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TaghiBeyglou B, Shamsollahi MB. ETucker: a constrained tensor decomposition for single trial ERP extraction. Physiol Meas 2023; 44:075005. [PMID: 37414004 DOI: 10.1088/1361-6579/ace510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 07/06/2023] [Indexed: 07/08/2023]
Abstract
Objective.In this paper, we propose a new tensor decomposition to extract event-related potentials (ERP) by adding a physiologically meaningful constraint to the Tucker decomposition.Approach.We analyze the performance of the proposed model and compare it with Tucker decomposition by synthesizing a dataset. The simulated dataset is generated using a 12th-order autoregressive model in combination with independent component analysis (ICA) on real no-task electroencephalogram (EEG) recordings. The dataset is manipulated to contain the P300 ERP component and to cover different SNR conditions, ranging from 0 to -30 dB, to simulate the presence of the P300 component in extremely noisy recordings. Furthermore, in order to assess the practicality of the proposed methodology in real-world scenarios, we utilized the brain-computer interface (BCI) competition III-dataset II.Main results.Our primary results demonstrate the superior performance of our approach compared to conventional methods commonly employed for single-trial estimation. Additionally, our method outperformed both Tucker decomposition and non-negative Tucker decomposition in the synthesized dataset. Furthermore, the results obtained from real-world data exhibited meaningful performance and provided insightful interpretations for the extracted P300 component.Significance.The findings suggest that the proposed decomposition is eminently capable of extracting the target P300 component's waveform, including latency and amplitude as well as its spatial location, using single-trial EEG recordings.
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Hüsser A, Caron-Desrochers L, Tremblay J, Vannasing P, Martínez-Montes E, Gallagher A. Parallel factor analysis for multidimensional decomposition of functional near-infrared spectroscopy data. NEUROPHOTONICS 2022; 9:045004. [PMID: 36405999 PMCID: PMC9665873 DOI: 10.1117/1.nph.9.4.045004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 09/29/2022] [Indexed: 06/16/2023]
Abstract
SIGNIFICANCE Current techniques for data analysis in functional near-infrared spectroscopy (fNIRS), such as artifact correction, do not allow to integrate the information originating from both wavelengths, considering only temporal and spatial dimensions of the signal's structure. Parallel factor analysis (PARAFAC) has previously been validated as a multidimensional decomposition technique in other neuroimaging fields. AIM We aimed to introduce and validate the use of PARAFAC for the analysis of fNIRS data, which is inherently multidimensional (time, space, and wavelength). APPROACH We used data acquired in 17 healthy adults during a verbal fluency task to compare the efficacy of PARAFAC for motion artifact correction to traditional two-dimensional decomposition techniques, i.e., target principal (tPCA) and independent component analysis (ICA). Correction performance was further evaluated under controlled conditions with simulated artifacts and hemodynamic response functions. RESULTS PARAFAC achieved significantly higher improvement in data quality as compared to tPCA and ICA. Correction in several simulated signals further validated its use and promoted it as a robust method independent of the artifact's characteristics. CONCLUSIONS This study describes the first implementation of PARAFAC in fNIRS and provides validation for its use to correct artifacts. PARAFAC is a promising data-driven alternative for multidimensional data analyses in fNIRS and this study paves the way for further applications.
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Affiliation(s)
- Alejandra Hüsser
- Research Center of the Sainte-Justine University Hospital, Neurodevelopmental Optical Imaging Laboratory (LIONlab), Montreal, Quebec, Canada
- Université de Montréal, Department of Psychology, Montréal, Quebec, Canada
| | - Laura Caron-Desrochers
- Research Center of the Sainte-Justine University Hospital, Neurodevelopmental Optical Imaging Laboratory (LIONlab), Montreal, Quebec, Canada
- Université de Montréal, Department of Psychology, Montréal, Quebec, Canada
| | - Julie Tremblay
- Research Center of the Sainte-Justine University Hospital, Neurodevelopmental Optical Imaging Laboratory (LIONlab), Montreal, Quebec, Canada
| | - Phetsamone Vannasing
- Research Center of the Sainte-Justine University Hospital, Neurodevelopmental Optical Imaging Laboratory (LIONlab), Montreal, Quebec, Canada
| | | | - Anne Gallagher
- Research Center of the Sainte-Justine University Hospital, Neurodevelopmental Optical Imaging Laboratory (LIONlab), Montreal, Quebec, Canada
- Université de Montréal, Department of Psychology, Montréal, Quebec, Canada
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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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Thanh LT, Dao NTA, Dung NV, Trung NL, Abed-Meraim K. Multi-channel EEG epileptic spike detection by a new method of tensor decomposition. J Neural Eng 2020; 17:016023. [PMID: 31905174 DOI: 10.1088/1741-2552/ab5247] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Epilepsy is one of the most common brain disorders. For epilepsy diagnosis or treatment, the neurologist needs to observe epileptic spikes from electroencephalography (EEG) data. Since multi-channel EEG records can be naturally represented by multi-way tensors, it is of interest to see whether tensor decomposition is able to analyze EEG epileptic spikes. APPROACH In this paper, we first proposed the problem of simultaneous multilinear low-rank approximation of tensors (SMLRAT) and proved that SMLRAT can obtain local optimum solutions by using two well-known tensor decomposition algorithms (HOSVD and Tucker-ALS). Second, we presented a new system for automatic epileptic spike detection based on SMLRAT. MAIN RESULTS We propose to formulate the problem of feature extraction from a set of EEG segments, represented by tensors, as the SMLRAT problem. Efficient EEG features were obtained, based on estimating the 'eigenspikes' derived from nonnegative GSMLRAT. We compared the proposed tensor analysis method with other common tensor methods in analyzing EEG signal and compared the proposed feature extraction method with the state-of-the-art methods. Experimental results indicated that our proposed method is able to detect epileptic spikes with high accuracy. SIGNIFICANCE Our method, for the first time, makes a step forward for automatic detection EEG epileptic spikes based on tensor decomposition. The method can provide a practical solution to distinguish epileptic spikes from artifacts in real-life EEG datasets.
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Affiliation(s)
- Le Trung Thanh
- Advanced Institute of Engineering and Technology (AVITECH), VNU University of Engineering and Technology, Vietnam National University, Hanoi, Vietnam
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Kim S, Shon K, Sung KR. Spatial and Temporal Characteristics of Visual Field Progression in Glaucoma Assessed by Parallel Factor Analysis. KOREAN JOURNAL OF OPHTHALMOLOGY 2019; 33:279-286. [PMID: 31179660 PMCID: PMC6557789 DOI: 10.3341/kjo.2019.0004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2019] [Revised: 02/22/2019] [Accepted: 02/27/2019] [Indexed: 11/28/2022] Open
Abstract
Purpose To explore spatial and temporal characteristics of glaucomatous visual field (VF) progression through multi-way decomposition of data. Methods Six serial VF exams with intervals of 6.0 ± 1.0 months in 121 pre-perimetric glaucoma eyes and 80 perimetric glaucoma eyes were arranged into a three-dimensional cube. The data were decomposed using parallel factor analysis. Results Three tri-linear components (i.e., spatial scores, temporal loadings, and subject-specific loadings) were identified. Component 1 clearly showed differences between superior and inferior hemispheres, linear trends over time, and wide variability in perimetric glaucoma. Findings were compatible with well-known characteristics of glaucomatous VF defects. Component 2 showed nasal and central areas in contrast with superior, inferior, and temporal peripheral locations, whereas component 3 showed a contrast between nasal and temporal hemispheres. Both components 2 and 3 failed to show clear temporal trends. Conclusions Identification of spatio-temporal patterns shows new possibilities for a multi-way decomposition methodology for earlier diagnosis and prediction of glaucomatous VF progression.
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Affiliation(s)
- Seungmo Kim
- Department of Ophthalmology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Kilhwan Shon
- Department of Ophthalmology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Kyung Rim Sung
- Department of Ophthalmology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
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Aldana YR, Hunyadi B, Reyes EJM, Rodriguez VR, Van Huffel S. Nonconvulsive Epileptic Seizure Detection in Scalp EEG Using Multiway Data Analysis. IEEE J Biomed Health Inform 2019; 23:660-671. [DOI: 10.1109/jbhi.2018.2829877] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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Haldar JP, Mosher JC, Nair DR, Gonzalez-Martinez JA, Leahy RM. Scalable and Robust Tensor Decomposition of Spontaneous Stereotactic EEG Data. IEEE Trans Biomed Eng 2018; 66:1549-1558. [PMID: 30307856 DOI: 10.1109/tbme.2018.2875467] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
OBJECTIVE Identification of networks from resting brain signals is an important step in understanding the dynamics of spontaneous brain activity. We approach this problem using a tensor-based model. METHODS We develop a rank-recursive scalable and robust sequential canonical polyadic decomposition (SRSCPD) framework to decompose a tensor into several rank-1 components. Robustness and scalability are achieved using a warm start for each rank based on the results from the previous rank. RESULTS In simulations we show that SRSCPD consistently outperforms the multi-start alternating least square (ALS) algorithm over a range of ranks and signal-to-noise ratios (SNRs), with lower computation cost. When applying SRSCPD to resting in-vivo stereotactic EEG (SEEG) data from two subjects with epilepsy, we found components corresponding to default mode and motor networks in both subjects. These components were also highly consistent within subject between two sessions recorded several hours apart. Similar components were not obtained using the conventional ALS algorithm. CONCLUSION Consistent brain networks and their dynamic behaviors were identified from resting SEEG data using SRSCPD. SIGNIFICANCE SRSCPD is scalable to large datasets and therefore a promising tool for identification of brain networks in long recordings from single subjects.
