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Keene JC, Loe ME, Fulton T, Keene M, Morrissey MJ, Tomko SR, Vesoulis ZA, Zempel JM, Ching S, Guerriero RM. A Comparison of Automatically Extracted Quantitative EEG Features for Seizure Risk Stratification in Neonatal Encephalopathy. J Clin Neurophysiol 2024:00004691-990000000-00136. [PMID: 38857366 DOI: 10.1097/wnp.0000000000001067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/12/2024] Open
Abstract
PURPOSE Seizures occur in up to 40% of neonates with neonatal encephalopathy. Earlier identification of seizures leads to more successful seizure treatment, but is often delayed because of limited availability of continuous EEG monitoring. Clinical variables poorly stratify seizure risk, and EEG use to stratify seizure risk has previously been limited by need for manual review and artifact exclusion. The goal of this study is to compare the utility of automatically extracted quantitative EEG (qEEG) features for seizure risk stratification. METHODS We conducted a retrospective analysis of neonates with moderate-to-severe neonatal encephalopathy who underwent therapeutic hypothermia at a single center. The first 24 hours of EEG underwent automated artifact removal and qEEG analysis, comparing qEEG features for seizure risk stratification. RESULTS The study included 150 neonates and compared the 36 (23%) with seizures with those without. Absolute spectral power best stratified seizure risk with area under the curve ranging from 63% to 71%, followed by range EEG lower and upper margin, median and SD of the range EEG lower margin. No features were significantly more predictive in the hour before seizure onset. Clinical examination was not associated with seizure risk. CONCLUSIONS Automatically extracted qEEG features were more predictive than clinical examination in stratifying neonatal seizure risk during therapeutic hypothermia. qEEG represents a potential practical bedside tool to individualize intensity and duration of EEG monitoring and decrease time to seizure recognition. Future work is needed to refine and combine qEEG features to improve risk stratification.
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Affiliation(s)
- Jennifer C Keene
- Division of Pediatric & Developmental Neurology, Department of Neurology. Washington University in St. Louis, St. Louis, Missouri U.S.A
| | - Maren E Loe
- Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, Missouri, U.S.A
- Medical Scientist Training Program, Washington University School of Medicine, St. Louis, Missouri, U.S.A
| | - Talie Fulton
- Washington University in St. Louis, St. Louis, Missouri, U.S.A.; and
| | - Maire Keene
- Division of Pediatric & Developmental Neurology, Department of Neurology. Washington University in St. Louis, St. Louis, Missouri U.S.A
- Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, Missouri, U.S.A
- Medical Scientist Training Program, Washington University School of Medicine, St. Louis, Missouri, U.S.A
- Washington University in St. Louis, St. Louis, Missouri, U.S.A.; and
- Division of Newborn Medicine, Department of Pediatrics. Washington University in St. Louis, St. Louis, Missouri, U.S.A
| | - Michael J Morrissey
- Division of Pediatric & Developmental Neurology, Department of Neurology. Washington University in St. Louis, St. Louis, Missouri U.S.A
| | - Stuart R Tomko
- Division of Pediatric & Developmental Neurology, Department of Neurology. Washington University in St. Louis, St. Louis, Missouri U.S.A
| | - Zachary A Vesoulis
- Division of Newborn Medicine, Department of Pediatrics. Washington University in St. Louis, St. Louis, Missouri, U.S.A
| | - John M Zempel
- Division of Pediatric & Developmental Neurology, Department of Neurology. Washington University in St. Louis, St. Louis, Missouri U.S.A
| | - ShiNung Ching
- Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, Missouri, U.S.A
| | - Réjean M Guerriero
- Division of Pediatric & Developmental Neurology, Department of Neurology. Washington University in St. Louis, St. Louis, Missouri U.S.A
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Keene JC, Loe ME, Fulton T, Keene M, Mathur A, Morrissey MJ, Tomko SR, Vesoulis ZA, Zempel JM, Ching S, Guerriero RM. Macroperiodic Oscillations: A Potential Novel Biomarker of Outcome in Neonatal Encephalopathy. J Clin Neurophysiol 2024; 41:344-350. [PMID: 37052470 PMCID: PMC10567988 DOI: 10.1097/wnp.0000000000001011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/14/2023] Open
Abstract
PURPOSE Neonatal encephalopathy (NE) is a common cause of neurodevelopmental morbidity. Tools to accurately predict outcomes after therapeutic hypothermia remain limited. We evaluated a novel EEG biomarker, macroperiodic oscillations (MOs), to predict neurodevelopmental outcomes. METHODS We conducted a secondary analysis of a randomized controlled trial of neonates with moderate-to-severe NE who underwent standardized clinical examination, magnetic resonance (MR) scoring, video EEG, and neurodevelopmental assessment with Bayley III evaluation at 18 to 24 months. A non-NE cohort of neonates was also assessed for the presence of MOs. The relationship between clinical examination, MR score, MOs, and neurodevelopmental assessment was analyzed. RESULTS The study included 37 neonates with 24 of whom survived and underwent neurodevelopmental assessment (70%). The strength of MOs correlated with severity of clinical encephalopathy. MO strength and spread significantly correlated with Bayley III cognitive percentile ( P = 0.017 and 0.046). MO strength outperformed MR score in predicting a combined adverse outcome of death or disability ( P = 0.019, sensitivity 100%, specificity 77% vs. P = 0.079, sensitivity 100%, specificity 59%). CONCLUSIONS MOs are an EEG-derived, quantitative biomarker of neurodevelopmental outcome that outperformed a comprehensive validated MRI injury score and a detailed systematic discharge examination in this small cohort. Future work is needed to validate MOs in a larger cohort and elucidate the underlying pathophysiology of MOs.
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Affiliation(s)
- Jennifer C Keene
- Division of Pediatric and Developmental Neurology, Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, U.S.A
| | - Maren E Loe
- Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, Missouri, U.S.A
- Medical Scientist Training Program, Washington University School of Medicine, St. Louis, Missouri, U.S.A
| | - Talie Fulton
- Washington University in St. Louis, St. Louis, Missouri, U.S.A
| | - Maire Keene
- Division of Pediatric and Developmental Neurology, Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, U.S.A
- Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, Missouri, U.S.A
- Medical Scientist Training Program, Washington University School of Medicine, St. Louis, Missouri, U.S.A
- Washington University in St. Louis, St. Louis, Missouri, U.S.A
- Division of Neonatal-Perinatal Medicine, Department of Pediatrics, Saint Louis University School of Medicine, St. Louis, Missouri, U.S.A. ; and
- Division of Newborn Medicine, Department of Pediatrics. Washington University in St. Louis, St. Louis, Missouri, U.S.A
| | - Amit Mathur
- Division of Neonatal-Perinatal Medicine, Department of Pediatrics, Saint Louis University School of Medicine, St. Louis, Missouri, U.S.A. ; and
| | - Michael J Morrissey
- Division of Pediatric and Developmental Neurology, Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, U.S.A
| | - Stuart R Tomko
- Division of Pediatric and Developmental Neurology, Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, U.S.A
| | - Zachary A Vesoulis
- Division of Newborn Medicine, Department of Pediatrics. Washington University in St. Louis, St. Louis, Missouri, U.S.A
| | - John M Zempel
- Division of Pediatric and Developmental Neurology, Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, U.S.A
| | - ShiNung Ching
- Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, Missouri, U.S.A
| | - Réjean M Guerriero
- Division of Pediatric and Developmental Neurology, Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, U.S.A
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Benedetti GM, Guerriero RM, Press CA. Review of Noninvasive Neuromonitoring Modalities in Children II: EEG, qEEG. Neurocrit Care 2023; 39:618-638. [PMID: 36949358 PMCID: PMC10033183 DOI: 10.1007/s12028-023-01686-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 01/30/2023] [Indexed: 03/24/2023]
Abstract
Critically ill children with acute neurologic dysfunction are at risk for a variety of complications that can be detected by noninvasive bedside neuromonitoring. Continuous electroencephalography (cEEG) is the most widely available and utilized form of neuromonitoring in the pediatric intensive care unit. In this article, we review the role of cEEG and the emerging role of quantitative EEG (qEEG) in this patient population. cEEG has long been established as the gold standard for detecting seizures in critically ill children and assessing treatment response, and its role in background assessment and neuroprognostication after brain injury is also discussed. We explore the emerging utility of both cEEG and qEEG as biomarkers of degree of cerebral dysfunction after specific injuries and their ability to detect both neurologic deterioration and improvement.
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Affiliation(s)
- Giulia M Benedetti
- Division of Pediatric Neurology, Department of Neurology, Seattle Children's Hospital and the University of Washington School of Medicine, Seattle, WA, USA.
