Researchers develop algorithm that detects sleep disorder patterns in EEG readings

Posted on
Electroencephalography, or EEG, measures electrical activity in the brain using electrodes placed on the scalp. It’s used by sleep specialists to diagnose and evaluate neurological disorders, which can be a tedious undertaking — annotating dips and spikes in hours of recorded brain activity requires specialized training and plenty of patience.

Researchers at Stanford and Université Paris-Saclay in France recently proposed an alternative: artificial intelligence that predicts the location, duration, and type of events in EEG charts. It’s described in a new paper (“A Deep Learning Architecture to Detect Events in EEG Signals During Sleep“) published on the preprint server

EEG pattern-detecting algorithms have been around a while, but the researchers note that most are event-specific; they’re hardwired to recognize known electrical patterns. By contrast, machine learning systems have the potential to learn events, like K-complexes (EEG waveforms that occur during stage 2 of NREM sleep) and sleep spindles (bursts of brain activity from the thalamus that occur during light sleep), as they’re trained on new data,

Leave a Reply

Your email address will not be published. Required fields are marked *