PhD Wearable Vital Signs Monitoring for Sleep Diagnosis

Eindhoven University of Technology (TU/e) is one of Europes top technological universities, situated in the heart of one of Europes largest high-tech innovation ecosystems. Research at TU/e is characterized by a combination of academic excellence and a strong real-world impact. This impact is often obtained via close collaboration with high-tech industries and key clinical centers. In the Healthcare field, TU/e has recently started a large-scale collaborative research program with Philips and leading clinical partners, aimed at improving the quality of care while lowering costs. The program focuses in part on improved diagnosis and treatment of sleep disorders, with the Kempenhaeghe Sleep Expertise Center serving as the clinical parter. For this focus area a multidisciplinary team of 7 PhD students is to be appointed, supervised by a multidisciplinary team of clinical, industrial and academic experts. To facilitate intensive multidisciplinary collaboration, students will be embedded for a part of their time at Philips Research and at Kempenhaeghe, both located in the direct vicinity of TU/e.  Research focus: Sleep is an essential behavior, taking up about 1/3 of our lives. Sleep apnea and chronic insomnia are the most common sleep disorders, together reaching a prevalence of almost 10% of the population. Sleep disorders are typically characterized by subjective symptoms that change over time and under the influence of external factors. Likewise, objective parameters fluctuate as well. In current clinical practice, these temporal aspects are unfortunately but inevitably completely ignored, because the appropriate instruments are lacking. Moreover, the available diagnostic techniques capture only a very limited part of the underlying pathophysiology, still using EEG-based polysomnographic measurements developed in the 1960s. Consequently, there is a strong need for advanced sleep monitoring techniques, that can obtain both subjective and objective data over the long term in natural settings, and that assess the various disorders on a deeper pathophysiological level. Finally, therapeutic interventions such as cognitive behavioral therapy for insomnia, would likely benefit from direct interactive input from sleep monitoring techniques. These challenges will be addressed by a multidisciplinary team involving clinical, industrial and academic experts along with 7 PhD students (5 engineers and 2 medical doctors). The vacancy at hand concerns of one these PhD positions and involves a close collaboration with all other team members. Vacancy: The current clinical standard for sleep monitoring uses polysomnography. This technique measures a combination of EEG, eye movement and EMG. It involves multiple sensors attached to the body of the patient, which may restrict freedom to move and consequently may affect the quality of sleep itself. Polysomnography can be used in an ambulatory setting, but is cumbersome and measurement duration is limited. This project explores an unobtrusive alternative in the form of a ‘smart watch with embedded sensor modalities such as accelerometry and photoplethysmography (PPG). For PPG, controlled light is emitted into the skin, and blood volume variations in the illuminated area cause a modulation in the reflected light, which is measured electrically. A smart watch can be used over longer periods of time, but only gives a surrogate measure of sleep based on activity patterns. Key scientific aims of this project are: – Development of pathophysiological models of the cardio-respiratory system that describe both healthy and breathing-disordered sleep. – Development of robust and reliable model-based analysis techniques to extract respiratory indicators for the severity of sleep disordered breathing, especially obstructive sleep apnea, from smart watch data. - Development of robust and reliable analysis techniques to extract sleep indicators related to insomnia (e.g. sleep staging, perfusion variability). – Clinical validation of these techniques vis a vis clinical gold standards such as polysomnography (jointly with clinical PhD students in the team). - Optimization of algorithms for long-term recording in unsupervised daily life settings.   We are looking for candidates who: – have a strong MSc degree in Electrical Engineering, Physics, or a related discipline; – have a strong background in probabilistic signal processing and data analysis, preferably in a biomedical context; – can fathom pathophysiology and sensor artefacts and capture both in simple mathematical models that can serve as a basis of robust signal analysis techniques; – can think out of the box, distinguish main lines from details, and provide structure to their work; – have excellent multidisciplinary team working and communication skills. We offer a challenging job at a dynamic and ambitious university through a fixed-term appointment for the period of 4 years. The positions must be concluded with writing a PhD thesis.  As an employee of the university you will receive a competitive salary as well as excellent employment conditions. A salary is offered starting at EUR 2,083 per month (gross) in the first year, increasing up to EUR 2,664 per month (gross) in the last year. Moreover, an 8% bonus share (holiday supplement) is provided annually. Assistance for finding accommodation can be given. The university offers an attractive package of fringe benefits such as excellent technical infrastructure, child care, savings schemes, and excellent sports facilities. TU/e also offers you the opportunity for personal development by developing your social and communication skills. We do this by offering every PhD student a series of courses that are part of the PROOF program as an excellent addition to your scientific education. More information on employment conditions can be found here: http://www.tue.nl/en/university/working-at-tue/working-conditions/ .   If interested, please use ‘apply now’-button at the top of this page. You should upload the following: a detailed curriculum vitae, a letter of motivation and portfolio with relevant work. Please keep in mind; you can upload only 5 documents up to 2 MB each! Application The application should consist of the following parts:

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