Sleep spindles are discrete, intermittent patterns of brain activity observed in human electroencephalographic data. Increasingly, these oscillations are of biological and clinical interest because ...of their role in development, learning and neurological disorders. We used an Internet interface to crowdsource spindle identification by human experts and non-experts, and we compared their performance with that of automated detection algorithms in data from middle- to older-aged subjects from the general population. We also refined methods for forming group consensus and evaluating the performance of event detectors in physiological data such as electroencephalographic recordings from polysomnography. Compared to the expert group consensus gold standard, the highest performance was by individual experts and the non-expert group consensus, followed by automated spindle detectors. This analysis showed that crowdsourcing the scoring of sleep data is an efficient method to collect large data sets, even for difficult tasks such as spindle identification. Further refinements to spindle detection algorithms are needed for middle- to older-aged subjects.
Highlights • Spindle identification is a difficult task, and more than one sleep expert is needed to reliably score spindles in EEG data. • The reliability of sleep staging may be improved by ...improving the reliability of spindle scoring, particularly for the discrimination of stage N1 and N2 sleep. • Reliability of sleep spindle scoring can be improved by using qualitative confidence scores, rather than a dichotomous yes/no scoring system.
Hypoglycaemia remains the main limiting factor in type 1 diabetes management. We developed an insulin‐dependent glucagon dosing regimen for treatment of mild hypoglycaemia based on simulations. A ...validated glucose–insulin–glucagon model was used to describe seven virtual patients with insulin pump‐treated type 1 diabetes. In each simulation, one of ten different and individualized subcutaneous insulin boluses was administered to decrease plasma glucose (PG) from 7.0 to ≤3.9 mmol/l. Insulin levels were estimated as ratio of actual to baseline serum insulin concentration (se/ba‐insulin), insulin on board (IOB) or percentage of IOB to total daily insulin dose (IOB/TDD). Insulin bolus sizes were chosen to provide pre‐defined insulin levels when PG reached 3.9 mmol/l, where one of 17 subcutaneous glucagon boluses was administered. Optimum glucagon bolus to treat mild hypoglycaemia at varying insulin levels was the lowest dose that in most patients caused PG peak between 5.0 and 10.0 mmol/l and sustained PG ≥ 3.9 mmol/l for 2 hr after the bolus. PG response to glucagon declined with increasing insulin levels. The glucagon dose to optimally treat mild hypoglycaemia depended exponentially on insulin levels, regardless of how insulin was estimated. A 125‐μg glucagon dose was needed to optimally treat mild hypoglycaemia when insulin levels were equal to baseline levels. In contrast, glucagon doses >500 μg were needed when se/ba‐insulin >2.5, IOB >2.0 U or IOB/TDD >6%. Although the proposed model‐based glucagon regimen needs confirmation in clinical trials, this is the first attempt to develop an insulin‐dependent glucagon dosing regimen for treatment of insulin‐induced mild hypoglycaemia in patients with type 1 diabetes.
Many of the automatic sleep spindle detectors currently used to analyze sleep EEG are either validated on young subjects or not validated thoroughly. The purpose of this study is to develop and ...validate a fast and reliable sleep spindle detector with high performance in middle aged subjects. An automatic sleep spindle detector using a bandpass filtering approach and a time varying threshold was developed. The validation was done on sleep epochs from EEG recordings with manually scored sleep spindles from 13 healthy subjects with a mean age of 57.9 ± 9.7 years. The sleep spindle detector reached a mean sensitivity of 84.6% and a mean specificity of 95.3%. The sleep spindle detector can be used to obtain measures of spindle count and density together with quantitative measures such as the mean spindle frequency, mean spindle amplitude, and mean spindle duration.
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