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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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Escribano P, Ródenas J, García M, Arias MA, Hidalgo VM, Calero S, Rieta JJ, Alcaraz R. Preoperative Prediction of Catheter Ablation Outcome in Persistent Atrial Fibrillation Patients through Spectral Organization Analysis of the Surface Fibrillatory Waves. J Pers Med 2022; 12:jpm12101721. [PMID: 36294860 PMCID: PMC9604697 DOI: 10.3390/jpm12101721] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Revised: 10/07/2022] [Accepted: 10/11/2022] [Indexed: 11/05/2022] Open
Abstract
Catheter ablation (CA) is a commonly used treatment for persistent atrial fibrillation (AF). Since its medium/long-term success rate remains limited, preoperative prediction of its outcome is gaining clinical interest to optimally select candidates for the procedure. Among predictors based on the surface electrocardiogram, the dominant frequency (DF) and harmonic exponential decay (γ) of the fibrillatory waves (f-waves) have reported promising but clinically insufficient results. Hence, the main goal of this work was to conduct a broader analysis of the f-wave harmonic spectral structure to improve CA outcome prediction through several entropy-based measures computed on different frequency bands. On a database of 151 persistent AF patients under radio-frequency CA and a follow-up of 9 months, the newly introduced parameters discriminated between patients who relapsed to AF and those who maintained SR at about 70%, which was statistically superior to the DF and approximately similar to γ. They also provided complementary information to γ through different combinations in multivariate models based on lineal discriminant analysis and report classification performance improvement of about 5%. These results suggest that the presence of larger harmonics and a proportionally smaller DF peak is associated with a decreased probability of AF recurrence after CA.
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Affiliation(s)
- Pilar Escribano
- Research Group in Electronic, Biomedical and Telecommunication Engineering, University of Castilla-La Mancha, 02071 Albacete, Spain
- Correspondence:
| | - Juan Ródenas
- Research Group in Electronic, Biomedical and Telecommunication Engineering, University of Castilla-La Mancha, 02071 Albacete, Spain
| | - Manuel García
- Research Group in Electronic, Biomedical and Telecommunication Engineering, University of Castilla-La Mancha, 02071 Albacete, Spain
| | - Miguel A. Arias
- Cardiac Arrhythmia Department, Complejo Hospitalario Universitario de Toledo, 45007 Toledo, Spain
| | - Víctor M. Hidalgo
- Cardiac Arrhythmia Department, Complejo Hospitalario Universitario de Albacete, 02006 Albacete, Spain
| | - Sofía Calero
- Cardiac Arrhythmia Department, Complejo Hospitalario Universitario de Albacete, 02006 Albacete, Spain
| | - José J. Rieta
- BioMIT.org, Electronic Engineering Department, Universitat Politecnica de Valencia, 46022 Valencia, Spain
| | - Raúl Alcaraz
- Research Group in Electronic, Biomedical and Telecommunication Engineering, University of Castilla-La Mancha, 02071 Albacete, Spain
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Dash DP, Kolekar MH, Chakraborty C, Khosravi MR. Review of Machine and Deep Learning Techniques in Epileptic Seizure Detection using Physiological Signals and Sentiment Analysis. ACM T ASIAN LOW-RESO 2022. [DOI: 10.1145/3552512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
Abstract
Epilepsy is one of the significant neurological disorders affecting nearly 65 million people worldwide. The repeated seizure is characterized as epilepsy. Different algorithms were proposed for efficient seizure detection using intracranial and surface EEG signals. In the last decade, various machine learning techniques based on seizure detection approaches were proposed. This paper discusses different machine learning and deep learning techniques for seizure detection using intracranial and surface EEG signals. A wide range of machine learning techniques such as support vector machine (SVM) classifiers, artificial neural network (ANN) classifier, and deep learning techniques such as a convolutional neural network (CNN) classifier, long-short term memory (LSTM) network for seizure detection are compared in this paper. The effectiveness of time-domain features, frequency domain features, and time-frequency domain features are discussed along with different machine learning techniques. Along with EEG, other physiological signals such as electrocardiogram are used to enhance seizure detection accuracy which are discussed in this paper. In recent years deep learning techniques based on seizure detection have found good classification accuracy. In this paper, an LSTM deep learning-network-based approach is implemented for seizure detection and compared with state-of-the-art methods. The LSTM based approach achieved 96.5% accuracy in seizure-nonseizure EEG signal classification. Apart from analyzing the physiological signals, sentiment analysis also has potential to detect seizure.
Impact Statement-
This review paper gives a summary of different research work related to epileptic seizure detection using machine learning and deep learning techniques. Manual seizure detetion is time consuming and requires expertise. So the artificial intelligence techniques such as machine learning and deep learning techniques are used for automatic seizure detection. Different physiological signals are used for seizure detection. Different researchers are working on developing automatic seizure detection using EEG, ECG, accelerometer, sentiment analysis. There is a need for a review paper that can discuss previous techniques and give further research direction. We have discussed different techniques for seizure detection with an accuracy comparison table. It can help the researcher to get an overview of both surface and intracranial EEG-based seizure detection approaches. The new researcher can easily compare different models and decide the model they want to start working on. A deep learning model is discussed to give a practical application of seizure detection. Sentiment analysis is another dimension of seizure detection and summerizing it will give a new prospective to the reader.
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Stone BT, Lin JY, Mahmood A, Sanford AJ, Katz DB. LiCl-induced sickness modulates rat gustatory cortical responses. PLoS Biol 2022; 20:e3001537. [PMID: 35877759 PMCID: PMC9352195 DOI: 10.1371/journal.pbio.3001537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 08/04/2022] [Accepted: 06/29/2022] [Indexed: 11/19/2022] Open
Abstract
Gustatory cortex (GC), a structure deeply involved in the making of consumption decisions, presumably performs this function by integrating information about taste, experiences, and internal states related to the animal's health, such as illness. Here, we investigated this assertion, examining whether illness is represented in GC activity, and how this representation impacts taste responses and behavior. We recorded GC single-neuron activity and local field potentials (LFPs) from healthy rats and rats made ill (via LiCl injection). We show (consistent with the extant literature) that the onset of illness-related behaviors arises contemporaneously with alterations in 7 to 12 Hz LFP power at approximately 12 min following injection. This process was accompanied by reductions in single-neuron taste response magnitudes and discriminability, and with enhancements in palatability-relatedness-a result reflecting the collapse of responses toward a simple "good-bad" code visible in the entire sample, but focused on a specific subset of GC neurons. Overall, our data show that a state (illness) that profoundly reduces consumption changes basic properties of the sensory cortical response to tastes, in a manner that can easily explain illness' impact on consumption.
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Affiliation(s)
- Bradly T. Stone
- Graduate Program in Neuroscience, Brandeis University, Waltham, Massachusetts, United States of America
| | - Jian-You Lin
- Department of Psychology, Neuroscience Program, and Volen National Center for Complex Systems, Brandeis University, Waltham, Massachusetts, United States of America
| | - Abuzar Mahmood
- Graduate Program in Neuroscience, Brandeis University, Waltham, Massachusetts, United States of America
| | - Alden J. Sanford
- Department of Psychology, Neuroscience Program, and Volen National Center for Complex Systems, Brandeis University, Waltham, Massachusetts, United States of America
| | - Donald B. Katz
- Graduate Program in Neuroscience, Brandeis University, Waltham, Massachusetts, United States of America
- Department of Psychology, Neuroscience Program, and Volen National Center for Complex Systems, Brandeis University, Waltham, Massachusetts, United States of America
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Deep learning helps EEG signals predict different stages of visual processing in the human brain. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102996] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Tataranno ML, Vijlbrief DC, Dudink J, Benders MJNL. Precision Medicine in Neonates: A Tailored Approach to Neonatal Brain Injury. Front Pediatr 2021; 9:634092. [PMID: 34095022 PMCID: PMC8171663 DOI: 10.3389/fped.2021.634092] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Accepted: 04/14/2021] [Indexed: 11/27/2022] Open
Abstract
Despite advances in neonatal care to prevent neonatal brain injury and neurodevelopmental impairment, predicting long-term outcome in neonates at risk for brain injury remains difficult. Early prognosis is currently based on cranial ultrasound (CUS), MRI, EEG, NIRS, and/or general movements assessed at specific ages, and predicting outcome in an individual (precision medicine) is not yet possible. New algorithms based on large databases and machine learning applied to clinical, neuromonitoring, and neuroimaging data and genetic analysis and assays measuring multiple biomarkers (omics) can fulfill the needs of modern neonatology. A synergy of all these techniques and the use of automatic quantitative analysis might give clinicians the possibility to provide patient-targeted decision-making for individualized diagnosis, therapy, and outcome prediction. This review will first focus on common neonatal neurological diseases, associated risk factors, and most common treatments. After that, we will discuss how precision medicine and machine learning (ML) approaches could change the future of prediction and prognosis in this field.
