Dataset Information

A Full-Stack Application for Detecting Seizures and Reducing Data During Continuous Electroencephalogram Monitoring.

ABSTRACT: Continuous electroencephalogram monitoring is associated with lower mortality in critically ill patients; however, it is underused due to the resource-intensive nature of manually interpreting prolonged streams of continuous electroencephalogram data. Here, we present a novel real-time, machine learning-based alerting and monitoring system for epilepsy and seizures that dramatically reduces the amount of manual electroencephalogram review.

Methods

We developed a custom data reduction algorithm using a random forest and deployed it within an online cloud-based platform, which streams data and communicates interactively with caregivers via a web interface to display algorithm results. We developed real-time, machine learning-based alerting and monitoring system for epilepsy and seizures on continuous electroencephalogram recordings from 77 patients undergoing routine scalp ICU electroencephalogram monitoring and tested it on an additional 20 patients.

Results

We achieved a mean seizure sensitivity of 84% in cross-validation and 85% in testing, as well as a mean specificity of 83% in cross-validation and 86% in testing, corresponding to a high level of data reduction. This study validates a platform for machine learning-assisted continuous electroencephalogram analysis and represents a meaningful step toward improving utility and decreasing cost of continuous electroencephalogram monitoring. We also make our high-quality annotated dataset of 97 ICU continuous electroencephalogram recordings public for others to validate and improve upon our methods.

SUBMITTER: Bernabei JM

PROVIDER: S-EPMC8280012 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A Full-Stack Application for Detecting Seizures and Reducing Data During Continuous Electroencephalogram Monitoring.

Bernabei John M JM Owoputi Olaoluwa O Small Shyon D SD Nyema Nathaniel T NT Dumenyo Elom E Kim Joongwon J Baldassano Steven N SN Painter Christopher C Conrad Erin C EC Ganguly Taneeta M TM Balu Ramani R Davis Kathryn A KA Levine Joshua M JM Pathmanathan Jay J Litt Brian B

Critical care explorations 20210713 7

Continuous electroencephalogram monitoring is associated with lower mortality in critically ill patients; however, it is underused due to the resource-intensive nature of manually interpreting prolonged streams of continuous electroencephalogram data. Here, we present a novel real-time, machine learning-based alerting and monitoring system for epilepsy and seizures that dramatically reduces the amount of manual electroencephalogram review.<h4>Methods</h4>We developed a custom data reduction algo ...[more]

PMID: 34278312

Similar Datasets

Project description:ImportanceNeonatal seizures pose a significant challenge in critical care, and continuous video electroencephalography (cEEG) monitoring holds promise for early detection of seizures. However, large-scale data on the incidence of neonatal seizures and monitoring systems in China are lacking.ObjectivesTo determine the incidence of neonatal seizures in infants with high risk in China.Design, setting, and participantsA large, cross-sectional multicenter study was conducted from January 2017 to December 2018 in the neonatal intensive care units (NICUs) of 7 tertiary medical centers in China. Neonates with high risk were included, and cEEG monitoring was conducted. Data were collected between January 1, 2017, and January 31, 2020. The data were analyzed between January 2021 and January 2022.Main outcomes and measuresThe incidence of neonatal seizures, categorized by etiology, and seizure burden.ResultsA total of 20 310 neonates with high risk were included (10 495 [51.7%] male; mean [SD] postmenstrual age, 37.7 [3.7] weeks), and seizures were observed in 3423 infants (16.9%). The highest proportion of seizures was attributed to acute neonatal encephalopathy (1448 [42.3%]). The incidence of seizures decreased with postmenstrual age and birth weight, with the highest occurrence observed in neonates with postmenstrual age of less than 28 weeks (237 of 879 [27.0%]) or birth weight of less than 1.0 kg (269 of 914 [29.4%]). Preterm infants had a higher proportion of moderate and severe seizure burdens compared with full-term infants (moderate severity: 248 of 1199 [20.7%] vs 454 of 2224 [20.4%]), but no significant differences were observed in etiology. Seizure burden was highest with genetic syndromes (49 of 188 [26.1%]), central nervous system malformations (33 of 127 [26.0%]), and inborn errors of metabolism (27 of 113 [23.9%]). During hospitalization, 7.8% of neonates with seizures died (267 neonates), with 81.3% of these cases having a moderate or severe seizure burden (217 neonates). Mortality was generally higher in preterm vs full-term infants (98 of 1199 [8.2%] vs 169 of 2224 [7.6%]) and increased with the severity of seizure burden (217 of 267 neonates with moderate or severe burden [81.3%]).Conclusions and relevanceThis cross-sectional study of neonatal seizures underscores the substantial burden seizures pose to high-risk infants with brain injury in China, particularly those who are born prematurely or who have congenital conditions.

Project description:ObjectiveWe evaluated the impact of monitoring indication, early electroencephalography (EEG), and clinical features on seizure risk in all neonates undergoing continuous EEG (cEEG) monitoring following a standardized monitoring protocol.MethodsAll cEEGs from unique neonates 34-48 weeks postmenstrual age monitored from 1/2011-10/2017 (n = 291) were included. We evaluated the impact of cEEG monitoring indication (acute neonatal encephalopathy [ANE], suspicious clinical events [SCEs], or other high-risk conditions [OHRs]), age, medication status, and early EEG abnormalities (including the presence of epileptiform discharges and abnormal background continuity, amplitude, asymmetry, asynchrony, excessive sharp transients, and burst suppression) on time to first seizure and overall seizure risk using Kaplan-Meier survival curves and multivariable Cox proportional hazards models.ResultsSeizures occurred in 28% of high-risk neonates. Discontinuation of monitoring after 24 hours of seizure-freedom would have missed 8.5% of neonates with seizures. Overall seizure risk was lower in neonates monitored for ANE compared to OHR (P = .004) and trended lower compared to SCE (P = .097). The time course of seizure presentation varied by group, where the probability of future seizure was less than 1% after 17 hours of seizure-free monitoring in the SCE group, but required 42 hours in the OHR group, and 73 hours in the ANE group. The presence of early epileptiform discharges increased seizure risk in each group (ANE: adjusted hazard ratio [aHR] 4.32, 95% confidence interval [CI] 1.23-15.13, P = .022; SCE: aHR 10.95, 95% CI 4.77-25.14, P < 1e-07; OHR: aHR 56.90, 95% CI 10.32-313.72, P < 1e-05).SignificanceNeonates who undergo cEEG are at high risk for seizures, and risk varies by monitoring indication and early EEG findings. Seizures are captured in nearly all neonates undergoing monitoring for SCE within 24 hours of cEEG monitoring. Neonates monitored for OHR and ANE can present with delayed seizures and require longer durations of monitoring. Early epileptiform discharges are the best early EEG feature to predict seizure risk.

Dataset Information

A Full-Stack Application for Detecting Seizures and Reducing Data During Continuous Electroencephalogram Monitoring.

Methods

Results

Publications

A Full-Stack Application for Detecting Seizures and Reducing Data During Continuous Electroencephalogram Monitoring.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets