RESUMEN
Identifying the location, the spatial extent and the electrical activity of distributed brain sources in the context of epilepsy through ElectroEncephaloGraphy (EEG) recordings is a challenging task because of the highly ill-posed nature of the underlying Electrophysiological Source Imaging (ESI) problem. To guarantee a unique solution, most existing ESI methods pay more attention to solve this inverse problem by imposing physiological constraints. This paper proposes an efficient ESI approach based on simulation-driven deep learning. Epileptic High-resolution 256-channels scalp EEG (Hr-EEG) signals are simulated in a realistic manner to train the proposed patient-specific model. More particularly, a computational neural mass model developed in our team is used to generate the temporal dynamics of the activity of each dipole while the forward problem is solved using a patient-specific three-shell realistic head model and the boundary element method. A Temporal Convolutional Network (TCN) is considered in the proposed model to capture local spatial patterns. To enable the model to observe the EEG signals from different scale levels, the multi-scale strategy is leveraged to capture the overall features and fine-grain features by adjusting the convolutional kernel size. Then, the Long Short-Term Memory (LSTM) is used to extract temporal dependencies among the computed spatial features. The performance of the proposed method is evaluated through three different scenarios of realistic synthetic interictal Hr-EEG data as well as on real interictal Hr-EEG data acquired in three patients with drug-resistant partial epilepsy, during their presurgical evaluation. A performance comparison study is also conducted with two other deep learning-based methods and four classical ESI techniques. The proposed model achieved a Dipole Localization Error (DLE) of 1.39 and Normalized Hamming Distance (NHD) of 0.28 in the case of one patch with SNR of 10 dB. In the case of two uncorrelated patches with an SNR of 10 dB, obtained DLE and NHD were respectively 1.50 and 0.28. Even in the more challenging scenario of two correlated patches with an SNR of 10 dB, the proposed approach still achieved a DLE of 3.74 and an NHD of 0.43. The results obtained on simulated data demonstrate that the proposed method outperforms the existing methods for different signal-to-noise and source configurations. The good behavior of the proposed method is also confirmed on real interictal EEG data. The robustness with respect to noise makes it a promising and alternative tool to localize epileptic brain areas and to reconstruct their electrical activities from EEG signals.
Asunto(s)
Aprendizaje Profundo , Epilepsia Refractaria , Epilepsia , Humanos , Encéfalo/diagnóstico por imagen , Epilepsia/diagnóstico por imagen , Electroencefalografía/métodos , Epilepsia Refractaria/diagnóstico por imagen , Mapeo Encefálico/métodosRESUMEN
OBJECTIVES: To access the performances of different algorithms for quantification of Intravoxel incoherent motion (IVIM) parameters D, f, [Formula: see text] in Vertebral Bone Marrow (VBM). MATERIALS AND METHODS: Five algorithms were studied: four deterministic algorithms (the One-Step and three segmented methods: Two-Step, Three-Step, and Fixed-[Formula: see text] algorithm) based on the least-squares (LSQ) method and a Bayesian probabilistic algorithm. Numerical simulations and quantification of IVIM parameters D, f, [Formula: see text] in vivo in vertebral bone marrow, were done on six healthy volunteers. The One-way repeated-measures analysis of variance (ANOVA) followed by Bonferroni's multiple comparison test (p value = 0.05) was applied. RESULTS: In numerical simulations, the Bayesian algorithm provided the best estimation of D, f, [Formula: see text] compared to the deterministic algorithms. In vivo VBM-IVIM, the values of D and f estimated by the Bayesian algorithm were close to those of the One-Step method, in contrast to the three segmented methods. DISCUSSION: The comparison of the five algorithms indicates that the Bayesian algorithm provides the best estimation of VBM-IVIM parameters, in both numerical simulations and in vivo data.
Asunto(s)
Imagen de Difusión por Resonancia Magnética , Procesamiento de Imagen Asistido por Computador , Humanos , Imagen de Difusión por Resonancia Magnética/métodos , Procesamiento de Imagen Asistido por Computador/métodos , Médula Ósea/diagnóstico por imagen , Teorema de Bayes , Algoritmos , Movimiento (Física)RESUMEN
Epilepsy is one of the most common neurological diseases, which can seriously affect the patient's psychological well-being and quality of life. An accurate and reliable seizure prediction system can generate alarm before epileptic seizures to provide patients and their caregivers with sufficient time to take appropriate action. This study proposes an efficient seizure prediction system based on deep learning in order to anticipate the onset of the seizure as early as possible. Handcrafted features extracted based on the prior knowledge and hidden deep features are complementarily fused through the feature fusion module, and then the hybrid features are fed into the multiplicative long short-term memory (MLSTM) to explore the temporal dependency in EEG signals. A one-dimensional channel attention mechanism is implemented to emphasize the more representative information in the multi-channel output of the MLSTM. Finally, a transfer learning strategy is proposed to transfer the weights of the base model trained on the EEG data of all patients to the target patient model, and the latter is then continuously trained using the EEG data of the target patient. The proposed method achieves an average sensitivity of 95.56% and a false positive rate (FPR) of 0.27/h on the SWEC-ETHZ intracranial EEG data. For the more challenging CHB-MIT scalp EEG database, an average sensitivity of 89.47% and a FPR of 0.34/h are obtained. Experimental results demonstrate that the proposed method has good robustness and generalization ability in both intracranial and scalp EEG signals.
