EEG Frequency Research Articles

Meta-analysis on QEEG Changes to Antidepressant Treatment Among Patients with Depression

Introduction: Diagnostic and treatment accuracy of depression can lead to a better and possibly earlier response and remission in patients. The literature, though scanty, seems to suggest that quantitative electroencephalography (QEEG) can predict the outcome of antidepressant effects. Methodology: Articles published between January 1990 and July 2019, including those dealing with QEEG recordings before and after the initiation of antidepressant medication, were included. The pooled effect size and subgroup analysis of waveforms were calculated to predict response to antidepressants. Result: In all, 572 results were retrieved from the searches, of which 20 studies were included. Pooled data using a random-effects model (REM) calculated an effect size of 0.80 (95% CI [0.64–0.97]). Heterogeneity of the sample was low with Tau² = 0.02; df = 18 ( P = .30); I² = 12%. Moreover, subgroup analysis showed that theta band frequencies were better at predicting response than alpha band frequencies (the standard mean difference [SMD] for theta was 0.91 compared to 0.68 for alpha waves). Conclusions: QEEG is a valuable predictor of the antidepressant response. Among the EEG frequencies, the theta band showed the most significant change with treatment.

EEG frequency tagging reveals the integration of dissimilar observed actions

Extensive research has demonstrated that visual and motor cortices can simultaneously represent multiple observed actions. This ability undoubtedly constitutes a crucial ingredient for the understanding of complex visual scenes involving different agents. However, it is still unclear how these distinct representations are integrated into coherent and meaningful percepts. In line with studies of perceptual binding, we hypothesized that similar movements would be more easily integrated. To test this hypothesis, we developed an EEG frequency tagging experiment in which two hand movements were displayed simultaneously at two different presentation rates. Crucially, the degree of similarity between the two movements varied along two dimensions, namely action identity (i.e., same or different performed movement), and agent identity (i.e., one agent performing a bimanual movement, or two agents moving each one hand). Contrary to our predictions, we found a larger intermodulation oscillatory component, indexing the integrated processing of the two individual movements, when they were less similar. We propose that integration-by-dissimilarity might serve as a top-down process to solve conflict caused by incongruent movements, thus contributing to the global understanding of distinct moving individuals in a complex social scene.

Neural encoding of melodic expectations in music across EEG frequency bands.

The human brain tracks regularities in the environment and extrapolates these to predict future events. Prior work on music cognition suggests that low-frequency (1-8Hz) brain activity encodes melodic predictions beyond the stimulus acoustics. Building on this work, we aimed to disentangle the frequency-specific neural dynamics linked to melodic prediction uncertainty (modelled as entropy) and prediction error (modelled as surprisal) for temporal (note onset) and content (note pitch) information. By using multivariate temporal response function (TRF) models, we re-analysed the electroencephalogram (EEG) from 20 subjects (10 musicians) who listened to Western tonal music. Our results show that melodic expectation metrics improve the EEG reconstruction accuracy in all frequency bands below the gamma range (< 30 Hz). Crucially, we found that entropy contributed more strongly to the reconstruction accuracy enhancement compared to surprisal in all frequency bands. Additionally, we found that the encoding of temporal, but not content, information metrics was not limited to low frequencies, rather it extended to higher frequencies (> 8Hz). An analysis of the TRF weights revealed that the temporal predictability of a note (entropy of note onset) may be encoded in the delta- (1-4Hz) and beta-band (12-30 Hz) brain activity prior to the stimulus, suggesting that these frequency bands associate with temporal predictions. Strikingly, we also revealed that melodic expectations selectively enhanced EEG reconstruction accuracy in the beta band for musicians, and in the alpha band (8-12 Hz) for non-musicians, suggesting that musical expertise influences the neural dynamics underlying predictive processing in music cognition.

Quantitative EEG biomarkers for STXBP1-related disorders.

