torso) posture as supine, left-lateral, right-lateral or prone, each within a 90-degree range. However, such broad categorization may limit the identification of subtle relationships between posture and OSA severity. The aim of this study was to quantify sleeping posture as a continuous variable; and to develop an intuitive tool for visualizing the relationship between body posture and OSA severity.

METHODS

A customized triaxial accelerometer-based posture sensor which quantifies torso posture as a continuous variable was developed. 38 participants attending the sleep laboratory for suspected OSA were recruited. Each participant underwent a diagnostic PSG with an additional customized posture sensor securely attached to the sternum. Individual data were presented using a novel circular histogram-based visualization which displays sleeping position and position-specific OSA severity.

RESULTS

Acceptable measurements were obtained in 21 participants. The mean ± standard deviation percentage of total sleep time spent within ± 15 degrees of the center of supine, left-lateral, right-lateral and prone was 59.7 ± 26.0%. A further 40.3 ± 26.0% of sleep time was spent in intermediate positions outside these traditional categorizations. The novel visualization revealed a wide variety of positional OSA phenotypes.

CONCLUSIONS

Quantification of torso posture as a continuous variable and analysis of these data using a novel visualization enables the identification of subtle relationships between body posture and OSA severity that are not apparent using standard clinical sensors and summary statistics.

Sleep and motor sequence learning consolidation in former iron deficient anemic adolescents

BACKGROUND

Iron deficiency is the most prevalent micronutrient deficiency worldwide. There is evidence that iron deficiency produces alterations in the developing brain, eventually leading to long-lasting effects on various cognitive functions.

METHODS

Here, we investigated motor learning and its consolidation after sleep in adolescents who sustained iron deficiency anemia (IDA) in infancy, compared to healthy controls, in the context of a long-term follow-up Chilean research project. Fifty-three adolescents who formerly had iron deficiency anemia as infants and 40 control adolescents practiced a sequential motor finger tapping task, before and after a night of sleep. Performance was measured at the end of learning, 30 min later (boost effect), and the next morning.

RESULTS

Revealed slower learning in subjects with infant iron deficiency anemia than control subjects, followed by a proportionally similar performance boost at 30 min. Performance remained stable overnight in healthy controls but further improved in infant IDA adolescents, suggesting a beneficial effect of post-training sleep on the consolidation of incompletely learned motor skills. In particular, overnight gains in performance were observed in female, but not male infant iron deficiency anemic subjects, suggesting a gender effect.

CONCLUSIONS

Our results indicate long-lasting motor learning deficits in infant IDA adolescents and provide support to the hypothesis that post-training sleep might, to some extent, compensate for hampered motor learning during wakefulness.

Arm-ECG Wireless Sensor System for Wearable Long-Term Surveillance of Heart Arrhythmias

This article presents the devising, development, prototyping and assessment of a wearable arm-ECG sensor system (WAMECG1) for long-term non-invasive heart rhythm monitoring, and functionalities for acquiring, storing, visualizing and transmitting high-quality far-field electrocardiographic signals. The system integrates the main building blocks present in a typical ECG monitoring device such as the skin surface electrodes, front-end amplifiers, analog and digital signal conditioning filters, flash memory and wireless communication capability. These are integrated into a comfortable, easy to wear, and ergonomically designed arm-band ECG sensor system which can acquire a bipolar ECG signal from the upper arm of the user over a period of 72 h. The small-amplitude bipolar arm-ECG signal is sensed by a reusable, long-lasting, Ag–AgCl based dry electrode pair, then digitized using a programmable sampling rate in the range of 125 to 500 Hz and transmitted via Wi-Fi. The prototype comparative performance assessment results showed a cross-correlation value of 99.7% and an error of less than 0.75% when compared to a reference high-resolution medical-grade ECG system. Also, the quality of the recorded far-field bipolar arm-ECG signal was validated in a pilot trial with volunteer subjects from within the research team, by wearing the prototype device while: (a) resting in a chair; and (b) doing minor physical activities. The R-peak detection average sensibilities were 99.66% and 94.64%, while the positive predictive values achieved 99.1% and 92.68%, respectively. Without using any additional algorithm for signal enhancement, the average signal-to-noise ratio (SNR) values were 21.71 and 18.25 for physical activity conditions (a) and (b) respectively. Therefore, the performance assessment results suggest that the wearable arm-band prototype device is a suitable, self-contained, unobtrusive platform for comfortable cardiac electrical activity and heart rhythm logging and monitoring.

R-DECO: an open-source Matlab based graphical user interface for the detection and correction of R-peaks

Many of the existing electrocardiogram (ECG) toolboxes focus on the derivation of heart rate variability features from RR-intervals. By doing so, they assume correct detection of the QRS-complexes. However, it is highly likely that not all detections are correct. Therefore, it is recommended to visualize the actual R-peak positions in the ECG signal and allow manual adaptations. In this paper we present R-DECO, an easy-to-use graphical user interface (GUI) for the detection and correction of R-peaks. Within R-DECO, the R-peaks are detected by using a detection algorithm which uses an envelope-based procedure. This procedure flattens the ECG and enhances the QRS-complexes. The algorithm obtained an overall sensitivity of 99.60% and positive predictive value of 99.69% on the MIT/BIH arrhythmia database. Additionally, R-DECO includes support for several input data formats for ECG signals, three basic filters, the possibility to load other R-peak locations and intuitive methods to correct ectopic, wrong, or missed heartbeats. All functionalities can be accessed via the GUI and the analysis results can be exported as Matlab or Excel files. The software is publicly available. Through its easy-to-use GUI, R-DECO allows both clinicians and researchers to use all functionalities, without previous knowledge.

A Performant Web-Based Visualization, Assessment, and Collaboration Tool for Multidimensional Biosignals

Biosignal-based research is often multidisciplinary and benefits greatly from multi-site collaboration. This requires appropriate tooling that supports collaboration, is easy to use, and is accessible. However, current software tools do not provide the necessary functionality, usability, and ubiquitous availability. The latter is particularly crucial in environments, such as hospitals, which often restrict users' permissions to install software. This paper introduces a new web-based application for interactive biosignal visualization and assessment. A focus has been placed on performance to allow for handling files of any size. The proposed solution can load local and remote files. It parses data locally on the client, and harmonizes channel labels. The data can then be scored, annotated, pseudonymized and uploaded to a clinical data management system for further analysis. The data and all actions can be interactively shared with a second party. This lowers the barrier to quickly visually examine data, collaborate and make informed decisions.

Dataset of EEG power integral, spontaneous recurrent seizure and behavioral responses following combination drug therapy in soman-exposed rats

This article investigated the efficacy of the combination of antiepileptic drug therapy in protecting against soman-induced seizure severity, epileptogenesis and performance deficits. Adult male rats with implanted telemetry transmitters for continuous recording of electroencephalographic (EEG) activity were exposed to soman and treated with atropine sulfate and the oxime HI-6 one minute after soman exposure and with midazolam, ketamine and/or valproic acid 40 min after seizure onset. Rats exposed to soman and treated with medical countermeasures were evaluated for survival, seizure severity, the development of spontaneous recurrent seizure and performance deficits; combination anti-epileptic drug therapy was compared with midazolam monotherapy. Telemetry transmitters were used to record EEG activity, and a customized MATLAB algorithm was used to analyze the telemetry data. Survival data, EEG power integral data, spontaneous recurrent seizure data and behavioral data are illustrated in figures and included as raw data. In addition, edf files of one month telemetry recordings from soman-exposed rats treated with delayed midazolam are provided as supplementary materials. Data presented in this article are related to research articles “Rational Polytherapy in the Treatment of Cholinergic Seizures” [1] and “Early polytherapy for benzodiazepine-refractory status epilepticus [4].