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Affiliation(s)
| | | | | | - Manon J. N. L. Benders
- Department of Neonatology, Wilhelmina Children's Hospital/University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
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Ghimatgar H, Kazemi K, Helfroush MS, Pillay K, Dereymaker A, Jansen K, Vos MD, Aarabi A. Neonatal EEG sleep stage classification based on deep learning and HMM. J Neural Eng 2020; 17:036031. [DOI: 10.1088/1741-2552/ab965a] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
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8
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Frassineti L, Barba C, Melani F, Piras F, Guerrini R, Manfredi C. Automatic detection and sonification of nonmotor generalized onset epileptic seizures: Preliminary results. Brain Res 2019; 1721:146341. [DOI: 10.1016/j.brainres.2019.146341] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2018] [Revised: 06/04/2019] [Accepted: 07/17/2019] [Indexed: 10/26/2022]
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9
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Classification of EEG Signals Using Hybrid Feature Extraction and Ensemble Extreme Learning Machine. Neural Process Lett 2018. [DOI: 10.1007/s11063-018-9919-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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Pillay K, Dereymaeker A, Jansen K, Naulaers G, Van Huffel S, De Vos M. Automated EEG sleep staging in the term-age baby using a generative modelling approach. J Neural Eng 2018; 15:036004. [DOI: 10.1088/1741-2552/aaab73] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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11
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Dereymaeker A, Pillay K, Vervisch J, De Vos M, Van Huffel S, Jansen K, Naulaers G. Review of sleep-EEG in preterm and term neonates. Early Hum Dev 2017; 113:87-103. [PMID: 28711233 PMCID: PMC6342258 DOI: 10.1016/j.earlhumdev.2017.07.003] [Citation(s) in RCA: 80] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Neonatal sleep is a crucial state that involves endogenous driven brain activity, important for neuronal survival and guidance of brain networks. Sequential EEG-sleep analysis in preterm infants provides insights into functional brain integrity and can document deviations of the biologically pre-programmed process of sleep ontogenesis during the neonatal period. Visual assessment of neonatal sleep-EEG, with integration of both cerebral and non-cerebral measures to better define neonatal state, is still considered the gold standard. Electrographic patterns evolve over time and are gradually time locked with behavioural characteristics which allow classification of quiet sleep and active sleep periods during the last 10weeks of gestation. Near term age, the neonate expresses a short ultradian sleep cycle, with two distinct active and quiet sleep, as well as brief periods of transitional or indeterminate sleep. Qualitative assessment of neonatal sleep is however challenged by biological and environmental variables that influence the expression of EEG-sleep patterns and sleep organization. Developing normative EEG-sleep data with the aid of automated analytic methods, can further improve our understanding of extra-uterine brain development and state organization under stressful or pathological conditions. Based on those developmental biomarkers of normal and abnormal brain function, research can be conducted to support and optimise sleep in the NICU, with the ultimate goal to improve therapeutic interventions and neurodevelopmental outcome.
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Affiliation(s)
- Anneleen Dereymaeker
- Department of Development and Regeneration, University Hospitals Leuven, Neonatal Intensive Care Unit, KU Leuven (University of Leuven), Leuven, Belgium.
| | - Kirubin Pillay
- Institute of Biomedical Engineering (IBME), Department of Engineering Science, University of Oxford, Oxford, United Kingdom..
| | - Jan Vervisch
- Department of Development and Regeneration, University Hospitals Leuven, Neonatal Intensive Care Unit, KU Leuven (University of Leuven), Leuven, Belgium; Department of Development and Regeneration, University Hospitals Leuven, Child Neurology, KU Leuven (University of Leuven), Leuven, Belgium.
| | - Maarten De Vos
- Institute of Biomedical Engineering (IBME), Department of Engineering Science, University of Oxford, Oxford, United Kingdom..
| | - Sabine Van Huffel
- KU Leuven (University of Leuven), Department of Electrical Engineering-ESAT, Division Stadius, Leuven, Belgium; Imec, Leuven, Belgium.
| | - Katrien Jansen
- Department of Development and Regeneration, University Hospitals Leuven, Neonatal Intensive Care Unit, KU Leuven (University of Leuven), Leuven, Belgium; Department of Development and Regeneration, University Hospitals Leuven, Child Neurology, KU Leuven (University of Leuven), Leuven, Belgium.
| | - Gunnar Naulaers
- Department of Development and Regeneration, University Hospitals Leuven, Neonatal Intensive Care Unit, KU Leuven (University of Leuven), Leuven, Belgium.
