An evaluation of cardiorespiratory and movement features with respect to sleep-stage classification.

Polysomnography (PSG) is considered the gold standard to assess sleep accurately, but it can be expensive, time-consuming, and uncomfortable, specifically in long-term sleep studies. Actigraphy, on the other hand, is both cheap and userfriendly, but depending on the application lacks detail and accuracy. Our aim was to evaluate cardiorespiratory and movement signals in discriminating between wake, rapid-eye-movement (REM), light (N1N2), and deep (N3) sleep. The dataset comprised 85 nights of PSG from a healthy population. Starting from a total of 750 characteristic variables (features), problem-specific subsets of 40 features were forwardly selected using the combination of a wrapper method (Cohen's kappa statistic on radial basis function (RBF)-kernel support vector machine (SVM) classifier) and filter method (minimum redundancy maximum relevance criterion on mutual information). Final classification was performed using an RBF-kernel SVM. Non-subject-specific wake versus sleep classification resulted in a Cohen's kappa value of 0.695, while REM versus NREM resulted in 0.558 and N3 versus N1N2 in 0.553. The broad pool of initial features gave insight in which features discriminated best between the different classes. The classification results demonstrate the possibility of making long-term sleep monitoring more widely available.

Biomechanics-based active control of bedding support properties and its influence on sleep.

Proper body support plays an import role in the recuperation of our body during sleep. Therefore, this study uses an automatically adapting bedding system that optimises spinal alignment throughout the night by altering the stiffness of eight comfort zones. The aim is to investigate the influence of such a dynamic sleep environment on objective and subjective sleep parameters. The bedding system contains 165 sensors that measure mattress indentation. It also includes eight actuators that control the comfort zones. Based on the measured mattress indentation, body movements and posture changes are detected. Control of spinal alignment is established by fitting personalized human models in the measured indentation. A total of 11 normal sleepers participated in this study. Sleep experiments were performed in a sleep laboratory where subjects slept three nights: a first night for adaptation, a reference night and an active support night (in counterbalanced order). Polysomnographic measurements were recorded during the nights, combined with questionnaires aiming at assessing subjective information. Subjective information on sleep quality, daytime quality and perceived number of awakenings shows significant improvements during the active support (ACS) night. Objective results showed a trend towards increased slow wave sleep. On the other hand, it was noticed that % N1-sleep was significantly increased during ACS night, while % N2-sleep was significantly decreased. No prolonged N1 periods were found during or immediately after steering.