Publications by authors named "A Humeau-Heurtier"
Entropy algorithms are widely applied in signal analysis to quantify the irregularity of data. In the realm of two-dimensional data, their two-dimensional forms play a crucial role in analyzing images. Previous works have demonstrated the effectiveness of one-dimensional increment entropy in detecting abrupt changes in signals.
View Article and Find Full Text PDFBackground: The onset of the symptoms of subcortical NDs is due to a unique part of the brain which strengthens the idea of reciprocal influence of physical activity and cognitive training in improving clinical symptoms. Consequently, protocols combining the two stimulations are becoming increasingly popular in NDs. Our threefold aim was to (A) describe the different combinations of physical and cognitive training used to alleviate the motor and cognitive symptoms of patients with subcortical neurodegenerative disorders, (B) compare the effects of these different combinations (sequential, dual tasking, synergical) on symptoms, and (C) recommend approaches for further studies.
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- Epilepsy is a neurological disorder that causes unpredictable seizures, increasing health risks for patients.
- The study introduces a novel method called Deep Embedded Gaussian Mixture (DEGM) for detecting seizures through EEG data, combining deep learning techniques with clustering algorithms.
- Results show DEGM significantly improves seizure detection performance on large datasets, offering better outcomes for patients and advancing research in this area.
Background And Objective: We present NLDyn, an open-source MATLAB toolbox tailored for in-depth analysis of nonlinear dynamics in biomedical signals. Our objective is to offer a user-friendly yet comprehensive platform for researchers to explore the intricacies of time series data.
Methods: NLDyn integrates approximately 80 distinct methods, encompassing both univariate and multivariate nonlinear dynamics, setting it apart from existing solutions.
Background: Nonlinear dynamical measures, such as fractal dimension (FD), entropy, and Lempel-Ziv complexity (LZC), have been extensively investigated individually for detecting information content in magnetoencephalograms (MEGs) from patients with Alzheimer's disease (AD).
Objective: To compare systematically the performance of twenty conventional and recently introduced nonlinear dynamical measures in studying AD versus mild cognitive impairment (MCI) and healthy control (HC) subjects using MEG.
Methods: We compared twenty nonlinear measures to distinguish MEG recordings from 36 AD (mean age = 74.