Quantitative Electroencephalogram Research Articles

Remediation of Learning Difficulty Utilizing School-Based Cognitive Behavioral Intervention Measured by EEG Theta-Alpha and Theta-Beta Ratio During Resting and Cognitive Task Performance Conditions.

Background. EEG is an effective tool due to its ability to capture and interpret the changes in brain activity under different situations. Quantitative EEG (qEEG) can be essential in evaluating and treating children's learning problems. Methods and procedure. Fifty school-going children with difficulty in learning were studied. Analysis of the difference between pre-intervention and postintervention EEG power ratio of frequency bands, including Theta to Beta and Theta to Alpha, while eyes-closed, eyes-open, hyperventilation, writing, and reading conditions and the values for relative powers were calculated. The study correlated postintervention theta/beta ratio (TBR) and theta/alpha ratio (TAR) values with behavioral measures. Results. The findings suggested that there was a significant difference in pre-intervention and postintervention relative TAR and TBR power values. A significant increase of TAR and TBR power values was observed in eyes-closed (resting), hyperventilation, writing, and reading task conditions, indicative of a state of arousal at FP1FP2, T3T4, and O1O2 scalp locations. During eye open conditions, the TAR and TBR were significantly low at all 3 scalp locations, indicating a relaxed, conscious, and aware state of mind. Postintervention TAR and TBR values were significantly correlated with behavioral measures during 5 task conditions on several scalp locations. Conclusion. These quantitative electroencephalogram findings in children with learning problems indicate that with the increased complexity of the cognitive tasks, TAR and TBR increase, while postintervention, children could attain a relaxed and conscious state of mind during eyes-open condition.

Relation between heart rate variability and spectral analysis of electroencephalogram in chronic neuropathic pain patients.

Chronic neuropathic pain (CNP) is a complex condition often arising from neural maladaptation after nerve injury. Understanding CNP complications involves the intricate interplay between brain-heart dynamics, assessed through quantitative electroencephalogram (qEEG) and heart rate variability (HRV). However, insights into their interaction in chronic pain are limited. Resting EEG and simultaneous electrocardiogram (lead II) of the participants were recorded for qEEG and HRV analysis. Correlations between HRV and qEEG parameters were calculated and compared with age, sex, and body mass index (BMI)-matched controls. CNP patients showed reduced HRV and significant increases in qEEG power spectral densities within delta, theta, and beta frequency ranges. A positive correlation was found between low frequency/ high frequency (LF/HF) ratio in HRV analysis and theta, alpha, and beta frequency bands in qEEG among CNP patients. However, no significant correlation was observed between parasympathetic indices and theta, beta bands in qEEG within CNP group, unlike age, sex, and BMI-matched healthy controls. CNP patients display significant HRV reductions and distinctive qEEG patterns. While healthy controls exhibit significant correlations between parasympathetic HRV parameters and qEEG spectral densities, these relationships are diminished or absent in CNP individuals. LF/HF ratio, reflecting sympathovagal balance, correlates significantly with qEEG frequency bands (theta, alpha, beta), illuminating autonomic dysregulation in CNP. These findings emphasize the intricate brain-heart interplay in chronic pain, warranting further exploration.

The Relationship between Self-Regulated Learning (SRL) and Cognitive Function with The Performance of qEEG Examination in First Year Medical Students

Introductions: Medical students are required to lifelong learning. Electroencephalog-raphy is a neuroscience tool that can be used to record brain wave activity related to a person’s cognitive function. Objective: This study was conducted to see the relationship between self-regulated learning and cognitive function with quantitative electroencepha-logram (qEEG) parameters. Methods: This study used an observa-tional analytic approach to assess self-regulated learning and cog-nitive function using the MSLQ and MoCA-INA questionnaires. qEEG recording to see brain wave activity was done when the eyes were open and closed and seen in alpha and beta waves. The sam-ple of this study amounted to 32 respondents who were willing to carry out each research procedure. Results: The study findings reveal a significant correlation between SRL and qEEG parameters. In the open-eye condition, such a correla-tion exists in the frontal and central regions with regard to alpha waves (P = 0.046 and P = 0.047). In contrast, in the closed-eye condition, it exists in the occipital, central, and parietal regions with alpha waves (P = 0.005, P = 0.021, and P = 0.049) and the central region with beta waves (P = 0.030). There is a significant correla-tion between cognitive function and qEEG parameters observed in both open and closed-eye conditions within the frontal region beta waves (P = 0.024, P = 0.044, and P = 0.021). Conclusions: There is a relationship between SRL and cognitive function with alpha and beta wave activity on qEEG recording.

