T
+34 946 567 842
F
+34 946 567 842
E
tteijeiro@bcamath.org
Information of interest
I received my PhD from the Centro Singular de Investigación en Tecnoloxías Intelixentes (CITIUS), University of Santiago de Compostela, Spain, in 2017. During my doctoral studies I developed a novel knowledge-based framework for time series interpretation based on abductive reasoning that has been successfully applied to automatic ECG interpretation and classification. In the 2018-2022 period I worked as a research associate with the Embedded Systems Laboratory (ESL) at the École polytechnique fédérale de Lausanne (EPFL), and during 2022 I was with the Mathmode group at the University of the Basque Country (UPV/EHU). Since January 2023 I am with the BCAM - Basque Center for Applied Mathematics with a Ramón y Cajal Research Fellowship. My research interests include knowledge representation, non-monotonic temporal reasoning, efficient machine learning, event-based sensing, and their application to biosignal abstraction and interpretation in energy-efficient setups.
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Cough-E: A multimodal, privacy-preserving cough detection algorithm for the edge
Albini, S.; Orlandic, L.; Dan, J.; Thevenot, J.P.R.; Teijeiro, T.
; Constantinescu, D.A.; Atienza, D. (2025-01-01)
Continuous cough monitors can greatly benefit doctors in home monitoring and treatment of respiratory diseases. Although many works propose algorithms to automate this task, they suffer of poor data privacy and short-term ...
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Acoustical features as knee health biomarkers: A critical analysis
Kechris, C.; Thevenot, J.P.R.; Teijeiro, T.; Stadelmann, V.A.; Maffiuletti, N.A.; Atienza, D. (2024-12-01)
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 field is currently led by machine learning ...
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M2SKD: Multi-to-Single Knowledge Distillation of Real-Time Epileptic Seizure Detection for Low-Power Wearable Systems
Baghersalimi, S.; Amirshahi, A.; Forooghifar, F.; Teijeiro, T.; Aminifar, A.; Atienza, D. (2024-10-17)
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 ...
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How to Count Coughs: An Event-Based Framework for Evaluating Automatic Cough Detection Algorithm Performance
Orlandic, L.; Dan, J.; Thevenot, J.P.R.; Teijeiro, T.; Sauty, A.; Atienza, D. (2024-01-01)
Chronic cough disorders are widespread and challenging to assess because they rely on subjective patient questionnaires about cough frequency. Wearable devices running Machine Learning (ML) algorithms are promising for ...
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A semi-supervised algorithm for improving the consistency of crowdsourced datasets: The COVID-19 case study on respiratory disorder classification
Orlandic, L.; Teijeiro, T.; Atienza, D. (2023-11-01)
Background and Objective: 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 contagious diseases, ...
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Machine learning discovery of optimal quadrature rules for isogeometric analysis
Teijeiro, T.; Taylor, J.M.; Hashemian, A.; Pardo, D. (2023-11-01)
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 consider 1D spline spaces of arbitrary ...
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Event-based sampled ECG morphology reconstruction through self-similarity
Zanoli, S.; Ansaloni, G.; Teijeiro, T.; Atienza, D. (2023-10-01)
Background and Objective: Event-based analog-to-digital converters allow for sparse bio-signal acquisition, enabling local sub-Nyquist sampling frequency. However, aggressive event selection can cause the loss of important ...
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Layer-Wise Learning Framework for Efficient DNN Deployment in Biomedical Wearable Systems
Baghersalimi, S.; Amirshahi, A.; Teijeiro, T.; Aminifar, A.; Atienza, D. (2023-10)
The development of low-power wearable systems requires specialized techniques to accommodate their unique requirements and constraints. While significant advancements have been made in the inference phase of artificial ...
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Neural network architecture optimization using automated machine learning for borehole resistivity measurements
Shahriari, M.; Pardo, D.; Kargaran, S.; Teijeiro, T. (2023-09)
Deep neural networks (DNNs) offer a real-time solution for the inversion of borehole resistivity measurements to approximate forward and inverse operators. Using extremely large DNNs to approximate the operators is possible, ...
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A Multimodal Dataset for Automatic Edge-AI Cough Detection
Orlandic, L.; Thevenot, J.P.R.; Teijeiro, T.; Atienza, D. (2023-07)
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. Automatic cough counting tools must provide ...
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Importance of methodological choices in data manipulation for validating epileptic seizure detection models
Pale, U.; Teijeiro, T.; Atienza, D. (2023-07)
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 ...
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An Error-Based Approximation Sensing Circuit for Event-Triggered Low-Power Wearable Sensors
Zanoli, S.; Ponzina, F.; Teijeiro, T.; Levisse, A.; Atienza, D. (2023-04-24)
Event-based sensors have the potential to optimize energy consumption at every stage in the signal processing pipeline, including data acquisition, transmission, processing, and storage. However, almost all state-of-the-art ...
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Decentralized Federated Learning for Epileptic Seizures Detection in Low-Power Wearable Systems
Baghersalimi, S.; Teijeiro, T.; Aminifar, A.; Atienza, D. (2023-01-01)
In healthcare, data privacy of patients regulations prohibits data from being moved outside the hospital, preventing international medical datasets from being centralized for AI training. Federated learning (FL) is a data ...
