Stroke or cerebrovascular accident occurs when the blood supply to the brain is cut off (an ischemic stroke) or when a blood vessel bursts (a hemorrhagic stroke). Most strokes are of the ischemic ...type. Without oxygen, brain cells begin to die. death or permanent disability can result. High blood pressure, smoking,D.M, and having had a previous stroke or heart attack increase a person’s chances of having a stroke. The aims of this study: Evaluate the role of
electroencephalography and visual evoked potential in patients presented with stroke. Determine the electroencephalographic abnormalities in stroke patients. Evaluate the clinical manifestations and medical history of patient with stroke. This study is a case-control study dealing with a total of 170(male and female) subjects, 85 of them as group presented with stroke and the other 85 considered asa control group. The electrophysiological tests were done at the neurophysiology unit of Mirjan Teaching center in Babylon City, during the period from 1ᵗʰ/12/ 2015 until 20ᵗʰ /5/2016.Electroencephalography and Visual evoked potential were performed for the patients and the control in parallel.This study shows the differences between patients with stroke and control by EEG changes there were significant differences between patients and control by EEG changes. There were 35% of stroke patient presented with abnormal EEG changes ,While 26%of stroke patient presented with abnormal VEP. The purpose of the study was to compare sensitivity and specificity of these two analytical procedures (EEG and VEP) in the diagnosis of stroke. The sensitivity and the specificity of EEG in stroke The results showed a sensitivity of35.3% and a specificity of 97.6%.p value less than 0.01 is highly significant.The sensitivity and the specificity of VEP in stroke The results showed a sensitivity of 25.9% and a specificity of 100% p value less than 0.01 is highly significant.The EEG abnormal findings in stroke patients were (35%) of all patient group (68%) of them were generalized while (32% )were partial seizure.The distribution of different EEG abnormalities in stroke patients were (slow wave 48%,spike wave26%,poly spike wave13% and sharp wave13%).The VEP abnormal findings in stroke patients were(26%) of allpatient group, the majority of abnormal VEP findings were prolonged latency of P100, P75 and P145 respectively.There were significant differences between stroke patients and control group regarding the clinical manifestations and medical history (DM, Headache, Dysarthria , Visual disorder,Facial weakness, dizziness hypertension and Hemiphgia.
Epileptic seizure detection using scalp electroencephalogram (sEEG) and intracranial electroencephalogram (iEEG) has attracted widespread attention in recent two decades. The accurate and rapid ...detection of seizures not only reflects the efficiency of the algorithm, but also greatly reduces the burden of manual detection during long-term electroencephalogram (EEG) recording. In this work, a stacked one-dimensional convolutional neural network (1D-CNN) model combined with a random selection and data augmentation (RS-DA) strategy is proposed for seizure onset detection. Firstly, we segmented the long-term EEG signals using 2-s sliding windows. Then, the 2-s interictal and ictal segments were classified by the stacked 1D-CNN model. During model training, a RS-DA strategy was applied to solve the problem of sample imbalance, and the patient-specific model was trained with event-based K-fold (K is the number of seizures per patient) cross validation for detecting all seizures of each patient. Finally, we evaluated the performances of the proposed approach in the two levels: the segment-based level and the event-based level. The proposed method was tested on two long-term EEG datasets: the CHB-MIT sEEG dataset and the SWEC-ETHZ iEEG dataset. For the CHB-MIT sEEG dataset, we achieved 88.14% sensitivity, 99.62% specificity and 99.54% accuracy in the segment-based level. From the perspective of the event-based level, 99.31% sensitivity, 0.2/h false detection rate (FDR) and mean 8.1-s latency were achieved. For the SWEC-ETHZ iEEG dataset, in the segment-based level, 90.09% sensitivity, 99.81% specificity and 99.73% accuracy were obtained. In the event-based level, 97.52% sensitivity, 0.07/h FDR and mean 13.2-s latency were attained. From these results, we can see that our method can effectively use both sEEG and iEEG data to detect epileptic seizures, and this may provide a reference for the clinical application of seizure onset detection.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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•Reviews the transfer learning methods in EEG signal analysis in recent years.•Discusses the challenges that transfer learning faces in the future development of EEG signal ...analysis.•Proposes the opportunities and application scenarios of transfer learning methods in EEG signal analysis.
Electroencephalogram (EEG) signal analysis, which is widely used for human-computer interaction and neurological disease diagnosis, requires a large amount of labeled data for training. However, the collection of substantial EEG data could be difficult owing to its randomness and non-stationary. Moreover, there is notable individual difference in EEG data, which affects the reusability and generalization of models. For mitigating the adverse effects from the above factors, transfer learning is applied in this field to transfer the knowledge learnt in one domain into a different but related domain. Transfer learning adjusts models with small-scale data of the task, and also maintains the learning ability with individual difference. This paper describes four main methods of transfer learning and explores their practical applications in EEG signal analysis in recent years. Finally, we discuss challenges and opportunities of transfer learning and suggest areas for further study.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The study objective was to characterize preoperative and postoperative continuous electroencephalogram metrics and hemodynamic adverse events as predictors of neurodevelopment in congenital heart ...disease infants undergoing cardiac surgery.
From 2010 to 2021, 320 infants underwent congenital heart disease surgery at our institution, of whom 217 had perioperative continuous electroencephalogram monitoring and were included in our study. Neurodevelopment was assessed in 76 patients by the Bayley Scales of Infant and Toddler Development, 3rd edition, consisting of cognitive, communication, and motor scaled scores. Patient and procedural factors, including hemodynamic adverse events, were included by means of the likelihood of covariate selection in our predictive model. Median (25th, 75th percentile) follow-up was 1.03 (0.09, 3.44) years with 3 (1, 6) Bayley Scales of Infant and Toddler Development, 3rd Edition evaluations per patient.
Median age at index surgery was 7 (4, 23) days, and 81 (37%) were female. Epileptiform discharges, encephalopathy, and abnormality (lethargy and coma) were more prevalent on postoperative continuous electroencephalograms, compared with preoperative continuous electroencephalograms (P < .005). In 76 patients with Bayley Scales of Infant and Toddler Development, 3rd edition evaluations, patients with diffuse abnormality (P = .009), waveform discontinuity (P = .007), and lack of continuity (P = .037) on preoperative continuous electroencephalogram had lower cognitive scores. Patients with synchrony (P < .005) on preoperative and waveform continuity (P = .009) on postoperative continuous electroencephalogram had higher fine motor scores. Patients with postoperative adverse events had lower cognitive (P < .005) and gross motor scores (P < .005).
Phenotypic patterns of perioperative continuous electroencephalogram metrics are associated with late-term neurologic injury in infants with congenital heart disease requiring surgery. Continuous electroencephalogram metrics can be integrated with hemodynamic adverse events in a predictive algorithm for neurologic impairment.
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Hyperexcitability of neuronal networks is central to the pathogenesis of Alzheimer's disease (AD). Pharmacological activation of Kv7 channels is an effective way to reduce neuronal firing. Our ...results showed that that pharmacologically activating the Kv7 channel with Retigabine (RTG) can alleviate cognitive impairment in mice without affecting spontaneous activity. RTG could also ameliorate damage to the Nissl bodies in cortex and hippocampal CA and DG regions in 9-month-old APP/PS1 mice. Additionally, RTG could reduce the Aβ plaque number in the hippocampus and cortex of both 6-month-old and 9-month-old mice. By recordings of electroencephalogram, we showed that a decrease in the number of abnormal discharges in the brains of the AD model mice when the Kv7 channel was opened. Moreover, Western blot analysis revealed a reduction in the expression of the p-Tau protein in both the hippocampus and cortex upon Kv7 channel opening. These findings suggest that Kv7 channel opener RTG may ameliorate cognitive impairment in AD, most likely by reducing brain excitability.