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Albera L, Becker H, Karfoul A, Gribonval R, Kachenoura A, Bensaid S, Senhadji L, Hernandez A, Merlet I. Localization of spatially distributed brain sources after a tensor-based preprocessing of interictal epileptic EEG data. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2016; 2015:6995-8. [PMID: 26737902 DOI: 10.1109/embc.2015.7320002] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
This paper addresses the localization of spatially distributed sources from interictal epileptic electroencephalographic data after a tensor-based preprocessing. Justifying the Canonical Polyadic (CP) model of the space-time-frequency and space-time-wave-vector tensors is not an easy task when two or more extended sources have to be localized. On the other hand, the occurrence of several amplitude modulated spikes originating from the same epileptic region can be used to build a space-time-spike tensor from the EEG data. While the CP model of this tensor appears more justified, the exact computation of its loading matrices can be limited by the presence of highly correlated sources or/and a strong background noise. An efficient extended source localization scheme after the tensor-based preprocessing has then to be set up. Different strategies are thus investigated and compared on realistic simulated data: the "disk algorithm" using a precomputed dictionary of circular patches, a standardized Tikhonov regularization and a fused LASSO scheme.
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Abstract
The cerebral function monitor is a device for trend monitoring of changes in the amplitude of the electroencephalogram, typically recorded from one or two pairs of electrodes. Initially developed and introduced to monitor cerebral activity in encephalopathic adult patients or during anaesthesia, it is now most widely used in newborns to assess the severity of encephalopathy and for determining prognosis. The duration and severity of abnormalities of the amplitude-integrated electroencephalogram tracing is highly predictive of subsequent neurologic outcome following neonatal hypoxic-ischemic encephalopathy, including in newborns receiving neuroprotective treatment with prolonged moderate hypothermia. The cerebral function monitor is also used for seizure detection and to monitor response to anticonvulsant therapies. Amplitude-integrated electroencephalography compares well with standard electroencephalography when used to assess the severity of neonatal encephalopathy, but a standard electroencephalogram is still required to provide important information about changes in frequency, and in the synchrony and distribution and other characteristics of cerebral cortical activity. The role of the amplitude-integrated electroencephalogram to identify brain injury in preterm infants remains to be determined.
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Affiliation(s)
- Denis Azzopardi
- Centre for the Developing Brain, Perinatal Imaging, King's College London, St Thomas' Hospital, London, UK.
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Cong F, Lin QH, Kuang LD, Gong XF, Astikainen P, Ristaniemi T. Tensor decomposition of EEG signals: A brief review. J Neurosci Methods 2015; 248:59-69. [DOI: 10.1016/j.jneumeth.2015.03.018] [Citation(s) in RCA: 107] [Impact Index Per Article: 11.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2014] [Revised: 02/17/2015] [Accepted: 03/12/2015] [Indexed: 10/23/2022]
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Becker H, Albera L, Comon P, Haardt M, Birot G, Wendling F, Gavaret M, Bénar CG, Merlet I. EEG extended source localization: tensor-based vs. conventional methods. Neuroimage 2014; 96:143-57. [PMID: 24662577 DOI: 10.1016/j.neuroimage.2014.03.043] [Citation(s) in RCA: 60] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2013] [Revised: 02/26/2014] [Accepted: 03/15/2014] [Indexed: 11/27/2022] Open
Abstract
The localization of brain sources based on EEG measurements is a topic that has attracted a lot of attention in the last decades and many different source localization algorithms have been proposed. However, their performance is limited in the case of several simultaneously active brain regions and low signal-to-noise ratios. To overcome these problems, tensor-based preprocessing can be applied, which consists in constructing a space-time-frequency (STF) or space-time-wave-vector (STWV) tensor and decomposing it using the Canonical Polyadic (CP) decomposition. In this paper, we present a new algorithm for the accurate localization of extended sources based on the results of the tensor decomposition. Furthermore, we conduct a detailed study of the tensor-based preprocessing methods, including an analysis of their theoretical foundation, their computational complexity, and their performance for realistic simulated data in comparison to conventional source localization algorithms such as sLORETA, cortical LORETA (cLORETA), and 4-ExSo-MUSIC. Our objective consists, on the one hand, in demonstrating the gain in performance that can be achieved by tensor-based preprocessing, and, on the other hand, in pointing out the limits and drawbacks of this method. Finally, we validate the STF and STWV techniques on real measurements to demonstrate their usefulness for practical applications.
