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Purpose: The clinical utility of pediatric ambulatory-EEG (A-EEG) has been studied for decades, but limited information exists regarding which variables influence its utility. The authors aimed to evaluate clinical/EEG variables that may influence A-EEG yields and to develop a pathway for A-EEG utilization in children.
Methods: Single-center retrospective review of A-EEGs performed from July 2019 to January 2021 in a tertiary referral center. The primary outcome was whether the A-EEG test successfully answered the referring physician's clinical question or influenced therapy. When it did, the A-EEG test was deemed useful. Clinical and EEG variables were assessed for their ability to predict utility. Further, the literature review generated 10 relevant prior studies whose details were used to generate a pathway for A-EEG utilization in children.
Results: One hundred forty-two A-EEG studies were included (mean age 8.8 years, 48% male patients, mean A-EEG duration 33.5 hours). Overall, A-EEG was considered useful in 106 children (75%) but heavily influenced by A-EEG indication. Specifically, it was deemed useful for 94% of patients evaluated for electrical status epilepticus in slow-wave sleep, 92% of those evaluated for interictal/ictal burden, and 63% of those undergoing spell classification. The test indication (P < 0.001), a diagnosis of epilepsy (P = 0.02), and an abnormal routine EEG (P = 0.04) were associated with A-EEG test utility, although the multivariate analysis confirmed the test indication as the only independent outcome predictor of A-EEG.
Conclusions: Pediatric A-EEG is extremely useful for evaluating electrical status epilepticus in slow-wave sleep and interictal/ictal burden and is often helpful for spell classification. Among all clinical and EEG variables analyzed, the test indication was the only independent outcome predictor of obtaining a helpful A-EEG.
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http://dx.doi.org/10.1097/WNP.0000000000000906 | DOI Listing |
J Pediatr
October 2025
Division of Neonatology, Department of Pediatrics, Willem-Alexander Children's Hospital, Leiden University Medical Center, Leiden, the Netherlands. Electronic address:
Objective: To evaluate the use of a fully automated trend measure of cortical activity, Brain State of the Newborn (BSN), in early stratification of infants for add-on neuroprotective therapies during therapeutic hypothermia (TH).
Study Design: Our retrospective cohort study included 167 infants with moderate-to-severe hypoxic-ischemic encephalopathy who underwent TH and continuous electroencephalography monitoring. The BSN trends were computed using fully automated pipelines, and we used a priori-defined thresholds at 6, 12, 24, and 36 hours after birth to assess prediction of an adverse postrewarming magnetic resonance imaging finding, defined as moderate-to-severe cortical or deep gray matter injury and/or severe white matter injury.
Cogn Neurodyn
December 2025
Wenzhou People's Hospital, Wenzhou, 325000 Zhejiang China.
Unlabelled: Acupuncture has been widely used as an effective treatment for post-stroke rehabilitation. However, the potential association between acupuncture sensation, an important factor influencing treatment efficacy, and brain functional network is unclear. This research sought to reveal and quantify the changes in brain functional network associated with acupuncture sensation.
View Article and Find Full Text PDFIEEE Trans Cybern
May 2025
Electroencephalography (EEG) is widely utilized for train driver state detection due to its high accuracy and low latency. However, existing methods for driver status detection rarely use the rich physiological information in EEG to improve detection performance. Moreover, there is currently a lack of EEG datasets for abnormal states of train drivers.
View Article and Find Full Text PDFBrain Sci
November 2024
College of Electronic Information Engineering, Taiyuan University of Technology, Taiyuan 030600, China.
: Studies have shown that emotion recognition based on electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) multimodal physiological signals exhibits superior performance compared to that of unimodal approaches. Nonetheless, there remains a paucity of in-depth investigations analyzing the inherent relationship between EEG and fNIRS and constructing brain networks to improve the performance of emotion recognition. : In this study, we introduce an innovative method to construct hybrid brain networks in the source space based on simultaneous EEG-fNIRS signals for emotion recognition.
View Article and Find Full Text PDFFood Chem
January 2025
School of Automation Engineering, Northeast Electric Power University, Jilin 132012, China. Electronic address: