Tomas Teijeiro Campo

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Related publications (29)

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

David Atienza Alonso, Lara Orlandic, Tomas Teijeiro Campo

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

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

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

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

The COUGHVID crowdsourcing dataset: A corpus for the study of large-scale cough analysis algorithms

David Atienza Alonso, Lara Orlandic, Tomas Teijeiro Campo

Overview 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. Th ...

Zenodo2021

Personalized Real-Time Federated Learning for Epileptic Seizure Detection

David Atienza Alonso, Amir Aminifar, Saleh Baghersalimi, Tomas Teijeiro Campo

Epilepsy is one of the most prevalent paroxystic neurological disorders. It is characterized by the occurrence of spontaneous seizures. About 1 out of 3 patients have drug-resistant epilepsy, thus their seizures cannot be controlled by medication. Automati ...

2021

Systematic Assessment of Hyperdimensional Computing for Epileptic Seizure Detection

David Atienza Alonso, Una Pale, Tomas Teijeiro Campo

Hyperdimensional computing is a promising novel paradigm for low-power embedded machine learning. It has been applied on different biomedical applications, and particularly on epileptic seizure detection. Unfortunately, due to differences in data preparati ...

2021

Interpreting deep learning models for epileptic seizure detection on EEG signals

David Atienza Alonso, Leila Cammoun, Valentin Alexandre Guy Gabeff, Marina Zapater Sancho, Tomas Teijeiro Campo

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 and poorly trusted by clinicians due to insufficient interpretability of neur ...

2021

An Event-Based System for Low-Power ECG QRS Complex Detection

David Atienza Alonso, Silvio Zanoli, Tomas Teijeiro Campo, Fabio Montagna

One of the greatest challenges in the design of modern wearable devices is energy efficiency. While data processing and communication have received a lot of attention from the industry and academia, leading to highly efficient microcontrollers and transmis ...

IEEE2020

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

Event-Triggered Sensing for High-Quality and Low-Power Cardiovascular Monitoring Systems

David Atienza Alonso, Amir Aminifar, Grégoire Casimir Joseph Surrel, Tomas Teijeiro Campo

In the context of wearable medical systems, resources are scarce while performance requirements are high. Traditional sampling strategies create large amounts of data, which hinders the device's battery lifetime. However, energy savings are possible when r ...

2019

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