Sleep disorders such as insomnia and sleep apnea affect around 50-70 million people in the United States. Lack of sufficient sleep and rest are directly linked to increased incidence of motor crashes, occupational accidents and industrial and medical disasters. Sleep disorders are often diagnosed using polysomnograms, where a patient is monitored during their sleep for changes in brain wave activity (EEG recordings), cardiac activity (ECG recordings), breathing, body and limb movement etc. Traditionally, the various stages of sleep (wake, REM sleep) are manually scored by technicians from the raw EEG and EMG data, and this process can take hours and is prone to human error.
A novel method of analyzing EEG data has been developed at the University of Michigan, which drastically speeds up this process and results in increased efficiency.
Efficient and quick coherence-based analysis of sleep stages
The technique is based on the findings that the sleep stages are tightly associated with electrical coherence of brain waves and that different sleep stages can be determined by coherence values of different frequency bands. Thus, instead of relying on raw EEG/EMG data, the new method relies on coherence analysis of long sets of EEG data without the need for monitoring EMG signals and without any manual scoring.
- Fast analysis of EEG recordings and different stages of sleep for use in detection of sleep disorders such as insomnia and sleep apnea
- Does not require the monitoring of EMG signals
- Does not need any manual scoring of EEG signals and therefore reduces analysis time from hours to minutes