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Diego Candia-Rivera
Affiliation
Sorbonne Université, Paris Brain Institute (ICM), CNRS UMR7225, INRIA Paris, INSERM U1127, AP-HP Hôpital Pitié Salpêtrière, Paris, France
Topic
Cardiac Activity,Cardiac Index,Heart Rate Variability,Interbeat Intervals,Long-term Fluctuations,Sympathetic System,Affective Computing,Approaches In The Literature,Autonomic Activity,Autonomic Dysfunction,Cardiac Sympathetic Activity,Changes In Duration,Classification Task,Convolutional Layers,Convolutional Neural Network,Dynamics Of Brain Activity,EEG Features,EEG Sensors,EEG Signals,Emotion Categories,Emotion Recognition,Emotion Recognition Task,Episodes In Patients,F1 Score,Frequency Band,Frequency Domain,Gradient Boosting,Image-based Approach,Image-based Methods,Input Representation,Machine Learning,Neural Network,Obstructive Sleep Apnea,Onset Of Effects,Parasympathetic Activity,Parasympathetic Index,Physiological Changes In Response,Polysomnography,Power Spectral Density,Pre-trained Deep Neural Networks,Pre-trained Network,Pre-trained Neural Network,Quadratic Discriminant Analysis,Random Forest,Recognition Performance,Sleep Apnea,Sliding Time Window,Spatial Information,Support Vector Machine,Functional Interplay,
Biography
Diego Candia-Rivera was born in Los Ángeles, Chile, in 1992. He received the dual B.S. degree in biotechnology and electrical engineering and the Electrical Engineering degree with a specialization in computational intelligence from the University of Chile, in 2014 and 2016, respectively, and the Ph.D. degree in information engineering from the University of Pisa, Italy, in 2022. From 2017 to 2019, he was a Research Engineer with École Normale Supérieure de Paris and the Paris Brain Institute. Since 2019, he has been with the Research Center E. Piaggio, University of Pisa. His research interests include the study of brain–heart interactions through generative models of brain dynamics and machine learning to understand their role in cognition and consciousness. He uncovered that brain–heart interactions reflect the presence and levels of consciousness in severely brain-damaged patients—this work was awarded by the Society of Neuroscience, USA, in 2022.