In neonatal intensive care units, there is a need for around the clock monitoring of electroencephalogram (EEG), especially for recognizing seizures. An automated seizure detector with an acceptable performance can partly fill this need. In order to develop a detector, an extensive dataset labeled by experts is needed. However, accurately defining neonatal seizures on EEG is a challenge, especially when seizure discharges do not meet exact definitions of repetitiveness or evolution in amplitude and frequency. When several readers score seizures independently, disagreement can be high. Commonly used metrics such as good detection rate (GDR) and false alarm rate (FAR) derived from data scored by multiple raters have their limitations. Therefore, new metrics are needed to measure the performance with respect to the different labels. In this paper, instead of defining the labels by consensus or majority voting, popular metrics including GDR, FAR, positive predictive value, sensitivity, specificity, and selectivity are modified such that they can take different scores into account. To this end, 353 hours of EEG data containing seizures from 81 neonates were visually scored by a clinical neurophysiologist, and then processed by an automated seizure detector. The scored seizures were mixed with false detections of an automated seizure detector and were relabeled by three independent EEG readers. Then, all labels were used in the proposed performance metrics and the result was compared with the majority voting technique and showed higher accuracy and robustness for the proposed metrics. Results were confirmed using a bootstrapping test.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1109/JBHI.2017.2750769 | DOI Listing |
Neurotherapeutics
January 2025
Department of Neurology, Massachusetts General Hospital, Boston MA, USA. Electronic address:
Electroencephalography (EEG) is invaluable in the management of acute neurological emergencies. Characteristic EEG changes have been identified in diverse neurologic conditions including stroke, trauma, and anoxia, and the increased utilization of continuous EEG (cEEG) has identified potentially harmful activity even in patients without overt clinical signs or neurologic diagnoses. Manual annotation by expert neurophysiologists is a major resource limitation in investigating the prognostic and therapeutic implications of these EEG patterns and in expanding EEG use to a broader set of patients who are likely to benefit.
View Article and Find Full Text PDFAnn Clin Transl Neurol
January 2025
Department of Pharmacology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA.
Objective: Interpretation of clinical genetic testing, which identifies a potential genetic etiology in 25% of children with epilepsy, is limited by variants of uncertain significance. Understanding functional consequences of variants can help distinguish pathogenic from benign alleles. We combined automated patch clamp recording with neurophysiological simulations to discern genotype-function-phenotype correlations in a real-world cohort of children with SCN1A-associated epilepsy.
View Article and Find Full Text PDFA A Pract
January 2025
Department of Psychology, Neuropsychology Lab, CarlVon Ossietzky Universität, Oldenburg, Germany.
An elderly patient with renal cell carcinoma underwent a robotic nephrectomy. After an uneventful intraoperative period, soon after extubation she developed generalized seizures and was diagnosed with posterior reversible encephalopathy syndrome (PRES) on neuroimaging. Management included antiepileptic and antihypertensive therapies, necessitating intensive care and neurorehabilitation.
View Article and Find Full Text PDFFront Med (Lausanne)
December 2024
Software Engineering Department, LUT University, Lahti, Finland.
Introduction: Neurodegenerative diseases, including Parkinson's, Alzheimer's, and epilepsy, pose significant diagnostic and treatment challenges due to their complexity and the gradual degeneration of central nervous system structures. This study introduces a deep learning framework designed to automate neuro-diagnostics, addressing the limitations of current manual interpretation methods, which are often time-consuming and prone to variability.
Methods: We propose a specialized deep convolutional neural network (DCNN) framework aimed at detecting and classifying neurological anomalies in MRI data.
Pediatr Qual Saf
January 2025
From the Department of Pediatrics Division of Pediatric Emergency Medicine, Nemours Children's Health, Wilmington, Del.
Introduction: Pediatric seizures account for approximately 1% of emergency department (ED) presentations. Laboratory evaluation and emergent electroencephalogram (EEG) are not indicated in patients with a new-onset, unprovoked, afebrile seizure with a normal physical examination. This study aimed to reduce unnecessary ED resource utilization.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!