Prevalence and predictors of subclinical seizures during scalp video-EEG monitoring in patients with epilepsy
Bo JinShan WangLinglin YangChun‐Hong ShenYao DingYi GuoZhongjin WangJunming ZhuShuang WangMeiping Ding
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This study first aimed to establish the prevalence and predictors of subclinical seizures in patients with epilepsy undergoing video electroencephalographic monitoring, then to evaluate the relationship of sleep/wake and circadian pattern with subclinical seizures.We retrospectively reviewed the charts of 742 consecutive patients admitted to our epilepsy center between July 2012 and October 2014. Demographic, electro-clinical data and neuroimage were collected.A total of 148 subclinical seizures were detected in 39 patients (5.3%) during video electroencephalographic monitoring. The mean duration of subclinical seizures was 47.18 s (range, 5-311). Pharmacoresistant epilepsy, abnormal MRI and the presence of interictal epileptiform discharges were independently associated with subclinical seizures in multivariate logistic regression analysis. Subclinical seizures helped localizing the presumed epileptogenic zone in 24 (61.5%) patients, and suggested multifocal epilepsy in five (12.8%). In addition, subclinical seizures occurred more frequently in sleep and night than wakefulness and daytime, respectively, and they were more likely seen between 21:00-03:00 h, and less likely seen between 09:00-12:00 h. Thirty patients (76.9%) had their first subclinical seizures within the first 24 h of monitoring while only 7.7% of patients had their first subclinical seizures detected within 20 min.Subclinical seizures are not uncommon in patients with epilepsy, particularly in those with pharmacoresistant epilepsy, abnormal MRI or interictal epileptiform discharges. Subclinical seizures occur in specific circadian patterns and in specific sleep/wake distributions. A 20-min VEEG monitoring might not be long enough to allow for their detection.Keywords:
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Objective To study the relationship between subclinical epileptiformdischarge and transitory cognitive impairment, Methods BM800 AEEG Was used to analyse subclinical epileptiformdischarge of 8 patients from August, 1998 to March, 2002. Results The results indicate that 8 patients suffered from subclinical epilep- tiformdischange are all accompanied with TCI. Conclusion The result is show that subclinical epileptifor- mdischarge is connected with TCI. It is necessary to inspecte by AEEG the patients who have difficulties to study but have not obvious symptoms, subclinical.
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Objective To analyze the variety and clinic signification of lipids in patients with subclinical hypothyr oidism. Methods The levels of TSH,FT3,FT4,TC,TG,LDL-C,HDL-C in 52 subclinical hypothyroidism patients,60 controls and 55clinical hypothyroidism patients were determined and compared. Results Compared with the control the levels of LDL-C,TSH in clinical hypothyroidism and subclinical hypothyroidism patiens were higher (P0.01); the levels of FT4,TC, TG in clinical hypothyroidism patients were higher (P0.01). The level of TC in subclinical hypothyroidism patiens and FT3 in clinical hypothyroidism patients was higher (P0.05). Compared with subclinical hypothyroidism,the levels of FT4,TC, TG,TSH、FT3 in clinical hypothyroidism patiens was higher (P 0.01 or P 0.05). Conclusion The level of lipids in subclinical hypothyroidism patients is abnormal and it amy be close to clinical hypothyroidism.
