Past and Future of Computer-Assisted Sleep Analysis and Drowsiness Assessment

Abstract

The development of computerized sleep analysis has been very much technology-driven by both mathematical tools and available hardware but, additionally and unfortunately, by the almost-30-year-old standard used for manual sleep stage scoring of paper recordings. There are no technical restrictions in terms of computing power, storage space, and costs anymore. However, the standards of visual sleep stage scoring have proven insufficient and ambiguous, and their utilization evidently provides misleading and erroneous information. The low temporal resolution provided by the one-page epoch, the crude division of the sleep processes into a few discrete stages, and the total ignorance of spatial information are the major drawbacks. It is meaningless to try to improve the computerised systems if the algorithms are based on erroneous concepts. Instead, the focus should be changed to studies dealing with the identification and modelling of true biological sleep-related processes. This work cannot be performed without the successful application of computerized methods, some of which have been used in related fields but have not yet been applied to sleep studies. It is extremely important that basic findings are confirmed with a wide variety of methods in several laboratories. The use of predetermined, fixed criteria for methods, waveforms, and states too early is scientifically erroneous and hazardous. Instead standards should describe the minimum requirements for the recording and analysis of the signals in terms of sampling rate, dynamic range, linearity, and documentation of the methods used. With the development of better technology, these standards ought to be constantly reevaluated and modified. The development toward more open commercial digital systems, including standardized programming methods and data formats, would have great positive impact to the field. These trends have long been established in many other fields of industry.

Computer-assisted sleep staging

“…Most previous efforts on automated staging [4], [5], [14], [28] have shown the separation of SWS into stages 3 and 4 to be generally poor. When these two stages are pooled, however, the overall performance improves drastically.…”

“…In all likelihood, the computer detection/separation of SWS into stages 3 and 4 is more consistent than manual scoring since it is based on an objective measurement. As suggested in [28], computer scoring of SWS should overrule manual scoring and not vice versa. It is our feeling that the visual distinction between stages 3 and 4 is bound to lead to less consistent performance as compared with an objective measure.…”

“…Standardized staging rules, such as the RK classification, are developed for scoring young normal healthy adult PSGs, where there is clear transition of stages [3], [28]. Thus, the application of these same rules to PSGs of subjects with disturbed sleep (with abnormal or unclear stage transitions) is bound to lead to significant subjectivity.…”

A Neural Network Method for Detection of Obstructive Sleep Apnea and Narcolepsy Based on Pupil Size and EEG

“…Millán et al [31] present a neural classifier to recognize mental tasks and get about 70% correct recognition. Researchers have found that fluctuations in wakefulness can be examined with EEG measurement from active subjects with eyes open and engaged in their usual awake activities [4], [20]. In these situations, intrusions of alpha and theta activity into the beta activity of active wakefulness have been interpreted as ensuing sleepiness.…”

Fuzzy detection of EEG alpha without amplitude thresholding

“…Visual analysis of the recordings requires a considerable amount of effort. Therefore, several attempts have been made to automate this process [4].…”