1
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Yeh CH, Zhang C, Shi W, Zhang B, An J. Quantifying Sharpness and Nonlinearity in Neonatal Seizure Dynamics. CYBORG AND BIONIC SYSTEMS 2024; 5:0076. [PMID: 38274711 PMCID: PMC10809840 DOI: 10.34133/cbsystems.0076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2023] [Accepted: 11/12/2023] [Indexed: 01/27/2024] Open
Abstract
The integration of multiple electrophysiological biomarkers is crucial for monitoring neonatal seizure dynamics. The present study aimed to characterize the temporal dynamics of neonatal seizures by analyzing intrinsic waveforms of epileptic electroencephalogram (EEG) signals. We proposed a complementary set of methods considering envelope power, focal sharpness changes, and nonlinear patterns of EEG signals of 79 neonates with seizures. Features derived from EEG signals were used as input to the machine learning classifier. All three characteristics were significantly elevated during seizure events, as agreed upon by all viewers (P < 0.0001). Envelope power was elevated in the entire seizure period, and the degree of nonlinearity rose at the termination of a seizure event. Epileptic sharpness effectively characterizes an entire seizure event, complementing the role of envelope power in identifying its onset. However, the degree of nonlinearity showed superior discriminability for the termination of a seizure event. The proposed computational methods for intrinsic sharp or nonlinear EEG patterns evolving during neonatal seizure could share some features with envelope power. Current findings may be helpful in developing strategies to improve neonatal seizure monitoring.
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Affiliation(s)
- Chien-Hung Yeh
- School of Information and Electronics,
Beijing Institute of Technology, Beijing, China
- Key Laboratory of Brain Health Intelligent Evaluation and Intervention (Beijing Institute of Technology), Ministry of Education, Beijing, China
| | - Chuting Zhang
- School of Information and Electronics,
Beijing Institute of Technology, Beijing, China
| | - Wenbin Shi
- School of Information and Electronics,
Beijing Institute of Technology, Beijing, China
- Key Laboratory of Brain Health Intelligent Evaluation and Intervention (Beijing Institute of Technology), Ministry of Education, Beijing, China
| | - Boyi Zhang
- School of Engineering,
University of Edinburgh, Edinburgh, UK
| | - Jianping An
- School of Information and Electronics,
Beijing Institute of Technology, Beijing, China
- School of Cyberspace Science and Technology,
Beijing Institute of Technology, Beijing, China
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2
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Raeisi K, Khazaei M, Tamburro G, Croce P, Comani S, Zappasodi F. A Class-Imbalance Aware and Explainable Spatio-Temporal Graph Attention Network for Neonatal Seizure Detection. Int J Neural Syst 2023; 33:2350046. [PMID: 37497802 DOI: 10.1142/s0129065723500466] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/28/2023]
Abstract
Seizures are the most prevalent clinical indication of neurological disorders in neonates. In this study, a class-imbalance aware and explainable deep learning approach based on Convolutional Neural Networks (CNNs) and Graph Attention Networks (GATs) is proposed for the accurate automated detection of neonatal seizures. The proposed model integrates the temporal information of EEG signals with the spatial information on the EEG channels through the graph representation of the multi-channel EEG segments. One-dimensional CNNs are used to automatically develop a feature set that accurately represents the differences between seizure and nonseizure epochs in the time domain. By employing GAT, the attention mechanism is utilized to emphasize the critical channel pairs and information flow among brain regions. GAT coefficients were then used to empirically visualize the important regions during the seizure and nonseizure epochs, which can provide valuable insight into the location of seizures in the neonatal brain. Additionally, to tackle the severe class imbalance in the neonatal seizure dataset using under-sampling and focal loss techniques are used. Overall, the final Spatio-Temporal Graph Attention Network (ST-GAT) outperformed previous benchmarked methods with a mean AUC of 96.6% and Kappa of 0.88, demonstrating its high accuracy and potential for clinical applications.
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Affiliation(s)
- Khadijeh Raeisi
- Department of Neuroscience, Imaging and Clinical Sciences, Universita Gabriele d'Annunzio, Chieti 66100, Italy
| | - Mohammad Khazaei
- Department of Neuroscience, Imaging and Clinical Sciences, Universita Gabriele d'Annunzio, Chieti 66100, Italy
| | - Gabriella Tamburro
- Department of Neuroscience, Imaging and Clinical Sciences-Behavioral Imaging and Neural Dynamics Center, Universita Gabriele d'Annunzio, Chieti 66100, Italy
| | - Pierpaolo Croce
- Department of Neuroscience, Imaging and Clinical Sciences-Behavioral Imaging and Neural Dynamics Center, Universita Gabriele d'Annunzio, Chieti 66100, Italy
| | - Silvia Comani
- Department of Neuroscience, Imaging and Clinical Sciences-Behavioral Imaging and Neural Dynamics Center, Universita Gabriele d'Annunzio, Chieti 66100, Italy
| | - Filippo Zappasodi
- Department of Neuroscience, Imaging and Clinical Sciences-Behavioral, Imaging and Neural Dynamics Center-Institute for, Advanced Biomedical Technologies, Universita Gabriele d'Annunzio, Chieti 66100, Italy
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3
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Chakravarthi B, Ng SC, Ezilarasan MR, Leung MF. EEG-based emotion recognition using hybrid CNN and LSTM classification. Front Comput Neurosci 2022; 16:1019776. [PMID: 36277613 PMCID: PMC9585893 DOI: 10.3389/fncom.2022.1019776] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 09/20/2022] [Indexed: 11/13/2022] Open
Abstract
Emotions are a mental state that is accompanied by a distinct physiologic rhythm, as well as physical, behavioral, and mental changes. In the latest days, physiological activity has been used to study emotional reactions. This study describes the electroencephalography (EEG) signals, the brain wave pattern, and emotion analysis all of these are interrelated and based on the consequences of human behavior and Post-Traumatic Stress Disorder (PTSD). Post-traumatic stress disorder effects for long-term illness are associated with considerable suffering, impairment, and social/emotional impairment. PTSD is connected to subcortical responses to injury memories, thoughts, and emotions and alterations in brain circuitry. Predominantly EEG signals are the way of examining the electrical potential of the human feelings cum expression for every changing phenomenon that the individual faces. When going through literature there are some lacunae while analyzing emotions. There exist some reliability issues and also masking of real emotional behavior by the victims. Keeping this research gap and hindrance faced by the previous researchers the present study aims to fulfill the requirements, the efforts can be made to overcome this problem, and the proposed automated CNN-LSTM with ResNet-152 algorithm. Compared with the existing techniques, the proposed techniques achieved a higher level of accuracy of 98% by applying the hybrid deep learning algorithm.
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Affiliation(s)
- Bhuvaneshwari Chakravarthi
- School of Computing and Information Science, Faculty of Science and Engineering, Anglia Ruskin University, Cambridge, United Kingdom
| | - Sin-Chun Ng
- School of Computing and Information Science, Faculty of Science and Engineering, Anglia Ruskin University, Cambridge, United Kingdom
- *Correspondence: Sin-Chun Ng,
| | - M. R. Ezilarasan
- Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India
| | - Man-Fai Leung
- School of Computing and Information Science, Faculty of Science and Engineering, Anglia Ruskin University, Cambridge, United Kingdom
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4
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Mendelsohn R, Lemyre B, Webster R, Mabilangan K, S.Bulusu, Pohl D. Real-time detection of neonatal seizures improves with on demand EEG interpretation. Clin Neurophysiol 2022; 143:166-171. [DOI: 10.1016/j.clinph.2022.08.017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 08/12/2022] [Accepted: 08/24/2022] [Indexed: 01/22/2023]
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5
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Ahmed MIB, Alotaibi S, Atta-ur-Rahman, Dash S, Nabil M, AlTurki AO. A Review on Machine Learning Approaches in Identification of Pediatric Epilepsy. SN COMPUTER SCIENCE 2022; 3:437. [PMID: 35965953 PMCID: PMC9364307 DOI: 10.1007/s42979-022-01358-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 06/26/2022] [Indexed: 10/26/2022]
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6
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Tapani KT, Nevalainen P, Vanhatalo S, Stevenson NJ. Validating an SVM-based neonatal seizure detection algorithm for generalizability, non-inferiority and clinical efficacy. Comput Biol Med 2022; 145:105399. [DOI: 10.1016/j.compbiomed.2022.105399] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 02/21/2022] [Accepted: 03/09/2022] [Indexed: 01/01/2023]
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7
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Montazeri S, Pinchefsky E, Tse I, Marchi V, Kohonen J, Kauppila M, Airaksinen M, Tapani K, Nevalainen P, Hahn C, Tam EWY, Stevenson NJ, Vanhatalo S. Building an Open Source Classifier for the Neonatal EEG Background: A Systematic Feature-Based Approach From Expert Scoring to Clinical Visualization. Front Hum Neurosci 2021; 15:675154. [PMID: 34135744 PMCID: PMC8200402 DOI: 10.3389/fnhum.2021.675154] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Accepted: 04/30/2021] [Indexed: 11/13/2022] Open
Abstract
Neonatal brain monitoring in the neonatal intensive care units (NICU) requires a continuous review of the spontaneous cortical activity, i.e., the electroencephalograph (EEG) background activity. This needs development of bedside methods for an automated assessment of the EEG background activity. In this paper, we present development of the key components of a neonatal EEG background classifier, starting from the visual background scoring to classifier design, and finally to possible bedside visualization of the classifier results. A dataset with 13,200 5-minute EEG epochs (8-16 channels) from 27 infants with birth asphyxia was used for classifier training after scoring by two independent experts. We tested three classifier designs based on 98 computational features, and their performance was assessed with respect to scoring system, pre- and post-processing of labels and outputs, choice of channels, and visualization in monitor displays. The optimal solution achieved an overall classification accuracy of 97% with a range across subjects of 81-100%. We identified a set of 23 features that make the classifier highly robust to the choice of channels and missing data due to artefact rejection. Our results showed that an automated bedside classifier of EEG background is achievable, and we publish the full classifier algorithm to allow further clinical replication and validation studies.
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Affiliation(s)
- Saeed Montazeri
- BABA Center, Pediatric Research Centre, Department of Clinical Neurophysiology, Children’s Hospital and HUS Diagnostic Center, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Elana Pinchefsky
- Division of Neurology, Department of Paediatrics, Sainte-Justine University Hospital Centre, University of Montreal, Montreal, QC, Canada
| | - Ilse Tse
- BABA Center, Pediatric Research Centre, Department of Clinical Neurophysiology, Children’s Hospital and HUS Diagnostic Center, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Viviana Marchi
- BABA Center, Pediatric Research Centre, Department of Clinical Neurophysiology, Children’s Hospital and HUS Diagnostic Center, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
- Department of Developmental Neuroscience, Stella Maris Scientific Institute, IRCCS Fondazione Stella Maris Foundation, Pisa, Italy
| | - Jukka Kohonen
- Department of Computer Science, Aalto University, Espoo, Finland
| | - Minna Kauppila
- BABA Center, Pediatric Research Centre, Department of Clinical Neurophysiology, Children’s Hospital and HUS Diagnostic Center, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Manu Airaksinen
- BABA Center, Pediatric Research Centre, Department of Clinical Neurophysiology, Children’s Hospital and HUS Diagnostic Center, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
- Department of Signal Processing and Acoustics, Aalto University, Espoo, Finland
| | - Karoliina Tapani
- BABA Center, Pediatric Research Centre, Department of Clinical Neurophysiology, Children’s Hospital and HUS Diagnostic Center, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Päivi Nevalainen
- BABA Center, Pediatric Research Centre, Department of Clinical Neurophysiology, Children’s Hospital and HUS Diagnostic Center, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Cecil Hahn
- Department of Paediatrics (Neurology), The Hospital for Sick Children and University of Toronto, Toronto, ON, Canada
| | - Emily W. Y. Tam
- Department of Paediatrics (Neurology), The Hospital for Sick Children and University of Toronto, Toronto, ON, Canada
| | - Nathan J. Stevenson
- Brain Modelling Group, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Sampsa Vanhatalo
- BABA Center, Pediatric Research Centre, Department of Clinical Neurophysiology, Children’s Hospital and HUS Diagnostic Center, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
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8
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Pisani F, Spagnoli C, Falsaperla R, Nagarajan L, Ramantani G. Seizures in the neonate: A review of etiologies and outcomes. Seizure 2021; 85:48-56. [PMID: 33418166 DOI: 10.1016/j.seizure.2020.12.023] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2020] [Revised: 12/24/2020] [Accepted: 12/26/2020] [Indexed: 12/21/2022] Open
Abstract
Neonatal seizures occur in their majority in close temporal relation to an acute brain injury or systemic insult, and are accordingly defined as acute symptomatic or provoked seizures. However less frequently, unprovoked seizures may also present in the neonatal period as secondary to structural brain abnormalities, thus corresponding to structural epilepsies, or to genetic conditions, thus corresponding to genetic epilepsies. Unprovoked neonatal seizures should be thus considered as the clinical manifestation of early onset structural or genetic epilepsies that often have the characteristics of early onset epileptic encephalopathies. In this review, we address the conundrum of neonatal seizures including acute symptomatic, remote symptomatic, provoked, and unprovoked seizures, evolving to post-neonatal epilepsies, and neonatal onset epilepsies. The different clinical scenarios involving neonatal seizures, each with their distinct post-neonatal evolution are presented. The structural and functional impact of neonatal seizures on brain development and the concept of secondary epileptogenesis, with or without a following latent period after the acute seizures, are addressed. Finally, we underline the need for an early differential diagnosis between an acute symptomatic seizure and an unprovoked seizure, since it is associated with fundamental differences in clinical evolution. These are crucial aspects for neonatal management, counselling and prognostication. In view of the above aspects, we provide an outlook on future strategies and potential lines of research in this field.
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Affiliation(s)
- Francesco Pisani
- Child Neuropsychiatry Unit, Medicine and Surgery Department, University of Parma, Italy
| | - Carlotta Spagnoli
- Child Neurology Unit, Department of Pediatrics, Azienda USL-IRCCS, Reggio Emilia, Italy
| | - Raffaele Falsaperla
- Neonatal Intensive Care Unit, University-Hospital Policlinico Vittorio Emanuele, Catania, Italy
| | - Lakshmi Nagarajan
- Children's Neuroscience Service, Department of Neurology, Perth Children's Hospital, Australia
| | - Georgia Ramantani
- Department of Neuropediatrics, University Children's Hospital Zurich, Switzerland.
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9
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Sandoval Karamian AG, Wusthoff CJ. Current and Future Uses of Continuous EEG in the NICU. Front Pediatr 2021; 9:768670. [PMID: 34805053 PMCID: PMC8595393 DOI: 10.3389/fped.2021.768670] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 10/12/2021] [Indexed: 11/28/2022] Open
Abstract
Continuous EEG (cEEG) is a fundamental neurodiagnostic tool in the care of critically ill neonates and is increasingly recommended. cEEG enhances prognostication via assessment of the background brain activity, plays a role in predicting which neonates are at risk for seizures when combined with clinical factors, and allows for accurate diagnosis and management of neonatal seizures. Continuous EEG is the gold standard method for diagnosis of neonatal seizures and should be used for detection of seizures in high-risk clinical conditions, differential diagnosis of paroxysmal events, and assessment of response to treatment. High costs associated with cEEG are a limiting factor in its widespread implementation. Centralized remote cEEG interpretation, automated seizure detection, and pre-natal EEG are potential future applications of this neurodiagnostic tool.
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Affiliation(s)
| | - Courtney J Wusthoff
- Division of Child Neurology, Lucile Packard Children's Hospital at Stanford, Palo Alto, CA, United States
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10
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Pavel AM, Rennie JM, de Vries LS, Blennow M, Foran A, Shah DK, Pressler RM, Kapellou O, Dempsey EM, Mathieson SR, Pavlidis E, van Huffelen AC, Livingstone V, Toet MC, Weeke LC, Finder M, Mitra S, Murray DM, Marnane WP, Boylan GB. A machine-learning algorithm for neonatal seizure recognition: a multicentre, randomised, controlled trial. THE LANCET. CHILD & ADOLESCENT HEALTH 2020; 4:740-749. [PMID: 32861271 PMCID: PMC7492960 DOI: 10.1016/s2352-4642(20)30239-x] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 05/19/2020] [Accepted: 07/03/2020] [Indexed: 01/19/2023]
Abstract
BACKGROUND Despite the availability of continuous conventional electroencephalography (cEEG), accurate diagnosis of neonatal seizures is challenging in clinical practice. Algorithms for decision support in the recognition of neonatal seizures could improve detection. We aimed to assess the diagnostic accuracy of an automated seizure detection algorithm called Algorithm for Neonatal Seizure Recognition (ANSeR). METHODS This multicentre, randomised, two-arm, parallel, controlled trial was done in eight neonatal centres across Ireland, the Netherlands, Sweden, and the UK. Neonates with a corrected gestational age between 36 and 44 weeks with, or at significant risk of, seizures requiring EEG monitoring, received cEEG plus ANSeR linked to the EEG monitor displaying a seizure probability trend in real time (algorithm group) or cEEG monitoring alone (non-algorithm group). The primary outcome was diagnostic accuracy (sensitivity, specificity, and false detection rate) of health-care professionals to identify neonates with electrographic seizures and seizure hours with and without the support of the ANSeR algorithm. Neonates with data on the outcome of interest were included in the analysis. This study is registered with ClinicalTrials.gov, NCT02431780. FINDINGS Between Feb 13, 2015, and Feb 7, 2017, 132 neonates were randomly assigned to the algorithm group and 132 to the non-algorithm group. Six neonates were excluded (four from the algorithm group and two from the non-algorithm group). Electrographic seizures were present in 32 (25·0%) of 128 neonates in the algorithm group and 38 (29·2%) of 130 neonates in the non-algorithm group. For recognition of neonates with electrographic seizures, sensitivity was 81·3% (95% CI 66·7-93·3) in the algorithm group and 89·5% (78·4-97·5) in the non-algorithm group; specificity was 84·4% (95% CI 76·9-91·0) in the algorithm group and 89·1% (82·5-94·7) in the non-algorithm group; and the false detection rate was 36·6% (95% CI 22·7-52·1) in the algorithm group and 22·7% (11·6-35·9) in the non-algorithm group. We identified 659 h in which seizures occurred (seizure hours): 268 h in the algorithm versus 391 h in the non-algorithm group. The percentage of seizure hours correctly identified was higher in the algorithm group than in the non-algorithm group (177 [66·0%; 95% CI 53·8-77·3] of 268 h vs 177 [45·3%; 34·5-58·3] of 391 h; difference 20·8% [3·6-37·1]). No significant differences were seen in the percentage of neonates with seizures given at least one inappropriate antiseizure medication (37·5% [95% CI 25·0 to 56·3] vs 31·6% [21·1 to 47·4]; difference 5·9% [-14·0 to 26·3]). INTERPRETATION ANSeR, a machine-learning algorithm, is safe and able to accurately detect neonatal seizures. Although the algorithm did not enhance identification of individual neonates with seizures beyond conventional EEG, recognition of seizure hours was improved with use of ANSeR. The benefit might be greater in less experienced centres, but further study is required. FUNDING Wellcome Trust, Science Foundation Ireland, and Nihon Kohden.
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Affiliation(s)
- Andreea M Pavel
- INFANT Research Centre, University College Cork, Cork, Ireland; Department of Paediatrics and Child Health, University College Cork, Cork, Ireland
| | - Janet M Rennie
- Institute for Women's Health, University College London, London, UK
| | - Linda S de Vries
- Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Mats Blennow
- Department of Neonatal Medicine, Karolinska University Hospital, Stockholm, Sweden; Division of Paediatrics, Department CLINTEC, Karolinska Institutet, Stockholm, Sweden
| | | | - Divyen K Shah
- Royal London Hospital, London, UK; London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Ronit M Pressler
- Department of Clinical Neurophysiology, Great Ormond Street Hospital for Children NHS Trust, London, UK
| | - Olga Kapellou
- Homerton University Hospital NHS Foundation Trust, London, UK
| | | | - Sean R Mathieson
- INFANT Research Centre, University College Cork, Cork, Ireland; Department of Paediatrics and Child Health, University College Cork, Cork, Ireland
| | - Elena Pavlidis
- INFANT Research Centre, University College Cork, Cork, Ireland; Department of Paediatrics and Child Health, University College Cork, Cork, Ireland
| | - Alexander C van Huffelen
- Clinical Neurophysiology, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Vicki Livingstone
- INFANT Research Centre, University College Cork, Cork, Ireland; Department of Paediatrics and Child Health, University College Cork, Cork, Ireland
| | - Mona C Toet
- Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Lauren C Weeke
- Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Mikael Finder
- Department of Neonatal Medicine, Karolinska University Hospital, Stockholm, Sweden; Division of Paediatrics, Department CLINTEC, Karolinska Institutet, Stockholm, Sweden
| | - Subhabrata Mitra
- Institute for Women's Health, University College London, London, UK
| | - Deirdre M Murray
- INFANT Research Centre, University College Cork, Cork, Ireland; Department of Paediatrics and Child Health, University College Cork, Cork, Ireland
| | | | - Geraldine B Boylan
- INFANT Research Centre, University College Cork, Cork, Ireland; Department of Paediatrics and Child Health, University College Cork, Cork, Ireland.
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11
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Takahashi H, Emami A, Shinozaki T, Kunii N, Matsuo T, Kawai K. Convolutional neural network with autoencoder-assisted multiclass labelling for seizure detection based on scalp electroencephalography. Comput Biol Med 2020; 125:104016. [PMID: 33022521 DOI: 10.1016/j.compbiomed.2020.104016] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Revised: 09/18/2020] [Accepted: 09/19/2020] [Indexed: 02/02/2023]
Abstract
OBJECTIVE In long-term video-monitoring, automatic seizure detection holds great promise as a means to reduce the workload of the epileptologist. A convolutional neural network (CNN) designed to process images of EEG plots demonstrated high performance for seizure detection, but still has room for reducing the false-positive alarm rate. METHODS We combined a CNN that processed images of EEG plots with patient-specific autoencoders (AE) of EEG signals to reduce the false alarms during seizure detection. The AE automatically logged abnormalities, i.e., both seizures and artifacts. Based on seizure logs compiled by expert epileptologists and errors made by AE, we constructed a CNN with 3 output classes: seizure, non-seizure-but-abnormal, and non-seizure. The accumulative measure of number of consecutive seizure labels was used to issue a seizure alarm. RESULTS The second-by-second classification performance of AE-CNN was comparable to that of the original CNN. False-positive seizure labels in AE-CNN were more likely interleaved with "non-seizure-but-abnormal" labels than with true-positive seizure labels. Consequently, "non-seizure-but-abnormal" labels interrupted runs of false-positive seizure labels before triggering an alarm. The median false alarm rate with the AE-CNN was reduced to 0.034 h-1, which was one-fifth of that of the original CNN (0.17 h-1). CONCLUSIONS A label of "non-seizure-but-abnormal" offers practical benefits for seizure detection. The modification of a CNN with an AE is worth considering because AEs can automatically assign "non-seizure-but-abnormal" labels in an unsupervised manner with no additional demands on the time of the epileptologist.
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Affiliation(s)
- Hirokazu Takahashi
- Department of Mechano-informatics, Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan; Research Center for Advanced Science and Technology, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo, 153-8904, Japan.
| | - Ali Emami
- Research Center for Advanced Science and Technology, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo, 153-8904, Japan
| | - Takashi Shinozaki
- CiNet, National Institute of Information and Communications Technology, 1-4 Yamadaoka, Suita City, Osaka, 565-0871, Japan
| | - Naoto Kunii
- Department of Neurosurgery, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan
| | - Takeshi Matsuo
- Tokyo Metropolitan Neurological Hospital, 2-6-1 Musashidai, Fuchu, Tokyo, 183-0042, Japan
| | - Kensuke Kawai
- Department of Neurosurgery, Jichi Medical University, 3311-1 Yakushiji, Shimotsuke-shi, Tochigi, 329-0498, Japan.