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12
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Hajjar YA, Al Hajjar AES, Daya B, Chauvet P. Determinant characteristics in EEG signal based on bursts amplitude segmentation for predicting pathological outcomes of a premature newborn. 2017 SENSORS NETWORKS SMART AND EMERGING TECHNOLOGIES (SENSET) 2017. [DOI: 10.1109/senset.2017.8125024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
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13
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Boashash B, Barki H, Ouelha S. Performance evaluation of time-frequency image feature sets for improved classification and analysis of non-stationary signals: Application to newborn EEG seizure detection. Knowl Based Syst 2017. [DOI: 10.1016/j.knosys.2017.06.015] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Alazrai R, Alwanni H, Baslan Y, Alnuman N, Daoud MI. EEG-Based Brain-Computer Interface for Decoding Motor Imagery Tasks within the Same Hand Using Choi-Williams Time-Frequency Distribution. SENSORS 2017; 17:s17091937. [PMID: 28832513 PMCID: PMC5621048 DOI: 10.3390/s17091937] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/21/2017] [Revised: 08/16/2017] [Accepted: 08/21/2017] [Indexed: 11/24/2022]
Abstract
This paper presents an EEG-based brain-computer interface system for classifying eleven motor imagery (MI) tasks within the same hand. The proposed system utilizes the Choi-Williams time-frequency distribution (CWD) to construct a time-frequency representation (TFR) of the EEG signals. The constructed TFR is used to extract five categories of time-frequency features (TFFs). The TFFs are processed using a hierarchical classification model to identify the MI task encapsulated within the EEG signals. To evaluate the performance of the proposed approach, EEG data were recorded for eighteen intact subjects and four amputated subjects while imagining to perform each of the eleven hand MI tasks. Two performance evaluation analyses, namely channel- and TFF-based analyses, are conducted to identify the best subset of EEG channels and the TFFs category, respectively, that enable the highest classification accuracy between the MI tasks. In each evaluation analysis, the hierarchical classification model is trained using two training procedures, namely subject-dependent and subject-independent procedures. These two training procedures quantify the capability of the proposed approach to capture both intra- and inter-personal variations in the EEG signals for different MI tasks within the same hand. The results demonstrate the efficacy of the approach for classifying the MI tasks within the same hand. In particular, the classification accuracies obtained for the intact and amputated subjects are as high as 88.8% and 90.2%, respectively, for the subject-dependent training procedure, and 80.8% and 87.8%, respectively, for the subject-independent training procedure. These results suggest the feasibility of applying the proposed approach to control dexterous prosthetic hands, which can be of great benefit for individuals suffering from hand amputations.
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Affiliation(s)
- Rami Alazrai
- Department of Computer Engineering, School of Electrical Engineering and Information Technology, German Jordanian University, Amman 11180, Jordan.
| | - Hisham Alwanni
- Faculty of Engineering, University of Freiburg, Freiburg 79098, Germany.
| | - Yara Baslan
- Department of Biomedical Engineering, School of Applied Medical Sciences, German Jordanian University, Amman 11180, Jordan.
| | - Nasim Alnuman
- Department of Biomedical Engineering, School of Applied Medical Sciences, German Jordanian University, Amman 11180, Jordan.
| | - Mohammad I Daoud
- Department of Computer Engineering, School of Electrical Engineering and Information Technology, German Jordanian University, Amman 11180, Jordan.
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The Potential Application of Multiscale Entropy Analysis of Electroencephalography in Children with Neurological and Neuropsychiatric Disorders. ENTROPY 2017; 19:e19080428. [PMID: 33535366 DOI: 10.3390/e19080428] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2017] [Revised: 08/11/2017] [Accepted: 08/16/2017] [Indexed: 01/25/2023]
Abstract
Electroencephalography (EEG) is frequently used in functional neurological assessment of children with neurological and neuropsychiatric disorders. Multiscale entropy (MSE) can reveal complexity in both short and long time scales and is more feasible in the analysis of EEG. Entropy-based estimation of EEG complexity is a powerful tool in investigating the underlying disturbances of neural networks of the brain. Most neurological and neuropsychiatric disorders in childhood affect the early stage of brain development. The analysis of EEG complexity may show the influences of different neurological and neuropsychiatric disorders on different regions of the brain during development. This article aims to give a brief summary of current concepts of MSE analysis in pediatric neurological and neuropsychiatric disorders. Studies utilizing MSE or its modifications for investigating neurological and neuropsychiatric disorders in children were reviewed. Abnormal EEG complexity was shown in a variety of childhood neurological and neuropsychiatric diseases, including autism, attention deficit/hyperactivity disorder, Tourette syndrome, and epilepsy in infancy and childhood. MSE has been shown to be a powerful method for analyzing the non-linear anomaly of EEG in childhood neurological diseases. Further studies are needed to show its clinical implications on diagnosis, treatment, and outcome prediction.
