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Tomas Teijeiro Campo

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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

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

David Atienza Alonso, Tomas Teijeiro Campo, Una Pale

Epilepsy is a chronic neurological disorder that affects a significant portion of the human population and imposes serious risks in the daily life. Despite advances in machine learning and IoT, small, non-stigmatizing wearable devices for continuous monito ...

New York2023

Approximate Zero-Crossing: A new interpretable, highly discriminative and low-complexity feature for EEG and iEEG seizure detection

David Atienza Alonso, Tomas Teijeiro Campo, Una Pale, Renato Zanetti

Objective: Long-term monitoring of people with epilepsy based on electroencephalography (EEG) and intracranial EEG (iEEG) has the potential to deliver key clinical information for personalised epilepsy treatment. More specifically, in outpatient settings, ...

2022

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

David Atienza Alonso, Elisabetta De Giovanni, Tomas Teijeiro Campo, Grégoire Millet

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 generally accurate. However, in conditions such as high-intensity exercise, sud ...

2022

Multi-Centroid Hyperdimensional Computing Approach for Epileptic Seizure Detection

David Atienza Alonso, Tomas Teijeiro Campo, Una Pale

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 solutions that allow continuous unobstructed monitoring and reliable detectio ...

2022

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

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

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

David Atienza Alonso, Giovanni Ansaloni, Tomas Teijeiro Campo, Renaud Marquis

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

Personalized seizure signature: An interpretable approach to false alarm reduction for long‐term epileptic seizure detection

David Atienza Alonso, Amir Aminifar, Tomas Teijeiro Campo, Dionisije Sopic

Objective Long-term automatic detection of focal seizures remains one of the major challenges in epilepsy due to the unacceptably high number of false alarms from state-of-the-art methods. Our aim was to investigate to what extent a new patient-specific ap ...

2022

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

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A Multimodal Dataset for Automatic Edge-AI Cough Detection

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

Zenodo2023

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

David Atienza Alonso, Tomas Teijeiro Campo, Una Pale

New York2023

Approximate Zero-Crossing: A new interpretable, highly discriminative and low-complexity feature for EEG and iEEG seizure detection

David Atienza Alonso, Tomas Teijeiro Campo, Una Pale, Renato Zanetti

2022

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

David Atienza Alonso, Elisabetta De Giovanni, Tomas Teijeiro Campo, Grégoire Millet

2022

Personalized seizure signature: An interpretable approach to false alarm reduction for long‐term epileptic seizure detection

David Atienza Alonso, Amir Aminifar, Tomas Teijeiro Campo, Dionisije Sopic

2022