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Stellenausschreibung / Job advertisement
The innovative joint project of the University Clinic for Neurology (Prof. Dr. Karsten Witt), the Department of Medical Physics (Prof. Dr. rer. nat. Dr. med. Birger Kollmeier), the University Clinic for Ear, Nose and Throat Medicine (Prof. Dr. Andreas Radeloff) and the Fraunhofer IDMT, Department of Hearing, Speech and Audio Technology (Dr. Insa Wolf) seeks to recruit as soon as possible a
(Salary according to TV-L E13, 3 years, m/f/d)
(Identification of biosignals for mobile sleep screening with special focus on apnea)
The thesis (project description see below) is planned at the interface between innovative sensor technology, data analysis using machine learning methods and data collection and will be supervised by the four project leaders mentioned above. Depending on the technical orientation of the research assistant assigned to this position, the initial supervision and integration into the respective working group will be determined by the three university project leaders. An increase by max. 25% from other project funds is possible depending on the individual fit in the respective project context.
Required is an academic university degree (Master or equivalent) in engineering physics, physics-technology-medicine, communications engineering, computer science, medical engineering, hearing technology and audiology or related subjects with above-average grades as well as experience in the field of signal processing, machine learning and/or measurement technology for biosignals or other fields relevant to the topic.
Participation in the course offerings provided by the respective department in accordance with the applicable LVVO is required.
The University of Oldenburg is dedicated to increasing the percentage of women in science. Therefore, female candidates are particularly encouraged to apply. In accordance with Lower Saxony regulations (§ 21 Section 3 NHG) female candidates with equal qualifications will be preferentially considered. Handicapped applicants will be given preference in case of equal qualification.
Please send applications (CV, letter of motivation, copies of the two most relevant references, name and contact address of at least one person willing to provide a reference) as one pdf file by 15.08.2022 to Prof. Dr. Dr. Birger Kollmeier, Department of Medical Physics and Acoustics, Carl von Ossietzky University, 26111 Oldenburg, email
Identification of biosignals for mobile sleep screening with special focus on apnea (IdA)
Background: Sleep apnea syndrome (SAS) is one of the most common sleep disorders, every 4th man and every 10th woman show relevant breathing pauses during sleep, which are a risk factor for vascular diseases . Patients with obstructive sleep apnea syndrome (OSAS) are mainly found in ENT clinic and neurology. 75% of all stroke patients have OSAS, but only 2% of these patients are referred for diagnosis, presumably because it is costly and resources are limited. In sleep medicine, the apnea-hypopnea index (AHI) refers to the average number of apnea and hypopnea episodes per hour. It defines and grades sleep-related breathing disorders such as OSAS in terms of severity and serves as a guide for treatment. Screening methods are either expensive or scientifically poor or not validated, so a ubiquitously available and inexpensive method that easily and effectively screens for suspected OSAS is lacking. The AHI is calculated from sleep laboratory parameters of respiratory flow, thoracic excursion, and blood oxygen desaturation, which leads to an arousal response. There are some promising approaches in the literature to combine simpler biomarkers to predict the AHI.
Hypothesis: Mobile recorded biosignals (acoustics, electroencephalogram, respiratory movements, electrocardiogram, pulsyoximetry) contain information that can be reduced using machine learning algorithms and the combination of which reliably predicts the AHI. This procedure leads to the development of a demonstrator for the inexpensive and simple AHI detection with mobile sensors.
Methods: Integration of sensor technology on a Raspberry Pi-based multi-sensor platform (EEG, audio), data collection in comparative studies of the multi-sensor platform in combination with polygraphy, which is the gold standard of OSAS diagnostics. Data analysis using machine learning methods to extract the essential parameters for a simple but reliable AHI prediction based on sensor technology.
Work program: (1) Literature research and familiarization with sleep analysis. Market exploration regarding extensions and subsequent purchase of these extensions that allow raw data analysis, this applies to ECG equipment, finger pulse oximeters. In cooperation with the Fraunhofer IDMT, integration of a system for contactless respiration detection. Preparation of an ethics application for comparative studies. (2) Study for data collection at the involved clinics (3) Implementation of analysis algorithms based on different sensor configurations to determine the AHI (4) Approach validation and optimization regarding the reduction of necessary sensor parameters. (5) Method validation by application to datasets collected in a sleep laboratory with full polysomnography
Prospect of results: Sleep disorders, such as OSAS, lead to an increased risk of, for example, cardiovascular disorders, dementia and depression. A robust AHI prediction based on a reduced sensor data set, which can be recorded with mobile sensors, would be the basis for a diagnostic support system for prevention.