Delighted our paper ‘Deep-spindle: An automated sleep spindle detection system for analysis of infant sleep spindles‘ has been published in Computers in Biology and Medicine.
The Deep-spindle webserver is also available online.
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.