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Navarro X, Porée F, Kuchenbuch M, Chavez M, Beuchée A, Carrault G. Multi-feature classifiers for burst detection in single EEG channels from preterm infants. J Neural Eng 2017; 14:046015. [DOI: 10.1088/1741-2552/aa714a] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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17
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Dereymaeker A, Pillay K, Vervisch J, Van Huffel S, Naulaers G, Jansen K, De Vos M. An Automated Quiet Sleep Detection Approach in Preterm Infants as a Gateway to Assess Brain Maturation. Int J Neural Syst 2017; 27:1750023. [PMID: 28460602 PMCID: PMC6342251 DOI: 10.1142/s012906571750023x] [Citation(s) in RCA: 43] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Sleep state development in preterm neonates can provide crucial information regarding functional brain maturation and give insight into neurological well being. However, visual labeling of sleep stages from EEG requires expertise and is very time consuming, prompting the need for an automated procedure. We present a robust method for automated detection of preterm sleep from EEG, over a wide postmenstrual age (PMA = gestational age + postnatal age) range, focusing first on Quiet Sleep (QS) as an initial marker for sleep assessment. Our algorithm, CLuster-based Adaptive Sleep Staging (CLASS), detects QS if it remains relatively more discontinuous than non-QS over PMA. CLASS was optimized on a training set of 34 recordings aged 27–42 weeks PMA, and performance then assessed on a distinct test set of 55 recordings of the same age range. Results were compared to visual QS labeling from two independent raters (with inter-rater agreement Kappa = 0. 93), using Sensitivity, Specificity, Detection Factor (DF = proportion of visual QS periods correctly detected by CLASS) and Misclassification Factor (MF = proportion of CLASS-detected QS periods that are misclassified). CLASS performance proved optimal across recordings at 31–38 weeks (median DF = 1.0, median MF 0–0.25, median Sensitivity 0.93–1.0, and median Specificity 0.80–0.91 across this age range), with minimal misclassifications at 35–36 weeks (median MF = 0). To illustrate the potential of CLASS in facilitating clinical research, normal maturational trends over PMA were derived from CLASS-estimated QS periods, visual QS estimates, and nonstate specific periods (containing QS and non-QS) in the EEG recording. CLASS QS trends agreed with those from visual QS, with both showing stronger correlations than nonstate specific trends. This highlights the benefit of automated QS detection for exploring brain maturation.
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Affiliation(s)
- Anneleen Dereymaeker
- 1 Department of Development and Regeneration, University Hospitals Leuven, Neonatal Intensive Care Unit, KU Leuven (University of Leuven), Leuven, Belgium
| | - Kirubin Pillay
- 2 Institute of Biomedical Engineering (IBME), Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| | - Jan Vervisch
- 3 Department of Development and Regeneration, University Hospitals Leuven, Neonatal Intensive Care, Unit & Child Neurology, KU Leuven, (University of Leuven), Leuven, Belgium
| | - Sabine Van Huffel
- 4 Department of Electrical Engineering-ESAT, Division Stadius, KU Leuven (University of Leuven), Leuven, Belgium.,5 imec, Leuven, Belgium
| | - Gunnar Naulaers
- 1 Department of Development and Regeneration, University Hospitals Leuven, Neonatal Intensive Care Unit, KU Leuven (University of Leuven), Leuven, Belgium
| | - Katrien Jansen
- 3 Department of Development and Regeneration, University Hospitals Leuven, Neonatal Intensive Care, Unit & Child Neurology, KU Leuven, (University of Leuven), Leuven, Belgium
| | - Maarten De Vos
- 6 Institute of Biomedical Engineering (IBME), Department of Engineering Science, University of Oxford, Old Road Campus Research Building, OX3 7DG, Oxford, United Kingdom
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Dunne JM, Wertheim D, Clarke P, Kapellou O, Chisholm P, Boardman JP, Shah DK. Automated electroencephalographic discontinuity in cooled newborns predicts cerebral MRI and neurodevelopmental outcome. Arch Dis Child Fetal Neonatal Ed 2017; 102:F58-F64. [PMID: 27103657 DOI: 10.1136/archdischild-2015-309697] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/03/2015] [Revised: 03/24/2016] [Accepted: 03/29/2016] [Indexed: 11/04/2022]
Abstract
BACKGROUND AND HYPOTHESIS Prolonged electroencephalographic (EEG) discontinuity has been associated with poor neurodevelopmental outcomes after perinatal asphyxia but its predictive value in the era of therapeutic hypothermia (TH) is unknown. In infants undergoing TH for hypoxic-ischaemic encephalopathy (HIE) prolonged EEG discontinuity is associated with cerebral tissue injury on MRI and adverse neurodevelopmental outcome. METHOD Retrospective study of term neonates from three UK centres who received TH for perinatal asphyxia, had continuous two channel amplitude-integrated EEG with EEG for a minimum of 48 h, brain MRI within 6 weeks of birth and neurodevelopmental outcome data at a median age of 24 months. Mean discontinuity was calculated using a novel automated algorithm designed for analysis of the raw EEG signal. RESULTS Of 49 eligible infants, 17 (35%) had MR images predictive of death or severe neurodisability (unfavourable outcome) and 29 (59%) infants had electrographic seizures. In multivariable logistic regression, mean discontinuity at 24 h and 48 h (both p=0.01), and high seizure burden (p=0.05) were associated with severe cerebral tissue injury on MRI. A mean discontinuity >30 s/min-long epoch, had a specificity and positive predictive value of 100%, sensitivity of 71% and a negative predictive value of 88% for unfavourable neurodevelopmental outcome at a 10 µV threshold. CONCLUSIONS In addition to seizure burden, excessive EEG discontinuity is associated with increased cerebral tissue injury on MRI and is predictive of abnormal neurodevelopmental outcome in infants treated with TH. The high positive predictive value of EEG discontinuity at 24 h may be valuable in selecting newborns with HIE for adjunctive treatments.
