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Proietti J, O'Toole JM, Murray DM, Boylan GB. Advances in Electroencephalographic Biomarkers of Neonatal Hypoxic Ischemic Encephalopathy. Clin Perinatol 2024; 51:649-663. [PMID: 39095102 DOI: 10.1016/j.clp.2024.04.006] [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] [Indexed: 08/04/2024]
Abstract
Electroencephalography (EEG) is a key objective biomarker of newborn brain function, delivering critical, cotside insights to aid the management of encephalopathy. Access to continuous EEG is limited, forcing reliance on subjective clinical assessments. In hypoxia ischaemia, the primary cause of encephalopathy, alterations in EEG patterns correlate with. injury severity and evolution. As HIE evolves, causing secondary neuronal death, EEG can track injury progression, informing neuroprotective strategies, seizure management and prognosis. Despite its value, challenges with interpretation and lack of on site expertise has limited its broader adoption. Technological advances, particularly in digital EEG and machine learning, are enhancing real-time analysis. This will allow EEG to expand its role in HIE diagnosis, management and outcome prediction.
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Affiliation(s)
- Jacopo Proietti
- Department of Engineering for Innovation Medicine, University of Verona, Strada le Grazie, Verona 37134, Italy; INFANT Research Centre, University College Cork, Cork, Ireland
| | - John M O'Toole
- INFANT Research Centre, University College Cork, Cork, Ireland; Cergenx Ltd., Dublin, Ireland
| | - Deirdre M Murray
- INFANT Research Centre, University College Cork, Cork, Ireland; Department of Paediatrics & Child Health, University College Cork, Paediatric Academic Unit, Cork University Hospital, Wilton, Cork, T12 DC4A, Ireland
| | - Geraldine B Boylan
- INFANT Research Centre, University College Cork, Cork, Ireland; Department of Paediatrics & Child Health, University College Cork, Paediatric Academic Unit, Cork University Hospital, Wilton, Cork, T12 DC4A, Ireland.
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2
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Kota S. ECI biocommentary: Srinivas Kota. Pediatr Res 2024:10.1038/s41390-024-03392-0. [PMID: 39039328 DOI: 10.1038/s41390-024-03392-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/11/2024] [Accepted: 06/21/2024] [Indexed: 07/24/2024]
Affiliation(s)
- Srinivas Kota
- Division of Neonatal-Perinatal Medicine, Department of Pediatrics, UT Southwestern Medical Center, Dallas, TX, USA.
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3
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Kota S, Kang S, Liu YL, Liu H, Montazeri S, Vanhatalo S, Chalak LF. Prognostic value of quantitative EEG in early hours of life for neonatal encephalopathy and neurodevelopmental outcomes. Pediatr Res 2024:10.1038/s41390-024-03255-8. [PMID: 39039325 DOI: 10.1038/s41390-024-03255-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 03/20/2024] [Accepted: 03/25/2024] [Indexed: 07/24/2024]
Abstract
BACKGROUND The ability to determine severity of encephalopathy is crucial for early neuroprotective therapies and for predicting neurodevelopmental outcome. The objective of this study was to assess a novel brain state of newborn (BSN) trend to distinguish newborns with presence of hypoxic ischemic encephalopathy (HIE) within hours after birth and predict neurodevelopmental outcomes at 2 years of age. METHOD This is a prospective cohort study of newborns at 36 weeks' gestation or later with and without HIE at birth. The Total Sanart Score (TSS) was calculated based on a modified Sarnat exam within 6 h of life. BSN was calculated from electroencephalogram (EEG) measurements initiated after birth. The primary outcome at 2 year of age was a diagnosis of death or disability using the Bayley Scales of Infant Development III. RESULTS BSN differentiated between normal and abnormal neurodevelopmental outcomes throughout the entire recording period from 6 h of life. Additionally, infants with lower BSN values had higher odds of neurodevelopmental impairment and HIE. BSN distinguished between normal (n = 86) and HIE (n = 46) and showed a significant correlation with the concomitant TSS. CONCLUSION BSN is a sensitive real-time marker for monitoring dynamic progression of encephalopathy and predicting neurodevelopmental impairment. IMPACT This is a prospective cohort study to investigate the ability of brain state of newborn (BSN) trend to predict neurodevelopmental outcome within the first day of life and identify severity of encephalopathy. BSN predicts neurodevelopmental outcomes at 2 years of age and the severity of encephalopathy severity. It also correlates with the Total Sarnat Score from the modified Sarnat exam. BSN could serve as a promising bedside trend aiding in accurate assessment and identification of newborns who may benefit from additional neuroprotection therapies.
