324 citations · 625 across the 27 of their papers we have counts for
27 papers
Why ML-based cough models do not generalize: a systematic cross-dataset evaluation for tuberculosis screening
Wensi Zhang, Tomas Teijeiro, Jérôme Thevenot +1
Cough acoustics are promising for non-invasive tuberculosis (TB) screening, yet whether machine learning (ML) models capture disease-related acoustics or artifacts of data collecti…
Efficient Numerical Integration for Finite Element Trunk Spaces in 2D and 3D using Machine Learning: A new Optimisation Paradigm to Construct Application-Specific Quadrature Rules
Tomas Teijeiro, Pouria Behnoudfar, Jamie M. Taylor +2
Finite element methods usually construct basis functions and quadrature rules for multidimensional domains via tensor products of one-dimensional counterparts. While straightforwar…
Mathematical model of parameters relevance in adaptive level-crossing sampling for electrocardiogram signals
Silvio Zanoli, Giovanni Ansaloni, Tomás Teijeiro +1
Digital acquisition of bio-signals has been mostly dominated by uniform time sampling following the Nyquist theorem. However, in recent years, new approaches have emerged, focused…
Cough-E: A multimodal, privacy-preserving cough detection algorithm for the edge
Stefano Albini, Lara Orlandic, Jonathan Dan +4
Continuous cough monitors can greatly aid doctors in home monitoring and treatment of respiratory diseases. Although many algorithms have been proposed, they still face limitations…
How to Count Coughs: An Event-Based Framework for Evaluating Automatic Cough Detection Algorithm Performance
Lara Orlandic, Jonathan Dan, Jerome Thevenot +3
Chronic cough disorders are widespread and challenging to assess because they rely on subjective patient questionnaires about cough frequency. Wearable devices running Machine Lear…
Acoustical Features as Knee Health Biomarkers: A Critical Analysis
Christodoulos Kechris, Jerome Thevenot, Tomas Teijeiro +3
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. The f…
Machine Learning Discovery of Optimal Quadrature Rules for Isogeometric Analysis
Tomas Teijeiro, Jamie M. Taylor, Ali Hashemian +1
We propose the use of machine learning techniques to find optimal quadrature rules for the construction of stiffness and mass matrices in isogeometric analysis (IGA). We initially…
Combining General and Personalized Models for Epilepsy Detection with Hyperdimensional Computing
Una Pale, Tomas Teijeiro, David Atienza
Epilepsy is a chronic neurological disorder with a significant prevalence. However, there is still no adequate technological support to enable epilepsy detection and continuous out…
Importance of methodological choices in data manipulation for validating epileptic seizure detection models
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. Despite advances in…
A Semi-Supervised Algorithm for Improving the Consistency of Crowdsourced Datasets: The COVID-19 Case Study on Respiratory Disorder Classification
Lara Orlandic, Tomas Teijeiro, David Atienza
Cough audio signal classification is a potentially useful tool in screening for respiratory disorders, such as COVID-19. Since it is dangerous to collect data from patients with su…
Many-to-One Knowledge Distillation of Real-Time Epileptic Seizure Detection for Low-Power Wearable Internet of Things Systems
Saleh Baghersalimi, Alireza Amirshahi, Farnaz Forooghifar +3
Integrating low-power wearable Internet of Things (IoT) systems into routine health monitoring is an ongoing challenge. Recent advances in the computation capabilities of wearables…
Automated machine learning for borehole resistivity measurements
M. Shahriari, D. Pardo, S. Kargaran +1
Deep neural networks (DNNs) offer a real-time solution for the inversion of borehole resistivity measurements to approximate forward and inverse operators. It is possible to use ex…
Event-based sampled ECG morphology reconstruction through self-similarity
Silvio Zanoli, Tomas Teijeiro, Giovanni Ansaloni +1
Background and Objective: Event-based analog-to-digital converters allow for sparse bio-signal acquisition, enabling local sub-Nyquist sampling frequency. However, aggressive event…
HDTorch: Accelerating Hyperdimensional Computing with GP-GPUs for Design Space Exploration
William Andrew Simon, Una Pale, Tomas Teijeiro +1
HyperDimensional Computing (HDC) as a machine learning paradigm is highly interesting for applications involving continuous, semi-supervised learning for long-term monitoring. Howe…
Hyperdimensional computing encoding for feature selection on the use case of epileptic seizure detection
Una Pale, Tomas Teijeiro, David Atienza
The healthcare landscape is moving from the reactive interventions focused on symptoms treatment to a more proactive prevention, from one-size-fits-all to personalized medicine, an…
Exploration of Hyperdimensional Computing Strategies for Enhanced Learning on Epileptic Seizure Detection
Una Pale, Tomas Teijeiro, David Atienza
Wearable and unobtrusive monitoring and prediction of epileptic seizures has the potential to significantly increase the life quality of patients, but is still an unreached goal du…
Adaptive R-Peak Detection on Wearable ECG Sensors for High-Intensity Exercise
Elisabetta De Giovanni, Tomas Teijeiro, Grégoire P. Millet +1
Objective: Continuous monitoring of biosignals via wearable sensors has quickly expanded in the medical and wellness fields. At rest, automatic detection of vital parameters is gen…
Multi-Centroid Hyperdimensional Computing Approach for Epileptic Seizure Detection
Una Pale, Tomas Teijeiro, David Atienza
Long-term monitoring of patients with epilepsy presents a challenging problem from the engineering perspective of real-time detection and wearable devices design. It requires new s…
An Error-Based Approximation Sensing Circuit for Event-Triggered, Low Power Wearable Sensors
Silvio Zanoli, Flavio Ponzina, Tomás Teijeiro +2
Event-based sensors have the potential to optimize energy consumption at every stage in the signal processing pipeline, including data acquisition, transmission, processing and sto…
Systematic Assessment of Hyperdimensional Computing for Epileptic Seizure Detection
Una Pale, Tomas Teijeiro, David Atienza
Hyperdimensional computing is a promising novel paradigm for low-power embedded machine learning. It has been applied on different biomedical applications, and particularly on epil…
Interpreting Deep Learning Models for Epileptic Seizure Detection on EEG signals
Valentin Gabeff, Tomas Teijeiro, Marina Zapater +4
While Deep Learning (DL) is often considered the state-of-the art for Artificial Intelligence-based medical decision support, it remains sparsely implemented in clinical practice a…
The COUGHVID crowdsourcing dataset: A corpus for the study of large-scale cough analysis algorithms
Lara Orlandic, Tomas Teijeiro, David Atienza
Cough audio signal classification has been successfully used to diagnose a variety of respiratory conditions, and there has been significant interest in leveraging Machine Learning…
Construe: a software solution for the explanation-based interpretation of time series
Tomas Teijeiro, Paulo Felix
This paper presents a software implementation of a general framework for time series interpretation based on abductive reasoning. The software provides a data model and a set of al…
Abductive reasoning as the basis to reproduce expert criteria in ECG Atrial Fibrillation identification
Tomás Teijeiro, Constantino A. García, Daniel Castro +1
Objective: This work aims at providing a new method for the automatic detection of atrial fibrillation, other arrhythmia and noise on short single lead ECG signals, emphasizing the…