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Reference : UMR5525-ELSGEN-011
Workplace : LA TRONCHE
Date of publication : Thursday, July 23, 2020
Type of Contract : FTC Scientist
Contract Period : 12 months
Expected date of employment : 1 October 2020
Proportion of work : Full time
Remuneration : Between 2648,79€ and 3054.06€ according to experiment
Desired level of education : PhD
Experience required : 1 to 4 years
To monitor the well-being of a fetus or for clinical diagnosis, one challenge is to extract a fetal electrocardiogram (fECG) signal of high quality from a limited number of non-invasive sensors on the maternal abdomen. The SurFAO project aims to enhance the current clinical routines. On the one hand, during childbirth, fetal monitoring is assessed by the fetal heart rate (FHR), classically from cardiotocography (CTG). The aim is to monitor FHR variability anomalies that reflect a too high fetal malaise. Improving the reliability of FHR estimation  by fECG analysis is of high clinical interest. On the other hand, in earlier stages of pregnancy, more rarely, fetal cardiac rhythm disorders can be detected. An access to fECG recordings to analyze the ECG waveforms morphology would allow more accurate diagnosis and a better tracking of treatment efficacy.
The approach proposed by a consortium form Grenoble (TIMC et GIPSA-Lab laboratories and CHU Grenoble Alpes) aims at coupling two complementary cardiac information acquired at the best-located position. It combines the use of electrophysiological sensors ECG and acoustic sensors of microphonic type that can register phonocardiographic signals (PCG)
The proposed position will be focused on the extraction of the fECG waveforms by enhancing the process extraction with PCG. Our previous works [2-5] based on Gaussian processes have shown the efficiency of this non-parametric modeling but its computational cost is too high. Extended Kalman model and kernel recursive least square (KRLS) algorithms will be investigated.
These methodological works will be conducted in parallel with the acquisition of real experimental data on pregnant women, in collaboration with the University Hospital of Grenoble, within clinical protocols. The proposed methods will be validated on the new acquired clinical data, based on clinical parameters analysis and interpretation. Industrial links are already identified for the development of a future medical device.
The candidate must have training in signal processing or applied mathematics. He / she should be interested in both theoretical and experimental aspects related to the application
The candidate will work in TIMC laboratory, in PRETA team, in collaboration with ViBS team of GIPSA-LAb, as part of the ANR project SurFAO 2018-2023 (Computer-assisted fetal monitoring).
TIMC-IMAG is a major laboratory in biomedical engineering. It gathers scientists and clinicians towards the use of computer science and applied mathematics for understanding and controlling normal and pathological processes in biology and healthcare. Among TIMC-IMAG teams, PRETA (Applied, Theoretical and Experimental cardio-Respiratory Physiology) interests cover cardio-respiratory physiology, including cardio-respiratory interactions, cell physiology and nutrition. The studies are multidisciplinary with a close collaboration between biologists, physiologists, engineers, theorists and clinicians. This collaboration allows developing new methods of non-invasive recording techniques that can be easily applied to patients as well as healthy persons. Experimental data and clinical observations are the bases for the development of explicative (mathematical) and descriptive (time-scale) models of the interactions between physiological systems. PRETA carries translational research and covers fundamental domains of physiology and applied fields of integrated physiology and physiopathology in humans and animals, with a high valorization potential.
GIPSA-Lab (Grenoble Images Parole Signal Automatique) and particularly the Department of Images and Signals is an expert laboratory in the area of digital signal processing. The ViBS team develops theoretical methods for biomedical signals (especially EEG and ECG) that are then applied on actual data. With the multiplicity of observations (multi-sensors, multi-modal, multi-components), it is important to model the relevance and the redundancy of each source by developing source separation methods as well as fusion processes.
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