Search Results for author: David Atienza

Found 8 papers, 2 papers with code

An Error-Based Approximation Sensing Circuit for Event-Triggered, Low Power Wearable Sensors

no code implementations25 Jun 2021 Silvio Zanoli, Flavio Ponzina, Tomás Teijeiro, Alexandre Levisse, David Atienza

At the same time, multi-channel signals (like the six-dimensional accelerometer signal) can still benefit from the designed circuit, achieving a reduction factor up to 80% with minor performance degradation.

ReBeatICG: Real-time Low-Complexity Beat-to-beat Impedance Cardiogram Delineation Algorithm

no code implementations4 May 2021 Una Pale, Nathan Müller, Adriana Arza, David Atienza

It achieves a detection Gmean accuracy of 94. 9%, 98. 6%, 90. 3%, and 84. 3% for the B, C, X, and O points, respectively.

Wearable and Continuous Prediction of Passage of Time Perception for Monitoring Mental Health

no code implementations3 May 2021 Lara Orlandic, Adriana Arza Valdes, David Atienza

Next, we classify each individual's POTP regardless of the task at hand, achieving an F-1 score of 77. 1% when distinguishing time passing faster rather than slower than usual.

Systematic Assessment of Hyperdimensional Computing for Epileptic Seizure Detection

1 code implementation3 May 2021 Una Pale, Tomas Teijeiro, David Atienza

Furthermore, we evaluate a post-processing strategy to adjust the predictions to the dynamics of epileptic seizures, showing that performance is significantly improved in all the approaches and also that after post-processing, differences in performance are much smaller between approaches.

Seizure Detection

The RECIPE Approach to Challenges in Deeply Heterogeneous High Performance Systems

no code implementations4 Mar 2021 Giovanni Agosta, William Fornaciari, David Atienza, Ramon Canal, Alessandro Cilardo, José Flich Cardo, Carles Hernandez Luz, Michal Kulczewski, Giuseppe Massari, Rafael Tornero Gavilá, Marina Zapater

RECIPE (REliable power and time-ConstraInts-aware Predictive management of heterogeneous Exascale systems) is a recently started project funded within the H2020 FETHPC programme, which is expressly targeted at exploring new High-Performance Computing (HPC) technologies.

Weather Forecasting Distributed, Parallel, and Cluster Computing

Interpreting Deep Learning Models for Epileptic Seizure Detection on EEG signals

no code implementations22 Dec 2020 Valentin Gabeff, Tomas Teijeiro, Marina Zapater, Leila Cammoun, Sylvain Rheims, Philippe Ryvlin, David Atienza

Specifically, we focused the discussion on three main aspects: 1) how to aggregate the classification results on signal segments provided by the DL model into a larger time scale, at the seizure-level; 2) what are the relevant frequency patterns learned in the first convolutional layer of different models, and their relation with the delta, theta, alpha, beta and gamma frequency bands on which the visual interpretation of EEG is based; and 3) the identification of the signal waveforms with larger contribution towards the ictal class, according to the activation differences highlighted using the DeepLIFT method.

EEG Seizure Detection

Synthetic Epileptic Brain Activities Using Generative Adversarial Networks

1 code implementation22 Jul 2019 Damian Pascual, Amir Aminifar, David Atienza, Philippe Ryvlin, Roger Wattenhofer

In this work, we generate synthetic seizure-like brain electrical activities, i. e., EEG signals, that can be used to train seizure detection algorithms, alleviating the need for recorded data.

EEG Seizure Detection

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