Tomas Teijeiro Campo

Cette personne a quitté l’EPFL

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Publications associées (29)

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

David Atienza Alonso, Tomas Teijeiro Campo, Una Pale

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 due to challenges of real-time detection and wearable devices design. Hyperdim ...

2022

A Multimodal Dataset for Automatic Edge-AI Cough Detection

David Atienza Alonso, Tomas Teijeiro Campo, Lara Orlandic, Jérôme Paul Rémy Thevenot

Counting the number of times a patient coughs per day is an essential biomarker in determining treatment efficacy for novel antitussive therapies and personalizing patient care. There is a need for wearable devices that employ multimodal sensors to perform ...

Zenodo2023

ExG Signal Feature Selection Using Hyperdimensional Computing Encoding

David Atienza Alonso, Tomas Teijeiro Campo, Una Pale

Wearable IoT devices and novel continuous monitoring algorithms are essential components of the healthcare transition from reactive interventions focused on symptom treatment to more proactive prevention, from one-size-fits-all to personalized medicine, an ...

IEEE2022

Knowledge, Machine Learning and Atrial Fibrillation: More Ingredients for a Tastier Cocktail

Tomas Teijeiro Campo

Fifty years after the publication of the first algorithms for the automatic detection of Atrial Fibrillation (AF), this cardiac condition is still the most studied from the computer science and engineering perspectives. Machine learning techniques are wide ...

2020

HDTorch: Accelerating Hyperdimensional Computing with GP-GPUs for Design Space Exploration

David Atienza Alonso, Tomas Teijeiro Campo, William Andrew Simon, Una Pale

The HyperDimensional Computing (HDC) Machine Learning (ML) paradigm is highly interesting for applications involving continuous, semi-supervised learning for long-term monitoring. However, its accuracy is not yet on par with other ML approaches, necessitat ...

Association for Computing MachineryNew YorkNYUnited States2022

Self-Aware Anomaly-Detection for Epilepsy Monitoring on Low-Power Wearable Electrocardiographic Devices

David Atienza Alonso, Amir Aminifar, Tomas Teijeiro Campo, Farnaz Forooghifar

Low-power wearable technologies offer a promising solution to pervasive epilepsy monitoring by removing the constraints concerning time and location, on one hand, and fulfilling long-term tracking, on the other hand. In the case of epileptic seizures, as t ...

2021

M2SKD: Multi-to-Single Knowledge Distillation of Real-Time Epileptic Seizure Detection for Low-Power Wearable Systems

David Atienza Alonso, Amir Aminifar, Tomas Teijeiro Campo, Alireza Amirshahi, Saleh Baghersalimi, Farnaz Forooghifar

Integrating low-power wearable systems into routine health monitoring is an ongoing challenge. Recent advances in the computation capabilities of wearables make it possible to target complex scenarios by exploiting multiple biosignals and using high-perfor ...

2024

The COUGHVID crowdsourcing dataset, a corpus for the study of large-scale cough analysis algorithms

David Atienza Alonso, Tomas Teijeiro Campo, Lara Orlandic

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 (ML) to provide widespread COVID-19 screening. The COUGHVID dataset provide ...

2021

INCLASS: Incremental Classification Strategy for Self-Aware Epileptic Seizure Detection

David Atienza Alonso, Giovanni Ansaloni, Tomas Teijeiro Campo

Wearable Health Companions allow the unobtrusive monitoring of patients affected by chronic conditions. In particular, by acquiring and interpreting bio-signals, they enable the detection of acute episodes in cardiac and neurological ailments. Nevertheless ...

IEEE2022