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Affiliation(s)
- Srinivas Kota
- Division of Neonatal-Perinatal Medicine, Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Shu Kang
- Department of Bioengineering, University of Texas at Arlington, Arlington, TX, USA
| | - Yu-Lun Liu
- Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Hanli Liu
- Department of Bioengineering, University of Texas at Arlington, Arlington, TX, USA
| | - Saeed Montazeri
- Department of Physiology, University of Helsinki, Helsinki, Finland
| | - Sampsa Vanhatalo
- Department of Physiology, University of Helsinki, Helsinki, Finland
| | - Lina F Chalak
- Division of Neonatal-Perinatal Medicine, Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, TX, USA.
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4
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Kota S, Chalak L. Perinatal asphyxia impact on networks of cortical activity. Pediatr Res 2024; 96:17-18. [PMID: 38499627 PMCID: PMC11257797 DOI: 10.1038/s41390-024-03085-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Revised: 01/15/2024] [Accepted: 01/28/2024] [Indexed: 03/20/2024]
Affiliation(s)
- Srinivas Kota
- Division of Neonatal-Perinatal Medicine, Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Lina Chalak
- Division of Neonatal-Perinatal Medicine, Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, TX, USA.
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5
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Damien J, Vannasing P, Tremblay J, Petitpas L, Marandyuk B, Balasingam T, El Jalbout R, Paquette N, Donofrio G, Birca A, Gallagher A, Pinchefsky EF. Relationship between EEG spectral power and dysglycemia with neurodevelopmental outcomes after neonatal encephalopathy. Clin Neurophysiol 2024; 163:160-173. [PMID: 38754181 DOI: 10.1016/j.clinph.2024.03.029] [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: 10/06/2023] [Revised: 02/28/2024] [Accepted: 03/23/2024] [Indexed: 05/18/2024]
Abstract
OBJECTIVE We investigated how electroencephalography (EEG) quantitative measures and dysglycemia relate to neurodevelopmental outcomes following neonatal encephalopathy (NE). METHODS This retrospective study included 90 neonates with encephalopathy who received therapeutic hypothermia. EEG absolute spectral power was calculated during post-rewarming and 2-month follow-up. Measures of dysglycemia (hypoglycemia, hyperglycemia, and glycemic lability) and glucose variability were computed for the first 48 h of life. We evaluated the ability of EEG and glucose measures to predict neurodevelopmental outcomes at ≥ 18 months, using logistic regressions (with area under the receiver operating characteristic [AUROC] curves). RESULTS The post-rewarming global delta power (average all electrodes), hyperglycemia and glycemic lability predicted moderate/severe neurodevelopmental outcome separately (AUROC = 0.8, 95%CI [0.7,0.9], p < .001) and even more so when combined (AUROC = 0.9, 95%CI [0.8,0.9], p < .001). After adjusting for NE severity and magnetic resonance imaging (MRI) brain injury, only global delta power remained significantly associated with moderate/severe neurodevelopmental outcome (odds ratio [OR] = 0.9, 95%CI [0.8,1.0], p = .04), gross motor delay (OR = 0.9, 95%CI [0.8,1.0], p = .04), global developmental delay (OR = 0.9, 95%CI [0.8,1.0], p = .04), and auditory deficits (OR = 0.9, 95%CI [0.8,1.0], p = .03). CONCLUSIONS In NE, global delta power post-rewarming was predictive of outcomes at ≥ 18 months. SIGNIFICANCE EEG markers post-rewarming can aid prediction of neurodevelopmental outcomes following NE.
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Affiliation(s)
- Janie Damien
- Neurodevelopmental Optical Imaging Laboratory (LION Lab), Sainte-Justine University Hospital Centre, Montreal, QC, Canada; Research Centre, Sainte-Justine University Hospital Centre, Montreal, QC, Canada; Department of Psychology, University of Montreal, Montreal, QC, Canada.
| | - Phetsamone Vannasing
- Neurodevelopmental Optical Imaging Laboratory (LION Lab), Sainte-Justine University Hospital Centre, Montreal, QC, Canada; Research Centre, Sainte-Justine University Hospital Centre, Montreal, QC, Canada.
| | - Julie Tremblay
- Neurodevelopmental Optical Imaging Laboratory (LION Lab), Sainte-Justine University Hospital Centre, Montreal, QC, Canada; Research Centre, Sainte-Justine University Hospital Centre, Montreal, QC, Canada.
| | - Laurence Petitpas
- Neurodevelopmental Optical Imaging Laboratory (LION Lab), Sainte-Justine University Hospital Centre, Montreal, QC, Canada; Research Centre, Sainte-Justine University Hospital Centre, Montreal, QC, Canada; Department of Psychology, University of Montreal, Montreal, QC, Canada.
| | - Bohdana Marandyuk
- Research Centre, Sainte-Justine University Hospital Centre, Montreal, QC, Canada.
| | - Thameya Balasingam
- Research Centre, Sainte-Justine University Hospital Centre, Montreal, QC, Canada.
| | - Ramy El Jalbout
- Department of Radiology, Sainte-Justine University Hospital Centre, Montreal, QC, Canada.
| | - Natacha Paquette
- Neurodevelopmental Optical Imaging Laboratory (LION Lab), Sainte-Justine University Hospital Centre, Montreal, QC, Canada; Research Centre, Sainte-Justine University Hospital Centre, Montreal, QC, Canada; Department of Psychology, University of Montreal, Montreal, QC, Canada.
| | - Gianluca Donofrio
- Department of Neurosciences Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DiNOGMI), University of Genoa, Via Gerolamo Gaslini 5, 16147 Genoa, Italy; Service of Neurology, Department of Pediatrics, Sainte-Justine University Hospital Centre, Montreal, QC, Canada.
| | - Ala Birca
- Research Centre, Sainte-Justine University Hospital Centre, Montreal, QC, Canada; Service of Neurology, Department of Pediatrics, Sainte-Justine University Hospital Centre, Montreal, QC, Canada
| | - Anne Gallagher
- Neurodevelopmental Optical Imaging Laboratory (LION Lab), Sainte-Justine University Hospital Centre, Montreal, QC, Canada; Research Centre, Sainte-Justine University Hospital Centre, Montreal, QC, Canada; Department of Psychology, University of Montreal, Montreal, QC, Canada.
| | - Elana F Pinchefsky
- Research Centre, Sainte-Justine University Hospital Centre, Montreal, QC, Canada; Service of Neurology, Department of Pediatrics, Sainte-Justine University Hospital Centre, Montreal, QC, Canada.
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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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7
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Tuiskula A, Pospelov AS, Nevalainen P, Montazeri S, Metsäranta M, Haataja L, Stevenson N, Tokariev A, Vanhatalo S. Quantitative EEG features during the first day correlate to clinical outcome in perinatal asphyxia. Pediatr Res 2024:10.1038/s41390-024-03235-y. [PMID: 38745028 DOI: 10.1038/s41390-024-03235-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 03/21/2024] [Accepted: 03/24/2024] [Indexed: 05/16/2024]
Abstract
OBJECTIVE To assess whether computational electroencephalogram (EEG) measures during the first day of life correlate to clinical outcomes in infants with perinatal asphyxia with or without hypoxic-ischemic encephalopathy (HIE). METHODS We analyzed four-channel EEG monitoring data from 91 newborn infants after perinatal asphyxia. Altogether 42 automatically computed amplitude- and synchrony-related EEG features were extracted as 2-hourly average at very early (6 h) and early (24 h) postnatal age; they were correlated to the severity of HIE in all infants, and to four clinical outcomes available in a subcohort of 40 newborns: time to full oral feeding (nasogastric tube NGT), neonatal brain MRI, Hammersmith Infant Neurological Examination (HINE) at three months, and Griffiths Scales at two years. RESULTS At 6 h, altogether 14 (33%) EEG features correlated significantly to the HIE grade ([r]= 0.39-0.61, p < 0.05), and one feature correlated to NGT ([r]= 0.50). At 24 h, altogether 13 (31%) EEG features correlated significantly to the HIE grade ([r]= 0.39-0.56), six features correlated to NGT ([r]= 0.36-0.49) and HINE ([r]= 0.39-0.61), while no features correlated to MRI or Griffiths Scales. CONCLUSIONS Our results show that the automatically computed measures of early cortical activity may provide outcome biomarkers for clinical and research purposes. IMPACT The early EEG background and its recovery after perinatal asphyxia reflect initial severity of encephalopathy and its clinical recovery, respectively. Computational EEG features from the early hours of life show robust correlations to HIE grades and to early clinical outcomes. Computational EEG features may have potential to be used as cortical activity biomarkers in early hours after perinatal asphyxia.
