Nocturnal paroxysmal dystonia – Case report

Nocturnal paroxysmal dystonia describes a syndrome consisting of recurrent motor episodes of dystonic–dyskinetic features arising from non-rapid eye movement (NREM) sleep. In the article, the authors present female case of nocturnal paroxysmal dystonia. The patient has had attacks since her childhood and was eventually diagnosed at the age of 48. Therapy with carbamazepine considerably reduced the frequency and entent of seizures. The present case evidences that nocturnal paroxysmal dystonia still is a diagnostic challenge for clinicians. Especially, we emphasize the importance of polysomnography in the verification of the diagnosis

Automatic Human Sleep Stage Scoring Using Deep Neural Networks

The classification of sleep stages is the first and an important step in the quantitative analysis of polysomnographic recordings. Sleep stage scoring relies heavily on visual pattern recognition by a human expert and is time consuming and subjective. Thus, there is a need for automatic classification. In this work we developed machine learning algorithms for sleep classification: random forest (RF) classification based on features and artificial neural networks (ANNs) working both with features and raw data. We tested our methods in healthy subjects and in patients. Most algorithms yielded good results comparable to human interrater agreement. Our study revealed that deep neural networks (DNNs) working with raw data performed better than feature-based methods. We also demonstrated that taking the local temporal structure of sleep into account a priori is important. Our results demonstrate the utility of neural network architectures for the classification of sleep

Narcolepsy - have we found all mysteries of the illness?

Narkolepsja jest przewlekłą chorobą neurologiczną, charakteryzującą się nadmierną sennością w ciągu dnia, katapleksją oraz zaburzeniami snu REM. Związana jest z niedoborem hipokretyny w ośrodkowym układzie nerwowym. Za przyczynę utraty komórek hipokretynowych odpowiada prawdopodobnie reakcja autoimmunologiczna, czego dowodzą prowadzone w ostatnich latach badania. Niniejszy artykuł przedstawia charakterystykę kliniczną choroby, metody diagnostyczne i terapeutyczne oraz najnowsze doniesienia na temat etiologii choroby.Narcolepsy is a chronic neurological disorder, characterized by excessive daytime sleepiness, cataplexy and disorders of REM sleep. Narcolepsy is linked with hypocretin deficiency in central nervous system. There is ongoing evidence that loss of hypocretin neurons is caused most probably by autoimmune process. This paper presents clinical characteristics of narcolepsy, diagnostic and therapeutic management in narcolepsy, as well as the most recent data on etiology of the disorder

Zaburzenia oddychania podczas snu u osób starszych - czynnik ryzyka i czynnik prognostyczny udaru mózgu

Stroke ranks third in mortality and is a key reason for permanent disability in the aging population over sixty years old. Results of resent studies show that obstructive sleep apnea syndrome (OSA) is a significant independent risk factor for stroke. After stroke, the presence of OSA is a strong predictive factor for an unfavorable clinical course. Studies in patients with stroke showed that OSA could be found as often as in every second patient. Therefore, an apnea screening should be performed in every patient with stroke and nocturnal snoring. The present paper presents a review of the literature on the relationship between sleep apnea and stroke, the data from two original studies performed in Poland in patients with stroke, and discusses the role of OSA in the pathogenesis of stroke.Udar mózgu jest trzecią co do częstości przyczyną śmiertelności oraz najważniejszą przyczyną inwalidztwa osób po 60. roku życia. Badania przeprowadzone w ostatnich latach wskazały, że obturacyjne zaburzenia oddychania podczas snu (OSA) stanowią istotny i niezależny czynnik ryzyka wystąpienia zaburzeń krążenia mózgowego. Po przebytym udarze mózgu obecność OSA jest silnym czynnikiem prognostycznym niekorzystnego przebiegu klinicznego. Wyniki przeprowadzonych badań wskazują, że w populacji osób z udarem mózgu OSA występują nawet u co drugiego pacjenta. Dlatego też badanie przesiewowe w kierunku OSA powinno się wykonywać rutynowo u każdego chorego z dodatnim wywiadem w kierunku chrapania. Celem artykułu jest przedstawienie piśmiennictwa na temat zaburzeń oddychania podczas snu u pacjentów z zaburzeniami krążenia mózgowego, zwłaszcza w starszym wieku oraz analiza danych z dwóch badań oryginalnych przeprowadzonych w Polsce, a także omówienie znaczenia OSA w patogenezie udaru mózgu

Automatic artefact detection in single-channel sleep EEG recordings

Quantitative electroencephalogram analysis (e.g. spectral analysis) has become an important tool in sleep research and sleep medicine. However, reliable results are only obtained if artefacts are removed or excluded. Artefact detection is often performed manually during sleep stage scoring, which is time consuming and prevents application to large datasets. We aimed to test the performance of mostly simple algorithms of artefact detection in polysomnographic recordings, derive optimal parameters and test their generalization capacity. We implemented 14 different artefact detection methods, optimized parameters for derivation C3A2 using receiver operator characteristic curves of 32 recordings, and validated them on 21 recordings of healthy participants and 10 recordings of patients (different laboratory) and considered the methods as generalizable. We also compared average power density spectra with artefacts excluded based on algorithms and expert scoring. Analyses were performed retrospectively. We could reliably identify artefact contaminated epochs in sleep electroencephalogram recordings of two laboratories (healthy participants and patients) reaching good sensitivity (specificity 0.9) with most algorithms. The best performance was obtained using fixed thresholds of the electroencephalogram slope, high-frequency power (25-90 Hz or 45-90 Hz) and residuals of adaptive autoregressive models. Artefacts in electroencephalogram data can be reliably excluded by simple algorithms with good performance, and average electroencephalogram power density spectra with artefact exclusion based on algorithms and manual scoring are very similar in the frequency range relevant for most applications in sleep research and sleep medicine, allowing application to large datasets as needed to address questions related to genetics, epidemiology or precision medicine

Automatic Human Sleep Stage Scoring Using Deep Neural Networks

The classification of sleep stages is the first and an important step in the quantitative analysis of polysomnographic recordings. Sleep stage scoring relies heavily on visual pattern recognition by a human expert and is time consuming and subjective. Thus, there is a need for automatic classification. In this work we developed machine learning algorithms for sleep classification: random forest (RF) classification based on features and artificial neural networks (ANNs) working both with features and raw data. We tested our methods in healthy subjects and in patients. Most algorithms yielded good results comparable to human interrater agreement. Our study revealed that deep neural networks (DNNs) working with raw data performed better than feature-based methods. We also demonstrated that taking the local temporal structure of sleep into account a priori is important. Our results demonstrate the utility of neural network architectures for the classification of sleep