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Affiliation(s)
| | | | - Paul Clarke
- Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich, UK
| | | | | | - James P Boardman
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh, UK
| | - Divyen K Shah
- Barts and the London Medical School, London, UK.,Royal London Hospital, London, UK
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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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Temko A, Doyle O, Murray D, Lightbody G, Boylan G, Marnane W. Multimodal predictor of neurodevelopmental outcome in newborns with hypoxic-ischaemic encephalopathy. Comput Biol Med 2015; 63:169-77. [DOI: 10.1016/j.compbiomed.2015.05.017] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2015] [Revised: 05/22/2015] [Accepted: 05/23/2015] [Indexed: 11/28/2022]
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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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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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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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Matic V, Cherian PJ, Jansen K, Koolen N, Naulaers G, Swarte RM, Govaert P, Visser GH, Van Huffel S, De Vos M. Automated EEG inter-burst interval detection in neonates with mild to moderate postasphyxial encephalopathy. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2013; 2012:17-20. [PMID: 23365821 DOI: 10.1109/embc.2012.6345860] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
EEG inter-burst interval (IBI) and its evolution is a robust parameter for grading hypoxic encephalopathy and prognostication in newborns with perinatal asphyxia. We present a reliable algorithm for the automatic detection of IBIs. This automated approach is based on adaptive segmentation of EEG, classification of segments and use of temporal profiles to describe the global distribution of EEG activity. A pediatric neurologist has blindly scored data from 8 newborns with perinatal postasphyxial encephalopathy varying from mild to severe. 15 minutes of EEG have been scored per patient, thus totaling 2 hours of EEG that was used for validation. The algorithm shows good detection accuracy and provides insight into challenging cases that are difficult to detect.
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Affiliation(s)
- Vladimir Matic
- Department of Electrical Engineering (ESAT-SCD), Katholieke Universiteit Leuven, Belgium.
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Löfhede J, Seoane F, Thordstein M. Textile electrodes for EEG recording--a pilot study. SENSORS 2012; 12:16907-19. [PMID: 23223149 PMCID: PMC3571817 DOI: 10.3390/s121216907] [Citation(s) in RCA: 51] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2012] [Revised: 12/05/2012] [Accepted: 12/06/2012] [Indexed: 11/16/2022]
Abstract
The overall aim of our research is to develop a monitoring system for neonatal intensive care units. Long-term EEG monitoring in newborns require that the electrodes don't harm the sensitive skin of the baby, an especially relevant feature for premature babies. Our approach to EEG monitoring is based on several electrodes distributed over the head of the baby, and since the weight of the head always will be on some of them, any type of hard electrode will inevitably cause a pressure-point that can irritate the skin. Therefore, we propose the use of soft conductive textiles as EEG electrodes, primarily for neonates, but also for other kinds of unobtrusive long-term monitoring. In this paper we have tested two types of textile electrodes on five healthy adults and compared them to standard high quality electrodes. The acquired signals were compared with respect to morphology, frequency distribution, spectral coherence, correlation and power line interference sensitivity, and the signals were found to be similar in most respects. The good measurement performance exhibited by the textile electrodes indicates that they are feasible candidates for EEG recording, opening the door for long-term EEG monitoring applications.
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Affiliation(s)
- Johan Löfhede
- School of Engineering, University of Borås, 501 90 Borås, Sweden
- Author to whom correspondence should be addressed; E-Mail: ; Tel.: +46-739-849-669
| | - Fernando Seoane
- School of Engineering, University of Borås, 501 90 Borås, Sweden
- School of Technology and Health, KTH-Royal Institute of Technology, 141 52 Huddinge, Sweden; E-Mail:
| | - Magnus Thordstein
- Institute of Neuroscience and Physiology, Sahlgrenska Academy, Sahlgrenska University Hospital, 413 45 Göteborg, Sweden; E-Mail:
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Stevenson NJ, Korotchikova I, Temko A, Lightbody G, Marnane WP, Boylan GB. An automated system for grading EEG abnormality in term neonates with hypoxic-ischaemic encephalopathy. Ann Biomed Eng 2012; 41:775-85. [PMID: 23519533 PMCID: PMC3605495 DOI: 10.1007/s10439-012-0710-5] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2012] [Accepted: 11/20/2012] [Indexed: 10/29/2022]
Abstract
Automated analysis of the neonatal EEG has the potential to assist clinical decision making for neonates with hypoxic-ischaemic encephalopathy. This paper proposes a method of automatically grading the degree of abnormality in an hour long epoch of neonatal EEG. The automated grading system (AGS) was based on a multi-class linear classifier grading of short-term epochs of EEG which were converted into a long-term grading of EEG using a majority vote operation. The features used in the AGS were summary measurements of two sub-signals extracted from a quadratic time-frequency distribution: the amplitude modulation and instantaneous frequency. These sub-signals were based on a model of EEG as a multiplication of a coloured random process with a slowly varying pseudo-periodic waveform and may be related to macroscopic neurophysiological function. The 4 grade AGS had a classification accuracy of 83% compared to human annotation of the EEG (level of agreement, κ = 0.76). Features estimated on the developed sub-signals proved more effective at grading the EEG than measures based solely on the EEG and the incorporation of additional sub-grades based on EEG states into the AGS also improved performance.
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Affiliation(s)
- N J Stevenson
- Neonatal Brain Research Group, University College Cork, Cork, Ireland.
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Mitchell TJ, Neil JJ, Zempel JM, Thio LL, Inder TE, Bretthorst GL. Automating the analysis of EEG recordings from prematurely-born infants: a Bayesian approach. Clin Neurophysiol 2012; 124:452-61. [PMID: 23014143 DOI: 10.1016/j.clinph.2012.09.003] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2011] [Revised: 07/15/2012] [Accepted: 09/04/2012] [Indexed: 11/15/2022]
Abstract
OBJECTIVE To implement an automated analysis of EEG recordings from prematurely-born infants and thus provide objective, reproducible results. METHODS Bayesian probability theory is employed to compute the posterior probability for developmental features of interest in EEG recordings. Currently, these features include smooth delta waves (0.5-1.5Hz, >100μV), delta brushes (delta portion: 0.5-1.5Hz, >100μV; "brush" portion: 8-22Hz, <75μV), and interburst intervals (<10μV), though the approach taken can be generalized to identify other EEG features of interest. RESULTS When compared with experienced electroencephalographers, the algorithm had a true positive rate between 72% and 79% for the identification of delta waves (smooth or "brush") and interburst intervals, which is comparable to the inter-rater reliability. When distinguishing between smooth delta waves and delta brushes, the algorithm's true positive rate was between 53% and 88%, which is slightly less than the inter-rater reliability. CONCLUSION Bayesian probability theory can be employed to consistently identify features of EEG recordings from premature infants. SIGNIFICANCE The identification of features in EEG recordings provides a first step towards the automated analysis of EEG recordings from premature infants.
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Affiliation(s)
- Timothy J Mitchell
- Department of Pediatrics, Washington University, St. Louis, MO 63110, USA.
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Piryatinska A, Woyczynski WA, Scher MS, Loparo KA. Optimal channel selection for analysis of EEG-sleep patterns of neonates. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2012; 106:14-26. [PMID: 22000642 DOI: 10.1016/j.cmpb.2011.08.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2011] [Revised: 07/05/2011] [Accepted: 08/11/2011] [Indexed: 05/31/2023]
Abstract
This paper extends our previous work on automated detection and classification of neonate EEG sleep stages. In [19] we adapted and integrated a range of computational, mathematical and statistical tools for the analysis of neonatal electroencephalogram (EEG) sleep recordings with the aim of facilitating the assessment of neonatal brain maturation and dismaturity by studying the structure and temporal patterns of their sleep. That work relied on algorithms using a single channel of EEG. The present paper builds on our previous work by incorporating a larger selection of EEG channels that capture both the spatial distribution and temporal patterns of EEG during sleep. Using a multivariate analysis approach, we obtain the "optimal" selection of the EEG channels and characteristics that are most suitable for EEG sleep state separation.
