34 citations · 115 across the 11 of their papers we have counts for
13 papers
Generative Pre-Trained Transformer for Cardiac Abnormality Detection
Pierre Louis Gaudilliere, Halla Sigurthorsdottir, Clémentine Aguet +3
ECG heartbeat classification plays a vital role in diagnosis of cardiac arrhythmia. The goal of the Physionet/CinC 2021 challenge was to accurately classify clinical diagnosis base…
KEVLAR-TZ: A Secure Cache for ARM TrustZone
Oscar Benedito, Ricard Delgado-Gonzalo, Valerio Schiavoni
Edge devices are increasingly in charge of storing privacy-sensitive data, in particular implantables, wearables, and nearables can potentially collect and process high-resolution…
Cardiac Arrhythmia Detection from ECG with Convolutional Recurrent Neural Networks
Jérôme Van Zaen, Ricard Delgado-Gonzalo, Damien Ferrario Mathieu Lemay
Except for a few specific types, cardiac arrhythmias are not immediately life-threatening. However, if not treated appropriately, they can cause serious complications. In particula…
ECG Classification with a Convolutional Recurrent Neural Network
Halla Sigurthorsdottir, Jérôme Van Zaen, Ricard Delgado-Gonzalo +1
We developed a convolutional recurrent neural network to classify 12-lead ECG signals for the challenge of PhysioNet/ Computing in Cardiology 2020 as team Pink Irish Hat. The model…
MQT-TZ: Hardening IoT Brokers Using ARM TrustZone
Carlos Segarra, Ricard Delgado-Gonzalo, Valerio Schiavoni
The publish-subscribe paradigm is an efficient communication scheme with strong decoupling between the nodes, that is especially fit for large-scale deployments. It adapts natively…
MQT-TZ: Secure MQTT Broker for Biomedical Signal Processing on the Edge
Carlos Segarra, Ricard Delgado-Gonzalo, Valerio Schiavoni
Physical health records belong to healthcare providers, but the information contained within belongs to each patient. In an increasing manner, more health-related data is being acq…
Convolutional-Recurrent Neural Networks on Low-Power Wearable Platforms for Cardiac Arrhythmia Detection
Antonino Faraone, Ricard Delgado-Gonzalo
Low-power sensing technologies, such as wearables, have emerged in the healthcare domain since they enable continuous and non-invasive monitoring of physiological signals. In order…
Classification of Cardiac Arrhythmias from Single Lead ECG with a Convolutional Recurrent Neural Network
Jérôme Van Zaen, Olivier Chételat, Mathieu Lemay +2
While most heart arrhythmias are not immediately harmful, they can lead to severe complications. In particular, atrial fibrillation, the most common arrhythmia, is characterized by…
Embedded Deep Learning for Sleep Staging
Engin Türetken, Jérôme Van Zaen, Ricard Delgado-Gonzalo
The rapidly-advancing technology of deep learning (DL) into the world of the Internet of Things (IoT) has not fully entered in the fields of m-Health yet. Among the main reasons ar…
Using Trusted Execution Environments for Secure Stream Processing of Medical Data
Carlos Segarra, Ricard Delgado-Gonzalo, Mathieu Lemay +3
Processing sensitive data, such as those produced by body sensors, on third-party untrusted clouds is particularly challenging without compromising the privacy of the users generat…
Detection of Beat-to-Beat Intervals from Wrist Photoplethysmography in Patients with Sinus Rhythm and Atrial Fibrillation after Surgery
Adrian Tarniceriu, Jarkko Harju, Antti Vehkaoja +5
Wrist photoplethysmography (PPG) allows unobtrusive monitoring of the heart rate (HR). PPG is affected by the capillary blood perfusion and the pumping function of the heart, which…
Learning a Physical Activity Classifier for a Low-power Embedded Wrist-located Device
Ricard Delgado-Gonzalo, Philippe Renevey, Adrian Tarniceriu +2
This article presents and evaluates a novel algorithm for learning a physical activity classifier for a low-power embedded wrist-located device. The overall system is designed for…
Respiratory and cardiac monitoring at night using a wrist wearable optical system
Philippe Renevey, Ricard Delgado-Gonzalo, Alia Lemkaddem +6
Sleep monitoring provides valuable insights into the general health of an individual and helps in the diagnostic of sleep-derived illnesses. Polysomnography, is considered the gold…