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Affiliation(s)
- Anna Tuiskula
- Department of Pediatrics, Children's Hospital, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.
- BABA Center, Pediatric Research Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.
| | - Alexey S Pospelov
- BABA Center, Pediatric Research Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Department of Physiology, University of Helsinki, Helsinki, Finland
| | - Päivi Nevalainen
- BABA Center, Pediatric Research Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Department of Clinical Neurophysiology, Children's Hospital, HUS Diagnostic Center, and Epilepsia Helsinki, full member of ERN EpiCare University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Saeed Montazeri
- BABA Center, Pediatric Research Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Department of Physiology, University of Helsinki, Helsinki, Finland
| | - Marjo Metsäranta
- Department of Pediatrics, Children's Hospital, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- BABA Center, Pediatric Research Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Leena Haataja
- BABA Center, Pediatric Research Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Department of Pediatric Neurology, Children's Hospital, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Nathan Stevenson
- Brain Modelling Group, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Anton Tokariev
- BABA Center, Pediatric Research Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Department of Physiology, University of Helsinki, Helsinki, Finland
| | - Sampsa Vanhatalo
- BABA Center, Pediatric Research Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Department of Physiology, University of Helsinki, Helsinki, Finland
- Department of Clinical Neurophysiology, Children's Hospital, HUS Diagnostic Center, and Epilepsia Helsinki, full member of ERN EpiCare University of Helsinki and Helsinki University Hospital, Helsinki, Finland
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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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9
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Abbasi H, Davidson JO, Dhillon SK, Zhou KQ, Wassink G, Gunn AJ, Bennet L. Deep Learning for Generalized EEG Seizure Detection after Hypoxia-Ischemia-Preclinical Validation. Bioengineering (Basel) 2024; 11:217. [PMID: 38534490 DOI: 10.3390/bioengineering11030217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 02/12/2024] [Accepted: 02/23/2024] [Indexed: 03/28/2024] Open
Abstract
Brain maturity and many clinical treatments such as therapeutic hypothermia (TH) can significantly influence the morphology of neonatal EEG seizures after hypoxia-ischemia (HI), and so there is a need for generalized automatic seizure identification. This study validates efficacy of advanced deep-learning pattern classifiers based on a convolutional neural network (CNN) for seizure detection after HI in fetal sheep and determines the effects of maturation and brain cooling on their accuracy. The cohorts included HI-normothermia term (n = 7), HI-hypothermia term (n = 14), sham-normothermia term (n = 5), and HI-normothermia preterm (n = 14) groups, with a total of >17,300 h of recordings. Algorithms were trained and tested using leave-one-out cross-validation and k-fold cross-validation approaches. The accuracy of the term-trained seizure detectors was consistently excellent for HI-normothermia preterm data (accuracy = 99.5%, area under curve (AUC) = 99.2%). Conversely, when the HI-normothermia preterm data were used in training, the performance on HI-normothermia term and HI-hypothermia term data fell (accuracy = 98.6%, AUC = 96.5% and accuracy = 96.9%, AUC = 89.6%, respectively). Findings suggest that HI-normothermia preterm seizures do not contain all the spectral features seen at term. Nevertheless, an average 5-fold cross-validated accuracy of 99.7% (AUC = 99.4%) was achieved from all seizure detectors. This significant advancement highlights the reliability of the proposed deep-learning algorithms in identifying clinically translatable post-HI stereotypic seizures in 256Hz recordings, regardless of maturity and with minimal impact from hypothermia.