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12
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Abbasi H, Unsworth CP. Applications of advanced signal processing and machine learning in the neonatal hypoxic-ischemic electroencephalogram. Neural Regen Res 2020; 15:222-231. [PMID: 31552887 PMCID: PMC6905345 DOI: 10.4103/1673-5374.265542] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2019] [Accepted: 05/24/2019] [Indexed: 01/15/2023] Open
Abstract
Perinatal hypoxic-ischemic-encephalopathy significantly contributes to neonatal death and life-long disability such as cerebral palsy. Advances in signal processing and machine learning have provided the research community with an opportunity to develop automated real-time identification techniques to detect the signs of hypoxic-ischemic-encephalopathy in larger electroencephalography/amplitude-integrated electroencephalography data sets more easily. This review details the recent achievements, performed by a number of prominent research groups across the world, in the automatic identification and classification of hypoxic-ischemic epileptiform neonatal seizures using advanced signal processing and machine learning techniques. This review also addresses the clinical challenges that current automated techniques face in order to be fully utilized by clinicians, and highlights the importance of upgrading the current clinical bedside sampling frequencies to higher sampling rates in order to provide better hypoxic-ischemic biomarker detection frameworks. Additionally, the article highlights that current clinical automated epileptiform detection strategies for human neonates have been only concerned with seizure detection after the therapeutic latent phase of injury. Whereas recent animal studies have demonstrated that the latent phase of opportunity is critically important for early diagnosis of hypoxic-ischemic-encephalopathy electroencephalography biomarkers and although difficult, detection strategies could utilize biomarkers in the latent phase to also predict the onset of future seizures.
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Affiliation(s)
- Hamid Abbasi
- Department of Engineering Science, The University of Auckland, Auckland, New Zealand
| | - Charles P. Unsworth
- Department of Engineering Science, The University of Auckland, Auckland, New Zealand
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13
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Continuous Electroencephalography Monitoring for Critically Ill Neonates: A Canadian Perspective. Neurol Sci 2019; 46:394-402. [PMID: 31030685 DOI: 10.1017/cjn.2019.36] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
BACKGROUND Continuous EEG monitoring, in the form of amplitude-integrated (aEEG) or conventional EEG (cEEG), is used in the neonatal intensive care unit (NICU) to detect subclinical central nervous system pathologies, inform management, and prognosticate neurodevelopmental outcomes. To learn more about provider attitudes and current practices in Canada, we evaluated neurologist and neonatologist opinions regarding NICU EEG monitoring. METHODS A 15-item electronic questionnaire was distributed to 114 pediatric neurologists and 176 neonatologists working across 25 sites. RESULTS The survey was completed by 87 of 290 physicians. Continuous EEG monitoring is utilized by 97% of pediatric neurologists and 92% of neonatologists. Neurologists and neonatologists differ in their EEG monitoring preferences. For seizure detection and diagnosis of encephalopathy, significantly more neonatologists favor aEEG alone or in combination with cEEG, whereas most neurologists prefer cEEG (p = 0.047, 0.001). There is a significant difference in the perceived gaps in monitoring patients with cEEG between neonatologists (13% would monitor more) and neurologists (41% would monitor more) (p = 0.007). Half of all respondents (53%) reported that they would be interested in attending an education session on neonatal EEG monitoring. CONCLUSIONS Canadian neurologists and neonatologists do not agree on the best monitoring approach for critically ill neonates. Furthermore, neonatologists perceive a smaller cEEG monitoring gap as compared with neurologists. However, many participants from both specialties would like to increase long-term EEG monitoring in the NICU setting. Facilitating access to EEG monitoring and enhancing education may help to address these needs.
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Ansari AH, Cherian PJ, Caicedo A, Naulaers G, De Vos M, Van Huffel S. Neonatal Seizure Detection Using Deep Convolutional Neural Networks. Int J Neural Syst 2019; 29:1850011. [DOI: 10.1142/s0129065718500119] [Citation(s) in RCA: 114] [Impact Index Per Article: 22.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Identifying a core set of features is one of the most important steps in the development of an automated seizure detector. In most of the published studies describing features and seizure classifiers, the features were hand-engineered, which may not be optimal. The main goal of the present paper is using deep convolutional neural networks (CNNs) and random forest to automatically optimize feature selection and classification. The input of the proposed classifier is raw multi-channel EEG and the output is the class label: seizure/nonseizure. By training this network, the required features are optimized, while fitting a nonlinear classifier on the features. After training the network with EEG recordings of 26 neonates, five end layers performing the classification were replaced with a random forest classifier in order to improve the performance. This resulted in a false alarm rate of 0.9 per hour and seizure detection rate of 77% using a test set of EEG recordings of 22 neonates that also included dubious seizures. The newly proposed CNN classifier outperformed three data-driven feature-based approaches and performed similar to a previously developed heuristic method.
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Affiliation(s)
- Amir H. Ansari
- Department of Electrical Engineering, KU Leuven, 3001 Leuven, Belgium
- IMEC VZW, 3001 Leuven, Belgium
| | - Perumpillichira J. Cherian
- Department of Neurology, Erasmus University Medical Center, 3015 CE Rotterdam, The Netherlands
- Department of Medicine, McMaster University, Hamilton, ON, Canada L8S 4L8 Canada
| | - Alexander Caicedo
- Department of Electrical Engineering, KU Leuven, 3001 Leuven, Belgium
- IMEC VZW, 3001 Leuven, Belgium
| | - Gunnar Naulaers
- Neonatal Intensive Care Unit, University Hospitals Leuven, Belgium
- Department of Development and Regeneration, KU Leuven, 3000 Leuven, Belgium
| | - Maarten De Vos
- Department of Engineering, University of Oxford, Oxford OX1 3PJ, UK
| | - Sabine Van Huffel
- Department of Electrical Engineering, KU Leuven, 3001 Leuven, Belgium
- IMEC VZW, 3001 Leuven, Belgium
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Buttle SG, Lemyre B, Sell E, Redpath S, Bulusu S, Webster RJ, Pohl D. Combined Conventional and Amplitude-Integrated EEG Monitoring in Neonates: A Prospective Study. J Child Neurol 2019; 34:313-320. [PMID: 30761936 DOI: 10.1177/0883073819829256] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
BACKGROUND/OBJECTIVE Seizure monitoring via amplitude-integrated EEG is standard of care in many neonatal intensive care units; however, conventional EEG is the gold standard for seizure detection. We compared the diagnostic yield of amplitude-integrated EEG interpreted at the bedside, amplitude-integrated EEG interpreted by an expert, and conventional EEG. METHODS Neonates requiring seizure monitoring received amplitude-integrated EEG and conventional EEG in parallel. Clinical events and amplitude-integrated EEG were interpreted at bedside. Subsequently, amplitude-integrated EEG and conventional EEG were independently analyzed by experienced neonatology and neurology readers. Sensitivity and specificity of bedside amplitude-integrated EEG as compared to expert amplitude-integrated EEG interpretation and conventional EEG were evaluated. RESULTS Thirteen neonates were monitored for an average duration of 33 hours (range 15-94, SD 25). Fourteen seizure-like events were detected by clinical observation, and 12 others by bedside amplitude-integrated EEG analysis. One of the clinical, and none of the bedside amplitude-integrated EEG events were confirmed as seizures on conventional EEG. Post hoc expert amplitude-integrated EEG interpretation revealed eight suspected seizures, all different from the ones detected by the bedside amplitude-integrated EEG team, of which one was confirmed via conventional EEG. Eight seizures were recorded on conventional EEG. Expert amplitude-integrated EEG interpretation had a sensitivity of 13% with 46% specificity for individual seizure detection, and a sensitivity of 50% with 46% specificity for detecting patients with seizures. CONCLUSION Real-world bedside amplitude-integrated EEG monitoring failed to detect all seizures evidenced via conventional EEG, while misclassifying other events as seizures. Even post hoc expert amplitude-integrated EEG interpretation provided limited sensitivity and specificity. Considering the poor sensitivity and specificity of bedside amplitude-integrated EEG interpretation, combined monitoring may provide limited clinical benefit.
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Affiliation(s)
- Sarah Grace Buttle
- 1 Division of Neurology, Children's Hospital of Eastern Ontario, University of Ottawa, Ottawa, Ontario, Canada
| | - Brigitte Lemyre
- 2 Division of Neonatology, Children's Hospital of Eastern Ontario, Ottawa, Ontario, Canada.,3 Department of Pediatrics, University of Ottawa, Ottawa, Ontario, Canada
| | - Erick Sell
- 1 Division of Neurology, Children's Hospital of Eastern Ontario, University of Ottawa, Ottawa, Ontario, Canada.,3 Department of Pediatrics, University of Ottawa, Ottawa, Ontario, Canada
| | - Stephanie Redpath
- 2 Division of Neonatology, Children's Hospital of Eastern Ontario, Ottawa, Ontario, Canada.,3 Department of Pediatrics, University of Ottawa, Ottawa, Ontario, Canada
| | - Srinivas Bulusu
- 4 Neurophysiology Laboratory, Children's Hospital of Eastern Ontario, Ottawa, Ontario, Canada
| | - Richard J Webster
- 5 Clinical Research Unit, Children's Hospital of Eastern Ontario, Ottawa, Ontario, Canada
| | - Daniela Pohl
- 1 Division of Neurology, Children's Hospital of Eastern Ontario, University of Ottawa, Ottawa, Ontario, Canada.,3 Department of Pediatrics, University of Ottawa, Ottawa, Ontario, Canada
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Boylan GB, Kharoshankaya L, Mathieson SR. Diagnosis of seizures and encephalopathy using conventional EEG and amplitude integrated EEG. HANDBOOK OF CLINICAL NEUROLOGY 2019; 162:363-400. [PMID: 31324321 DOI: 10.1016/b978-0-444-64029-1.00018-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Seizures are more common in the neonatal period than at any other time of life, partly due to the relative hyperexcitability of the neonatal brain. Brain monitoring of sick neonates in the NICU using either conventional electroencephalography or amplitude integrated EEG is essential to accurately detect seizures. Treatment of seizures is important, as evidence increasingly indicates that seizures damage the brain in addition to that caused by the underlying etiology. Prompt treatment has been shown to reduce seizure burden with the potential to ameliorate seizure-mediated damage. Neonatal encephalopathy most commonly caused by a hypoxia-ischemia results in an alteration of mental status and problems such as seizures, hypotonia, apnea, and feeding difficulties. Confirmation of encephalopathy with EEG monitoring can act as an important adjunct to other investigations and the clinical examination, particularly when considering treatment strategies such as therapeutic hypothermia. Brain monitoring also provides useful early prognostic indicators to clinicians. Recent use of machine learning in algorithms to continuously monitor the neonatal EEG, detect seizures, and grade encephalopathy offers the exciting prospect of real-time decision support in the NICU in the very near future.
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Affiliation(s)
- Geraldine B Boylan
- Department of Paediatrics and Child Health, Irish Centre for Fetal and Neonatal Translational Research (INFANT), University College Cork, Cork, Ireland.
| | - Liudmila Kharoshankaya
- Department of Paediatrics and Child Health, Irish Centre for Fetal and Neonatal Translational Research (INFANT), University College Cork, Cork, Ireland
| | - Sean R Mathieson
- Department of Paediatrics and Child Health, Irish Centre for Fetal and Neonatal Translational Research (INFANT), University College Cork, Cork, Ireland
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Ansari AH, Cherian PJ, Caicedo Dorado A, Jansen K, Dereymaeker A, De Wispelaere L, Dielman C, Vervisch J, Govaert P, De Vos M, Naulaers G, Huffel SV. Weighted Performance Metrics for Automatic Neonatal Seizure Detection Using Multiscored EEG Data. IEEE J Biomed Health Inform 2018; 22:1114-1123. [DOI: 10.1109/jbhi.2017.2750769] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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18
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Pisani F, Pavlidis E. The role of electroencephalogram in neonatal seizure detection. Expert Rev Neurother 2017; 18:95-100. [DOI: 10.1080/14737175.2018.1413352] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Affiliation(s)
- Francesco Pisani
- Child Neuropsychiatry Unit, Medicine & Surgery Department, University of Parma, Parma, Italy
| | - Elena Pavlidis
- Child Neuropsychiatry Unit, Medicine & Surgery Department, University of Parma, Parma, Italy
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Dereymaeker A, Ansari AH, Jansen K, Cherian PJ, Vervisch J, Govaert P, De Wispelaere L, Dielman C, Matic V, Dorado AC, De Vos M, Van Huffel S, Naulaers G. Interrater agreement in visual scoring of neonatal seizures based on majority voting on a web-based system: The Neoguard EEG database. Clin Neurophysiol 2017; 128:1737-1745. [DOI: 10.1016/j.clinph.2017.06.250] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2016] [Revised: 06/16/2017] [Accepted: 06/22/2017] [Indexed: 01/15/2023]
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Ansari AH, Cherian PJ, Caicedo A, De Vos M, Naulaers G, Van Huffel S. Improved neonatal seizure detection using adaptive learning. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2017:2810-2813. [PMID: 29060482 DOI: 10.1109/embc.2017.8037441] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
In neonatal intensive care units performing continuous EEG monitoring, there is an unmet need for around-the-clock interpretation of EEG, especially for recognizing seizures. In recent years, a few automated seizure detection algorithms have been proposed. However, these are suboptimal in detecting brief-duration seizures (<; 30s), which frequently occur in neonates with severe neurological problems. Recently, a multi-stage neonatal seizure detector, composed of a heuristic and a data-driven classifier was proposed by our group and showed improved detection of brief seizures. In the present work, we propose to add a third stage to the detector in order to use feedback of the Clinical Neurophysiologist and adaptively retune a threshold of the second stage to improve the performance of detection of brief seizures. As a result, the false alarm rate (FAR) of the brief seizure detections decreased by 50% and the positive predictive value (PPV) increased by 18%. At the same time, for all detections, the FAR decreased by 35% and PPV increased by 5% while the good detection rate remained unchanged.
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22
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Ansari A, Cherian P, Dereymaeker A, Matic V, Jansen K, De Wispelaere L, Dielman C, Vervisch J, Swarte R, Govaert P, Naulaers G, De Vos M, Van Huffel S. Improved multi-stage neonatal seizure detection using a heuristic classifier and a data-driven post-processor. Clin Neurophysiol 2016; 127:3014-3024. [DOI: 10.1016/j.clinph.2016.06.018] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2016] [Accepted: 06/06/2016] [Indexed: 10/21/2022]
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23
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Matic V, Cherian PJ, Jansen K, Koolen N, Naulaers G, Swarte RM, Govaert P, Van Huffel S, De Vos M. Improving Reliability of Monitoring Background EEG Dynamics in Asphyxiated Infants. IEEE Trans Biomed Eng 2015; 63:973-983. [PMID: 26390441 DOI: 10.1109/tbme.2015.2477946] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
The goal of this study is to develop an automated algorithm to quantify background electroencephalography (EEG) dynamics in term neonates with hypoxic ischemic encephalopathy. The recorded EEG signal is adaptively segmented and the segments with low amplitudes are detected. Next, depending on the spatial distribution of the low-amplitude segments, the first part of the algorithm detects (dynamic) interburst intervals (dIBIs) and performs well on the relatively artifact-free EEG periods and well-defined burst-suppression EEG periods. However, on testing the algorithm on EEG recordings of more than 48 h per neonate, a significant number of misclassified and dubious detections were encountered. Therefore, as the next step, we applied machine learning classifiers to differentiate between definite dIBI detections and misclassified ones. The developed algorithm achieved a true positive detection rate of 98%, 97%, 88%, and 95% for four duration-related dIBI groups that we subsequently defined. We benchmarked our algorithm with an expert diagnostic interpretation of EEG periods (1 h long) and demonstrated its effectiveness in clinical practice. We show that the detection algorithm effectively discriminates challenging cases encountered within mild and moderate background abnormalities. The dIBI detection algorithm improves identification of neonates with good clinical outcome as compared to the classification based on the classical burst-suppression interburst interval.
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Ansari AH, Matic V, De Vos M, Naulaers G, Cherian PJ, Van Huffel S. Improvement of an automated neonatal seizure detector using a post-processing technique. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2015; 2015:5859-5862. [PMID: 26737624 DOI: 10.1109/embc.2015.7319724] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Visual recognition of neonatal seizures during continuous EEG monitoring in neonatal intensive care units (NICUs) is labor-intensive, has low inter-rater agreement and requires special expertise that is not available around the clock. Development of an accurate automated seizure detection system with a low false alarm rate will support clinical decision making and alleviate significantly the workload. However, this is an ongoing difficult challenge for engineers as the neonatal EEG signal is non-stationary and often includes complex patterns of seizures and artifacts. In this study, we show an improvement of our previously developed neonatal seizure detector (developed using heuristic if-then rules). In order to improve the detection accuracy, mean phase coherence as a new feature is used to characterize artifacts and also support vector machine is applied to perform the post-processing step to remove false detections. As a result, the false alarm rate drops 42% (from 2.6 h(-1) to 1.5 h(-1)), whereas the good detection rate reduces only by 4%.