Neurosteroid and benzodiazepine combination therapy reduces status epilepticus and long-term effects of whole-body sarin exposure in rats

OBJECTIVE

Our objective was to evaluate the protective efficacy of the neurosteroid pregnanolone (3α-hydroxy-5β pregnan-20-one), a GABAA receptor-positive allosteric modulator, as an adjunct to benzodiazepine therapy against the chemical warfare nerve agent (CWNA) sarin (GB), using whole-body exposure, an operationally relevant route of exposure to volatile GB.

METHODS

Rats implanted with telemetry transmitters for the continuous measurement of cortical electroencephalographic (EEG) activity were exposed for 60 minutes to 3.0 LCt50 of GB via whole-body exposure. At the onset of toxic signs, rats were administered an intramuscular injection of atropine sulfate (2 mg/kg) and the oxime HI-6 (93.6 mg/kg) to increase survival rate and, 30 minutes after seizure onset, treated subcutaneously with diazepam (10 mg/kg) and intravenously with pregnanolone (4 mg/kg) or vehicle. Animals were evaluated for GB-induced status epilepticus (SE), spontaneous recurrent seizures (SRS), impairment in spatial memory acquisition, and brain pathology, and treatment groups were compared.

RESULTS

Delayed dual therapy with pregnanolone and diazepam reduced time in SE in GB-exposed rats compared to those treated with delayed diazepam monotherapy. The combination therapy of pregnanolone with diazepam also prevented impairment in the Morris water maze and reduced the neuronal loss and neuronal degeneration, evaluated at one and three months after exposure.

SIGNIFICANCE

Neurosteroid administration as an adjunct to benzodiazepine therapy offers an effective means to treat benzodiazepine-refractory SE, such as occurs following delayed treatment of GB exposure. This study is the first to present data on the efficacy of delayed pregnanolone and diazepam dual therapy in reducing seizure activity, performance deficits and brain pathology following an operationally relevant route of exposure to GB and supports the use of a neurosteroid as an adjunct to standard anticonvulsant therapy for the treatment of CWNA-induced SE.

Cognitive effort decreases beta, alpha, and theta coherence and ends afterdischarges in human brain

OBJECTIVE

Mental activation has been reported to modify the occurrence of epileptiform activity. We studied its effect on afterdischarges.

METHOD

In 15 patients with implanted electrodes we presented cognitive tasks when afterdischarges occurred. We developed a wavelet cross-coherence function to analyze the electrocorticography before and after the tasks and compared findings when cognitive tasks did or did not result in afterdischarge termination. Six patients returned for functional MRI (fMRI) testing, using similar tasks.

RESULTS

Cognitive tasks often could terminate afterdischarges when direct abortive stimulation could not. Wavelet cross-coherence analysis showed that, when afterdischarges stopped, there was decreased coherence throughout the brain in the 7.13-22.53 Hz frequency ranges (p values 0.008-0.034). This occurred a) regardless of whether an area activated on fMRI and b) regardless of whether there were afterdischarges in the area.

CONCLUSIONS

It is known that cognitive tasks can alter localized or network synchronization. Our results show that they can change activity throughout the brain. These changes in turn can terminate localized epileptiform activity.

SIGNIFICANCE

Cognitive tasks result in diffuse brain changes that can modify focal brain activity. Combined with a seizure detection device, cognitive activation might provide a non-invasive method of terminating or modifying seizures.

EEG-BIDS, an extension to the brain imaging data structure for electroencephalography

The Brain Imaging Data Structure (BIDS) project is a rapidly evolving effort in the human brain imaging research community to create standards allowing researchers to readily organize and share study data within and between laboratories. Here we present an extension to BIDS for electroencephalography (EEG) data, EEG-BIDS, along with tools and references to a series of public EEG datasets organized using this new standard.