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Affiliation(s)
- Hamid Abbasi
- Department of Physiology, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1023, New Zealand
- Auckland Bioengineering Institute (ABI), University of Auckland, Auckland 1010, New Zealand
| | - Joanne O Davidson
- Department of Physiology, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1023, New Zealand
| | - Simerdeep K Dhillon
- Department of Physiology, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1023, New Zealand
| | - Kelly Q Zhou
- Department of Physiology, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1023, New Zealand
| | - Guido Wassink
- Department of Physiology, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1023, New Zealand
| | - Alistair J Gunn
- Department of Physiology, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1023, New Zealand
| | - Laura Bennet
- Department of Physiology, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1023, New Zealand
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10
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Ding X, Shen Z. Electroencephalography Prediction of Neurological Outcomes After Hypoxic-Ischemic Brain Injury: A Systematic Review and Meta-Analysis. Clin EEG Neurosci 2023:15500594231211105. [PMID: 37941351 DOI: 10.1177/15500594231211105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/10/2023]
Abstract
Background. Predicting neurological outcomes after hypoxic-ischemic brain injury (HIBI) is difficult. Objective. Electroencephalography (EEG) can identify acute and subacute brain abnormalities after hypoxic brain injury and predict HIBI recovery. We examined EEG's ability to predict neurologic outcomes following HIBI. Method. A PRISMA-compliant search was conducted in the Medline, Embase, Cochrane, and Central databases until January 2023. EEG-predicted neurological outcomes in HIBI patients were selected from relevant perspective and retrospective cohort studies. RevMan did meta-analysis, while QDAS2 assessed research quality. Results. Eleven studies with 3761 HIBI patients met the inclusion and exclusion criteria. We aggregated study-level estimates of sensitivity and specificity for EEG patterns determined a priori using random effect bivariate and univariate meta-analysis when appropriate. Positive indicators and anatomical area heterogeneity impacted prognosis accuracy. Funnel plots analyzed publication bias. Significant heterogeneity of greater than 80% was among the included studies with P < 0.001. The area under the curve was 0.94, the threshold effect was P < 0.001, and the sensitivity and specificity, with 95% confidence intervals, were 0.91 (0.84-0.99) and 0.86 (0.75-0.97). EEG detects status epilepticus and burst suppression with good sensitivity, specificity, and little probability of false-negative impairment result attribution. Study quality varied by domain, but patient flow and timing were well conducted in all. Conclusion. EEG can predict the outcome of HIBI with good prognostic accuracy, but more standardized cross-study protocols and descriptions of EEG patterns are needed to better evaluate its prognostic use for patients with HIBI.
