Tim Willemen

TL;DR: Evaluating cardiorespiratory and movement signals in discriminating between wake, rapid-eye-movement (REM), light (N1N2), and deep (N3) sleep demonstrated the possibility of making long-term sleep monitoring more widely available.

TL;DR: The Primary Insomnia-group overestimated Sleep Onset Latency and this overestimation was correlated with elevated EEG activity, and the strong association found between K-alpha (K-complex within one second followed by 8-12 Hz EEG activity) in Stage2 sleep and a lower parasympathetic Autonomic Nervous System dominance (less high frequency HR) in Slow-wave sleep, further assumes a state of hyperarousal continuing through sleep in Primary Ins insomnia.

TL;DR: Both multi-modal algorithms showed significant increases in performance of up to 8.65% for noisy multimodal datasets compared to when only the ECG signal is used.

TL;DR: The proposed approach demonstrated the potential for unobtrusive screening of sleep apnea patients at home and the synchronization framework enabled supervised analysis of the commercial Emfit sensor for future sleep monitoring, which can be extended to other multi-modal systems that record movements during sleep.

TL;DR: The presence of apneic events proved to have a significant impact on the performance of a cardiac and respiratory based algorithm for sleep stage classification, and alternative probabilistic visual representations were presented, referred to as the hypnocorrogram and apneacorrogram.