Are you inspired by signal processing and artificial intelligence for biomedical applications? This project is aimed at sleep apnea, one of the most prevalent sleep disorders. The goal is to develop new predictors of clinical relevance, severity and therapy outcome using a combination of signal processing and big data approaches.

Obstructive Sleep Apnea (OSA) is one of the most prevalent sleep disorders with a strong negative impact on quality of life, and increased risk of cardiovascular complications. Current screening and diagnosis of OSA is biased towards a specific group: middle-aged obese men with frequent snoring.  However, there are many patients that do not fit this stereotype, and presentation of the disease can be highly variable. Current diagnostic outcomes often fail at linking symptom severity (e.g. sleepiness) to objective measures of sleep. This makes it difficult to predict future risk factors or selection of treatment.  There is therefore a need for better screening, triaging and diagnosis tools that -besides identifying groups with clinically relevant sleep apnea- indicate which therapy will likely be accepted and effective in alleviating night- and day-time symptoms.

This project ultimately aims to develop improved screening, triaging and diagnosing tools for OSA, based on a) discovering clinically relevant OSA phenotypes based on big data from an existing large multimodal retrospective data base, and b) classification of patients into these new phenotypes using a combination of sensing strategies and clinical information.

The aim of this project is twofold:

• Using advanced signal processing techniques, this project will start by identifying and implementing features expressing unique physiological characteristics based on traditional polysomnographic sleep recordings, in addition to novel sensing modalities such as wrist-worn photoplethysmography or chest-worn seismocardiography. These features will be used to discover and describe clusters or sub-groups of patients that share not only relevant physiological traits, but also clinical aspects such as the possible presence of comorbid conditions, daytime symptoms and complaints and response to different therapies.
• After these groups are identified and described, this project will focus on techniques that allow new patients to be classified into these new phenotypes, using simple and broadly applicable sensing strategies,

This project will make use of existing patient data, including one or two-night clinical PSG recordings, multiple day/night recordings of wearable photoplethysmography, night-time seismocardiography, and questionnaires and sleep/wake diaries.  When possible outcome variables such as success of treatment will be included in the analysis.

The candidate should have:

• A Master's Degree in Electrical or Biomedical Engineering or equivalent
• Strong skills and experience in the processing of physiological signals
• Affinity with clinical topics
• Strong communication skills, to interact with a multidisciplinary team including clinical and industrial stakeholders.
• A research-oriented attitude
• Capability to coordinate your own research and interact with various disciplines and levels of expertise.
• Fluent in spoken and written English

A PhD position in a vibrant and dynamic high-tech environment. You can perform research on a highly relevant topic that can influence the quality of life for many patients, with the potential to see and help your research results get implemented in clinical practice. Furthermore, we offer

• A meaningful job in a dynamic and ambitious university with the possibility to present your work at international conferences.
• A full-time employment for four years, with an intermediate evaluation after one year.
• To support you during your PhD and to prepare you for the rest of your career, you will have free access to a personal development program for PhD students (PROOF program).
• A gross monthly salary and benefits in accordance with the Collective Labor Agreement for Dutch Universities.
• Additionally, an annual holiday allowance of 8% of the yearly salary, plus a year-end allowance of 8.3% of the annual salary.
• A broad package of fringe benefits, including an excellent technical infrastructure, moving expenses, and savings schemes.
• Family-friendly initiatives are in place, such as an international spouse program, and excellent on-campus children day care and sports facilities.

Embedding:

This project will be embedded within the Eindhoven MedTech Innovation Center (e/MTIC). This is a public-private partnership that aims to create a fast track to high-tech-health innovations in the perinatal, cardiovascular and sleep fields. It combines an academic partner (TU Eindhoven) with an industrial partner (Philips Research) and 3 semi-academic hospitals: Maxima Medical Center, Catharina Hospital, and Kempenhaeghe. On a project-specific basis e/MTIC also involves other parties that can help reinforce these projects.