PhD Position in Cardiorespiratory Diagnostics Using Wearable Sensors
Catholic University of Leuven Department of Electrical Engineering
Belgium

 
This project is hosted by the STADIUS Center for Dynamical Systems, Signal Processing, and Data Analytics group of the Department of Electrical Engineering (ESAT) at KU Leuven. STADIUS is an academic research center, with a research focus on mathematical engineering, where mathematical tools from numerical linear and multilinear algebra, statistics and optimization are engineered for applications of dynamical systems and control, signal processing, data modeling and analytics. https://www.esat.kuleuven.be/stadius/

Project

Two PhD positions are funded, which focus on the development of software tools for smart diagnostics by means of long-term cardiorespiratory monitoring using wearable sensors. These PhD research positions fit into a multidisciplinary collaboration between KU Leuven and imec, a world-leading R&D and innovation hub in nanoelectronics and digital technologies (https://www.imec.be/nl/).

Wearable sensors generate tons of data (continuous / repetitive multimodal recordings), often monitoring the human physiology, but data as such is ‘useless’ without automated intelligent analysis and interpretation. Such raw data contain many artifacts, making some periods not suitable for any analysis.  

The main goal is to develop software tools that allow to extract relevant and interpretable information from data collected using wearable systems. Implementing these tools into the existing imec sensors will make them SMART: ready for online use in strategic applications in chronic illness monitoring such as long-term follow-up of heart failure patients and kidney patients at home in daily life, and lifestyle sensing such as stress follow-up at work. To achieve this goal, two vacant PhD positions are defined with focus on the following topics:

  • Development of a robust multimodal signal quality indicator that distinguishes relevant epochs from too noisy segments in raw multimodal data streams (e.g. ECG or PPG, Respiration, ACM, bioimpedance,… ). The indicator gives a continuous quality label, correlated to the noise level and adaptable to the user needs. Novel unsupervised or semi-supervised algorithms will be developed using AI-based deep learning methodologies, smart data fusion and Blind Source Separation. Artefact correction and/or removal are envisaged depending on the user needs.
  • Novel class of more powerful biomarkers assessing cardiac and respiratory activity, as well as their interactions, using ECG/PPG, respiration and bioimpedance. Algorithms using advanced machine learning and signal processing tools (such as transfer entropy, subspace projections, graph theory and tensor algebra) will be considered and adapted for implementation in wearable imec sensors.

The tools developed during this project will be validated on data collected from healthy populations and from patients suffering from chronic diseases such as heart failure, kidney disease, and sleep apnea.

Profile

You should have a master's degree in information technology, biomedical engineering, electrical or mathematical engineering, artificial intelligence, or a similar degree with an equivalent academic level. A genuine interest in signal processing and machine learning should motivate your application. The candidate should have strong social abilities allowing an active participation to the multidisciplinary network, fruitful exchanges with other students and researchers, and an excellent integration in the team of your research group.

Offer

Duration: 48 Months

The selected candidate will be able to take advantage of the multidisciplinary team, encompassing different faculties within KU Leuven and imec. Regular meetings with all partners involved will be organized, where research progress and industrial applications will be discussed. PhD candidates involved in this project will become independent researchers with improved career prospects in both the academic and non-academic sectors, and will contribute to the impact of wearable technology on health monitoring, thereby facilitating the early diagnosis and follow-up of different diseases and conditions in a home environment.

The work will be performed within the research division STADIUS (Stadius Centre for Dynamical Systems, Signal Processing, and Data Analytics) at the Electrical Engineering Department (ESAT) at KU Leuven, Europe’s most innovative university (Reuters, 2018). STADIUS's major research objective is to contribute to the development of improved digital (control and signal processing) systems that incorporate advanced mathematical modeling techniques as a crucial new ingredient.

Interested?

For more information please contact Prof. dr. Carolina Varon Perez, mail carolina.varon@kuleuven.be or Prof. dr. ir. Sabine Van Huffel, tel.: +32 16 32 17 03, mail: sabine.vanhuffel@kuleuven.be.

You can apply for this job no later than August 17, 2020 via the
KU Leuven seeks to foster an environment where all talents can flourish, regardless of gender, age, cultural background, nationality or impairments. If you have any questions relating to accessibility or support, please contact us at diversiteit.HR@kuleuven.be.
  • Employment percentage: Voltijds
  • Location: Leuven
  • Apply before: August 17, 2020
  • Tags: Ingenieurswetenschappen


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