Towards unobtrusive automated sleep stage classification:polysomnography using electrodes on the face

\u3cp\u3eAlthough sleep stage annotation (SSA) is historically known from clinical practice and typically performed by a certified expert on the basis of visual examination of polysomnography (PSG) signals. Automatic SSA has emerged as a tool to assist sleep experts and to accelerate the analysis of PSG data. New advances in signal processing and sensor technology start to enable the application of SSA in home solutions as well. In today's busy lives, sleep plays a central role and good quality sleep helps us to deal with the stress of everyday life. Being able to enhance sleep quality thus is a major opportunity to help people in reducing the influence of stress on their live, health and wellbeing. The advent of consumer products aimed at enhancing the sleep experience has propelled the need for home sleep monitoring and inducing solutions which can i) provide automatic SSA using sensors that interfere minimally with the sleep process and ii) provide sleep stage information in real-time in order to be suitable for closed-loop sleep inducing solutions. In this paper, we examine two possible alternatives for unobtrusive sleep monitoring. The first one uses respiratory, cardiac and wrist actigraphy signals while the second one relies on Facial PSG electrodes positioned on the facial area which allow for unobtrusive and comfortable sensors arrangements.\u3c/p\u3

Sleep EEG characteristics associated with sleep onset misperception

Study objective\u3cbr/\u3eTo study sleep EEG characteristics associated with misperception of Sleep Onset Latency (SOL).\u3cbr/\u3e\u3cbr/\u3eMethods\u3cbr/\u3eData analysis was based on secondary analysis of standard in-lab polysomnographic recordings in 20 elderly people with insomnia and 21 elderly good sleepers. Parameters indicating sleep fragmentation, such as number of awakenings, wake after sleep onset (WASO) and percentage of NREM1 were extracted from the polsysomnogram, as well as spectral power, microarousals and sleep spindle index. The correlation between these parameters during the first sleep cycle and the amount of misperceived sleep was assessed in the insomnia group. Additionally, we made a model of the minimum duration that a sleep fragment at sleep onset should have in order to be perceived as sleep, and we fitted this model to subjective SOLs of both subject groups.\u3cbr/\u3e\u3cbr/\u3eResults\u3cbr/\u3eMisperception of SOL was associated with increased percentage of NREM1 and more WASO during sleep cycle 1. For insomnia subjects, the best fit of modelled SOL with subjective SOL was found when assuming that sleep fragments shorter than 30 min at sleep onset were perceived as wake. The model indicated that healthy subjects are less sensitive to sleep interruptions and perceive fragments of 10 min or longer as sleep.\u3cbr/\u3e\u3cbr/\u3eConclusions\u3cbr/\u3eOur findings suggest that sleep onset misperception is related to sleep fragmentation at the beginning of the night. Moreover, we show that people with insomnia needed a longer duration of continuous sleep for the perception as such compared to controls. Further expanding the model could provide more detailed information about the underlying mechanisms of sleep misperception

Validation of photoplethysmography-based sleep staging compared with polysomnography in healthy middle-aged adults

\u3cp\u3eStudy Objectives: To compare the accuracy of automatic sleep staging based on heart rate variability measured from photoplethysmography (PPG) combined with body movements measured with an accelerometer, with polysomnography (PSG) and actigraphy. Methods: Using wrist-worn PPG to analyze heart rate variability and an accelerometer to measure body movements, sleep stages and sleep statistics were automatically computed from overnight recordings. Sleep-wake, 4-class (wake/N1 + N2/N3/REM) and 3-class (wake/NREM/REM) classifiers were trained on 135 simultaneously recorded PSG and PPG recordings of 101 healthy participants and validated on 80 recordings of 51 healthy middle-aged adults. Epoch-byepoch agreement and sleep statistics were compared with actigraphy for a subset of the validation set. Results: The sleep-wake classifier obtained an epoch-by-epoch Cohen's κ between PPG and PSG sleep stages of 0.55 ± 0.14, sensitivity to wake of 58.2 ± 17.3%, and accuracy of 91.5 ± 5.1%. ? and sensitivity were significantly higher than with actigraphy (0.40 ± 0.15 and 45.5 ± 19.3%, respectively). The 3-class classifier achieved a κ of 0.46 ± 0.15 and accuracy of 72.9 ± 8.3%, and the 4-class classifier, a κ of 0.42 ± 0.12 and accuracy of 59.3 ± 8.5%. Conclusions: The moderate epoch-by-epoch agreement and, in particular, the good agreement in terms of sleep statistics suggest that this technique is promising for long-term sleep monitoring, although more evidence is needed to understand whether it can complement PSG in clinical practice. It also offers an improvement in sleep/wake detection over actigraphy for healthy individuals, although this must be confirmed on a larger, clinical population.\u3c/p\u3