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Affiliation(s)
- Xina Ding
- Department of Brain Function, Hospital of Nantong University, No. 20 Xisi Road, Chongchuan District, Nantong City, Jiangsu Province, 226001, China
| | - Zhixiao Shen
- Department of Brain Function, Hospital of Nantong University, No. 20 Xisi Road, Chongchuan District, Nantong City, Jiangsu Province, 226001, China
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Hermans T, Carkeek K, Dereymaeker A, Jansen K, Naulaers G, Van Huffel S, De Vos M. Partial wavelet coherence as a robust method for assessment of neurovascular coupling in neonates with hypoxic ischemic encephalopathy. Sci Rep 2023; 13:457. [PMID: 36627381 PMCID: PMC9832127 DOI: 10.1038/s41598-022-27275-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 12/29/2022] [Indexed: 01/12/2023] Open
Abstract
In neonates with hypoxic ischemic encephalopathy, the computation of wavelet coherence between electroencephalogram (EEG) power and regional cerebral oxygen saturation (rSO2) is a promising method for the assessment of neurovascular coupling (NVC), which in turn is a promising marker for brain injury. However, instabilities in arterial oxygen saturation (SpO2) limit the robustness of previously proposed methods. Therefore, we propose the use of partial wavelet coherence, which can eliminate the influence of SpO2. Furthermore, we study the added value of the novel NVC biomarkers for identification of brain injury compared to traditional EEG and NIRS biomarkers. 18 neonates with HIE were monitored for 72 h and classified into three groups based on short-term MRI outcome. Partial wavelet coherence was used to quantify the coupling between C3-C4 EEG bandpower (2-16 Hz) and rSO2, eliminating confounding effects of SpO2. NVC was defined as the amount of significant coherence in a frequency range of 0.25-1 mHz. Partial wavelet coherence successfully removed confounding influences of SpO2 when studying the coupling between EEG and rSO2. Decreased NVC was related to worse MRI outcome. Furthermore, the combination of NVC and EEG spectral edge frequency (SEF) improved the identification of neonates with mild vs moderate and severe MRI outcome compared to using EEG SEF alone. Partial wavelet coherence is an effective method for removing confounding effects of SpO2, improving the robustness of automated assessment of NVC in long-term EEG-NIRS recordings. The obtained NVC biomarkers are more sensitive to MRI outcome than traditional rSO2 biomarkers and provide complementary information to EEG biomarkers.
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Affiliation(s)
- Tim Hermans
- Department of Electrical Engineering (ESAT), STADIUS, KU Leuven, Leuven, Belgium.
| | - Katherine Carkeek
- grid.5596.f0000 0001 0668 7884Department of Development and Regeneration, KU Leuven, Leuven, Belgium ,grid.410569.f0000 0004 0626 3338Neonatal Intensive Care Unit, UZ Leuven, Leuven, Belgium ,grid.48769.340000 0004 0461 6320Neonatal Intensive Care Unit, Cliniques Universitaires Saint Luc, Brussels, Belgium
| | - Anneleen Dereymaeker
- grid.5596.f0000 0001 0668 7884Department of Development and Regeneration, KU Leuven, Leuven, Belgium ,grid.410569.f0000 0004 0626 3338Neonatal Intensive Care Unit, UZ Leuven, Leuven, Belgium
| | - Katrien Jansen
- grid.5596.f0000 0001 0668 7884Department of Development and Regeneration, KU Leuven, Leuven, Belgium ,grid.410569.f0000 0004 0626 3338Child Neurology, UZ Leuven, Leuven, Belgium
| | - Gunnar Naulaers
- grid.5596.f0000 0001 0668 7884Department of Development and Regeneration, KU Leuven, Leuven, Belgium ,grid.410569.f0000 0004 0626 3338Neonatal Intensive Care Unit, UZ Leuven, Leuven, Belgium
| | - Sabine Van Huffel
- grid.5596.f0000 0001 0668 7884Department of Electrical Engineering (ESAT), STADIUS, KU Leuven, Leuven, Belgium
| | - Maarten De Vos
- grid.5596.f0000 0001 0668 7884Department of Electrical Engineering (ESAT), STADIUS, KU Leuven, Leuven, Belgium ,grid.5596.f0000 0001 0668 7884Department of Development and Regeneration, KU Leuven, Leuven, Belgium
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Wang X, Liu H, Ortigoza EB, Kota S, Liu Y, Zhang R, Chalak LF. Feasibility of EEG Phase-Amplitude Coupling to Stratify Encephalopathy Severity in Neonatal HIE Using Short Time Window. Brain Sci 2022; 12:brainsci12070854. [PMID: 35884659 PMCID: PMC9313332 DOI: 10.3390/brainsci12070854] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Revised: 06/27/2022] [Accepted: 06/28/2022] [Indexed: 12/02/2022] Open
Abstract
Goal: It is challenging to clinically discern the severity of neonatal hypoxic ischemic encephalopathy (HIE) within hours after birth in time for therapeutic decision-making for hypothermia. The goal of this study was to determine the shortest duration of the EEG based PAC index to provide real-time guidance for clinical decision-making for neonates with HIE. Methods: Neonates were recruited from a single-center Level III NICU between 2017 and 2019. A time-dependent, PAC-frequency-averaged index, tPACm, was calculated to characterize intrinsic coupling between the amplitudes of 12−30 Hz and the phases of 1−2 Hz oscillation from 6-h EEG data at electrode P3 during the first day of life, using different sizes of moving windows including 10 s, 20 s, 1 min, 2 min, 5 min, 10 min, 20 min, 30 min, 60 min, and 120 min. Time-dependent receiver operating characteristic (ROC) curves were generated to examine the performance of the accurate window tPACm as a neurophysiologic biomarker. Results: A total of 33 neonates (mild-HIE, n = 15 and moderate/severe HIE, n = 18) were enrolled. Mixed effects models demonstrated that tPACm between the two groups was significantly different with window time segments of 3−120 min. By observing the estimates of group differences in tPACm across different window sizes, we found 20 min was the shortest window size to optimally distinguish the two groups (p < 0.001). Time-varying ROC showed significant average area-under-the-curve of 0.82. Conclusions: We demonstrated the feasibility of using tPACm with a 20 min EEG time window to differentiate the severity of HIE and facilitate earlier diagnosis and treatment initiation.
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Affiliation(s)
- Xinlong Wang
- Department of Bioengineering, University of Texas at Arlington, Arlington, TX 75220, USA; (X.W.); (H.L.)
| | - Hanli Liu
- Department of Bioengineering, University of Texas at Arlington, Arlington, TX 75220, USA; (X.W.); (H.L.)
| | - Eric B. Ortigoza
- Division of Neonatal-Perinatal Medicine, Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, TX 75220, USA;
| | - Srinivas Kota
- Department of Neurosurgery, University of Texas Southwestern Medical Center, Dallas, TX 75220, USA;
| | - Yulun Liu
- Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX 75220, USA;
| | - Rong Zhang
- Departments of Internal Medicine and Neurology, University of Texas Southwestern Medical Center, Dallas, TX 75220, USA;
| | - Lina F. Chalak
- Division of Neonatal-Perinatal Medicine, Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, TX 75220, USA;
- Correspondence: ; Tel.: +1-214-648-3753; Fax: +1-214-648-2481
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Wang X, Liu H, Kota S, Das Y, Liu Y, Zhang R, Chalak L. EEG phase-amplitude coupling to stratify encephalopathy severity in the developing brain. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 214:106593. [PMID: 34959157 DOI: 10.1016/j.cmpb.2021.106593] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 11/19/2021] [Accepted: 12/15/2021] [Indexed: 06/14/2023]
Abstract
BACKGROUND Neonatal hypoxic ischemic encephalopathy (HIE) is difficult to classify within the narrow therapeutic window of hypothermia. Neurophysiological biomarkers are needed for timely differentiation of encephalopathy severity within the short therapeutic window for initiation of hypothermia therapy. METHODS A novel analysis of mean Phase Amplitude Coupling index, PACm, of amplitudes high frequencies (12-30 Hz) coupled with phases of low (1,2 Hz) frequencies was calculated from the 6 h EEG recorded during the first day of life. PACm values were compared to identify differences between mild versus higher-grade HIE, respectively, for each of the EEG electrodes. A receiver operating characteristic curve was generated to examine the performance of PACm. RESULTS 38 newborns with different HIE grades were enrolled in the first 6 h of life. Threshold PACm 0.001 at Fz, O1, O2, P3, and P4 had AUC >0.9 to differentiate HIE severity and predict the persistence of moderate to severe encephalopathy that requires treatment with hypothermia. CONCLUSION PAC is a promising biomarker to identify mild from higher severity of HIE after birth.
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Affiliation(s)
- Xinlong Wang
- Department of Bioengineering, University of Texas at Arlington, Arlington, TX, United States
| | - Hanli Liu
- Department of Bioengineering, University of Texas at Arlington, Arlington, TX, United States
| | - Srinivas Kota
- Department of Neurosurgery, University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Yudhajit Das
- Department of Bioengineering, University of Texas at Arlington, Arlington, TX, United States
| | - Yulun Liu
- Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Rong Zhang
- Departments of Internal Medicine and Neurology, University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Lina Chalak
- Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, TX, United States.
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