High-performance Diagnosis of Sleep Disorders: A Novel, Accurate and Fast Machine Learning Approach Using Electroencephalographic Data

While diagnosing sleep disorders by physicians using electroencephalographic data is protracted and inaccurate, we report promising results from a novel, fast and reliable machine learning approach. Our approach only needs an electroencephalographic recording snippet of 10 minutes instead of eight hours to correctly classify the disorder with an accuracy of over 90 percent. The Rapid Eye Movement sleep behavior disorder can lead to secondary diseases like Parkinson or Dementia. Therefore, it is important to classify the disorder fast and with a high level of accuracy - which is now possible with our approach

Development of a Machine Learning Based Algorithm To Accurately Detect Schizophrenia based on One-minute EEG Recordings

While diagnosing schizophrenia by physicians based on patients' history and their overall mental health is inaccurate, we report on promising results using a novel, fast and reliable machine learning approach based on electroencephalography (EEG) recordings. We show that a fine granular division of EEG spectra in combination with the Random Forest classifier allows a distinction to be made between paranoid schizophrenic (ICD-10 F20.0) and non-schizophrenic persons with a very good balanced accuracy of 96.77 percent. We evaluate our approach on EEG data from an open neurological and psychiatric repository containing 499 one-minute recordings of n=28 participants (14 paranoid schizophrenic and 14 healthy controls). Since the fact that neither diagnostic tests nor biomarkers are available yet to diagnose paranoid schizophrenia, our approach paves the way to a quick and reliable diagnosis with a high accuracy. Furthermore, interesting insights about the most predictive subbands were gained by analyzing the electroencephalographic spectrum up to 100 Hz

High-performance detection of epilepsy in seizure-free EEG recordings: A novel machine learning approach using very specific epileptic EEG sub-bands

We applied machine learning to diagnose epilepsy based on the fine-graded spectral analysis of seizure-free (resting state) EEG recordings. Despite using unspecific agglomerated EEG spectra, our fine-graded spectral analysis specifically identified the two EEG resting state sub-bands differentiating healthy people from epileptics (1.5-2 Hz and 11-12.5 Hz). The rigorous evaluation of completely unseen data of 100 EEG recordings (50 belonging to epileptics and the other 50 to healthy people) shows that the approach works successfully, achieving an outstanding accuracy of 99 percent, which significantly outperforms the current benchmark of 70% to 95% by a panel of up to three experienced neurologists. Our epilepsy diagnosis classifier can be implemented in modern EEG analysis devices, especially in intensive care units where early diagnosis and appropriate treatment are decisive in life and death scenarios and where physicians’ error rates are particularly high. Our approach is accurate, robust, fast, and cost-efficient and substantially contributes to Information Systems research in healthcare. The approach is also of high practical and theoretical relevance

Machine Learning Based Diagnostics of Developmental Coordination Disorder using Electroencephalographic Data

We report on promising results concerning the fast and accurate diagnosis of developmental coordination disorder (DCD) which heavily impacts the life of affected children with emotional and behavioral issues. Using a machine learning classifier on spectral data of electroencephalography (EEG) recordings and unfolding the traditional frequency bandwidth in a fine-graded equidistant 99-point spectrum we were able to reach an accuracy of over 99.35 percent having only one misclassification. Our machine learning work contributes to healthcare and information systems research. While current diagnostic methods in use are either complicated, time-consuming, or inaccurate, our automated machine-based approach is accurate and reliable. Our results also provide more insights into the relationship between DCD and brain activity which could stimulate future work in medicine

Endocrine and neurophysiological examination of sleep disorders in Williams syndrome

Background: A high rate of sleep disturbances have been reported in individuals with Williams syndrome (WS), but the underlying aetiology has yet to be identified. Melatonin and cortisol levels are known to affect and regulate sleep/wake patterns. We investigated the changing levels of these hormones in order to explore any relationship with sleep disturbances in children with WS. Methods: Twenty seven children with WS and 27 typically developing (TD) children were recruited. Sleep was monitored using actigraphy and pulse oximetry. Parents completed Children’s Sleep Habit Questionnaire (CSHQ). Saliva and first void morning urine samples were collected from the children. Saliva was collected at three time points: 4-6pm, before bedtime and first thing after awakening. Levels of salivary melatonin and cortisol were analysed by enzyme linked immunoassays. For determination of melatonin, cortisol and their metabolites in urine samples, specific Ultra-High Performance Liquid Chromatography-Tandem Mass Spectrometry (UHPLC-MS/MS) method was developed. Results: CSHQ and actigraphy indicated that children with WS were significantly affected by several types of sleep disturbances, including: abnormally high sleep latency and excessive night waking. Children in WS group had shallower falls in salivary cortisol levels and less pronounced rises in salivary melatonin at bedtime compared to TD controls (p < 0.01 and p = 0.04 respectively). Furthermore, it was found that children with WS also had significantly higher levels of bedtime cortisol compared to TD controls (p = 0.03). Using UHPLC-MS/MS analysis it was shown that children with WS secrete less melatonin during the night compared to healthy controls (p < 0.01). Also, levels of cortisone, a metabolite of cortisol were significantly higher in the WS group (p = 0.05). Conclusions: We found that children with WS had significant sleep disturbances which may be associated with their increased bedtime cortisol and lower evening melatonin. Both hormones play a significant role in the circadian rhythm and sleep/wake cycle, therefore it was necessary to look closely at these endocrine markers in individuals suffering from sleep disorders. Sleep problems in children with WS may adversely affect daytime activity and the quality of life, as well as social, emotional, health and economic functioning of the entire family. Hence, finding their cause is of great importance for affected children and their families

Hypocretin deficiency : neuronal loss and functional consequences

The first part deals with the hypothalamic hypocretin system in disorders that are accompanied by narcolepsy-like sleep disturbances, i.e. Prader-Willi Syndrome, Parkinson__s Disease and Huntington__s Disease. To determine whether the hypocretin system is affected in these disorders, the total number of hypocretin neurons was determined using quantitative techniques in post-mortem human hypothalami. The reason why hypocretin neurons disappear in narcolepsy is still a mystery. A putative autoimmune aetiology has been hypothesized, but a screening for autoantibodies and a n=1 trial with intravenous immunoglobulins yielded no unequivocal results in favor of this hypothesis. In the second part, the consequences of hypocretin deficiency in narcoleptic patients are explored, focussing on vigilance, metabolism and the autonomic nervous system and skin temperature regulation. The ability of a specific neuropsychological test to measure vigilance as a severity indicator for narcolepsy is explored. Two possible causes for the obesity commonly seen in narcolepsy are a decreased basal metabolic rate and a changed autonomic tone. To assess the influence of hypocretin deficiency on skin temperature regulation, thermoregulatory profiles of the proximal and distal skin of narcoleptic subjects were compared to profiles of healthy controls during a daytime sleep registration.Boehringer Ingelheim BV, JE Jurriaanse Stichting, Netherlands Institute for Neuroscience, Nederlandse Vereniging voor Narcolepsie, Netherlands Society for Sleep Wake Research, Novartis Pharma BV, UCB Pharma BV.UBL - phd migration 201

The Impact of Total Sleep Deprivation on Neuropsychological Functioning.

Extended sleep deprivation has been shown to produce impairments in sustained attention and vigilance, especially if the deprivation period is greater than 48 hours. However little is known about the effects of sleep deprivation on performance of cognitive tasks considered to be measures of higher cortical functioning such as cognitive flexibility and the capacity to shift response set. These two activities are associated with intact functioning of the frontal lobes of the cerebral cortex while attention and vigilance tasks are not considered to be part of this type of cognitive activity and are not associated with frontal lobe function. One current hypothesis is that sleep deprivation of a shorter duration (34-36 hours) adversely affects higher cortical function while effects on attention and vigilance are relatively mild. Performance on an intelligence test, a test of sustained attention and tests designed to measure higher cortical function were compared in a group of 29 subjects who underwent 34-36 hours of continuous sleep deprivation and 32 normal sleeping control subjects. No significant group performance differences were noted on any measure. One night of total sleep deprivation does not appear to significantly impair performance on tasks that are designed to assess higher cortical functioning or frontal lobe function