Sound Analysis for Sleep Apnea Detection

26 Novembre 2024

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Catégorie : Stagiaire

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Location : Clermont-Ferrand, France

Host institute: EnCoV Lab, Faculty of Medicine, University of Clermont Auvergne and CNRS

Duration: 3 to 6 months

Supervisors: Dr. Navid Rabbani, Prof. Adrien Bartoli

Stipend: 4.35€ Net per hour, approx 600€ / month

Brief Description of the project:

We offer a 3 to 6-month internship as part of a research project focused on analysing sound data recorded during sleep to detect sleep apnea, a common sleep disorder characterised by repeated interruptions in breathing during sleep. If left untreated, sleep apnea can lead to significant health issues, including cardiovascular problems and daytime fatigue. The intern will work with sound signals, applying signal processing and machine learning techniques to identify patterns and markers indicative of sleep apnea. Probable solutions include developing algorithms to process and classify sound signals, detecting breathing irregularities, and distinguishing apnea episodes from normal sleep sounds. Techniques such as feature extraction, time-frequency analysis, and supervised learning models may play a crucial role in achieving accurate detection. The results will be combined with video analysis for which we have already established a feature extraction method.

This project provides hands-on experience with real-world data and cutting-edge methodologies at the intersection of health and technology. Applicants with a background in signal analysis, programming in Python, and an interest in biomedical research are encouraged to apply.

The internship is hosted by the EnCoV research group in University of Clermont Auvergne and CNRS, providing a collaborative and innovative research environment.

Needed Software skills:

Skills: Signal processing, Machine learning

Programming Languages: Python (numpy, librosa, scipy, scikit)

Application:

Please send your application, along with your CV, to: navid_rabbani@yahoo.com.