Intracerebral recordings, microelectrodes, epilepsy, spike sorting
|DURATION||2 years (possible renewal)|
|BRIEF DESCRIPTION OF THE PROJECT
Single unit and LFP analysis in human epileptic patients
The general goal of this project is to improve knowledge on pathophysiology of epilepsy, in particular the interictal events and the epileptic seizures. With the development of intracerebral recordings with microelectrodes in our epileptic patients, we are now able since several years to perform continuous intracerebral recordings at different scales, from usual macroelectrodes to single neuron recordings, in the interictal period but also during seizures, in multiple intracerebral areas.
The main role of the position will be to perform electrophysiological analysis of intracerebral signals at different periods: during the interictal period and preictal period and to study the link with multiunit/single unit activities in cortical areas.
|Main activities||The doc /post-doc will be integrated in a multidisciplinary team including researchers, engineers, clinical electrophysiologists, neuroradiologists and neurosurgeons. He/she will receive a full assistance from an engineer for the data acquisition and storage. In addition, he/she will have inputs from several researchers/methodologists in signal analysis.|
|Qualification & experience required||The candidate should have a PhD or equivalent in Signal analysis or Neuroscience related field. Expertise in signal processing and good analytical skills (MATLAB) are required, notably in spike sorting analysis. Since the analytic approaches needed for this study have been extensively developed and used in animals, previous experience in animal in vivo or in vitro experiments is a plus.|
|Application Process||Candidates should submit curriculum vitae, a cover letter and contact information for two referees. Application should be sent to Pr Vincent Navarro (firstname.lastname@example.org).|
Undergrad intern positions
Video-based Clinical Monitoring of Dyspnea for ICU Patients
Host laboratories: The “Network dynamics and cellular excitability” Team of the Brain Institute (CNRS-UMR-7225), and the Unit “Neurophysiologie Respiratoire Expérimentale et Clinique” (INSERM-SU UMRS_S 1158), both at La Pitié-Salpêtrière hospital in Paris, propose an M2/engineering internship in the field of clinical monitoring using video recordings.
Introduction: Dyspnea is a subjective experience of breathing discomfort, or “not getting enough air” that shares many characteristics and physiological pathways with pain. In intensive care unit (ICU) patients, this sensation of respiratory impairment is frequent, severe and it is associated with immediate panic or fear and delayed unfavorable outcomes. Despite its deleterious impact on critically ill patients, its precise characterization and evaluation remains elusive, especially because ICU patients cannot directly report their symptoms (e.g. sedative drugs, delirium, endotracheal tube). Providing an automatic and continuous surveillance of ICU patients could therefore help caregivers to identify and treat this suffering, but also could provide an early identification of patients with high risk of clinical deterioration, in the ward, for timely and appropriate decision to ICU admission.
Non-contact methods for measurement physiological parameters (e.g. heart and respiratory rates) have a significant potential in remote sensing or tele-monitoring . Current solutions for non-contact measurements of heart and respiratory activity include laser Doppler and microwave Doppler radar. Recent advances in video and computer vision technologies have allowed video camera systems to become a useful alternative for non-contact monitoring of heart and respiratory rates [2-3] and also face expressions. Different works have shown that temperature variations due to the air and blood flow can be measured with thermal systems under the nose during the respiratory cycle. Such fluctuations contain information related to cardiac and respiratory activities . Physiological measurements can be thus obtained by statistical analysis of these video images.
Objective of the internship: The objective of this internship is to implement a dyspnea detection algorithm through video monitoring system that can extract and transform physiological parameters correlated to dyspnea, into a novel index or alarm that may help in the care and treatment of seriously ill patients at ICU. The monitoring of cardio-respiratory dynamics from distant video recordings has already been studied in our group. A python-based toolbox has been recently developed for standard video recordings (webcams). The first task of the student will be the implementation of computer vision algorithms to automatically detect and track, from thermal images, the region under the nose. From this Region of Interest, the algorithm will extract signals that contain vital physiological information, such respiratory and heart rate. The student will also evaluate the statistical agreement between the real cardio-respiratory rates and their video-based estimates. She/he will also assess the robustness of this video-based monitoring system to body/head movements/position and subject-camera distance. The monitoring system must be implemented using the open-source toolbox OpenCV.
- Minimal duration: 4-6 months
- Good programming skills (C++ or Python) are required for this position,
- Basic knowledge of image processing tools (filtering, segmentation, etc.) and OpenCV library
- A minimal level of scientific English is preferable,
- Candidate should have excellent interpersonal skills and the ability to work independently.
The successful candidate will have the opportunity to join a dynamic and scientifically grounded research team. His/her tasks will be directly supervised by M Chavez (CNRS) and M Decavèle (AP-HP). Applicants should send their CV and a cover letter to: email@example.com and firstname.lastname@example.org
Bibliographic references: SS. Ulyanov; VV. Tuchin (1993). Pulse-wave monitoring by means of focused laser beams scattered by skin surface and membranes. In Proc. SPIE “Static and Dynamic Light Scattering in Medicine and Biology”, 1884: 160-167.  W. Verkruysse, LO Svaasand, and J S. Nelson (2008). Remote plethysmographic imaging using ambient light. Opt Express. 16(26): 21434–21445.  Poh MZ, McDuff DJ, Picard RW (2010). Non-contact, automated cardiac pulse measurements using video imaging and blind source separation. Opt Express. 18(10):10762-10774  M Garbey, N Sun, A Merla, I Pavlidis (2007). Contact-free measurement of cardiac pulse based on the analysis of thermal imagery. IEEE Trans Biomed Eng, 54 (8):1418-1426
 Researcher-ID: http://www.researcherid.com/rid/D-8495-2011
We permanently seek talented undergraduate or graduate students from different backgrounds interested in our scientific research areas. Such disciplines include electrophysiology, clinical neurosciences, or biomedical signals processing.
Inquiries or applications should include a cover letter and updated CV. Specific job opportunities are regularly posted in our team website.
Here are some useful links to international organisms offering scholar/fellowships: