Conveners
Use case 2: polysomnography
- Nicolai Spicher (UMG)
Description
Polysomnography (PSG) is a multimodal measurement of multiple biosignals such as electroencephalography (EEG), electrocardiography (ECG), electromyography (EMG) and is used in clinical practice for sleep stage scoring. Traditionally, a somnologist visually assesses the signals in parallel in 30s windows and classifies them in one out of five different sleep stages. This is a tedious and time-consuming task which also suffers from intra- and inter-rater variability. Recently, this led to the development of end-to-end machine learning approaches for processing PSG and computing sleep stages which automatically leads to high performance computing (HPC) for effective processing. However, due to their multimodality, PSGs are highly sensible: They allow to identify multiple types of disease and it has already been demonstrated that individuals can be identified from ECG or EEG data to a certain degree.
Therefore, the Biosignal Processing Group of the Department of Medical Informatics at the University Medical Center Göttingen uses the GWDG HPC Secure Workflow with the aim of providing end-to-end sleep staging based on protected PSG data.
