Sander Theodoor Pastoor

Online single EEG channel based automatic sleep staging

Recent evidence supports the positive effects of external intervention during specific sleep stages (e.g. enhanced memory consolidation and depression relief). To enable timely intervention, online automated sleep staging is required and preferably with short latency. In this paper, we propose an approach to achieve this based on the analysis of spectral features of a single electroencephalogram (EEG) channel and the use of Gaussian Mixture Models. We compare among several choices for the EEG signal location, the type of spectral features, and the duration of the signal segment (epoch) that is required to automatically identify the sleep stage. The performance metric used for comparison purposes is the kappa statistic, which measures the agreement between the automatic and manual sleep staging. The performance is higher when central EEG locations (C3, C4), longer epochs, and the power in five frequency bands are used. However, good results (kappa=0.6) can also be obtained for an epoch duration of 12 seconds.

Automatic characterization of sleep need dissipation dynamics using a single EEG signal.

In the two-process model of sleep regulation, slow-wave activity (SWA, i.e. the EEG power in the 0.5-4 Hz frequency band) is considered a direct indicator of sleep need. SWA builds up during non-rapid eye movement (NREM) sleep, declines before the onset of rapid-eye-movement (REM) sleep, remains low during REM and the level of increase in successive NREM episodes gets progressively lower. Sleep need dissipates with a speed that is proportional to SWA and can be characterized in terms of the initial sleep need, and the decay rate. The goal in this paper is to automatically characterize sleep need from a single EEG signal acquired at a frontal location. To achieve this, a highly specific and reasonably sensitive NREM detection algorithm is proposed that leverages the concept of a single-class Kernel-based classifier. Using automatic NREM detection, we propose a method to estimate the decay rate and the initial sleep need. This method was tested on experimental data from 8 subjects who recorded EEG during three nights at home. We found that on average the estimates of the decay rate and the initial sleep need have higher values when automatic NREM detection was used as compared to manual NREM annotation. However, the average variability of these estimates across multiple nights of the same subject was lower when the automatic NREM detection classifier was used. While this method slightly over estimates the sleep need parameters, the reduced variability across subjects makes it more effective for within subject statistical comparisons of a given sleep intervention.

Closed-loop system to enhance slow-wave activity

OBJECTIVE Recent evidence reports cognitive, metabolic, and sleep restoration benefits resulting from the enhancement of sleep slow-waves using auditory stimulation. Our objective is to make this concept practical for consumer use by developing and validating an electroencephalogram (EEG) closed-loop system to deliver auditory stimulation during sleep to enhance slow-waves. APPROACH The system automatically detects slow-wave sleep with 74% sensitivity and 97% specificity and optimally delivers stimulation in the form of 50 ms-long tones separated by a constant one-second inter-tone interval at a volume that is dynamically modulated such that louder tones are delivered when sleep is deeper. The system was tested in a study involving 28 participants (18F, 10M; 36.9  ±  7.3 years old; median age: 40 years old) who used the system for ten nights (five nights in a sham condition and five in a stimulation condition). Four nights in each condition were recorded at-home and the fifth one in-lab. MAIN RESULTS The analysis in two age groups defined by the median age of participants in the study shows significant slow wave activity enhancement (+16.1%, p  <  0.01) for the younger group and absence of effect on the older group. However, the older group received only a fraction (57%) of the stimulation compared to the younger group. Changes in sleep architecture and EEG properties due to aging have influenced the amount of stimulation. The analysis of the stimulation timing suggests an entrainment-like phenomenon where slow-waves align to the stimulation periodicity. In addition, enhancement of spindle power in the stimulation condition was found. SIGNIFICANCE We show evidence of the viability of delivering auditory stimulation during sleep, at home, to enhance slow wave activity. The system ensures the stimulation delivery to be at the right time during sleep without causing disturbance.