Tomas Teijeiro Campo

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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 ...

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 accurate information, while runnin ...

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 monitoring. Edge-AI is a promising ...

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 quantifying daily coughs, providing c ...

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 ...

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, many research teams have turned ...

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 bio-markers, not recoverable with ...

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 ...

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 intelligence, the training phase rema ...

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 ...

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 systems are still built upon th ...

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 ...

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 privacy-focused method that trains ...

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 ...

Epilepsy is a highly prevalent chronic neurological disorder with great negative impact on patients' daily lives. Despite this there is still no adequate technological support to enable epilepsy detection and continuous outpatient monitoring in everyday li ...

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 ...

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 ...

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 ...

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 ...

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 ...

Tomas Teijeiro Campo

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Personalized Real-Time Federated Learning for Epileptic Seizure Detection

A Multimodal Dataset for Automatic Edge-AI Cough Detection

Cough-E: A multimodal, privacy-preserving cough detection algorithm for the edge

How to Count Coughs: An Event-Based Framework for Evaluating Automatic Cough Detection Algorithm Performance

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

A semi-supervised algorithm for improving the consistency of crowdsourced datasets: The COVID-19 case study on respiratory disorder classification

Event-based sampled ECG morphology reconstruction through self-similarity

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

Layer-Wise Learning Framework for Efficient DNN Deployment in Biomedical Wearable Systems

Interpreting deep learning models for epileptic seizure detection on EEG signals

An Error-Based Approximation Sensing Circuit for Event-Triggered Low-Power Wearable Sensors

Multi-Centroid Hyperdimensional Computing Approach for Epileptic Seizure Detection

Decentralized Federated Learning for Epileptic Seizures Detection in Low-Power Wearable Systems

Systematic Assessment of Hyperdimensional Computing for Epileptic Seizure Detection

Combining general and personal models for epilepsy detection with hyperdimensional computing

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

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

A Multimodal Dataset for Automatic Edge-AI Cough Detection

ExG Signal Feature Selection Using Hyperdimensional Computing Encoding

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