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Abstract Objective Electroencephalography (EEG) interpretations through visual (by human raters) and automated (by computer technology) analysis are still not reliable for the diagnosis of non-convulsive status epilepticus (NCSE). This study aimed to identify typical pitfalls in the EEG analysis and make suggestions as to how those pitfalls might be avoided. Methods We analyzed the EEG recordings of individuals who had clinically confirmed or suspected NCSE. Epileptiform EEG activity during seizures (ictal discharges) were visually analyzed by two independent raters. We investigated whether unreliable EEG visual interpretations quantified by low inter-rater agreement can be predicted by the characteristics of ictal discharges and individuals’ clinical data. In addition, the EEG recordings were automatically analyzed by in-house algorithms. To further explore the causes of unreliable EEG interpretations, two epileptologists analyzed EEG patterns most likely misinterpreted as ictal discharges based on the differences between the EEG interpretations through the visual and automated analysis. Results Short ictal discharges with a gradual onset (developing over 3 seconds in length) were liable to be misinterpreted. An extra 2 minutes of ictal discharges contributed to an increase in the kappa statistics of > 0.1. Other problems were the misinterpretation of abnormal background activity (slow wave activities, other abnormal brain activity, and the ictal-like movement artifacts), continuous interictal discharges, and continuous short ictal discharges. Conclusion A longer duration criterion for NCSE-EEGs than 10 seconds that commonly used in NCSE working criteria is needed. Using knowledge of historical EEGs, individualized algorithms, and context-dependent alarm thresholds may also avoid the pitfalls.
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Abstract Objective . The aim of this study was to clarify the effect of a stable concentration of propofol on interictal high‐frequency oscillations (HFOs), which may contribute to identifying the epileptogenic zone intraoperatively for resection surgery. Methods . Nine patients with drug‐resistant focal epilepsy who underwent invasive pre‐surgical evaluation with chronic subdural electrodes were recruited. Five‐minute electrocorticograms during wakefulness, slow‐wave sleep, and under a stable brain concentration of propofol were recorded with the same electrodes. In each patient, 1–10 pairs of electrodes were selected for both electrodes with EEG changes within 5 seconds from the ictal onset (ictal pattern for 5 seconds [IP5]) and those outside the area of IP5 with no interictal epileptiform discharges (non‐epileptiform [nEPI]). The numbers of ripples (80‐250 Hz) and fast ripples (>250 Hz) were measured semi‐automatically using an established algorithm. Statistical testing was performed with a mixed effect model. Results . Thirty‐seven pairs of electrodes from nine patients were analysed for IP5 and 29 pairs from seven patients were analysed for nEPI. The numbers of HFOs differed between the areas (IP5 and nEPI) and among the conditions (wakefulness, slow‐wave sleep, propofol anaesthesia) (all p <0.01). The HFO occurrence rates were significantly higher for IP5 than those for nEPI in all conditions (for both ripples and fast ripples in all conditions; p <0.01). Significance . The occurrence rates of HFOs for IP5 were significantly higher than those for nEPI under propofol anaesthesia. These are fundamental findings for intraoperative HFO analysis, however, the following limitations should be considered: physiological HFOs could not be completely differentiated from pathological HFOs; in order to apply an HFO detector, an appropriate cut‐off threshold is needed; an artefact of the impulse response filter appears as an HFO; and the series was comprised of a small number of heterogeneous patients.
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Background: Registered EEG technologists (RETs) are trained in both the technical aspects of EEG and in preliminary EEG interpretation. However, there is little research evaluating the accuracy of EEG interpretation by RETs. Methods: Retrospective study of consecutive routine EEG recordings performed at SickKids Hospital. Preliminary reports by RETs and final reports by neurophysiologists were compared in 5 domains: background activity, focal abnormalities, ictal and inter-ictal epileptiform discharges and summary. Results: 500 EEG recordings were analyzed. Sensitivity and specificity of RET reports was high for the assessment of background (85%, 93%), focal slowing (84%, 93%) and inter-ictal epileptiform discharges (92%, 90%). RET reports identified ictal EEG patterns in 32 cases vs. 29 cases identified by neurophysiologists. RET reports were 100% accurate for noting no EEG change for all of 11 cases with non-epileptic events. Conclusions: Preliminary EEG reports by RETs were sensitive and specific for all EEG domains analyzed. In the majority of cases, the preliminary interpretation made by the RET was concordant with the final report of the neurophysiologist. Given these findings, RETs may be able to participate in the screening of routine EEG recordings in order to enhance the productivity of busy EEG laboratories.
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