Real-Time Excitation of Slow Oscillations during Deep Sleep Using Acoustic Stimulation

Slow-wave synchronous acoustic stimulation is a promising research and therapeutic tool. It is essential to clearly understand the principles of the synchronization methods, to know their performances and limitations, and, most importantly, to have a clear picture of the effect of stimulation on slow-wave activity (SWA). This paper covers the mentioned and currently missing parts of knowledge that are essential for the appropriate development of the method itself and future applications. Artificially streamed real sleep EEG data were used to quantitatively compare the two currently used real-time methods: the phase-locking loop (PLL) and the fixed-step stimulus in our own implementation. The fixed-step stimulation method was concluded to be more reliable and practically applicable compared to the PLL method. The sleep experiment with chronic insomnia patients in our sleep laboratory was analyzed in order to precisely characterize the effect of sound stimulation during deep sleep. We found that there is a significant phase synchronization of delta waves, which were shown to be the most sensitive metric of the effect of acoustic stimulation compared to commonly used averaged signal and power analyses. This finding may change the understanding of the effect and function of the SWA stimulation described in the literature

An Unsupervised Multichannel Artifact Detection Method for Sleep EEG Based on Riemannian Geometry

International audienceIn biomedical signal processing, we often face the problem of artifacts that distort the original signals. This concerns also sleep recordings, such as EEG. Artifacts may severely affect or even make impossible visual inspection, as well as automatic processing. Many proposed methods concentrate on certain artifact types. Therefore, artifact-free data are often obtained after sequential application of different methods. Moreover, single-channel approaches must be applied to all channels alternately. The aim of this study is to develop a multichannel artifact detection method for multichannel sleep EEG capable of rejecting different artifact types at once. The inspiration for the study is gained from recent advances in the field of Riemannian geometry. The method we propose is tested on real datasets. The performance of the proposed method is measured by comparing detection results with the expert labeling as a reference and evaluated against a simpler method based on Riemannian geometry that has previously been proposed, as well as against the state-of-the-art method FASTER. The obtained results prove the effectiveness of the proposed method

Apnea Detection in Polysomnographic Recordings Using Machine Learning Techniques

Sleep disorders are diagnosed in sleep laboratories by polysomnography, a multi-parameter examination that monitors biological signals during sleep. The subsequent evaluation of the obtained records is very time-consuming. The goal of this study was to create an automatic system for evaluation of the airflow and SpO2 channels of polysomnography records, through the use of machine learning techniques and a large database, for apnea and desaturation detection (which is unusual in other studies). To that end, a convolutional neural network (CNN) was designed using hyperparameter optimization. It was then trained and tested for apnea and desaturation. The proposed CNN was compared with the commonly used k-nearest neighbors (k-NN) method. The classifiers were designed based on nasal airflow and blood oxygen saturation signals. The final neural network accuracy for apnea detection reached 84%, and that for desaturation detection was 74%, while the k-NN classifier reached accuracies of 83% and 64% for apnea detection and desaturation detection, respectively

Validity of Quinpirole Sensitization Rat Model of OCD: Linking Evidence from Animal and Clinical Studies

Obsessive-compulsive disorder (OCD) is a neuropsychiatric disorder with 1-3% prevalence. OCD is characterized by recurrent thoughts (obsessions) and repetitive behaviors (compulsions). The pathophysiology of OCD remains unclear, stressing the importance of pre-clinical studies. The aim of this article is to critically review a proposed animal model of OCD that is characterized by the induction of compulsive checking and behavioral sensitization to the D2/D3 dopamine agonist quinpirole.. Changes in this model have been reported at the level of brain structures, neurotransmitter systems and other neurophysiological aspects. In this review, we consider these alterations in relation to the clinical manifestations in OCD, with the aim to discuss and evaluate axes of validity of this model. Our analysis shows that some axes of validity of quinpirole sensitization model (QSM) are strongly supported by clinical findings, such as behavioral phenomenology or roles of brain structures. Evidence on predictive validity is contradictory and ambiguous. It is concluded that this model is useful in the context of searching for the underlying pathophysiological basis of the disorder because of the relatively strong biological similarities with OCD