EEG patterns and quantitative EEG (qEEG) features have been poorly explored in monogenic epilepsies. Herein, we investigate regional differences in EEG frequency composition in patients with STXBP1 developmental and epileptic encephalopathy (STXBP1-DEE). We conducted a retrospective study collecting electroclinical data of patients with STXBP1-DEE and two control groups of patients with DEEs of different etiologies and typically developing individuals matched for age and sex. We performed a (1) visual EEG assessment, (b) qEEG analysis, and (c) electrical source imaging (ESI). We quantified the relative power (RP) of four frequency bands (α β, θ, δ), in two electrode groups (anterior/posterior), and compared their averages and dynamics (standard deviation [SD] over time). The ESI was performed by applying the standard Distributed Source Modeling algorithm. We analyzed 42 EEG studies in 19 patients with STXBP1-DEE (10 female), with a median age at recordings of 9.6 years (range 9 months to 29 years). The δRP was higher in recordings of STXBP1-DEE (p < .001) compared to both control groups, suggesting the pathogenicity and STXBP1-specificity of these findings. In STXBP1-DEE, the δRP was significantly higher in the anterior electrode group compared to the posterior one (p = .003). There was no correlation between the anterior δRP and the epilepsy focus, age at recordings, and concomitant medications The ESI modeling of this activity showed a widespread involvement of the dorsomesial frontal cortex, suggesting a large corticosubcortical pathologic network. Finally, we identified two groups of recordings: cluster.1 with higher anterior δRP and low dynamics and cluster.2 with lower δRP and higher dynamics. Patients in cluster.1 had a more severe epilepsy and neurological phenotype compared to patients in cluster 2. The qEEG analysis showed a predominant frontal slow activity as a specific STXBP1 feature that correlates with the severity of the phenotype and may represent a biomarker for prospective longitudinal studies of STXBP1-DEE.

Investigating Brain Responses to Transcutaneous Electroacupuncture Stimulation: A Deep Learning Approach

This study investigates the neurophysiological effects of transcutaneous electroacupuncture stimulation (TEAS) on brain activity, using advanced machine learning techniques. This work analyzed the electroencephalograms (EEG) of 48 study participants, in order to analyze the brain’s response to different TEAS frequencies (2.5, 10, 80, and sham at 160 pulses per second (pps)) across 48 participants through pre-stimulation, during-stimulation, and post-stimulation phases. Our approach introduced several novel aspects. EEGNet, a convolutional neural network specifically designed for EEG signal processing, was utilized in this work, achieving over 95% classification accuracy in detecting brain responses to various TEAS frequencies. Additionally, the classification accuracies across the pre-stimulation, during-stimulation, and post-stimulation phases remained consistently high (above 92%), indicating that EEGNet effectively captured the different time-based brain responses across different stimulation phases. Saliency maps were applied to identify the most critical EEG electrodes, potentially reducing the number needed without sacrificing accuracy. A phase-based analysis was conducted to capture time-based brain responses throughout different stimulation phases. The robustness of EEGNet was assessed across demographic and clinical factors, including sex, age, and psychological states. Additionally, the responsiveness of different EEG frequency bands to TEAS was investigated. The results demonstrated that EEGNet excels in classifying EEG signals with high accuracy, underscoring its effectiveness in reliably classifying EEG responses to TEAS and enhancing its applicability in clinical and therapeutic settings. Notably, gamma band activity showed the highest sensitivity to TEAS, suggesting significant effects on higher cognitive functions. Saliency mapping revealed that a subset of electrodes (Fp1, Fp2, Fz, F7, F8, T3, T4) could achieve accurate classification, indicating potential for more efficient EEG setups.

Abnormal EEG Effects of Acute Apomorphine Injection in 5xFAD Transgenic Mice Are Partially Normalized in Those Chronically Pretreated with Apomorphine: The Time–Frequency Clustering of EEG Spectra

Background: In experimental and clinical studies of pharmacological treatments for Alzheimer’s disease (AD), the electroencephalogram (EEG) frequency spectrum approach has demonstrated its efficacy in determining the characteristics of pathological changes in the functioning of different cerebral structures, interconnections between them, and disturbances in the brain neurotransmitter systems. The main results have been obtained in frames of traditionally used so-called “classical” EEG frequency bands: delta, theta, alpha, and beta. Objective: This unified approach simplifies comparing data from different studies but loses the dynamic peculiarities of the effects because of their time-dependent transition through the borders of the “classical” bands. Methods: In this study on non-narcotized freely moving 5xFAD transgenic mice, a model of AD, chronically pretreated with a non-selective dopamine (DA) receptor agonist, apomorphine (APO), we analyze the transitory EEG effects of acute APO injection in different brain areas by use of our “time–frequency” clustering program. The acute injection of APO was used to compare DA receptor sensitivity in 5xFAD mice pretreated with either APO or saline vs. wild-type (WT) mice pretreated with saline. Results: After acute APO injection, the clusters of enhanced EEG activity centered in the theta–alpha frequency range observed in WT mice disappeared in 5xFAD mice pretreated with saline and practically recovered in 5xFAD mice pretreated with APO. Conclusions: In 5xFAD mice pretreated with saline, the sensitivity of DA receptors was disturbed; chronic APO pretreatment mainly recovered this characteristic in 5xFAD mice. The “clustering” of pharmacological EEG effects and their time-dependent transition between classical frequency bands is a new effective approach for analyzing cerebral neurotransmission in neurodegenerative pathologies.