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25
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Shellhaas RA. Continuous long-term electroencephalography: the gold standard for neonatal seizure diagnosis. Semin Fetal Neonatal Med 2015; 20:149-53. [PMID: 25660396 DOI: 10.1016/j.siny.2015.01.005] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Newborn infants at risk for cerebral dysfunction, such as those with acute brain injury or with disorders of brain development, often have encephalopathy and seizures. Conventional electroencephalography (EEG) monitoring can enhance the care of these highly vulnerable patients, through identification of prognostically significant EEG background patterns and accurate diagnosis of seizures and non-seizure paroxysmal events. Neonatal seizures are usually subclinical, and abnormal neonatal movements are often not the result of seizures. Judicious use of conventional EEG monitoring can provide precise diagnosis, quantify seizures, and guide treatment--neonates with EEG-proven seizures should receive appropriate medications and those whose events are not seizures may be spared unnecessary exposure to medications that have potentially important side-effects.
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Affiliation(s)
- Renée A Shellhaas
- Department, Division of Pediatric Neurology, C.S. Mott Children's Hospital, University of Michigan Health System, Ann Arbor, MI, USA.
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26
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Mathieson SR, Stevenson NJ, Low E, Marnane WP, Rennie JM, Temko A, Lightbody G, Boylan GB. Validation of an automated seizure detection algorithm for term neonates. Clin Neurophysiol 2015; 127:156-168. [PMID: 26055336 PMCID: PMC4727504 DOI: 10.1016/j.clinph.2015.04.075] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2014] [Revised: 03/24/2015] [Accepted: 04/30/2015] [Indexed: 02/08/2023]
Abstract
OBJECTIVE The objective of this study was to validate the performance of a seizure detection algorithm (SDA) developed by our group, on previously unseen, prolonged, unedited EEG recordings from 70 babies from 2 centres. METHODS EEGs of 70 babies (35 seizure, 35 non-seizure) were annotated for seizures by experts as the gold standard. The SDA was tested on the EEGs at a range of sensitivity settings. Annotations from the expert and SDA were compared using event and epoch based metrics. The effect of seizure duration on SDA performance was also analysed. RESULTS Between sensitivity settings of 0.5 and 0.3, the algorithm achieved seizure detection rates of 52.6-75.0%, with false detection (FD) rates of 0.04-0.36FD/h for event based analysis, which was deemed to be acceptable in a clinical environment. Time based comparison of expert and SDA annotations using Cohen's Kappa Index revealed a best performing SDA threshold of 0.4 (Kappa 0.630). The SDA showed improved detection performance with longer seizures. CONCLUSION The SDA achieved promising performance and warrants further testing in a live clinical evaluation. SIGNIFICANCE The SDA has the potential to improve seizure detection and provide a robust tool for comparing treatment regimens.
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Affiliation(s)
- Sean R Mathieson
- Academic Research Department of Neonatology, Institute for Women's Health, University College London, London, United Kingdom
| | - Nathan J Stevenson
- Neonatal Brain Research Group, Irish Centre for Fetal and Neonatal Translational Research, Department of Paediatrics and Child Health, University College Cork, Cork, Ireland
| | - Evonne Low
- Neonatal Brain Research Group, Irish Centre for Fetal and Neonatal Translational Research, Department of Paediatrics and Child Health, University College Cork, Cork, Ireland
| | - William P Marnane
- Neonatal Brain Research Group, Irish Centre for Fetal and Neonatal Translational Research, Department of Paediatrics and Child Health, University College Cork, Cork, Ireland
| | - Janet M Rennie
- Academic Research Department of Neonatology, Institute for Women's Health, University College London, London, United Kingdom
| | - Andrey Temko
- Neonatal Brain Research Group, Irish Centre for Fetal and Neonatal Translational Research, Department of Paediatrics and Child Health, University College Cork, Cork, Ireland
| | - Gordon Lightbody
- Neonatal Brain Research Group, Irish Centre for Fetal and Neonatal Translational Research, Department of Paediatrics and Child Health, University College Cork, Cork, Ireland
| | - Geraldine B Boylan
- Neonatal Brain Research Group, Irish Centre for Fetal and Neonatal Translational Research, Department of Paediatrics and Child Health, University College Cork, Cork, Ireland.
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Matic V, Cherian PJ, Koolen N, Ansari AH, Naulaers G, Govaert P, Van Huffel S, De Vos M, Vanhatalo S. Objective differentiation of neonatal EEG background grades using detrended fluctuation analysis. Front Hum Neurosci 2015; 9:189. [PMID: 25954174 PMCID: PMC4407610 DOI: 10.3389/fnhum.2015.00189] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2014] [Accepted: 03/20/2015] [Indexed: 12/22/2022] Open
Abstract
A quantitative and objective assessment of background electroencephalograph (EEG) in sick neonates remains an everyday clinical challenge. We studied whether long range temporal correlations quantified by detrended fluctuation analysis (DFA) could be used in the neonatal EEG to distinguish different grades of abnormality in the background EEG activity. Long-term EEG records of 34 neonates were collected after perinatal asphyxia, and their background was scored in 1 h epochs (8 h in each neonate) as mild, moderate or severe. We applied DFA on 15 min long, non-overlapping EEG epochs (n = 1088) filtered from 3 to 8 Hz. Our formal feasibility study suggested that DFA exponent can be reliably assessed in only part of the EEG epochs, and in only relatively short time scales (10-60 s), while it becomes ambiguous if longer time scales are considered. This prompted further exploration whether paradigm used for quantifying multifractal DFA (MF-DFA) could be applied in a more efficient way, and whether metrics from MF-DFA paradigm could yield useful benchmark with existing clinical EEG gradings. Comparison of MF-DFA metrics showed a significant difference between three visually assessed background EEG grades. MF-DFA parameters were also significantly correlated to interburst intervals quantified with our previously developed automated detector. Finally, we piloted a monitoring application of MF-DFA metrics and showed their evolution during patient recovery from asphyxia. Our exploratory study showed that neonatal EEG can be quantified using multifractal metrics, which might offer a suitable parameter to quantify the grade of EEG background, or to monitor changes in brain state that take place during long-term brain monitoring.
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Affiliation(s)
- Vladimir Matic
- Department of Electrical Engineering (ESAT), STADIUS Centre for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven Leuven, Belgium ; iMinds Medical IT Department Leuven, Belgium
| | - Perumpillichira Joseph Cherian
- Section of Clinical Neurophysiology, Department of Neurology, Erasmus MC, University Medical Center Rotterdam, The Netherlands
| | - Ninah Koolen
- Department of Electrical Engineering (ESAT), STADIUS Centre for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven Leuven, Belgium ; iMinds Medical IT Department Leuven, Belgium
| | - Amir H Ansari
- Department of Electrical Engineering (ESAT), STADIUS Centre for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven Leuven, Belgium ; iMinds Medical IT Department Leuven, Belgium
| | - Gunnar Naulaers
- Neonatal Intensive Care Unit, University Hospital Gasthuisberg Leuven, Belgium
| | - Paul Govaert
- Section of Neonatology, Department of Pediatrics, Erasmus MC-Sophia Children's Hospital, University Medical Center Rotterdam, Netherlands
| | - Sabine Van Huffel
- Department of Electrical Engineering (ESAT), STADIUS Centre for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven Leuven, Belgium ; iMinds Medical IT Department Leuven, Belgium
| | - Maarten De Vos
- Department of Engineering, Institute of Biomedical Engineering, University of Oxford Oxford, UK
| | - Sampsa Vanhatalo
- Department of Children's Clinical Neurophysiology, HUS Medical Imaging Center and Children's Hospital, Helsinki University Central Hospital and University of Helsinki Helsinki, Finland
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Zwanenburg A, Andriessen P, Jellema RK, Niemarkt HJ, Wolfs TGAM, Kramer BW, Delhaas T. Using trend templates in a neonatal seizure algorithm improves detection of short seizures in a foetal ovine model. Physiol Meas 2015; 36:369-84. [DOI: 10.1088/0967-3334/36/3/369] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Temko A, Marnane W, Boylan G, Lightbody G. Clinical implementation of a neonatal seizure detection algorithm. DECISION SUPPORT SYSTEMS 2015; 70:86-96. [PMID: 25892834 PMCID: PMC4394138 DOI: 10.1016/j.dss.2014.12.006] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/23/2014] [Revised: 12/09/2014] [Accepted: 12/20/2014] [Indexed: 06/04/2023]
Abstract
Technologies for automated detection of neonatal seizures are gradually moving towards cot-side implementation. The aim of this paper is to present different ways to visualize the output of a neonatal seizure detection system and analyse their influence on performance in a clinical environment. Three different ways to visualize the detector output are considered: a binary output, a probabilistic trace, and a spatio-temporal colormap of seizure observability. As an alternative to visual aids, audified neonatal EEG is also considered. Additionally, a survey on the usefulness and accuracy of the presented methods has been performed among clinical personnel. The main advantages and disadvantages of the presented methods are discussed. The connection between information visualization and different methods to compute conventional metrics is established. The results of the visualization methods along with the system validation results indicate that the developed neonatal seizure detector with its current level of performance would unambiguously be of benefit to clinicians as a decision support system. The results of the survey suggest that a suitable way to visualize the output of neonatal seizure detection systems in a clinical environment is a combination of a binary output and a probabilistic trace. The main healthcare benefits of the tool are outlined. The decision support system with the chosen visualization interface is currently undergoing pre-market European multi-centre clinical investigation to support its regulatory approval and clinical adoption.
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Affiliation(s)
- Andriy Temko
- Neonatal Brain Research Group, INFANT Research Centre, Dept. Electrical and Electronic Engineering, University College Cork, Cork, Ireland
| | - William Marnane
- Neonatal Brain Research Group, INFANT Research Centre, Dept. Electrical and Electronic Engineering, University College Cork, Cork, Ireland
| | - Geraldine Boylan
- Neonatal Brain Research Group, INFANT Research Centre, Dept. Pediatrics and Child Health, University College Cork, Cork, Ireland
| | - Gordon Lightbody
- Neonatal Brain Research Group, INFANT Research Centre, Dept. Electrical and Electronic Engineering, University College Cork, Cork, Ireland
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Matic V, Cherian PJ, Koolen N, Naulaers G, Swarte RM, Govaert P, Van Huffel S, De Vos M. Holistic approach for automated background EEG assessment in asphyxiated full-term infants. J Neural Eng 2014; 11:066007. [DOI: 10.1088/1741-2560/11/6/066007] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Pisani F, Spagnoli C, Pavlidis E, Facini C, Kouamou Ntonfo GM, Ferrari G, Raheli R. Real-time automated detection of clonic seizures in newborns. Clin Neurophysiol 2014; 125:1533-40. [DOI: 10.1016/j.clinph.2013.12.119] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2013] [Revised: 12/04/2013] [Accepted: 12/27/2013] [Indexed: 12/17/2022]
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Koolen N, Jansen K, Vervisch J, Matic V, De Vos M, Naulaers G, Van Huffel S. Line length as a robust method to detect high-activity events: automated burst detection in premature EEG recordings. Clin Neurophysiol 2014; 125:1985-94. [PMID: 24631012 DOI: 10.1016/j.clinph.2014.02.015] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2013] [Revised: 01/30/2014] [Accepted: 02/17/2014] [Indexed: 10/25/2022]
Abstract
OBJECTIVE EEG is a valuable tool for evaluation of brain maturation in preterm babies. Preterm EEG constitutes of high voltage burst activities and more suppressed episodes, called interburst intervals (IBIs). Evolution of background characteristics provides information on brain maturation and helps in prediction of neurological outcome. The aim is to develop a method for automated burst detection. METHODS Thirteen polysomnography recordings were used, collected at preterm postmenstrual age of 31.4 (26.1-34.4)weeks. We developed a burst detection algorithm based on the feature line length and compared it with manual scorings of clinical experts and other published methods. RESULTS The line length-based algorithm is robust (84.27% accuracy, 84.00% sensitivity, 85.70% specificity). It is not critically dependent on the number of measurement channels, because two channels still provide 82% accuracy. Furthermore, it approximates well clinically relevant features, such as median IBI duration 5.45 (4.00-7.11)s, maximum IBI duration 14.02 (8.73-18.80)s and burst percentage 48.89 (35.45-60.12)%, with a median deviation of respectively 0.65s, 1.96s and 6.55%. CONCLUSION Automated assessment of long-term preterm EEG is possible and its use will optimize EEG interpretation in the NICU. SIGNIFICANCE This study takes a first step towards fully automatic analysis of the preterm brain.
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Affiliation(s)
- Ninah Koolen
- Department of Electrical Engineering (ESAT), Division SCD, KU Leuven, Leuven, Belgium; iMinds-KU Leuven Future Health Department, Leuven, Belgium.
| | - Katrien Jansen
- Department of Pediatrics, University Hospital Gasthuisberg, Leuven, Belgium
| | - Jan Vervisch
- Department of Pediatrics, University Hospital Gasthuisberg, Leuven, Belgium
| | - Vladimir Matic
- Department of Electrical Engineering (ESAT), Division SCD, KU Leuven, Leuven, Belgium; iMinds-KU Leuven Future Health Department, Leuven, Belgium
| | - Maarten De Vos
- Cluster of Excellence "Hearing4all" & Methods in Neurocognitive Psychology, University of Oldenburg, Oldenburg, Germany; Department of Electrical Engineering (ESAT), Division SCD, KU Leuven, Leuven, Belgium
| | - Gunnar Naulaers
- Neonatal Intensive Care Unit, University Hospital Gasthuisberg, Leuven, Belgium
| | - Sabine Van Huffel
- Department of Electrical Engineering (ESAT), Division SCD, KU Leuven, Leuven, Belgium; iMinds-KU Leuven Future Health Department, Leuven, Belgium
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33
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Temko A, Marnane W, Boylan G, O'Toole JM, Lightbody G. Neonatal EEG audification for seizure detection. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2014; 2014:4451-4454. [PMID: 25570980 DOI: 10.1109/embc.2014.6944612] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Technologies for automated detection of neonatal seizures are gradually moving towards cot-side implementation. The aim of this paper is to present an alternative way to visualize the output of a neonatal seizure detection algorithm. For this purpose audified neonatal EEG is considered. The EEG is audified with the aid of the neonatal seizure detection algorithm which selects the representative channels for stereo audio image and controls the signal gain. A survey on the usefulness and accuracy of the presented audification method has been performed. The results of the audification method compare favourably to that of using amplitude integrated EEG for detection of neonatal seizures.
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Zhou W, Liu Y, Yuan Q, Li X. Epileptic Seizure Detection Using Lacunarity and Bayesian Linear Discriminant Analysis in Intracranial EEG. IEEE Trans Biomed Eng 2013; 60:3375-81. [DOI: 10.1109/tbme.2013.2254486] [Citation(s) in RCA: 106] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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35
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Despotovic I, Cherian PJ, De Vos M, Hallez H, Deburchgraeve W, Govaert P, Lequin M, Visser GH, Swarte RM, Vansteenkiste E, Van Huffel S, Philips W. Relationship of EEG sources of neonatal seizures to acute perinatal brain lesions seen on MRI: a pilot study. Hum Brain Mapp 2013; 34:2402-17. [PMID: 22522744 PMCID: PMC6870156 DOI: 10.1002/hbm.22076] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2011] [Revised: 02/10/2012] [Accepted: 02/13/2012] [Indexed: 11/12/2022] Open
Abstract
Even though it is known that neonatal seizures are associated with acute brain lesions, the relationship of electroencephalographic (EEG) seizures to acute perinatal brain lesions visible on magnetic resonance imaging (MRI) has not been objectively studied. EEG source localization is successfully used for this purpose in adults, but it has not been sufficiently explored in neonates. Therefore, we developed an integrated method for ictal EEG dipole source localization based on a realistic head model to investigate the utility of EEG source imaging in neonates with postasphyxial seizures. We describe here our method and compare the dipole seizure localization results with acute perinatal lesions seen on brain MRI in 10 full-term infants with neonatal encephalopathy. Through experimental studies, we also explore the sensitivity of our method to the electrode positioning errors and the variations in neonatal skull geometry and conductivity. The localization results of 45 focal seizures from 10 neonates are compared with the visual analysis of EEG and MRI data, scored by expert physicians. In 9 of 10 neonates, dipole locations showed good relationship with MRI lesions and clinical data. Our experimental results also suggest that the variations in the used values for skull conductivity or thickness have little effect on the dipole localization, whereas inaccurate electrode positioning can reduce the accuracy of source estimates. The performance of our fused method indicates that ictal EEG source imaging is feasible in neonates and with further validation studies, this technique can become a useful diagnostic tool.
