The 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) in conjunction with the 43rd Annual Conference of the Canadian Medical and Biological Engineering Society was offered virtually via the EMBS Learning Academy, July 20th – 24th, 2020.
Lan Wei presented a full paper entitled ‘Random Forest-based
Algorithm for Sleep Spindle Detection in Infant EEG’. This paper co-authored by Dr. Catherine Mooney (UCD), Professor Madeleine Lowery (UCD), Soraia Ventura (UCC), Dr. Sean Mathieson(UCC), Professor Geraldine B. Boylan (UCC) as well as Mary Anne Ryan (UCC).
Sleep spindles are associated with normal brain development, memory consolidation and infant sleep-dependent brain plasticity and can be used by clinicians in the assessment of brain development in infants. Sleep spindles can be detected in EEG, however, identifying sleep spindles in 
EEG recordings manually is very time-consuming and typically requires highly trained experts. Research on the automatic detection of sleep spindles in infant EEGs has been limited to-date. In this study, we present a novel supervised machine learning-based algorithm to detect sleep spindles in infant EEG recordings, which has the potential to assist researchers and clinicians in the automated analysis of sleep spindles in infant EEG.
The conference was full of interesting talks and presentations, and it was my first time to attend a large virtual conference, which gave me a different experience.
NB: The paper is available here!
— Lan Wei