  Polysomnographic recordings are essential for diagnosing many sleep
disorders, yet their detailed analysis presents considerable challenges. With
the rise of machine learning methodologies, researchers have created various
algorithms to automatically score and extract clinically relevant features from
polysomnography, but less research has been devoted to how exactly the
algorithms should be incorporated into the workflow of sleep technologists.
This paper presents a sophisticated data collection platform developed under
the Sleep Revolution project, to harness polysomnographic data from multiple
European centers. A tripartite platform is presented: a user-friendly web
platform for uploading three-night polysomnographic recordings, a dedicated
splitter that segments these into individual one-night recordings, and an
advanced processor that enhances the one-night polysomnography with
contemporary automatic scoring algorithms. The platform is evaluated using
real-life data and human scorers, whereby scoring time, accuracy and trust are
quantified. Additionally, the scorers were interviewed about their trust in the
platform, along with the impact of its integration into their workflow.
