Publication

Ten seconds of my nights: Exploring methods to measure brightness, loudness and attendance and their associations with alcohol use from video clips

Publications associées (30)

Novel spatiotemporal processing tools for body-surface potential map signals for the prediction of catheter ablation outcome in persistent atrial fibrillation

Jean-Marc Vesin, Etienne Pruvot, Adrian Luca

Background: Signal processing tools are required to efficiently analyze data collected in body-surface-potential map (BSPM) recordings. A limited number of such tools exist for studying persistent atrial fibrillation (persAF). We propose two novel, spatiot ...

FRONTIERS MEDIA SA2022

Fading of brain network fingerprint in Parkinson's disease predicts motor clinical impairment

Enrico Amico, Antonella Romano

The clinical connectome fingerprint (CCF) was recently introduced as a way to assess brain dynamics. It is an approach able to recognize individuals, based on the brain network. It showed its applicability providing network features used to predict the cog ...

Wiley2023

Kernel Density Estimation of Electromyographic Signals and Ensemble Learning for Highly Accurate Classification of a Large Set of Hand/Wrist Motions

Parviz Ghaderi

The performance of myoelectric control highly depends on the features extracted from surface electromyographic (sEMG) signals. We propose three new sEMG features based on the kernel density estimation. The trimmed mean of density (TMD), the entropy of dens ...

FRONTIERS MEDIA SA2022

Improving the efficiency of identifying malignant pulmonary nodules before surgery via a combination of artificial intelligence CT image recognition and serum autoantibodies

Yi Ding, Zhen Gao, Jiancheng Yang

ObjectiveTo construct a new pulmonary nodule diagnostic model with high diagnostic efficiency, non-invasive and simple to measure. MethodsThis study included 424 patients with radioactive pulmonary nodules who underwent preoperative 7-autoantibody (7-AAB) ...

SPRINGER2022

Impaired cognitive flexibility and heightened urgency are associated with increased alcohol consumption in rodent models of excessive drinking

Emanuela De Falco

Alcohol use disorder (AUD) is characterized by impairments in decision-making that can exist as stable traits or transient states. Cognitive inflexibility reflects an inability to update information that guides decision-making and is thought to contribute ...

WILEY2021

Fast screening of covariates in population models empowered by machine learning

Jan Sickmann Hesthaven, Nadia Terranova

One of the objectives of Pharmacometry (PMX) population modeling is the identification of significant and clinically relevant relationships between parameters and covariates. Here, we demonstrate how this complex selection task could benefit from supervise ...

2021

Multi-ion-sensing emulator and multivariate calibration optimization by machine learning models

Giovanni De Micheli, Sandro Carrara, Mandresy Ivan Ny Hanitra, Francesca Criscuolo

One paramount challenge in multi-ion-sensing arises from ion interference that degrades the accuracy of sensor calibration. Machine learning models are here proposed to optimize such multivariate calibration. However, the acquisition of big experimental da ...

2021

Real-Time Multi-Ion-Monitoring Front-End With Interference Compensation by Multi-Output Support Vector Regressor

Giovanni De Micheli, Sandro Carrara, Mandresy Ivan Ny Hanitra, Francesca Criscuolo

Ion-sensors play a major role in physiology and healthcare monitoring since they are capable of continuously collecting biological data from body fluids. Nevertheless, ion interference from background electrolytes present in the sample is a paramount chall ...

2021

Non-invasive monitoring of key hemodynamical and cardiac parameters using physics-based modelling and artificial intelligence

Vasiliki Bikia

In a progressively aging population, it is of utmost importance to develop reliable, non-invasive, and cost-effective tools to estimate biomarkers that can be indicative of cardiovascular risk. Clinical parameters directly measured in the heart or the aort ...

EPFL2021

Impact of quantum-chemical metrics on the machine learning prediction of electron density

Anne-Clémence Corminboeuf, Alberto Fabrizio, Ksenia Briling

Machine learning (ML) algorithms have undergone an explosive development impacting every aspect of computational chemistry. To obtain reliable predictions, one needs to maintain a proper balance between the black-box nature of ML frameworks and the physics ...

AMER INST PHYSICS2021