CBB member Edith Law is actively working on CrowdEEG, an innovative tool for medical time series data.
CrowdEEG is a platform that integrates machine and human intelligence for the accurate analysis of clinical EEG recordings. With the use of an open-source collaborative annotation tool, CrowdEEG enables both expert and non-expert crowds to perform feature detection or high-level classification tasks on medical time series data. Additionally, CrowdEEG implements a smart data aggregation component that uses state-of-the-art machine learning to combine insights from machines, experts and non-expert crowds for accurate analysis results on a large scale. CrowdEEG is currently focusing on specific analysis tasks, such as epileptic seizure detection, as well as sleep stage classification.
Through the development of CrowdEEG, Law seeks to prepare a high-quality dataset of human clinical polysomnograms, such as multi-channel biosignal recordings of human subjects during sleep, in order to advance other research endeavours in this field. Additionally, CrowdEEG seeks to make medical-grade EEG analysis more affordable and accessible for patients both in Canada and abroad.
More about CrowdEEG can be found here, as well as on Law's webpage.