Construction of machine learning models for recognizing comorbid anxiety in epilepsy patients based on their clinical and quantitative EEG features

BackgroundThis study aimed to construct prediction models for the recognizing of anxiety disorders (AD) in patients with epilepsy (PWEs) by combining clinical features with quantitative electroencephalogram (qEEG) features and using machine learning (ML). MethodsNineteen clinical features and 20-min resting-state EEG were collected from 71 PWEs comorbid with AD and another 60 PWEs without AD who met the inclusion-exclusion criteria of this study. The EEG were preprocessed and 684 Phase Locking Value (PLV) and 76 Lempel–Ziv Complexity (LZC) features on four bands were extracted. The Fisher score method was used to rank all the derived features. We constructed four models for recognizing AD in PWEs, whether PWEs based on different combinations of features using eXtreme gradient boosting (XGboost) and evaluated these models using the five-fold cross-validation method. ResultsThe prediction model constructed by combining the clinical, PLV, and LZC features showed the best performance, with an accuracy of 96.18%, precision of 94.29%, sensitivity of 98.33%, F1-score of 96.06%, and Area Under the Curve (AUC) of 0.96. The Fisher score ranking results displayed that the top ten features were depression, educational attainment, α_P3LZC, α_T6-PzPLV, α_F7LZC, β_Fp2-O1PLV, θ_T4-CzPLV, θ_F7-PzPLV, α_Fp2LZC, and θ_T4-PzPLV. ConclusionsThe model, constructed by combining the clinical and qEEG features PLV and LZC, efficiently identified the presence of AD comorbidity in PWEs and might have the potential to complement the clinical diagnosis. Our findings suggest that LZC features in the α band and PLV features in Fp2-O1 may be potential biomarkers for diagnosing AD in PWEs.

Cognitive function and quantitative electroencephalogram analysis in subjects recovered from COVID-19 infection

Background & ObjectivesObjective assessment of post-COVID-19 cognitive dysfunction is highly warranted. This study aimed to evaluate the cognitive dysfunction of COVID-19 survivors with cognitive complaints, both clinically and neurophysiologically, using Quantitative Electroencephalogram (QEEG).MethodsThis case–control study was conducted on 50 recovered subjects from COVID-19 infection with cognitive complaints and 50 age, sex, and educational-matched healthy controls. Both groups were subjected to the following neurocognitive tests: Paired associate learning Test (PALT) and Paced Auditory Serial Addition Test (PASAT). The neurophysiological assessment was also done for both groups using QEEG.ResultsCOVID-19 survivors had significantly lower PALT scores than controls (P < 0.001). QEEG analysis found significantly higher levels of Theta / Beta ratio in both central and parietal areas in patients than in the controls (P < 0.001 for each). The interhemispheric coherence for the frontal, central, and parietal regions was also significantly lower in patients than in the control group regarding alpha and beta bands. There were statistically significant lower scores of PALT and PASAT among cases with severe COVID-19 infection (P = 0.011, 0.005, respectively) and those who needed oxygen support (P = 0.04, 0.01, respectively). On the other hand, a statistically significantly lower mean of frontal alpha inter-hemispheric coherence among patients with severe COVID-19 infection (P = 0.01) and those needing mechanical ventilation support (P = 0.04).ConclusionEpisodic memory deficit is evident in COVID-19 survivors with subjective cognitive complaints accompanied by lower inter-hemispheric coherence in frontal regions. These clinical and neurophysiological changes are associated with hypoxia and COVID-19 severity.

Predictive Value of Quantitative Electroencephalogram Combined with Transcranial Doppler Ultrasound in Delayed Cerebral Ischemia after Subarachnoid Hemorrhage

ObjectiveTo explore the predictive value of transcranial Doppler ultrasound (TCD) combined with quantitative electroencephalogram (QEEG) in delayed cerebral ischemia (DCI) caused by aneurysmal subarachnoid hemorrhage (aSAH). MethodsThe subjects were 105 patients with aSAH from June 2020 to December 2022. Patients were divided into DCI group (n=34) and non-DCI group (n=71) according to the presence of DCI 14d after onset. Further comparison was conducted on the baseline data as well as the parameters of QEEG and TCD within 24h after admission. Multivariate logistic analysis was performed to investigate risk factors related to DCI within 14 days of admission in aSAH patients. ResultsThere were significant differences in the comparison of the proportion of Hunt-Hess grading, relative δ power (RDP), relative α power (RAP), relative α/β power ratio (ADR), as well as peak systolic velocity (Vs), mean blood flow velocity (MBFV) and pulsatility index (PI) of middle cerebral artery between the two groups (P<0.05). Furthermore, Logistic regression analysis revealed that ADR [OR=1.668, 95%CI (1.369, 4.345)] and MBFV of middle cerebral artery [OR=3.279, 95%CI (2.332, 6.720)] were risk factors for the occurrence of DCI in aSAH patients (P<0.05). In addition, evaluation of the predictive value revealed that combined use of the two indicators showed the highest predictive value (AUC: 0.959, 95%CI: 0.896∼0.993). ConclusionPatients with aSAH complicated by DCI have relatively higher MBFV of middle cerebral artery and ADR. Combined use of the two indicators can provide reference for early prediction of DCI in aSAH patients.

Unique Effects of (R)-Ketamine Compared to (S)-Ketamine on EEG Theta Power in Rats.

Differences in the pharmacological effects of (S)-ketamine and (R)-ketamine are at the focus of research. Clinical data and our rat studies confirmed the antidepressant effect of (S)- but not (R)-ketamine, with similar differences in quantitative electroencephalogram (EEG) and sleep effects. In contrast, studies mainly on mice showed some stronger, preferable effects of (R)-ketamine. EEG theta (5-9 Hz) rhythm originates from the hippocampus, and its power is associated with cognitive functions, attention, and decreased anxiety. To find a brain parameter that is not associated with the antidepressant effect of drugs and may confirm potent in vivo effects of (R)-ketamine in rats, theta EEG power-inducing effects of the two enantiomers were measured and compared for 23 h. EEG-equipped Wistar rats were treated with (R)-ketamine (7.5, 15, 30 mg/kg i.p.), (S)-ketamine (7.5 and 15 mg/kg i.p.), or vehicle at the beginning of the passive phase. Frontoparietal EEG, electromyogram, and motor activity were recorded. (R)-ketamine but not (S)-ketamine dose-dependently increased EEG theta power during wakefulness and rapid eye movement (REM) sleep for 23 h. These results suggest that (R)-ketamine has an effect on a hippocampal function that was not affected by (S)-ketamine and may be associated with neural plasticity and memory encoding.

Intravenous Ganaxolone: Pharmacokinetics, Pharmacodynamics, Safety, and Tolerability in Healthy Adults.

Ganaxolone, a neuroactive steroid anticonvulsant that modulates both synaptic and extrasynaptic γ-aminobutyric acid type A (GABAA ) receptors, is in development for treatment of status epilepticus (SE) and rare epileptic disorders, and has been approved in the United States for treatment of seizures associated with cyclin-dependent kinase-like 5 deficiency disorder in patients ≥2 years old. This phase 1 study in 36 healthy volunteers evaluated the pharmacokinetics, pharmacodynamics, and safety of intravenous ganaxolone administered as a (i) single bolus, (ii) infusion, and (iii) bolus followed by continuous infusion. After a single bolus over 2 minutes (20mg) or 5 minutes (10 or 30mg), ganaxolone was detected in plasma with a median Tmax of 5 minutes, whereas a 60-minute infusion (10 or 30mg) or a bolus (6mg over 5minutes) followed by infusion (20mg/h) for 4 hours achieved a median Tmax of approximately 1 and 3 hours, respectively. Cmax was dose and administration-time dependent, ranging from 73.8ng/mL (10mg over 5minutes) to 1240ng/mL (30mg over 5minutes). Bolus doses above 10mg of ganaxolone markedly influenced the bispectral index score with a rapid decline; smaller changes occurred on the Modified Observer's Assessment of Alertness/Sedation scale and in quantitative electroencephalogram. Most adverse events were of mild severity, with 2 events of moderate severity; none were reported as serious. No effects on systemic hemodynamics or respiratory functions were reported. Overall, ganaxolone was generally well tolerated at the doses studied and demonstrated pharmacokinetic and pharmacodynamic properties suitable to treat SE.

