Pediatric EEG in the intensive care unit (ICU) requires specific technical requirements in order to yield relevant data depending upon clinical scenario: diagnosis of electroclinical or subclinical seizures, their quantification before and after therapeutic changes and sometimes evaluation of severity of cortical dysfunction. The urgent nature of these indications implies the rapid set-up of the EEG system by qualified staff and possibility of maintaining the electrodes in place during long periods of time. Various techniques are available today for EEG monitoring, the interpretation of which depends on the contribution of an experienced physician. Among recent techniques, those most commonly used are trend curves obtained via signal analysis such as amplitude EEG (a-EEG) and density spectral array (DSA) or compressed spectral array (CSA). Trend curves enable the digital creation of a display graph containing several hours of transformed and compressed EEG recorded data. Visualized on one sole display graph, these trend curves can facilitate the identification of very slow changes in EEG background activity and their variation (alertness cycles, changes linked to treatment administrations) as well as seizure patterns and their quantification. In this chapter, we propose a brief overview of monitoring techniques, followed by a review of the various data yielded by EEG monitoring as well as the relevance of this type of management; finally, detailed clinical indications will be discussed after thorough analysis of the literature.
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http://dx.doi.org/10.1016/j.neucli.2014.11.010 | DOI Listing |
Entropy (Basel)
January 2025
School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China.
Emotion recognition is an advanced technology for understanding human behavior and psychological states, with extensive applications for mental health monitoring, human-computer interaction, and affective computing. Based on electroencephalography (EEG), the biomedical signals naturally generated by the brain, this work proposes a resource-efficient multi-entropy fusion method for classifying emotional states. First, Discrete Wavelet Transform (DWT) is applied to extract five brain rhythms, i.
View Article and Find Full Text PDFFront Hum Neurosci
January 2025
Student Affairs Office, Guilin Normal College, Guilin, China.
Introduction: Attention classification based on EEG signals is crucial for brain-computer interface (BCI) applications. However, noise interference and real-time signal fluctuations hinder accuracy, especially in portable single-channel devices. This study proposes a robust Kalman filtering method combined with a norm-constrained extreme learning machine (ELM) to address these challenges.
View Article and Find Full Text PDFResuscitation
January 2025
Division of Neurology, Department of Pediatrics, The Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, USA.
Aims: To determine which patient and cardiac arrest factors were associated with obtaining neuroimaging after in-hospital cardiac arrest, and among those patients who had neuroimaging, factors associated with which neuroimaging modality was obtained.
Methods: Retrospective cohort study of patients who survived in-hospital cardiac arrest (IHCA) and were enrolled in the ICU-RESUS trial (NCT02837497).
Results: We tabulated ultrasound (US), CT, and MRI frequency within 7 days following IHCA and identified patient and cardiac arrest factors associated with neuroimaging modalities utilized.
Pediatr Neurol
January 2025
Department of Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing, China; Beijing Key Laboratory of Epilepsy, Beijing, China; Center of Epilepsy, Beijing Institute of Brain Disorders, Collaborative Innovation Center for Brain Disorders, Capital Medical University, Beijing, China. Electronic address:
Background: Sturge-Weber syndrome (SWS) is a rare congenital neurocutaneous disorder, often complicated by epilepsy. Approximately 50% of patients with SWS with epilepsy develop drug-resistant seizures, leaving limited treatment options. Vagus nerve stimulation (VNS) is a known therapy for refractory epilepsy, modulating neural activity to reduce seizures.
View Article and Find Full Text PDFHealth Sci Rep
January 2025
Department of Research The Medical Research Circle (MedReC) Goma Democratic Republic of the Congo.
Background And Aim: Epilepsy is a major neurological challenge, especially for pediatric populations. It profoundly impacts both developmental progress and quality of life in affected children. With the advent of artificial intelligence (AI), there's a growing interest in leveraging its capabilities to improve the diagnosis and management of pediatric epilepsy.
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