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Affiliation(s)
- H Becker
- Univ. Nice Sophia Antipolis, CNRS, I3S, UMR 7271, F-06900 Sophia Antipolis, France; INSERM, U1099, Rennes F-35000, France; Université de Rennes 1, LTSI, Rennes F-35000, France; GIPSA-Lab, CNRS UMR5216, Grenoble Campus BP.46, F-38402 St Martin d'Heres Cedex, France
| | - L Albera
- INSERM, U1099, Rennes F-35000, France; Université de Rennes 1, LTSI, Rennes F-35000, France; Centre INRIA Rennes-Bretagne Atlantique, Rennes F-35042, France.
| | - P Comon
- GIPSA-Lab, CNRS UMR5216, Grenoble Campus BP.46, F-38402 St Martin d'Heres Cedex, France
| | - M Haardt
- Ilmenau University of Technology, Communications Research Laboratory, P.O. Box 10 05 65, D-98684 Ilmenau, Germany
| | - G Birot
- INSERM, U1099, Rennes F-35000, France; Université de Rennes 1, LTSI, Rennes F-35000, France
| | - F Wendling
- INSERM, U1099, Rennes F-35000, France; Université de Rennes 1, LTSI, Rennes F-35000, France
| | - M Gavaret
- INSERM, UMR 1106, F-13005 Marseille, France; Aix-Marseille Université, F-13005 Marseille, France; AP-HM, Hopital Timone, F-13005 Marseille, France
| | - C G Bénar
- INSERM, UMR 1106, F-13005 Marseille, France; Aix-Marseille Université, F-13005 Marseille, France
| | - I Merlet
- INSERM, U1099, Rennes F-35000, France; Université de Rennes 1, LTSI, Rennes F-35000, France
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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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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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Verleger R, Paulick C, Möcks J, Smith JL, Keller K. Parafac and go/no-go: Disentangling CNV return from the P3 complex by trilinear component analysis. Int J Psychophysiol 2013; 87:289-300. [DOI: 10.1016/j.ijpsycho.2012.08.003] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2012] [Revised: 03/27/2012] [Accepted: 08/07/2012] [Indexed: 11/28/2022]
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Vanderperren K, Mijović B, Novitskiy N, Vanrumste B, Stiers P, Van den Bergh BRH, Lagae L, Sunaert S, Wagemans J, Van Huffel S, De Vos M. Single trial ERP reading based on parallel factor analysis. Psychophysiology 2012; 50:97-110. [DOI: 10.1111/j.1469-8986.2012.01405.x] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2011] [Accepted: 05/12/2012] [Indexed: 11/27/2022]
Affiliation(s)
| | | | - Nikolay Novitskiy
- Laboratory of Experimental Psychology; Katholieke Universiteit Leuven; Leuven; Belgium
| | | | - Peter Stiers
- Faculty of Psychology and Neuroscience; Maastricht University; Maastricht; The Netherlands
| | | | - Lieven Lagae
- Department of Pediatric Neurology; Katholieke Universiteit Leuven; Leuven; Belgium
| | - Stefan Sunaert
- Department of Radiology; Katholieke Universiteit Leuven; Leuven; Belgium
| | - Johan Wagemans
- Laboratory of Experimental Psychology; Katholieke Universiteit Leuven; Leuven; Belgium
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Prada MA, Toivola J, Kullaa J, Hollmén J. Three-way analysis of structural health monitoring data. Neurocomputing 2012. [DOI: 10.1016/j.neucom.2011.07.030] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
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Orekhov VY, Jaravine VA. Analysis of non-uniformly sampled spectra with multi-dimensional decomposition. PROGRESS IN NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY 2011; 59:271-92. [PMID: 21920222 DOI: 10.1016/j.pnmrs.2011.02.002] [Citation(s) in RCA: 251] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2011] [Accepted: 02/21/2011] [Indexed: 05/04/2023]
Affiliation(s)
- Vladislav Yu Orekhov
- Swedish NMR Centre, University of Gothenburg, Box 465, 40530 Gothenburg, Sweden.
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Unraveling superimposed EEG rhythms with multi-dimensional decomposition. J Neurosci Methods 2011; 195:47-60. [DOI: 10.1016/j.jneumeth.2010.11.010] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2010] [Revised: 11/15/2010] [Accepted: 11/21/2010] [Indexed: 11/20/2022]
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A Nonlinear Model of Newborn EEG with Nonstationary Inputs. Ann Biomed Eng 2010; 38:3010-21. [DOI: 10.1007/s10439-010-0041-3] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2009] [Accepted: 04/06/2010] [Indexed: 10/19/2022]
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