Predicting the therapeutic response to valproic acid in childhood absence epilepsy through electroencephalogram analysis using machine learning

Childhood absence epilepsy (CAE) is a common type of idiopathic generalized epilepsy, manifesting as daily multiple absence seizures. Although seizures in most patients can be adequately controlled with first-line antiseizure medication (ASM), approximately 25 % of patients respond poorly to first-line ASM. In addition, an accurate method for predicting first-line medication responsiveness is lacking. We used the quantitative electroencephalogram (QEEG) features of patients with CAE along with machine learning to predict the therapeutic effects of valproic acid in this population. We enrolled 25 patients with CAE from multiple medical centers. Twelve patients who required additional medication for seizure control or who were shifted to another ASM and 13 patients who achieved seizure freedom with valproic acid within 6 months served as the nonresponder and responder groups. Using machine learning, we analyzed the interictal background EEG data without epileptiform discharge before ASM. The following features were analyzed: EEG frequency bands, Hjorth parameters, detrended fluctuation analysis, Higuchi fractal dimension, Lempel–Ziv complexity (LZC), Petrosian fractal dimension, and sample entropy (SE). We applied leave-one-out cross-validation with support vector machine, K-nearest neighbor (KNN), random forest, decision tree, Ada boost, and extreme gradient boosting, and we tested the performance of these models. The responders had significantly higher alpha band power and lower delta band power than the nonresponders. The Hjorth mobility, LZC, and SE values in the temporal, parietal, and occipital lobes were higher in the responders than in the nonresponders. Hjorth complexity was higher in the nonresponders than in the responders in almost all the brain regions, except for the leads FP1 and FP2. Using KNN classification with theta band power in the temporal lobe yielded optimal performance, with sensitivity of 92.31 %, specificity of 76.92 %, accuracy of 84.62 %, and area under the curve of 88.46 %.We used various EEG features along with machine learning to accurately predict whether patients with CAE would respond to valproic acid. Our method could provide valuable assistance for pediatric neurologists in selecting suitable ASM.

Alteration in Resting-State Brain Activity in Stroke Survivors After Repetitive Finger Stimulation.

This quasi-experimental study examined the effect of repetitive finger stimulation on brain activation in eight stroke and seven control subjects, measured by quantitative electroencephalogram. We applied 5 mins of 2-Hz repetitive bilateral index finger transcutaneous electrical nerve stimulation and compared differences pre- and post-transcutaneous electrical nerve stimulation using quantitative electroencephalogram metrics delta/alpha ratio and delta-theta/alpha-beta ratio. Between-group differences before and after stimulation were significantly different in the delta/alpha ratio ( z = -2.88, P = 0.0040) and the delta-theta/alpha-beta ratio variables ( z = -3.90 with P < 0.0001). Significant decrease in the delta/alpha ratio and delta-theta/alpha-beta ratio variables after the transcutaneous electrical nerve stimulation was detected only in the stroke group (delta/alpha ratio diff = 3.87, P = 0.0211) (delta-theta/alpha-beta ratio diff = 1.19, P = 0.0074). The decrease in quantitative electroencephalogram metrics in the stroke group may indicate improved brain activity after transcutaneous electrical nerve stimulation. This finding may pave the way for a future novel therapy based on transcutaneous electrical nerve stimulation and quantitative electroencephalogram measures to improve brain recovery after stroke.

QEEG‐based TabNet classifier for dementia pathologies: Alzheiemer’s disease and Lewy body disease

AbstractBackgroundEarly screening and prevention of dementia are widely discussed and studied in the current healthcare paradigm. However, about 70 percent of dementia diagnoses are categorized into Alzheimer’s disease (AD), neglecting the presence and influence of Lewy body. The present study aims to distinguish Alzheimer’s disease dementia (pure AD) and Lewy body dementia (pure LBD) through quantitative electroencephalogram (QEEG)‐based classification algorithm. The mixed dementia that exhibits both AD and LBD related propensities were further tested for the verification of the established classification model.Method19 channel EEG data were recorded in accordance with the 10‐20 system in eyes‐closed resting state. The acquired data were bandpass filtered into the range of interest (1‐45Hz) and re‐referenced into common average reference. Artifacts were removed by bad epoch rejection and independent component analysis (ICA) through an automated, cloud‐based QEEG analysis platform iSyncBrain®. Henceforth, sensor‐level features were extracted and computed for the training of TabNet structure. Due to the data imbalance (N = 146, Pure AD = 48, Pure LBD = 102), time window‐based augmentation has been applied to establish the final dataset (N = 7292, Pure AD = 3376, Pure LBD = 3916), which were then split into 8 to 2 ratio for the training and testing of the classification model. In addition, mixed dementia (N = 136) data were used to further validate the model’s performance.ResultThe best classification model yielded test accuracy of 80% with pure AD sensitivity at 80% and pure LBD sensitivity 80%. Moreover, the predicted probability distribution for the mixed dementia data were prominently concentrated around 0.5, which reflect that both pure AD and LBD properties exist.ConclusionAlong with the promising classification performance of the established classification model, the centered probability distribution of the mixed dementia data reflect that the model can well distinguish the characteristics that are specific to pure AD and LBD.

Disgust-reduction evaluative conditioning (DREC) and brain stimulation in patients with contamination-based obsessive-compulsive disorder: a protocol for a randomized control trial

BackgroundThe negative emotional valence of a stimulus can be altered if paired with a pleasant stimulus, a phenomenon referred to as evaluative conditioning. Disgust, as a central emotion in obsessive-compulsive disorder (OCD), particularly in the contamination subtype, may be an appropriate target for such a method. We know that disgust processing and OCD pathophysiology share in some brain areas, including the orbitofrontal cortex, as the neuromodulation techniques targeted in this area have been able to decrease OCD symptoms. We aim to conduct a randomized clinical trial to investigate the evaluative conditioning effect on disgust reduction in patients with contamination-based OCD when administered with or without neuromodulation targeted orbitofrontal cortex.MethodIn a single-blind randomized control trial (RCT), 55 patients with contamination-based OCD will be randomly assigned to four arms. In a factorial design, they will receive 10 sessions of evaluative conditioning training (either sham or real) plus cathodal transcranial direct current stimulation (tDCS) over the orbitofrontal cortex (either sham or real). The intensity of disgust experience and clinical symptoms will be investigated as primary outcomes and quantitative electroencephalogram and cognitive functions as secondary outcomes. The data will be collected at three assessment levels: baseline, after completing intervention sessions, and 2-month follow-up.DiscussionThe present RCT is the first study that applies evaluative conditioning training in the OCD clinical sample. It will clarify the effect of the evaluative conditioning method alone and with tDCS on disgust reduction in patients with contamination-based OCD. It will provide initial evidence for such an emotion modulation method in the OCD population. The effect of this emotion-focused protocol on cognitive functions and electroencephalogram components is also of interest.Trial registrationClinicalTrials.gov, NCT05907369. Registered on 16 June 2023. Retrospectively registered.

"Drum Tower Hospital" strategy for acute type A aortic dissection with coma.