•Activation of Kv7 channels alleviate cognitive deficits in the brains of both 6- and 9-month-old APP/PS1 mice.•Activation of Kv7 channels reduced hyperexcitability within the brains of both 6- and 9-month-old APP/PS1 mice.•Activation of Kv7 channels may have therapeutic potential in neuropsychiatric disorders such as Alzheimer's disease.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Remimazolam is an ultrashort-acting benzodiazepine intravenous anesthetic that has recently been released. When using the total intravenous anesthesia technique, electroencephalogram（ECG）monitoring ...is recommended to maintain adequate depth of sedation. However, ECG during the administration of remimazolam sometimes differs from that of the previously standardized propofol in that beta waves are more likely to be observed, Burst Suppression is less likely to be detected, while higher processed ECG values are likely to be calculated. These factors make ECG monitoring challenging to interpret, so the condition of the patient must be comprehensively monitored when remimazolam is used.
We present an automated algorithm for unified rejection and repair of bad trials in magnetoencephalography (MEG) and electroencephalography (EEG) signals. Our method capitalizes on cross-validation ...in conjunction with a robust evaluation metric to estimate the optimal peak-to-peak threshold – a quantity commonly used for identifying bad trials in M/EEG. This approach is then extended to a more sophisticated algorithm which estimates this threshold for each sensor yielding trial-wise bad sensors. Depending on the number of bad sensors, the trial is then repaired by interpolation or by excluding it from subsequent analysis. All steps of the algorithm are fully automated thus lending itself to the name Autoreject.
In order to assess the practical significance of the algorithm, we conducted extensive validation and comparisons with state-of-the-art methods on four public datasets containing MEG and EEG recordings from more than 200 subjects. The comparisons include purely qualitative efforts as well as quantitatively benchmarking against human supervised and semi-automated preprocessing pipelines. The algorithm allowed us to automate the preprocessing of MEG data from the Human Connectome Project (HCP) going up to the computation of the evoked responses. The automated nature of our method minimizes the burden of human inspection, hence supporting scalability and reliability demanded by data analysis in modern neuroscience.
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•A strategy for artifact rejection in M/EEG using peak-to-peak thresholds is proposed•The thresholds are estimated using cross-validation with a robust error metric•The method detects and repairs outlier data segments for each sensor•Comparison with competing methods on 200 subjects with ground truth responses
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Summary
Autosomal dominant mutations
S326fs328X
and
A322D
in the GABA
A
receptor α1 subunit are associated with human absence epilepsy and juvenile myoclonic epilepsy, respectively. Because these ...mutations substantially reduce α1 subunit protein expression in vitro, it was hypothesized that they produce epilepsy by causing α1 subunit haploinsufficiency. However, in a mixed background strain of mice, α1 subunit deletion does not reduce viability or cause visually apparent seizures; the effects of α1 subunit deletion on electroencephalography (EEG) waveforms were not investigated. Here, we determined the effects of α1 subunit loss on viability, EEG spike‐wave discharges and seizures in congenic C57BL/6J and DBA/2J mice. Deletion of α1 subunit caused strain‐ and sex‐dependent reductions in viability. Heterozygous mice experienced EEG discharges and absence‐like seizures within both background strains, and exhibited a sex‐dependent effect on the discharges and viability in the C57BL/6J strain. These findings suggest that α1 subunit haploinsufficiency can produce epilepsy and may be a major mechanism by which the
S326fs328X
and
A322D
mutations cause these epilepsy syndromes.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Clozapine, a drug effective in treatment resistant schizophrenia, can modulate the brain’s electrical activity as measured by an electroencephalogram (EEG). Past reviews have focused on synthesizing ...literature related to epileptiform activity or rate of seizures in clozapine treated individuals. The aim of this review was to determine whether clozapine’s mediated effects on measurements related to neural oscillations can inform its therapeutic effects. Here, literature pertaining to studies that implemented pre-post designed investigations of clozapine and measured frequency characteristics of neural oscillations in individuals with schizophrenia were reviewed. The synthesis of findings suggests that while clozapine is associated with alterations in all neural oscillations, slower waves (delta and theta) are consistently increased in power by clozapine. We then further discuss potential mechanisms that may underlie these effects of clozapine. Future research can implement the findings of this review to motivate hypothesis-driven investigations into clozapine responsiveness biomarkers.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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