Asunto(s)
Epilepsia , Calidad de Vida , Algoritmos , Electroencefalografía/métodos , Epilepsia/diagnóstico , Humanos , Aprendizaje Automático , Redes Neurales de la Computación , Convulsiones/diagnósticoRESUMEN
BACKGROUND AND OBJECTIVE: Recently, spectral Dynamic Causal Modelling (DCM) has been used increasingly to infer effective connectivity from epileptic intracranial electroencephalographic (iEEG) signals. In this context, the Physiology-Based Model (PBM), a neural mass model, is used as a generative model. However, previous studies have highlighted out the inability of PBM to properly describe iEEG signals with specific power spectral densities (PSDs). More precisely, PSDs that have multiple peaks around ß and γ rhythms (i.e. spectral characteristics at seizure onset) are concerned. METHODS: To cope with this limitation, an alternative neural mass model, called the complete PBM (cPBM), is investigated. The spectral DCM and two recent variants are used to evaluate the relevance of cPBM over PBM. RESULTS: The study is conducted on both simulated signals and real epileptic iEEG recordings. Our results confirm that, compared to PBM, cPBM shows (i) more ability to model the desired PSDs and (ii) lower numerical complexity whatever the method. CONCLUSIONS: Thanks to its intrinsic and extrinsic connectivity parameters as well as the input coming into the fast inhibitory subpopulation, the cPBM provides a more expressive model of PSDs, leading to a better understanding of epileptic patterns and DCM-based effective connectivity inference.
Asunto(s)
Epilepsia , Red Nerviosa , Encéfalo , Electroencefalografía , Ritmo Gamma , Humanos , Modelos Neurológicos , Modelos Teóricos , ConvulsionesRESUMEN
In this paper, a new method to track brain effective connectivity networks in the context of epilepsy is proposed. It relies on the combination of partial directed coherence with a constrained low-rank canonical polyadic tensor decomposition. With such combination being established, the most dominating directed graph structures underlying each time window of intracerebral electroencephalographic signals are optimally inferred. Obtained time and frequency signatures of inferred brain networks allow respectively to track the time evolution of these networks and to define frequency bands on which they are operating. Besides, the proposed method allows also to track brain connectivity networks through several epileptic seizures of the same patient. Understanding the most dominating directed graph structures over epileptic seizures and investigating their behavior over time and frequency plans are henceforth possible. Since only few but the the most important directed connections in the graph structure are of interest and also for a meaningful interpretation of obtained signatures to be guaranteed, the low-rank canonical polyadic tensor decomposition is prompted respectively by the sparsity and the non-negativity constraints on the tensor loading matrices. The main objective of this contribution is to propose a new way of tracking brain networks in the context of epileptic iEEG data by identifying the most dominant effective connectivity patterns underlying the observed iEEG signals at each time window. The performance of the proposed method is firstly evaluated on simulated data imitating brain activities and secondly on real intracerebral electroencephalographic signals obtained from an epileptic patient. The partial directed coherence-based tensor has been decomposed into space, time, and frequency signatures in accordance with the expected ground truth for each consecutive sequence of the simulated data. The method is also in accordance with the clinical expertise for iEEG epileptic signals, where the signatures were investigated through a simultaneous multi-seizure analysis.
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Encéfalo , Epilepsia , Mapeo Encefálico , Electroencefalografía , Humanos , ConvulsionesRESUMEN
IntraVoxel Incoherent Motion (IVIM) Diffusion-Weighted Magnetic Resonance Imaging (DW-MRI) is of great interest for evaluating tissue diffusion and perfusion and producing parametric maps in clinical applications for liver pathologies. However, the presence of macroscopic blood vessels (not capillaries) in a given Region of Interest (ROI) results in a confounding effect that bias the quantification of tissue perfusion. Therefore, it is necessary to identify those voxels affected by blood vessels. In this paper, an efficient algorithm for an automatic identification of blood vessels in a given ROI is proposed. It relies on the sparsity of the spatial distribution of blood vessels. This sparsity prior can be easily incorporated using the all-voxel IVIM-MRI model introduced in this paper. In addition to the identification of blood vessels, the proposed algorithm provides a quantification of blood vessels, tissue diffusion and tissue perfusion of all voxels in a given ROI, in one single step. Besides, two strategies are proposed in this paper to deal with the nonnegativity of the model parameters. The efficiency of the proposed algorithm compared to the Non-Negative Least Square (NNLS)-based method, recently introduced to deal with the confounding blood vessel effect in the IVIM-MRI model, is confirmed using both realistic and real DW-MR images.
Asunto(s)
Algoritmos , Imagen de Difusión por Resonancia Magnética/métodos , Procesamiento de Imagen Asistido por Computador/métodos , Hígado/irrigación sanguínea , Humanos , Método de Montecarlo , Movimiento (Física)RESUMEN
This paper addresses the question of effective connectivity in the human cerebral cortex in the context of epilepsy. Among model based approaches to infer brain connectivity, spectral Dynamic Causal Modelling is a conventional technique for which we propose an alternative to estimate cross spectral density. The proposed strategy we investigated tackles the sub-estimation of the free energy using the well-known variational Expectation-Maximization algorithm highly sensitive to the initialization of the parameters vector by a permanent local adjustment of the initialization process. The performance of the proposed strategy in terms of effective connectivity identification is assessed using simulated data generated by a neuronal mass model (simulating unidirectional and bidirectional flows) and real epileptic intracerebral Electroencephalographic signals. Results show the efficiency of proposed approach compared to the conventional Dynamic Causal Modelling and the one wherein a deterministic annealing scheme is employed.