Sleep spindles are an indicator of the development and integrity of the central nervous system in infants. Identifying sleep spindles manually in EEG is time-consuming and typically requires experienced experts. Automated detection of sleep spindles would greatly facilitate this analysis. Deep learning methods have been widely used recently in EEG analysis. We have developed a deep learning-based automated sleep spindle detection system, Deep-spindle, which employs a convolutional neural network (CNN) combined with a bidirectional Long Short-Term Memory (LSTM) network, which could assist in the analysis of infant sleep spindles. The Deep-spindle system can reduce physicians’ workload, demonstrating the potential to assist physicians in the automated analysis of sleep spindles in infants.
We have developed the Deep-spindle as a publicly available web server. If you want to know more about Deep-spindle, please visit https://lisda.ucd.ie/Deep-spindle/