Body sounds are one of the oldest physiological parameters known to be used in diagnostics. Even today the stereotypical image of a physician is a person with a stethoscope around the neck. Indeed, body sounds are still today a very important parameters that can relay a lot of disease information. Heart rate and abnormal respiration patterns are clear and well-known examples. However today body sounds are still primarily recorded via (digitally amplified) stethoscopes and analysis is done by the physician. A clear trend in healthcare is towards more and more remote and automatic monitoring. We will explore the possibility to apply similar strategies to detect abnormal respiration patterns in recorded body sounds to classify for example certain specific respiratory diseases. The scientific hypothesis is that certain analysis of body sounds can be considered as a pattern recognition and classification problem. The candidate will be asked to first explore suitable algorithms. This will also require investigation into miniaturized recording of body sounds in a wearable form factor since it is to be expected that the recordings will be of significantly lower quality (ambient noise pickup, artifacts from motion, etc.) than high-fidelity audio recordings with a digital stethoscope. The candidate will use these learnings combined with high-quality recordings from publicly available databases to develop as suitable large database. This database will then be used to develop and validate machine-learning algorithms.
Type of project: Internship, Thesis, Combination of internship and thesis
Required degree: Master of Engineering Technology, Master of Bioengineering, Master of Engineering Science, Master of Science
Required background: Biomedical engineering, Computer Science, Electrotechnics/Electrical Engineering
Supervising scientist(s): For further information or for application, please contact: Dwaipayan Biswas (Dwaipayan.Biswas@imec.be)
Imec allowance will be provided.