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On the Selection of Time-Frequency Features for Improving the Detection and Classification of Newborn EEG Seizure Signals and Other Abnormalities. NEURAL INFORMATION PROCESSING 2012. [DOI: 10.1007/978-3-642-34478-7_77] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
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Temko A, Nadeu C, Marnane W, Boylan G, Lightbody G. EEG signal description with spectral-envelope-based speech recognition features for detection of neonatal seizures. IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE : A PUBLICATION OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY 2011; 15:839-47. [PMID: 21690018 PMCID: PMC3428725 DOI: 10.1109/titb.2011.2159805] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
In this paper, features which are usually employed in automatic speech recognition (ASR) are used for the detection of seizures in newborn EEG. In particular, spectral envelope-based features, composed of spectral powers and their spectral derivatives are compared to the established feature set which has been previously developed for EEG analysis. The results indicate that the ASR features which model the spectral derivatives, either full-band or localized in frequency, yielded a performance improvement, in comparison to spectral-power-based features. Indeed it is shown here that they perform reasonably well in comparison with the conventional EEG feature set. The contribution of the ASR features was analyzed here using the support vector machines (SVM) recursive feature elimination technique. It is shown that the spectral derivative features consistently appear among the top-rank features. The study shows that the ASR features should be given a high priority when dealing with the description of the EEG signal.
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Affiliation(s)
- Andriy Temko
- Department of Electrical and Electronic Engineering and the Neonatal Brain Research Group, University College Cork, Ireland.
| | - Climent Nadeu
- Speech Processing Group, TALP Research Center, Department of Signal Theory and Communication, Univesitat Politècnica de Catalunya, Barcelona, Spain.
| | - William Marnane
- Department of Electrical and Electronic Engineering and the Neonatal Brain Research Group, University College Cork, Ireland.
| | - Geraldine Boylan
- Department of Pediatrics and Child Health and the Neonatal Brain Research Group, University College Cork, Ireland.
| | - Gordon Lightbody
- Department of Electrical and Electronic Engineering and the Neonatal Brain Research Group, University College Cork, Ireland.
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Flisberg A, Kjellmer I, Löfhede J, Lindecrantz K, Thordstein M. Prognostic capacity of automated quantification of suppression time in the EEG of post-asphyctic full-term neonates. Acta Paediatr 2011; 100:1338-43. [PMID: 21615787 DOI: 10.1111/j.1651-2227.2011.02323.x] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
AIM To evaluate the prognostic capacity of a new method for automatic quantification of the length of suppression time in the electroencephalogram (EEG) of a group of asphyxiated newborn infants. METHODS Twenty-one full-term newborn infants who had been resuscitated for severe birth asphyxia were studied. Eight channel continuous EEG was recorded for prolonged time periods during the first days of life. Artefact detection or rejection was not applied to the signals. The signals were fed through a pretrained classifier and then segmented into burst and suppression periods. Total suppression length per hour was calculated. All surviving patients were followed with structured neurodevelopmental assessments to at least 18 months of age. RESULTS The patients who developed neurodevelopmental disability or died had significant suppression periods in their EEG during the first days of life while the patients who had a normal follow-up had no or negligible amount of suppression. CONCLUSIONS This new method for automatic quantification of suppression periods in the raw, neonatal EEG discriminates infants with good from those with poor outcome.
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Affiliation(s)
- A Flisberg
- Institute of Clinical Science, Department of Pediatrics, Division of Neonatology, The Queen Silvia Children's Hospital Sahlgrenska University Hospital, Gothenburg, Sweden.
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Korotchikova I, Stevenson N, Walsh B, Murray D, Boylan G. Quantitative EEG analysis in neonatal hypoxic ischaemic encephalopathy. Clin Neurophysiol 2011; 122:1671-8. [DOI: 10.1016/j.clinph.2010.12.059] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2010] [Revised: 12/17/2010] [Accepted: 12/18/2010] [Indexed: 10/18/2022]
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Wulsin DF, Gupta JR, Mani R, Blanco JA, Litt B. Modeling electroencephalography waveforms with semi-supervised deep belief nets: fast classification and anomaly measurement. J Neural Eng 2011; 8:036015. [PMID: 21525569 DOI: 10.1088/1741-2560/8/3/036015] [Citation(s) in RCA: 126] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Clinical electroencephalography (EEG) records vast amounts of human complex data yet is still reviewed primarily by human readers. Deep belief nets (DBNs) are a relatively new type of multi-layer neural network commonly tested on two-dimensional image data but are rarely applied to times-series data such as EEG. We apply DBNs in a semi-supervised paradigm to model EEG waveforms for classification and anomaly detection. DBN performance was comparable to standard classifiers on our EEG dataset, and classification time was found to be 1.7-103.7 times faster than the other high-performing classifiers. We demonstrate how the unsupervised step of DBN learning produces an autoencoder that can naturally be used in anomaly measurement. We compare the use of raw, unprocessed data--a rarity in automated physiological waveform analysis--with hand-chosen features and find that raw data produce comparable classification and better anomaly measurement performance. These results indicate that DBNs and raw data inputs may be more effective for online automated EEG waveform recognition than other common techniques.
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Affiliation(s)
- D F Wulsin
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
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