Grégoire Surrel a reçu le grade de Master en Génie Électrique. Pendant ses études, il a travaillé en tant que développeur Android de manière à intégrer et la réception et le traitement de données pour le compte du Laboratoire des Systèmes Intégrés (LSI) à l'EPFL.
Actuellement, il travaille en tant que doctorant au Laboratoire des Systèmes Embarqués (ESL) à l'EPFL avec le Prof. David Atienza. Ses recherches incluent la conception et l'optimisation d'un système hétérogènes à multiples curs pour traiter simultanément différents bio-signaux en temps réel.
Entrées triées anti-chronologiquementLes donées en-ligne ne sont pas accessibles
Online Obstructive Sleep Apnea Detection on Medical Wearable Sensors
IEEE Transactions on Biomedical Circuits and Systems. 2018-08.
DOI : 10.1109/TBCAS.2018.2824659.
Obstructive Sleep Apnea (OSA) is one of the main under-diagnosed sleep disorder. It is an aggravating factor for several serious cardiovascular diseases, including stroke. There is, however, a lack of medical devices for long-term ambulatory monitoring of OSA since current systems are rather bulky, expensive, intrusive, and cannot be used for long-term monitoring in ambulatory settings. In this paper, we propose a wearable, accurate, and energy efficient system for monitoring obstructive sleep apnea on a long-term basis. As an embedded system for Internet of Things (IoT), it reduces the gap between home health-care and professional supervision. Our approach is based on monitoring the patient using a single-channel electrocardiogram (ECG) signal. We develop an efficient time-domain analysis to meet the stringent resources constraints of embedded systems to compute the sleep apnea score. Our system, for a publicly available database (PhysioNet Apnea-ECG), has a classification accuracy of up to 88.2% for our new online and patient-specific analysis, which takes the distinct profile of each patient into account. While accurate, our approach is also energy efficient and can achieve a battery lifetime of 46 days for continuous screening of OSA.
Wireless Monitoring of Endogenous and Exogenous Biomolecules on an Android Interface
IEEE Sensors Journal. 2016.
DOI : 10.1109/Jsen.2016.2524631.
Monitoring patients in intensive care units is generally expensive and time consuming. A prompter medical intervention for those critical patients is a key factor for their safety. Therefore, a system that offers immediate visualization of the monitored data represents a great advance in the field. In this paper, the design, the development, and the validation of an android interface for the continuous and wireless monitoring of up to five compounds are described. Continuous monitoring of the biomolecules is addressed by using a fully integrated hardware platform consisting of biosensors connected to a read-out circuit on a printed circuit board. The electrochemical platform uses Roving's Bluetooth module RN-42 to send the measured data to the mobile device. For the validation of the system, some biomolecules are taken as reference: glucose and lactate for endogenous metabolites and paracetamol for exogenous biomolecules. Chronoamperometries are performed at +650 mV for glucose and lactate and at +450 mV for paracetamol. Multi-walled carbon nanotubes are deposited on working electrodes for glucose and lactate for enhanced signal. Instead, for paracetamol, bare working electrodes are used. The measured data are continuously displayed on the screen of the mobile device because of the android interface. Current step for every variation of glucose, lactate, or paracetamol is clearly visible by the trend of the graphs.
Low-Power Wearable System for Real-Time Screening of Obstructive Sleep Apnea
2016. IEEE Computer Society Annual Symposium on VLSI , Pittsburgh, Pennsylvania, U.S.A. , July 11-13, 2016. p. 230-235.
DOI : 10.1109/Isvlsi.2016.51.
Obstructive Sleep Apnea (OSA) is one of the main sleep disorders, but only 10% of the cases are diagnosed. Moreover, there is a lack of tools for long-term monitoring of OSA, since current systems are too bulky and intrusive to be used continuously. In this context, recent studies have shown that it is possible to detect it automatically based on single- lead ECG recordings. This approach can be used in non-invasive smart wearable sensors which measure and process bio-signals online. This work focuses on the implementation, optimization and integration of an algorithm for OSA detection for preventive health-care. It relies on a frequency-domain analysis while tar- geting an ultra-low power embedded wearable device. As it must share its resources usage with other computations, it must be as lightweight as possible. Our current results based on publicly available signals show a classification accuracy of up to 83.2% for both the offline analysis and the embedded online one. This system gives an even better classification accuracy than the best offline algorithm when using the same features for classification
Design of Ultra-Low-Power Smart Wearable Systems
2015. IEEE 16th Latin-American Test Symposium 2015 (LATS) , Puerto Vallarta, Mexico , March 25-27, 2015. p. 1-2.
DOI : 10.1109/LATW.2015.7102527.
Latest progress in microelectronics have enabled a new generation of low cost, low power, miniaturized, yet, smart sensor nodes. This new generation of wearable sensor nodes promise to deploy automated complex bio-signals analysis. In this paper, we present INYU, a wearable sensor device for physical and emotional health monitoring. The device obtains key vital signs of the user, namely Electrocardiogram (ECG), respiration and skin conductance continuously. Using this information, INYU can deliver a novel real-time algorithm for on-line heart-beat classification and correction that relies on a probabilistic model to determine whether a heartbeat is likely to happen under certain timing conditions. Thus, using this algorithm INYU can quickly decide if a beat is occurring at an expected time or if there is a problem in the series (e.g., a skipped, an extra or a misplaced beat). This new algorithm has been integrated in the processing pipeline of automated Heart-Rate Variability (HRV) analysis, both for time-domain (RMSSD, SDNN) and frequency-domain (LF/HF) algorithms.
Real-Time Probabilistic Heart Beat Classification and Correction for Embedded Systems
2015. Computing in Cardiology 2015 , Nice, France , September 06-09, 2015.
With the emergence of wearable and non-intrusive med- ical devices, one major challenge is the real-time analysis of the acquired signals in real-life and ambulatory con- ditions. This paper presents a lightweight algorithm for on-line heart beat classification and correction that relies on a probabilistic model to determine whether a heart beat is likely to happen under certain timing conditions or not. It can quickly decide if a beat is occurring at an expected time or if there is a problem in the series (e.g., a skipped, an extra or a misplaced beat). If an error is detected, the series is repaired accordingly. The algorithm has been carefully optimized to minimize the required processing power and memory usage in order to enable its real-time embedded implementation on a wearable sensing device. Our experimental results, based on the PhysioNet Fanta- sia database, show that the proposed algorithm achieves 99.5% sensitivity in the detection and correction of erro- neous beats. In addition, it features a fast response time when the activity level of the user changes, thus enabling its use in situations where the heart rate quickly changes.
Full System for Translational Studies of Personalized Medicine with Free-Moving Mice (invited)
2015. International Symposium on Circuits and Systems (ISCAS) , Lisbon, Portugal , May 24-27, 2015. p. 1774-1777.
DOI : 10.1109/ISCAS.2015.7168998.
A full remotely powered system for metabolism monitoring of free-moving mice is presented here. The fully implantable sensing platform hosts two ASICs, one off-the-shelf micro-controller, four biosensors, two other sensors, a coil to receive power, and an antenna to transmit data. Proper enzymes ensure specificity for animal metabolites while Multi-Walled Carbon Nanotubes ensure the due sensitivity. The remote powering is indeed provided by inductive coils located under the floor of the mouse' cage. Two different approaches were investigated to ensure freedom of movement to the animal. The application to studies for personalized medicine is demonstrated by showing continuous monitoring of both glucose and paracetamol.