Dennis van de Sande
Spectroscopy is a crucial technology in fields like chemistry, medicine, and environmental and safety services. It uses the electromagnetic spectrum to generate unique fingerprints of molecular structures. But, the process requires human experts to acquire, process, and analyse spectral data, which makes the overall workflow very complex for non-experts and relatively time-consuming. Spectralligence is changing that by introducing artificial intelligence (AI) into the spectroscopy process. Our team at TU/e is working with Philips, Maastricht UMC, and other spectroscopy companies to develop cross-domain validated neural networks that aims to revolutionize spectroscopy across industries. Our TU/e team focuses mainly on Magnetic Resonance Spectroscopy (MRS), a non-invasive technique used to study the metabolism of the human brain. With partners from different spectroscopic domains, we are able to bring fresh perspectives and innovative ideas to the table. Some of the AI solutions we're currently developing include generative models for spectral data generation, models for artifact removal, and classification tools for clustering or determining the quality of spectra. Visit our website to stay up-to-date with our latest achievements and join us on our journey to revolutionize spectroscopy with Spectralligence!
Project: Multimodal unobtrusive monitoring of snoring and sleep apnea detection including acoustic signal analysis
Obstructive sleep apnea (OSA) is one of the most common sleep disorders, which affects 9% to 17% of the general adult population by estimation. Polysomnography (PSG) is the gold standard for diagnosing OSA. However, PSG requires costly sleep center facilities, overnight hospitalization, and instrument disrupting sleepers. Thus, there is an urge to have unobtrusive and reliable OSA screening techniques. This project is to investigate and develop an OSA monitoring platform with multimodal unobtrusive measurements that can be used in a home environment. In this project, multiple signals were collected from Kempenhaeghe sleep center. A PhD candidate and researchers from Kempenhaeghe, TU/e, and Phillips cooperate to study and develop OSA screening methods based on those signals. Our first focus is on audio signal, we detected and used the snoring sound from audio to estimate OSA severity. Then, we used algorithms based on ECG signal to detect sleep disorder breathing. Next, the focus will be accurate OSA detection combining multimodal signals including acoustic signal. Multiple signal processing and machine learning technologies are applied in this project.