Acute type A aortic dissection (ATAAD) with persistent coma is a life-threatening condition associated with high mortality and poor neurological outcomes. The optimal timing for surgical intervention in these patients remains uncertain, and many patients are not eligible for surgery due to their poor prognosis. In this case, a 53-year-old man with hypertension presented to the emergency department in a coma that had lasted for 9hours. The patient was diagnosed with ATAAD and underwent the "Drum Tower Hospital" strategy, which involved preoperative assessments, including computed tomography angiography (CTA) and quantitative electroencephalogram (qEEG) monitoring. Surgical interventions, such as emergency stenting and aortic replacement, were performed to restore blood flow and repair the aorta. Postoperative monitoring, including qEEG, showed improvements in brain function. Despite the patient experiencing hemiplegia and a neurological deficit, the "Drum Tower Hospital" strategy, guided by comprehensive brain assessments, showed promise in managing ATAAD with coma. However, further research is needed to establish effective treatment strategies for these patients. Overall, ATAAD with persistent coma is a critical condition with limited treatment options. The "Drum Tower Hospital" strategy, supported by multimodal brain assessment, offers a potential approach to improve outcomes in these patients.

Quantitative electroencephalogram in term neonates under different sleep states.

Electroencephalogram (EEG) can be used to assess depth of consciousness, but interpreting EEG can be challenging, especially in neonates whose EEG undergo rapid changes during the perinatal course. EEG can be processed into quantitative EEG (QEEG), but limited data exist on the range of QEEG for normal term neonates during wakefulness and sleep, baseline information that would be useful to determine changes during sedation or anesthesia. We aimed to determine the range of QEEG in neonates during awake, active sleep and quiet sleep states, and identified the ones best at discriminating between the three states. Normal neonatal EEG from 37 to 46weeks were analyzed and classified as awake, quiet sleep, or active sleep. After processing and artifact removal, total power, power ratio, coherence, entropy, and spectral edge frequency (SEF) 50 and 90 were calculated. Descriptive statistics were used to summarize the QEEG in each of the three states. Receiver operating characteristic (ROC) curves were used to assess discriminatory ability of QEEG. 30 neonates were analyzed. QEEG were different between awake vs asleep states, but similar between active vs quiet sleep states. Entropy beta, delta2 power %, coherence delta2, and SEF50 were best at discriminating awake vs active sleep. Entropy beta had the highest AUC-ROC ≥ 0.84. Entropy beta, entropy delta1, theta power %, and SEF50 were best at discriminating awake vs quiet sleep. All had AUC-ROC ≥ 0.78. In active sleep vs quiet sleep, theta power % had highest AUC-ROC > 0.69, lower than the other comparisons. We determined the QEEG range in healthy neonates in different states of consciousness. Entropy beta and SEF50 were best at discriminating between awake and sleep states. QEEG were not as good at discriminating between quiet and active sleep. In the future, QEEG with high discriminatory power can be combined to further improve ability to differentiate between states of consciousness.

Quantitative EEG and brain network analysis: predicting awakening from early coma after cardiopulmonary resuscitation

ABSTRACT Objective For patients in early coma after cardiopulmonary resuscitation (CPR), quantitative electroencephalogram (EEG) and brain network analysis was performed to identify relevant indicators of awakening. Methods A prospective cohort study was conducted on comatose patients after CPR in the neuro-critical care unit. The included patients received clinical evaluation. The bedside high-density (64-lead) EEG monitoring was performed for visual grading and calculation of power spectrum and brain network parameters. A 3-month prognostic assessment was performed and the patients were dichotomized into the awakening group and the unawakening group. Results A total of 25 patients were included. The awakening group had higher GCS score, more slow wave pattern and reactive EEG than the unawakening group (P = 0.003, P < 0.001, P < 0.001, respectively). Compared with the unawakening group, (1) the awakening group had significantly higher absolute and relative θ power and slow/fast band ratio of the whole brain (P < 0.05), (2) the awakening group had stronger connection based on coherence, phase synchronization, phase lag index and cross-correlation (P < 0.05), (3) the awakening group had higher small-worldness, clustering coefficient and average path length based on graph theory (P < 0.05). Conclusions The power spectrum and brain network characteristics in patients in early coma after CPR have predictive value for recovery.

Quantitative EEG as a diagnostic and prognostic tool in hemispheric stroke patients undergoing type A aortic dissection surgery.

