Researchers develop algorithm that detects sleep disorder patterns in EEG readings

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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 Arxiv.org.

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,

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