Hypertension is a major risk factor for cardiovascular disease, yet cuffless blood pressure monitoring remains challenging because most existing methods rely on intermittent cuff-based measurements or multimodal physiological signals. This work proposes PPG-FusionNet, a dual-branch deep learning architecture for simult...
Eduardo Martínez-Duque, G. Daza-Santacoloma, D. Cárdenas-Peña· Computers· 0 citations
The Contextualized Transfer Entropy Network (CTE-Net) is proposed, an end-to-end deep-learning architecture that combines global content-based contextualization with nonlinear and directed EEG connectivity estimation and exhibited the lowest within-subject dispersion among the analyzed representation stages.
Alejandra Gomez-Rivera, J. D. Pastrana-Cortés, A. Álvarez-Meza et al.· Italian National Conference...· 0 citations
This work introduces Hidden Markov Model-Induced Stationary RKHS Distance Learning (HIS), a probabilistic framework that represents each subject by a Hidden Markov Model with Gaussian-mixture emissions trained directly from frontal EEG recordings.
Leonardo Lopez-Ortiz, Cristhian K. Valencia-Marin, J. Gil-González et al.· Italian National Conference...· 0 citations
Detecting localized morphological anomalies in three-dimensional point clouds is difficult because geometric deviations are entangled with rigid pose variation, residual registration error, sampling noise, and normal inter-subject variability. This challenge is particularly relevant in translational neuroimaging, where...
A. Jiménez-García, Jonnatan Arias-Garcia, H. García et al.· Machine Learning and Knowled...· 0 citations
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