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Affiliation(s)
- Ivana Despotovic
- MEDISIP-IPI-IBBT, Ghent University, Sint-Pietersnieuwstraat 41, 9000 Ghent, Belgium
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Abstract
Neonatal seizures are a neurological emergency and prompt treatment is required. Seizure burden in neonates can be very high, status epilepticus a frequent occurrence, and the majority of seizures do not have any clinical correlate. Detection of neonatal seizures is only possible with continuous electroencephalogram (EEG) monitoring. EEG interpretation requires special expertise that is not available in most neonatal intensive care units (NICUs). As a result, a simplified method of EEG recording incorporating an easy-to-interpret compressed trend of the EEG output (amplitude integrated EEG) from one of the EEG output from one or two channels has emerged as a popular way to monitor neurological function in the NICU. This is not without limitations; short duration and low amplitude seizures can be missed, artefacts are problematic and may mimic seizure-like activity and only a restricted area of the brain is monitored. Continuous multichannel EEG is the gold standard for detecting seizures and monitoring response to therapy but expert interpretation of the EEG output is generally not available. Some centres have set up remote access for neurophysiologists to the cot-side EEG, but reliable interpretation is wholly dependent on the 24 h availability of experts, an expensive solution. A more practical solution for the NICU without such expertise is an automated seizure detection system. This review outlines the current state of the art regarding cot-side monitoring of neonatal seizures in the NICU.
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Affiliation(s)
- Geraldine B Boylan
- Neonatal Brain Research Group, Department of Paediatrics & Child Health, University College Cork, Ireland.
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37
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TEMKO ANDRIY, BOYLAN GERALDINE, MARNANE WILLIAM, LIGHTBODY GORDON. Robust neonatal EEG seizure detection through adaptive background modeling. Int J Neural Syst 2013; 23:1350018. [PMID: 23746291 PMCID: PMC3957205 DOI: 10.1142/s0129065713500184] [Citation(s) in RCA: 50] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Adaptive probabilistic modeling of the EEG background is proposed for seizure detection in neonates with hypoxic ischemic encephalopathy. The decision is made based on the temporal derivative of the seizure probability with respect to the adaptively modeled level of background activity. The robustness of the system to long duration "seizure-like" artifacts, in particular those due to respiration, is improved. The system was developed using statistical leave-one-patient-out performance assessment, on a large clinical dataset, comprising 38 patients of 1479 h total duration. The developed technique was then validated by a single test on a separate totally unseen randomized prospective dataset of 51 neonates totaling 2540 h of duration. By exploiting the proposed adaptation, the ROC area is increased from 93.4% to 96.1% (41% relative improvement). The number of false detections per hour is decreased from 0.42 to 0.24, while maintaining the correct detection of seizure burden at 70%. These results on the unseen data were predicted from the rigorous leave-one-patient-out validation and confirm the validity of our algorithm development process.
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Affiliation(s)
- ANDRIY TEMKO
- Neonatal Brain Research Group, Department of Electrical and Electronic Engineering, University College Cork, Ireland
| | - GERALDINE BOYLAN
- Neonatal Brain Research Group, Department of Paediatrics and Child Health, University College Cork, Ireland
| | - WILLIAM MARNANE
- Neonatal Brain Research Group, Department of Electrical and Electronic Engineering, University College Cork, Ireland
| | - GORDON LIGHTBODY
- Neonatal Brain Research Group, Department of Electrical and Electronic Engineering, University College Cork, Ireland
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38
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Abstract
Neonatal seizures are common, often require EEG monitoring for diagnosis and management, may be associated with worse neurodevelopmental outcome, and can often be treated with existing anticonvulsants. A neonatal electrographic seizure is defined as a sudden, repetitive, evolving, and stereotyped event of abnormal electrographic pattern with amplitude of at least 2 μV and a minimum duration of 10 seconds. The diagnosis of neonatal seizures relies heavily on the neurophysiologist's interpretation of EEG. Consideration of specific criteria for the definition of a neonatal seizure, including seizure duration, location, morphology, evolution, semiology, and overall seizure burden, has utility for both the clinician and the researcher. The importance of EEG in the diagnosis and management of neonatal seizures, the electrographic characteristics of neonatal seizures, the impact of neonatal seizures on outcome, and tools to aid in the identification of neonatal seizures are reviewed.
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Thomas EM, Temko A, Marnane WP, Boylan GB, Lightbody G. Discriminative and Generative Classification Techniques Applied to Automated Neonatal Seizure Detection. IEEE J Biomed Health Inform 2013; 17:297-304. [PMID: 24235107 DOI: 10.1109/jbhi.2012.2237035] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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40
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Abstract
As more critically ill term and premature neonates are surviving their acute illness, their long-term neurodevelopmental morbidity is being recognized. Continuous monitoring of cerebral function, with electroencephalography or derived digital trends, can provide key information regarding seizures and background patterns, with direct treatment and prognostic implications. Conventional video-electroencephalography remains the gold standard for neonatal seizure diagnosis and quantification, but can be supplemented by digital trending modalities. Both conventional and amplitude-integrated electroencephalography can provide valuable data regarding the background trends. This review describes indications and methods for continuous electroencephalography monitoring in high-risk neonates.
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Affiliation(s)
- Renée A Shellhaas
- Department of Pediatrics & Communicable Diseases, Division of Pediatric Neurology, University of Michigan, Room 12-733, C.S. Mott Children's Hospital, 1540 East Hospital Drive SPC 4279, Ann Arbor, MI 48109-4279, USA.
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41
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Widening the horizon of neonatal neurophysiology. Clin Neurophysiol 2012; 123:1475-6. [DOI: 10.1016/j.clinph.2012.01.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2011] [Revised: 01/10/2012] [Accepted: 01/12/2012] [Indexed: 11/20/2022]
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42
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Temko A, Stevenson N, Marnane W, Boylan G, Lightbody G. Inclusion of temporal priors for automated neonatal EEG classification. J Neural Eng 2012; 9:046002. [PMID: 22713600 DOI: 10.1088/1741-2560/9/4/046002] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
The aim of this paper is to use recent advances in the clinical understanding of the temporal evolution of seizure burden in neonates with hypoxic ischemic encephalopathy to improve the performance of automated detection algorithms. Probabilistic weights are designed from temporal locations of neonatal seizure events relative to time of birth. These weights are obtained by fitting a skew-normal distribution to the temporal seizure density and introduced into the probabilistic framework of the previously developed neonatal seizure detector. The results are validated on the largest available clinical dataset, comprising 816.7 h. By exploiting these priors, the receiver operating characteristic area is increased by 23% (relative) reaching 96.74%. The number of false detections per hour is decreased from 0.45 to 0.25, while maintaining the correct detection of seizure burden at 70%.
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Affiliation(s)
- Andriy Temko
- Department of Electrical and Electronic Engineering, University College Cork, Ireland.
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43
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Temko A, Stevenson N, Marnane W, Boylan G, Lightbody G. Temporal evolution of seizure burden for automated neonatal EEG classification. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2012; 2012:4915-4918. [PMID: 23367030 DOI: 10.1109/embc.2012.6347096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
The aim of this paper is to use recent advances in the clinical understanding of the temporal evolution of seizure burden in neonates with hypoxic ischemic encephalopathy to improve the performance of automated detection algorithms. Probabilistic weights are designed from temporal locations of neonatal seizure events relative to time of birth. These weights are obtained by fitting a skew-normal distribution to the temporal seizure density and introduced into the probabilistic framework of the previously developed neonatal seizure detector. The results are validated on the largest available clinical dataset, comprising 816.7 hours. By exploiting these priors, the ROC area is increased by 23% (relative) reaching 96.75%. The number of false detections per hour is decreased from 0.72 to 0.36, while maintaining the correct detection of seizure burden at 75%.
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Affiliation(s)
- Andriy Temko
- Department of Electrical and Electronic Engineering and the Neonatal Brain, Research Group, University College Cork, Ireland.
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44
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Automated artifact removal as preprocessing refines neonatal seizure detection. Clin Neurophysiol 2011; 122:2345-54. [DOI: 10.1016/j.clinph.2011.04.026] [Citation(s) in RCA: 63] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2010] [Revised: 04/13/2011] [Accepted: 04/26/2011] [Indexed: 11/17/2022]
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45
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Alegre M, Urrestarazu E. Neonatal automated seizure detection: Going ahead into clinical use. Clin Neurophysiol 2011; 122:1480-1. [DOI: 10.1016/j.clinph.2011.01.044] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2011] [Revised: 01/28/2011] [Accepted: 01/29/2011] [Indexed: 11/30/2022]
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