Cortical activation in REM behavior disorder mimics voluntary movement. An electroencephalography study.

OBJECTIVES: Motor symptoms of Parkinson's disease improve during REM sleep behavior disorder movement episodes. Our aim was to study cortical activity during these movement episodes, in patients with and without Parkinson's disease, in order to investigate the cortical involvement in the generation of its electromyographic activity and its potential relationship with Parkinson's disease. METHODS: We looked retrospectively in our polysomnography database for patients with REM sleep behavior disorder, analyzing fifteen patients in total, seven with idiopathic REM sleep behavior disorder and eight associated with Parkinson's disease. We selected segments of REM sleep with the presence of movements (evidenced by electromyographic activation), and studied movement-related changes in cortical activity by averaging the electroencephalographic signal (premotor potential) and by means of time/frequency transforms. RESULTS: We found a premotor potential and an energy decrease of alpha-beta oscillatory activity preceding the onset of electromyographic activity, together with an increase of gamma activity for the duration of the movement. All these changes were similarly present in REM sleep behavior disorder patients with and without Parkinson's disease. CONCLUSIONS: Movement-related changes in electroencephalographic activity observed in REM sleep behavior disorder are similar to those observed during voluntary movements, regardless of the presence of Parkinson's disease motor symptoms. SIGNIFICANCE: These results suggest a main involvement of the cortex in the generation of the movements during REM sleep.

Simple and Autonomous Sleep Signal Processing System for the Detection of Obstructive Sleep Apneas.

Obstructive sleep apnea (OSA) is a common sleep disorder characterized by repetitive upper airway obstruction, intermittent hypoxemia, and recurrent awakenings during sleep. The most used treatment for this syndrome is a device that generates a positive airway pressure­Continuous Positive Airway Pressure (CPAP), but it works continuously, whether or not there is apnea. An alternative consists on systems that detect apnea episodes and produce a stimulus that eliminates them. Article focuses on the development of a simple and autonomous processing system for the detection of obstructive sleep apneas, using polysomnography (PSG) signals: electroencephalography (EEG), electromyography (EMG), respiratory effort (RE), respiratory flow (RF), and oxygen saturation (SO2). The system is evaluated using, as a gold standard, 20 PSG tests labeled by sleep experts and it performs two analyses. A first analysis detects awake/sleep stages and is based on the accumulated amplitude in a channel-dependent frequency range, according to the criteria of the American Academy of Sleep Medicine (AASM). The second analysis detects hypopneas and apneas, based on analysis of the breathing cycle and oxygen saturation. The results show a good estimation of sleep events, where for 75% of the cases of patients analyzed it is possible to determine the awake/asleep states with an effectiveness of >92% and apneas and hypopneas with an effectiveness of >55%, through a simple processing system that could be implemented in an electronic device to be used in possible OSA treatments.

An interactive framework for the detection of ictal and interictal activities: Cross-species and stand-alone implementation.

BACKGROUND AND OBJECTIVE: Despite advances on signal analysis and artificial intelligence, visual inspection is the gold standard in event detection on electroencephalographic recordings. This process requires much time of clinical experts on both annotating and training new experts for this same task. In scenarios where epilepsy is considered, the need for automatic tools is more prominent, as both seizures and interictal events can occur on hours- or days-long recordings. Although other solutions have already been proposed, most of them are not integrated on clinical and basic science environments due to their complexity and required specialization. Here we present a pipeline that arises from coordinated efforts between life-science researchers, clinicians and data scientists to develop an interactive and iterative workflow to train machine-learning tools for the automatic detection of electroencephalographic events in a variety of scenarios. METHODS: The approach consists on a series of subsequent steps covering data loading and configuration, event annotation, model training/re-training and event detection. With slight modifications, the combination of these blocks can cope with a variety of scenarios. To illustrate the flexibility and robustness of the approach, three datasets from clinical (patients of Dravet Syndrome) and basic research environments (mice model of the same disease) were evaluated. From them, and in response to researchers' daily needs, four real world examples of interictal event detection and seizure classification tasks were selected and processed. RESULTS: Results show that the current approach was of great aid for event annotation and model development. It was capable of creating custom machine-learning solutions for each scenario with slight adjustments on the analysis protocol, easily accessible to users without programming skills. Final annotator similarity metrics reached values above 80% on all cases of use, reaching 92.3% on interictal event detection on human recordings. CONCLUSIONS: The presented framework is easily adaptable to multiple real world scenarios and the interactive and ease-to-use approach makes it manageable to clinical and basic researches without programming skills. Nevertheless, it is conceived so data scientists can optimize it for specific scenarios, improving the knowledge transfer between these fields.