With over two decades of groundbreaking innovation pioneering the field of computer- assisted sleep scoring, The Siesta Group is setting the benchmark in measuring, analyzing, and interpreting bio-signals related to the workings of the brain during sleep and wakefulness, as well as physical activity.
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Sleep Disorders
For many major sleep disorders, such as Primary Insomnia or Narcolepsy, the supervised in-lab polysomnography (PSG) and derived endpoints like Wake-after-sleep-onset or Sleep Efficiency are the gold standard in drug trials. In addition, daytime measurements like the Multiple Sleep Latency Test are standard EEG-based measurements for vigilance or somnolence. But there are many more biomarkers that can be extracted from PSG, including the micro-structure of sleep (e.g. sleep spindles, REM density, etc.) or quantitative EEG during stable sleep stages.
The Apnea Hypopnea Index (AHI) derived from in-lab polysomnography (PSG) is the most well-known biomarker for Obstructive Sleep Apnea (OSA). But there are alternatives, such as the Hypoxic Burden Index, shown to correlate better to many outcome measures than the AHI. Lately, it has also been used in respiratory phenotyping.
Event-related Potentials (ERP), such as the well-known P300 in an acoustic oddball paradigm has been proven to be a major biomarker detecting and predicting cognitive decline in both Alzheimer’s and Parkinson’s disease. But also, quantitative EEG variables provide important information about the effects of the diseases. More recently, sleep-related biomarkers such as sleep spindle densities have been shown to be rather characteristic for changes in Alzheimer’s disease.
Quantitative EEG is known to reveal important changes in the brain due to Major Depression Disorder (MDD), such as increases in the Delta bands. But also measuring sleep in MDD provides valuable endpoints for related drug trials. For instance, the relationship between latency to and amount of REM sleep to the extent of the disorder can guide early phase trials.