Long-term administration of paroxetine increases cortical EEG beta and gamma band activities in healthy awake rats

Understanding the electrophysiological properties of antidepressant medications is important to resolve the response heterogeneity of these drugs in clinical practice. Administration of paroxetine, a selective serotonin reuptake inhibitor, has been shown to increase serotonin levels that affect cortical activities in healthy subjects. However, the extent to which cortical oscillations can be altered by ongoing administration of paroxetine is not known. Here, we develop EEG biomarkers showing long-term effects of paroxetine. EEG changes were analyzed using Neuroscan in healthy wakeful rats administered paroxetine (4 mg/kg/day) for six weeks. Subsequent EEG recordings taken at 3 and 6 weeks after treatment showed differences in cortical oscillations obtained from both hemispheres and frontal-central-parietal regions. Chronic paroxetine administration resulted in an increase in gamma band activity. Comparison of EEG frequency bands of paroxetine and saline groups showed an enhancement in higher frequency activities at third weeks after the treatment. Higher activity of alpha oscillations in the temporal cortex was persistent at sixth week of the administration. Overall, our results suggest that chronic paroxetine administration affects cortical oscillations across an expansive network.

Improved ADHD Diagnosis Using EEG Connectivity and Deep Learning through Combining Pearson Correlation Coefficient and Phase-Locking Value.

Attention Deficit Hyperactivity Disorder (ADHD) is a widespread neurobehavioral disorder affecting children and adolescents, requiring early detection for effective treatment. EEG connectivity measures can reveal the interdependencies between EEG recordings, highlighting brain network patterns and functional behavior that improve diagnostic accuracy. This study introduces a novel ADHD diagnostic method by combining linear and nonlinear brain connectivity maps with an attention-based convolutional neural network (Att-CNN). Pearson Correlation Coefficient (PCC) and Phase-Locking Value (PLV) are used to create fused connectivity maps (FCMs) from various EEG frequency subbands, which are then inputted into the Att-CNN. The attention module is strategically placed after the latest convolutional layer in the CNN. The performance of different optimizers (Adam and SGD) and learning rates are assessed. The suggested model obtained 98.88%, 98.41%, 98.19%, and 98.30% for accuracy, precision, recall, and F1 Score, respectively, using the SGD optimizer in the FCM of the theta band with a learning rate of 1e-1. With the use of FCM, Att-CNN, and advanced optimizers, the proposed technique has the potential to produce trustworthy instruments for the early diagnosis of ADHD, greatly enhancing both patient outcomes and diagnostic accuracy.

Electroencephalographic depression after abruptly increasing partial pressure of end-tidal carbon dioxide: a case series

BackgroundProlonged electroencephalographic depression during surgery is associated with poor outcomes for patients. However, the published literature on electroencephalographic depression caused by a sudden increase in the partial pressure of end-tidal carbon dioxide (PETCO2) is lacking.Case presentationWe report four patients who were scheduled for laparoscopic liver surgery under general anesthesia. During the process of EEG monitoring with Sedline, four patients experienced electroencephalographic depression closely after a sudden increase in PETCO2. The four patients showed that electroencephalographic depression mainly manifested as a slow in EEG frequency, a reduction in the amplitude and power of EEG, and a decrease in spectral edge frequency. Patient state index was elevated in three cases.ConclusionsTo summarize, our patients showed EEG depression when PETCO2 suddenly increased, which suggests that clinical doctors should be alert to electroencephalographic depression when the PETCO2 abruptly increases. EEG monitoring devices should be applied in patients with possible hypercapnia. Anesthesiologists must comprehensively interpret the raw EEG, spectral edge frequency, and density spectral array data, in addition to patient sedation index values.

Linking brain-heart interactions to emotional arousal in immersive virtual reality.

The subjective experience of emotions is linked to the contextualized perception and appraisal of changes in bodily (e.g., heart) activity. Increased emotional arousal has been related to attenuated high-frequency heart rate variability (HF-HRV), lower EEG parieto-occipital alpha power, and higher heartbeat-evoked potential (HEP) amplitudes. We studied emotional arousal-related brain-heart interactions using immersive virtual reality (VR) for naturalistic yet controlled emotion induction. Twenty-nine healthy adults (13 women, age: 26 ± 3) completed a VR experience that included rollercoasters while EEG and ECG were recorded. Continuous emotional arousal ratings were collected during a video replay immediately after. We analyzed emotional arousal-related changes in HF-HRV as well as in BHIs using HEPs. Additionally, we used the oscillatory information in the ECG and the EEG to model the directional information flows between the brain and heart activity. We found that higher emotional arousal was associated with lower HEP amplitudes in a left fronto-central electrode cluster. While parasympathetic modulation of the heart (HF-HRV) and parieto-occipital EEG alpha power were reduced during higher emotional arousal, there was no evidence for the hypothesized emotional arousal-related changes in bidirectional information flow between them. Whole-brain exploratory analyses in additional EEG (delta, theta, alpha, beta and gamma) and HRV (low-frequency, LF, and HF) frequency bands revealed a temporo-occipital cluster, in which higher emotional arousal was linked to decreased brain-to-heart (i.e., gamma→HF-HRV) and increased heart-to-brain (i.e., LF-HRV → gamma) information flow. Our results confirm previous findings from less naturalistic experiments and suggest a link between emotional arousal and brain-heart interactions in temporo-occipital gamma power.

Assessing the Effectiveness of Audio-Visual vs. Visual Neurofeedback for Attention Enhancement: A Pilot Study with Neurological, Behavioural, and Neuropsychological Measures.

Electroencephalogram (EEG) based Neurofeedback training has gained traction as a practical method for enhancing executive functions, particularly attention, among healthy individuals. The neurofeedback protocols based on EEG channel locations, frequency bands, or EEG features has been tested. However, the improvement in attention was not measured by comparing different feedback stimulus types. We believe that multisensory nature feedback even with few training sessions may induce strong effect. Therefore, this study compares the effect of audio-visual and visual feedback stimuli for attention enhancement utilizing neurophysiological, behavioural and neuropsychological measures. Total 21 subjects were recruited, undergoing six alternate days of neurofeedback training sessions to upregulate EEG beta power of frontocentral (FC5). Dwell time, fractional occupancy and transition probability were also estimated from the EEG beta power. Audiovisual group (G1) as compared to visual group (G2) demonstrate a significant increase of global EEG beta activity alongside improved dwell time (t = 2.76, p = 0.003), fractional occupancy (t = 1.73, p = 0.042) and transition probability (t = 2.46, p = 0.008) over the course of six neurofeedback training sessions. Similarly, the group (G1) shows higher scores (t = 2.13, p = 0.032) and faster reaction times (t = 2.22, p = 0.028) in Stroop task, along with increased score in Mindfulness Attention Awareness Scale (MAAS-15) questionnaire (t = 2.306, p = 0.012). Audiovisual neurofeedback may enhance training effectiveness, potentially achieving the same outcomes in fewer sessions compared to visual-only feedback. However, sufficient training days are essential for effect consolidation. This highlights the feasibility of completing neurofeedback training, a significant challenge in practice.

Comparative Electroencephalographic Profile of a New Anesthetic and Anticonvulsant That Is Selective for the GABAAR Slow Receptor Subtype.