The diagnostic and prognostic value of quantitative electroencephalogram (qEEG) parameters, specifically the symmetry of amplitude-integrated electroencephalography (aEEG) and relative band power (RBP), in the postoperative stroke of the cerebral hemisphere following type A aortic dissection, remains an area of inquiry. We analyzed and processed 56 patients with type A aortic dissection who underwent bedside qEEG monitoring and analyzed the qEEG indices, brain CT, and clinical data of these patients. qEEG (symmetry of aEEG and RBP, and affected/unaffected hemisphere) indices were analyzed at discharge and 60 days after discharge. A total of 56 patients were studied. The 60-day mortality rate was 12.5%. The affected hemisphere's diagnosis and mortality after 1-year follow-up were evaluated, and RBP beta demonstrated the highest area under the curve values with 95% confidence intervals (CI) of .849 (95% CI: .771-.928) and .91 (95% CI: .834-.986), respectively. According to the results of the logistic regression analysis, we have identified the strongest predictors for cerebral hemisphere stroke and 1-year mortality in stroke patients. Specifically, aEEGmin exhibited the highest predictive power with an odds ratio (OR) of .735 for cerebral hemisphere stroke, whereas DTABR was confirmed as one of the strongest predictors with an OR of 1.619 for 1-year mortality in stroke patients, indicating a high level of reliability. Spearman correlation coefficients showed that aEEGmax and aEEGmin were positively correlated with Alberta Stroke Program Early CT Score (aEEGmax: rho=.50, p<.001; aEEGmin: rho=.44, p<.001). QEEG has been proven to be a sensitive indicator for monitoring brain function and can be monitored continuously. It can help clinicians detect and treat these patients early and improve long-term prognosis.

Electroencephalographic slowdowns during sleepare associated with cognitive impairment in patients who have obstructive sleep apneabut no dementia.

To research the relationship between quantitative electroencephalogram (qEEG) and impaired cognitive function patients who have obstructive sleep apnea (OSA)but no dementia. Subjects who complained of snoring between March 2020 and April 2021 in the Sleep Medicine Center of Weihai Municipal Hospital were included. All subjects underwent overnight in-laboratory polysomnography (PSG) and were assessed using a neuropsychological scale. Standard fast fourier transform (FFT) was usedto obtain the electroencephalogram (EEG) power spectral density curve, and tocalculate the delta, theta, alpha, and beta relative power and the ratio between slow and fast frequencies. Binary logistic regression was used to assess the risk factors for cognitive impairment in patients who had OSAbut no dementia. Correlation analysis was performed to determine the relationship between qEEG and cognitive impairment. A total of 175 participants without dementiawho met the inclusion criteria were included in this study. There were 137 patients withOSA, including 76 with mild cognitive impairment (OSA + MCI), 61 without mild cognitive impairment (OSA-MCI), and 38 participants without OSA (non-OSA). The relative theta power in the frontal lobe in stage 2 of non-rapid eye movement sleep (NREM 2) in OSA + MCI was higher than that in OSA-MCI (P = 0.038) and non-OSA (P = 0.018). Pearson correlation analysis showed that the relative theta power in the frontal lobe in NREM 2 was negatively correlated with Mini-Mental State Examination (MMSE) scores, Montreal Cognitive Assessment (MoCA) Beijing version scores, and MoCA subdomains scores (visual executive function, naming, attention, language, abstraction, delayed recall and orientation) outside language. In patients who had OSAbut no dementia, the EEG slower frequency power increased. The relative theta power in the frontal lobe in NREM 2 was associated with MCI of patients withOSA. These results suggest that the slowing of theta activity may be one of the neurophysiological changes in the early stage of cognitive impairment in patients withOSA.

The potential of the Lempel-Ziv complexity of the EEG in diagnosing cognitive impairment in patients with temporal lobe epilepsy.