- Division of Pediatric Neurology, Department of Pediatrics, C.S. Mott Children's Hospital and the University of Michigan, 1540 E Hospital Drive, Ann Arbor, MI, 48109-4279, USA.
| | - Rejéan M Guerriero
- Division of Pediatric and Developmental Neurology, Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Craig A Press
- Departments of Neurology and Pediatric, Children's Hospital of Philadelphia and Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
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The Role of Electroencephalography in the Prognostication of Clinical Outcomes in Critically Ill Children: A Review. CHILDREN 2022; 9:children9091368. [PMID: 36138677 PMCID: PMC9497701 DOI: 10.3390/children9091368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 08/30/2022] [Accepted: 09/02/2022] [Indexed: 11/16/2022]
Abstract
Electroencephalography (EEG) is a neurologic monitoring modality that allows for the identification of seizures and the understanding of cerebral function. Not only can EEG data provide real-time information about a patient’s clinical status, but providers are increasingly using these results to understand short and long-term prognosis in critical illnesses. Adult studies have explored these associations for many years, and now the focus has turned to applying these concepts to the pediatric literature. The aim of this review is to characterize how EEG can be utilized clinically in pediatric intensive care settings and to highlight the current data available to understand EEG features in association with functional outcomes in children after critical illness. In the evaluation of seizures and seizure burden in children, there is abundant data to suggest that the presence of status epilepticus during illness is associated with poorer outcomes and a higher risk of mortality. There is also emerging evidence indicating that poorly organized EEG backgrounds, lack of normal sleep features and lack of electrographic reactivity to clinical exams portend worse outcomes in this population. Prognostication in pediatric critical illness must be informed by the comprehensive evaluation of a patient’s clinical status but the utilization of EEG may help contribute to this assessment in a meaningful way.
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Loe ME, Morrissey MJ, Tomko SR, Guerriero RM, Ching S. Detecting slow narrowband modulation in EEG signals. J Neurosci Methods 2022; 378:109660. [PMID: 35779689 DOI: 10.1016/j.jneumeth.2022.109660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 06/08/2022] [Accepted: 06/25/2022] [Indexed: 10/17/2022]
Abstract
BACKGROUND We observed an unusual modulatory phenomenon in the electroencephalogram (EEG) of pediatric patients with acquired brain injury. The modulation is orders of magnitude slower than the fast EEG background activity, necessitating new analysis procedures to systematically detect and quantify the phenomenon. NEW METHOD We propose a method for analyzing spatial and temporal relationships associated with slow, narrowband modulation of EEG. We extract envelope signals from physiological frequency bands of EEG. Then, we construct a sparse representation of the spectral content of the envelope signal across sliding windows. For the latter, we use an augmented LASSO regression to incorporate spatial and temporal filtering into the solution. The method can be applied to windows of variable length, depending on the desired frequency resolution. RESULTS The sparse estimates of the envelope power spectra enable the detection of narrowband modulation in the millihertz frequency range. Subsequently, we are able to assess non-stationarity in the frequency and spatial relationships across channels. The method can be paired with unsupervised anomaly detection to identify windows with significant modulation. We validated such findings by applying our method to a control set of EEGs. COMPARISON WITH EXISTING METHODS To our knowledge, no methods have been previously proposed to quantify second order modulation at such disparate time-scales. CONCLUSIONS We provide a general EEG analysis framework capable of detecting signal content below 0.1Hz, which is especially germane to clinical recordings that may contain multiple hours worth of continuous data.
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Affiliation(s)
- Maren E Loe
- Department of Electrical and Systems Engineering, Washington University in St. Louis, 1 Brookings Drive, St. Louis, 63130, Missouri, USA
| | - Michael J Morrissey
- St. Louis Children's Hospital, One Children's Place, St. Louis, 63110, Missouri, USA
| | - Stuart R Tomko
- Division of Pediatric Neurology, Department of Neurology, Washington University School of Medicine, 660 S. Euclid Ave, St. Louis, 63110, Missouri, USA
| | - Réjean M Guerriero
- Division of Pediatric Neurology, Department of Neurology, Washington University School of Medicine, 660 S. Euclid Ave, St. Louis, 63110, Missouri, USA
| | - ShiNung Ching
- Department of Electrical and Systems Engineering, Washington University in St. Louis, 1 Brookings Drive, St. Louis, 63130, Missouri, USA.
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