Anesthetics like propofol increase electroencephalography (EEG) power in delta frequencies (0.1-4 Hz), with a decrease of power in bandwidths >30 Hz. Propofol is nonselective for gamma amino butyric acid type A receptor subtypes (GABAAR) as it enhances all 3 GABAAR subtypes (slow, fast, and tonic). Our newly developed anesthetic class selectively targets GABAAR-slow synapses to depress brain responsiveness. We hypothesized that a selective GABAAR-slow agonist, KSEB 01-S2, would produce a different EEG signature compared to the broad-spectrum GABAAR agonist (propofol), and tested this using rat EEG recordings. Male rats were studied after Institutional Animal Care and Use Committees (IACUC) approval from the US Army Medical Research Institute of Chemical Defense and the University of Michigan. Rats were anesthetized using isoflurane (3%-5% induction, 1%-3% maintenance) with oxygen at 0.5 to 1.0 L/min. Stainless steel screws were placed in the skull and used to record subcranial cortical EEG signals. After recovery, either propofol or KSEB 01-S2 was administered and effects on EEG signals were analyzed. As previously reported, propofol produced increased power in delta frequencies (0.1-4 Hz) compared to predrug recordings and produced a decrease in EEG power >30 Hz but no significant changes were seen within ±20 seconds of losing the righting reflex. By contrast, KSEB 01-S2 produced a significant increase in theta frequency percent power (median 14.7%, 16.2/13.8, 75/25 confidence interval; to 34.7%, 35/31.8; P < .015) and a significant decrease in low gamma frequency percent power (16.9%, 18.6/15.8; to 5.45%, 5.5/5.39; P < .015) for all rats at ± 20 seconds of loss of consciousness (LOC). Both anesthetics produced a flattening of chaotic attractor plots from nonlinear dynamic analyses, like that produced by volatile and dissociative anesthetics at LOC. KSEB 01-S2 produced a markedly different EEG pattern, with a selective increase observed in the theta frequency range. KSEB 01-S2 also differs markedly in its activity at the GABAAR-slow receptor subtype, suggesting a possible mechanistic link between receptor subtype specificity and EEG frequency band signatures. Increased theta together with depressed gamma frequencies is interesting because GABAAR slow synapses have previously been suggested to underlie theta frequency oscillations, while fast synapses control gamma activity. These reciprocal effects support a previous model for theta and nested gamma oscillations based on inhibitory connections between GABAAR fast and slow interneurons. Although each anesthetic produced a unique EEG response, propofol and KSEB 01-S2 both increased slow wave activity and flattened chaotic attractor plots at the point of LOC.

A hierarchical random effects state-space model for modeling brain activities from electroencephalogram data.

Mental disorders present challenges in diagnosis and treatment due to their complex and heterogeneous nature. Electroencephalogram (EEG) has shown promise as a source of potential biomarkers for these disorders. However, existing methods for analyzing EEG signals have limitations in addressing heterogeneity and capturing complex brain activity patterns between regions. This paper proposes a novel random effects state-space model (RESSM) for analyzing large-scale multi-channel resting-state EEG signals, accounting for the heterogeneity of brain connectivities between groups and individual subjects. We incorporate multi-level random effects for temporal dynamical and spatial mapping matrices and address non-stationarity so that the brain connectivity patterns can vary over time. The model is fitted under a Bayesian hierarchical model framework coupled with a Gibbs sampler. Compared to previous mixed-effects state-space models, we directly model high-dimensional random effects matrices of interest without structural constraints and tackle the challenge of identifiability. Through extensive simulation studies, we demonstrate that our approach yields valid estimation and inference. We apply RESSM to a multi-site clinical trial of major depressive disorder (MDD). Our analysis uncovers significant differences in resting-state brain temporal dynamics among MDD patients compared to healthy individuals. In addition, we show the subject-level EEG features derived from RESSM exhibit a superior predictive value for the heterogeneous treatment effect compared to the EEG frequency band power, suggesting the potential of EEG as a valuable biomarker for MDD.

Evaluation of wakefulness electroencephalogram in OSA patients.

Obstructive sleep apnea (OSA) has been associated with an elevated cardiovascular risk, increased daytime sleepiness, cognitive decline, and slower electroencephalographic activity (EEG). This study assesses EEG patterns during wakefulness in OSA patients compared to those without sleep-disordered breathing. This retrospective study analyzed 30 OSA patients with an Apnea/Hypopnea Index (AHI) of 15 or higher, as well as 29 individuals without sleep-disordered breathing (AHI of 5 or lower) who underwent hospital polysomnography and met all inclusion criteria. Sociodemographic and EEG data were obtained from the sleep laboratory database. Blinded EEG analysis was conducted by two observers, assessing activity in the frontal, central, and occipital regions. No significant differences were observed in EEG activity between OSA and non-OSA patients. However, a weak correlation was found between decreased C3 EEG frequency and higher AHI (p = 0.033), as well as increased total sleep time and higher O2 EEG frequency (p = 0.038). Lower amplitudes in C3 (p = 0.043) and O1 (p = 0.031) were correlated with reduced average oxygen saturation. Our findings suggest that OSA-related hypoxemia may impact neuronal activity, highlighting the need to address this sleep-disordered breathing in order to potentially prevent the cognitive decline observed in OSA patients.

Electrophysiological and hemodynamic mechanisms underlying load modulations in visuospatial working memory: A functional near-infrared spectroscopy (fNIRS) and electroencephalogram (EEG) study.

The n-back task has been widely used to study working memory. Previous studies investigating the electrophysiological (electroencephalogram [EEG]) and hemodynamic correlates (functional near-infrared spectroscopy [fNIRS]) of the n-back task have been generally based on verbal stimuli and only investigated EEG frequency bands. We simultaneously acquired the EEG and fNIRS in 35 participants (16 males; age = 26.4 ± 4.3 years; educational attainment = 18 ± 2 years) during a visuospatial n-back task. The task encompassed a control condition and a low (requiring to recall one previous stimulus) and a high (requiring to recall two previous stimuli) working memory load experimental conditions. Accuracy decreased and reaction times slowed in the high compared to both low load and control conditions. Regarding EEG, P3a showed higher amplitude in the experimental conditions compared to the control one, and P3b exhibited higher amplitude in the low compared to the high load condition. Regarding fNIRS, the high load condition showed higher deoxygenated hemoglobin compared to the control one. Moreover, the central frontopolar cortex showed higher activation compared with the left frontal cortex. Our study showed that working memory load during a visuospatial n-back task influenced behavioral and electrophysiological indices. Even if the load effect was only observed for deoxygenated hemoglobin on hemodynamic data, this was in line with previous studies and coherent with its electrophysiological correlates. Thus, our study confirms that EEG and fNIRS can be successfully used in multimodal acquisitions, but also highlights that future studies are needed to develop a novel version of the task. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

FREQUENCY OF NORMAL INTERICTAL EEG IN ADULTS PRESENTING WITH FIRST SEIZURE

First-time seizure often triggers elaborate neurologic examination to define its etiology and assess the likelihood of its repetition. Objective: The study's main objective is to find the frequency of normal interictal EEG in adults presenting with the first seizure. Methods: This cross-sectional study used data collected from 75 patients. Patient demographic details, seizure characteristics, and EEG findings were documented. EEG results were categorized as normal or abnormal (presence of epileptiform discharges, spikes, or sharp waves). Results: Data were collected from 75 patients with a mean age of 38.23 ± 12.09 years, ranging from 18 to 72 years. Among them, 53% were male, and 47% were female. Generalized tonic-clonic seizures were the most common, seen in 67% of patients, while 33% had focal seizures. EEG results showed that 43% had normal interictal EEGs, and 57% had abnormal findings. Imaging revealed structural abnormalities in 35% of patients, while 65% had normal imaging. At the 6-month follow-up, seizure recurrence was observed in 38% of those with normal EEGs and 58% in those with abnormal EEGs. Conclusion: It is concluded that a significant proportion of adults presenting with their first seizure may have normal interictal EEGs yet still face a risk of seizure recurrence.

Mortality Risk Assessment Using Deep Learning-Based Frequency Analysis of EEG and EOG in Sleep

Abstract Study Objectives To assess whether the frequency content of electroencephalography (EEG) and electrooculography (EOG) during nocturnal polysomnography (PSG) can predict all-cause mortality. Methods Power spectra from PSGs of 8,716 participants, included from the MrOS Sleep Study and the Sleep Heart Health Study (SHHS), were analyzed in deep learning-based survival models. The best-performing model was further examined using SHapley Additive Explanation (SHAP) for data-driven sleep-stage specific definitions of power bands, which were evaluated in predicting mortality using Cox Proportional Hazards models. Results Survival analyses, adjusted for known covariates, identified multiple EEG frequency bands across all sleep stages predicting all-cause mortality. For EEG, we found an all-cause mortality hazard ratio (HR) of 0.90 (CI95% 0.85–0.96) for 12–15 Hz in N2, 0.86 (CI95% 0.82–0.91) for 0.75–1.5 Hz in N3, and 0.87 (CI95% 0.83–0.92) for 14.75–33.5 Hz in REM sleep. For EOG, we found several low-frequency effects including an all-cause mortality HR of 1.19 (CI95% 1.11–1.28) for 0.25 Hz in N3, 1.11 (CI95% 1.03–1.21) for 0.75 Hz in N1, and 1.11 (CI95% 1.03–1.20) for 1.25–1.75 Hz in Wake. The gain in the concordance index (C-index) for all-cause mortality is minimal, with only a 0.24% increase: The best single mortality predictor was EEG N3 (0-0.5 Hz) with C-index of 77.78% compared to 77.54% for confounders alone. Conclusion Spectral power features, possibly reflecting abnormal sleep microstructure, are associated with mortality risk. These findings add to a growing literature suggesting that sleep contains incipient predictors of health and mortality.

Depression of cortical neuronal activity after a low-dose fentanyl in preterm infants.

Opioids might be harmful to the developing brain and dosing accuracy is important. We aimed at investigating fentanyl effects on cortical activity in infants using computational re-analysis of bedside recorded EEG signals. Fifteen infants born at median 26.4 gestational weeks (range 23.3-34.1), with a birth weight 740 grams (530-1420) and postnatal age 7 days (5-11) received fentanyl 0.5 or 2 μg/kg intravenously before a skin-breaking procedure or tracheal intubation, respectively. Cortical activity was continuously recorded using amplitude-integrated electroencephalography (aEEG). Analyses using three computational EEG features representing cortical synchrony and signal power, were conducted five minutes pre- and 10 minutes post the drug administration. Visual assessment of trends displayed from the EEG metrics did not indicate systematic changes. However, the magnitude of the changes in the parietal and right hemisphere signals after the dose was significantly correlated (ρ < -0.5, p < 0.05) to the EEG amplitude and frequency power level before drug administration. This effect started after 3-4 min. Fentanyl, even in small doses, may affect cortical activity in the preterm brain. The effect is robustly related to the state of cortical activity prior to drug treatment, which must be taken into account when analysing the effects of sedative drugs.

Nonictal electroencephalographic measures for the diagnosis of functional seizures.

Functional seizures (FS) look like epileptic seizures but are characterized by a lack of epileptic activity in the brain. Approximately one in five referrals to epilepsy clinics are diagnosed with this condition. FS are diagnosed by recording a seizure using video-electroencephalography (EEG), from which an expert inspects the semiology and the EEG. However, this method can be expensive and inaccessible and can present significant patient burden. No single biomarker has been found to diagnose FS. However, the current limitations in FS diagnosis could be improved with machine learning to classify signal features extracted from EEG, thus providing a potentially very useful aid to clinicians. The current study has investigated the use of seizure-free EEG signals with machine learning to identify subjects with FS from those with epilepsy. The dataset included interictal and preictal EEG recordings from 48 subjects with FS (mean age = 34.76 ± 10.55 years, 14 males) and 29 subjects with epilepsy (mean age = 38.95 ± 13.93 years, 18 males) from which various statistical, temporal, and spectral features from the five EEG frequency bands were extracted then analyzed with threshold accuracy, five machine learning classifiers, and two feature importance approaches. The highest classification accuracy reported from thresholding was 60.67%. However, the temporal features were the best performing, with the highest balanced accuracy reported by the machine learning models: 95.71% with all frequency bands combined and a support vector machine classifier. Machine learning was much more effective than using individual features and could be a powerful aid in FS diagnosis. Furthermore, combining the frequency bands improved the accuracy of the classifiers in most cases, and the lowest performing EEG bands were consistently delta and gamma.

Variational Mode Decomposition Analysis of Electroencephalograms during General Anesthesia: Using the Grey Wolf Optimizer to Determine Hyperparameters.

Frequency analysis via electroencephalography (EEG) during general anesthesia is used to develop techniques for measuring anesthesia depth. Variational mode decomposition (VMD) enables mathematical optimization methods to decompose EEG signals into natural number intrinsic mode functions with distinct narrow bands. However, the analysis requires the a priori determination of hyperparameters, including the decomposition number (K) and the penalty factor (PF). In the VMD analysis of EEGs derived from a noninterventional and noninvasive retrospective observational study, we adapted the grey wolf optimizer (GWO) to determine the K and PF hyperparameters of the VMD. As a metric for optimization, we calculated the envelope function of the IMF decomposed via the VMD method and used its envelope entropy as the fitness function. The K and PF values varied in each epoch, with one epoch being the analytical unit of EEG; however, the fitness values showed convergence at an early stage in the GWO algorithm. The K value was set to 2 to capture the α wave enhancement observed during the maintenance phase of general anesthesia in intrinsic mode function 2 (IMF-2). This study suggests that using the GWO to optimize VMD hyperparameters enables the construction of a robust analytical model for examining the EEG frequency characteristics involved in the effects of general anesthesia.