To analyze whether the Lempel-Ziv Complexity (LZC) in quantitative electroencephalogram differs between the temporal lobe epilepsy (TLE) patients with or without cognitive impairment (CI) and explore the diagnostic value of LZC for identifying CI in TLE patients. Twenty-two clinical features and 20-min EEG recordings were collected from 48 TLE patients with CI and 27 cognitively normal (CON) TLE patients. Seventy-six LZC features were calculated for 19 leads in four frequency bands (alpha, beta, delta, and theta). The clinical and LZC features were compared between the two groups. A support vector machine (SVM) was subsequently constructed using the leave-one-out method of cross-validation for LZC features with statistical differences. Regarding the clinical features, the level of education (p < .001), hippocampal atrophy and sclerosis (p = .029), and depression (p = .037) were statistically different between the two groups. For the LZC features, there were statistically significant differences in the alpha (Fp1, Fz, Cz, Pz, C3, C4, T3, T4, T5, T6, F3, F4, F7, F8, O1, and O2), beta (Fp2), and theta (F7) oscillations. The mean LZC in the alpha band was higher in the TLE-CI group than that in the CON group, and there were no differences in the remaining bands. The SVM model showed 74.51% accuracy, 79.63% sensitivity, 84.30% F1 score, 68.75% specificity, and .85 area under the curve scores. The LZC in the alpha band might have the potential to be used as a biomarker for the diagnosis of TLE combined with CI. The TLE-CI group, on the other hand, exhibited a higher degree of complexity in alpha oscillations, which were widespread and occurred in all brain regions.

Research diagnostic criteria for mild cognitive impairment with Lewy bodies: A systematic review and meta-analysis.

Operationalized research criteria for mild cognitive impairment with Lewy bodies (MCI-LB) were published in 2020. The aim of this systematic review and meta-analysis was to review the evidence for the diagnostic clinical features and biomarkers in MCI-LB set out in the criteria. MEDLINE, PubMed, and Embase were searched on 9/28/22 for relevant articles. Articles were included if they presented original data reporting the rates of diagnostic features in MCI-LB. Fifty-seven articles were included. The meta-analysis supported the inclusion of the current clinical features in the diagnostic criteria. Evidence for striatal dopaminergic imaging and meta-iodobenzylguanidine cardiac scintigraphy, though limited, supports their inclusion. Quantitative electroencephalogram (EEG) and fluorodeoxyglucose positron emission tomography (PET) show promise as diagnostic biomarkers. The available evidence largely supports the current diagnostic criteria for MCI-LB. Further evidence will help refine the diagnostic criteria and understand how best to apply them in clinical practice and research. A meta-analysis of the diagnostic features of MCI-LB was carried out. The four core clinical features were more common in MCI-LB than MCI-AD/stable MCI. Neuropsychiatric and autonomic features were also more common in MCI-LB. More evidence is needed for the proposed biomarkers. FDG-PET and quantitative EEG show promise as diagnostic biomarkers in MCI-LB.

An Attempt to Develop a Model of Brain Waves Using Quantitative Electroencephalography with Closed Eyes in K1 Kickboxing Athletes-Initial Concept.

Brain injuries are a common problem in combat sports, especially in disciplines such as kickboxing. Kickboxing is a combat sport that has several variations of competition, with the most contact-oriented fights being carried out under the format of K-1 rules. While these sports require a high level of skill and physical endurance, frequent micro-traumas to the brain can have serious consequences for the health and well-being of athletes. According to studies, combat sports are one of the riskiest sports in terms of brain injuries. Among the sports disciplines with the highest number of brain injuries, boxing, mixed martial arts (MMA), and kickboxing are mentioned. The study was conducted on a group of 18 K-1 kickboxing athletes who demonstrate a high level of sports performance. The subjects were between the ages 18 and 28. QEEG (quantitative electroencephalogram) is a numeric spectral analysis of the EEG record, where the data is digitally coded and statistically analysed using the Fourier transform algorithm. Each examination of one person lasts about 10 min with closed eyes. The wave amplitude and power for specific frequencies (Delta, Theta, Alpha, Sensorimotor Rhythm (SMR), Beta 1, and Beta2) were analysed using 9 leads. High values were shown in the Alpha frequency for central leads, SMR in the Frontal 4 (F4 lead), Beta 1 in leads F4 and Parietal 3 (P3), and Beta2 in all leads. The high activity of brainwaves such as SMR, Beta and Alpha can have a negative effect on the athletic performance of kickboxing athletes by affecting focus, stress, anxiety, and concentration. Therefore, it is important for athletes to monitor their brainwave activity and use appropriate training strategies to achieve optimal results.