Automatic slow eye movement (SEM) detection of sleep onset in patients with obstructive sleep apnea syndrome (OSAS): comparison between multiple sleep latency test (MSLT) and maintenance of wakefulness test (MWT)

Inside the clinical evaluation of sleepiness: subjective and objective tools

PURPOSE

To critically review the available tools for evaluating excessive daytime sleepiness (EDS) in clinical practice.

METHODS

Objective tests and subjective scales were divided into three groups in accordance with the different dimensions of sleepiness they measure, namely physiological, manifest, and introspective. Strengths, weaknesses, and limitations of each test have been analysed and discussed along with the available recommendations for their use in clinical practice.

RESULTS

The majority of the tests developed for sleepiness evaluation do not have practical usefulness outside the research setting. The suboptimal correlation between different tests mainly depends on the different dimensions of sleepiness they analyse. Most importantly in-laboratory tests poorly correlate with sleepiness in real-life situations and, to date, none is able to predict the risk of injuries related to EDS, especially on an individual level.

CONCLUSIONS

There exists not the one best test to assess EDS, however, clinicians can choose a more specific test to address a specific diagnostic challenge on the individual level. The development of novel performance tests with low cost and easy to administer is advisable for both screening purposes and fitness for duty evaluations in populations at high risk of EDS-related injuries, for example professional drivers.

Envelope filter sequence to delete blinks and overshoots

Background

Eye movements have been used in control interfaces and as indicators of somnolence, workload and concentration. Different techniques can be used to detect them: we focus on the electrooculogram (EOG) in which two kinds of interference occur: blinks and overshoots. While they both draw bell-shaped waveforms, blinks are caused by the eyelid, whereas overshoots occur due to target localization error and are placed on saccade. They need to be extracted from the EOG to increase processing effectiveness.

Methods

This paper describes off- and online processing implementations based on lower envelope for removing bell-shaped noise; they are compared with a 300-ms-median filter. Techniques were analyzed using two kinds of EOG data: those modeled from our own design, and real signals. Using a model signal allowed to compare filtered outputs with ideal data, so that it was possible to quantify processing precision to remove noise caused by blinks, overshoots, and general interferences. We analyzed the ability to delete blinks and overshoots, and waveform preservation.

Results

Our technique had a high capacity for reducing interference amplitudes (>97%), even exceeding median filter (MF) results. However, the MF obtained better waveform preservation, with a smaller dependence on fixation width.

Conclusions

The proposed technique is better at deleting blinks and overshoots than the MF in model and real EOG signals.

Assessment of sleep quality in powernapping

The purpose of this study is to assess the Sleep Quality (SQ) in powernapping. The contributed factors for SQ assessment are time of Sleep Onset (SO), Sleep Length (SL), Sleep Depth (SD), and detection of sleep events (K-complex (KC) and Sleep Spindle (SS)). Data from daytime nap for 10 subjects, 2 days each, including EEG and ECG were recorded. The SD and sleep events were analyzed by applying spectral analysis. The SO time was detected by a combination of signal spectral analysis, Slow Rolling Eye Movement (SREM) detection, Heart Rate Variability (HRV) analysis and EEG segmentation using both Autocorrelation Function (ACF), and Crosscorrelation Function (CCF) methods. The EEG derivation FP1-FP2 filtered in a narrow band and used as an alternative to EOG for SREM detection. The ACF and CCF segmentation methods were also applied for detection of sleep events. The ACF method detects segment boundaries based on single channel analysis, while the CCF includes spatial variation from multiple EEG derivation. The results indicate that SREM detection using EEG is possible and can be used as input together with power spectral analysis to enhance SO detection. Both segmentation methods could detect SO as a segment boundary. Additionally they were able to contribute to detection of KC and SS events. The CCF method was more sensitive to spatial EEG changes and the exact segment boundaries varied slightly between the two methods. The HRV analysis revealed, that low and very low frequency variations in the heart rate was highly correlated with the EEG changes during both SO and variations in SD. Analyzing the relationship between the sleep events and SD showed a negative correlation between the Delta and Sigma activity. Analyzing the subjective measurement (SM) showed that there were a positive correlation between the SL and rated SQ. This preliminary study showed that the factors contributing to the overall SQ during powernapping can be assessed markedly better using a fusion of multiple methods. Future studies will include measures of individual performance before and after powernapping and investigate its relation to the assessed SQ.