Search Results for author: Tomas Teijeiro

Found 16 papers, 6 papers with code

How to Count Coughs: An Event-Based Framework for Evaluating Automatic Cough Detection Algorithm Performance

no code implementations3 Jun 2024 Lara Orlandic, Jonathan Dan, Jerome Thevenot, Tomas Teijeiro, Alain Sauty, David Atienza

We provide examples and best practice guidelines in event-based cough counting as a necessary first step to assess algorithm performance with clinical relevance.


Acoustical Features as Knee Health Biomarkers: A Critical Analysis

no code implementations23 May 2024 Christodoulos Kechris, Jerome Thevenot, Tomas Teijeiro, Vincent A. Stadelmann, Nicola A. Maffiuletti, David Atienza

Acoustical knee health assessment has long promised an alternative to clinically available medical imaging tools, but this modality has yet to be adopted in medical practice.


Machine Learning Discovery of Optimal Quadrature Rules for Isogeometric Analysis

1 code implementation4 Apr 2023 Tomas Teijeiro, Jamie M. Taylor, Ali Hashemian, David Pardo

The quadrature rule search is posed as an optimization problem and solved by a machine learning strategy based on gradient-descent.

Combining General and Personalized Models for Epilepsy Detection with Hyperdimensional Computing

no code implementations26 Mar 2023 Una Pale, Tomas Teijeiro, David Atienza

In this work, we demonstrate a few additional aspects in which HD computing, and the way its models are built and stored, can be used for further understanding, comparing, and creating more advanced machine learning models for epilepsy detection.

Transfer Learning

Importance of methodological choices in data manipulation for validating epileptic seizure detection models

no code implementations21 Feb 2023 Una Pale, Tomas Teijeiro, David Atienza

Epilepsy is a chronic neurological disorder that affects a significant portion of the human population and imposes serious risks in the daily life of patients.

Seizure Detection

A Semi-Supervised Algorithm for Improving the Consistency of Crowdsourced Datasets: The COVID-19 Case Study on Respiratory Disorder Classification

no code implementations9 Sep 2022 Lara Orlandic, Tomas Teijeiro, David Atienza

In this work, we use a semi-supervised learning (SSL) approach to improve the labeling consistency of the COUGHVID dataset and the robustness of COVID-19 versus healthy cough sound classification.

Sound Classification

Event-based sampled ECG morphology reconstruction through self-similarity

no code implementations5 Jul 2022 Silvio Zanoli, Tomas Teijeiro, Giovanni Ansaloni, David Atienza

In this work, we leverage the self-similarity of the electrocardiogram (ECG) signal to recover missing features in event-based sampled ECG signals, dynamically selecting patient-representative templates together with a novel dynamic time warping algorithm to infer the morphology of event-based sampled heartbeats.

Dynamic Time Warping

Hyperdimensional computing encoding for feature selection on the use case of epileptic seizure detection

no code implementations16 May 2022 Una Pale, Tomas Teijeiro, David Atienza

As a result, we believe it can support the ML community to further foster the research in multiple directions related to feature and channel selection, as well as model interpretability.

EEG feature selection +1

Exploration of Hyperdimensional Computing Strategies for Enhanced Learning on Epileptic Seizure Detection

1 code implementation24 Jan 2022 Una Pale, Tomas Teijeiro, David Atienza

Yet, most of them have not been tested on the challenging task of epileptic seizure detection, and it stays unclear whether they can increase the HD computing performance to the level of the current state-of-the-art algorithms, such as random forests.

EEG Seizure Detection

Adaptive R-Peak Detection on Wearable ECG Sensors for High-Intensity Exercise

1 code implementation8 Dec 2021 Elisabetta De Giovanni, Tomas Teijeiro, Grégoire P. Millet, David Atienza

Additionally, the online adaptive process achieves an F1 score of 99% across five different exercise intensities, with a total energy consumption of 1. 55+-0. 54~mJ.

Multi-Centroid Hyperdimensional Computing Approach for Epileptic Seizure Detection

1 code implementation16 Nov 2021 Una Pale, Tomas Teijeiro, David Atienza

At the same time, the total number of sub-classes is not significantly increased compared to the balanced dataset.

EEG Seizure Detection

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

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

Construe: a software solution for the explanation-based interpretation of time series

1 code implementation17 Mar 2020 Tomas Teijeiro, Paulo Felix

This paper presents a software implementation of a general framework for time series interpretation based on abductive reasoning.

Atrial Fibrillation Detection Heartbeat Classification +2

Cannot find the paper you are looking for? You can Submit a new open access paper.