Deep learning for automated sleep monitoring

Wearable electroencephalography (EEG) is a technology that is revolutionising the longitudinal monitoring of neurological and mental disorders, improving the quality of life of patients and accelerating the relevant research. As sleep disorders and other conditions related to sleep quality affect a large part of the population, monitoring sleep at home, over extended periods of time could have significant impact on the quality of life of people who suffer from these conditions. Annotating the sleep architecture of patients, known as sleep stage scoring, is an expensive and time-consuming process that cannot scale to a large number of people. Using wearable EEG and automating sleep stage scoring is a potential solution to this problem. In this thesis, we propose and evaluate two deep learning algorithms for automated sleep stage scoring using a single channel of EEG. In our first method, we use time-frequency analysis for extracting features that closely follow the guidelines that human experts follow, combined with an ensemble of stacked sparse autoencoders as our classification algorithm. In our second method, we propose a convolutional neural network (CNN) architecture for automatically learning filters that are specific to the problem of sleep stage scoring. We achieved state-of-the-art results (mean F1-score 84%; range 82-86%) with our first method and comparably good results with the second (mean F1-score 81%; range 79-83%). Both our methods effectively account for the skewed performance that is usually found in the literature due to sleep stage duration imbalance. We propose a filter analysis and visualisation methodology for CNNs to understand the filters that CNNs learn. Our results indicate that our CNN was able to robustly learn filters that closely follow the sleep scoring guidelines.Open Acces

An easy to calculate equation to estimate GFR based on inulin clearance

Background. For the estimation of renal function on the basis of serum creatinine, either the Cockcroft-Gault (CG) equation or the MDRD formula is commonly used. Compared to MDRD (using power functions), CG has the advantage of easy calculability at the bedside. MDRD, however, approaches glomerular filtration rate (GFR) more precisely than CG and gives values corrected for a body surface area (BSA) of 1.73 m2. We wondered whether CG could be adapted to estimate GFR rather than creatinine clearance without losing the advantage of easy calculability. In this prospective study, inulin clearance under well-defined conditions was taken as the gold standard for GFR. Methods. In 182 living kidney donors, inulin clearance was measured under standardized conditions (protein, salt and water intake, overnight stay) before and after nephrectomy. Together with the serum creatinine level, and demographic and clinical data, 281 measurements of inulin clearance were used to compare the accuracy of different estimation equations. Using stepwise multiple regression, a new set of constants was defined for a CG-like equation in order to estimate GFR. Results. The MDRD equation underestimated GFR by 9%, and the quadratic equation suggested by Rule overestimated GFR by 12.4%. The new CG-like equation, even when calculated with ‘mental arithmetic-friendly' rounded parameters, showed significantly less bias (1.2%). The adapted equation is Conclusions. We propose the CG-like equation called IB-eGFR (Inulinclearance Based eGFR) to estimate GFR more reliably than MDRD, Rule's equation or the original Cockcroft-Gault equation. As our data represent a Caucasian population, the adapted equation is still to be validated for patients of other ethnicit

Weekly low-dose treatment with intravenous iron sucrose maintains iron status and decreases epoetin requirement in iron-replete haemodialysis patients

Background. Haemodialysis patients need sustained treatment with intravenous iron because iron deficiency limits the efficacy of recombinant human epoetin therapy in these patients. However, the optimal intravenous iron maintenance dose has not been established yet. Methods. We performed a prospective multicentre clinical trial in iron-replete haemodialysis patients to evaluate the efficacy of weekly low-dose (50 mg) intravenous iron sucrose administration for 6 months to maintain the iron status, and to examine the effect on epoetin dosage needed to maintain stable haemoglobin values in these patients. Fifty patients were enrolled in this prospective, open-label, single arm, phase IV study. Results. Forty-two patients (84%) completed the study. After 6 months of intravenous iron sucrose treatment, the mean ferritin value showed a tendency to increase slightly from 405 ± 159 at baseline to 490 ± 275 µg/l at the end of the study, but iron, transferrin levels and transferrin saturation did not change. The haemoglobin level remained stable (12 ± 1.1 at baseline and 12.1 ± 1.5 g/dl at the end of the study). The mean dose of darbepoetin alfa could be reduced from 0.75 to 0.46 µg/kg/week; epoetin alfa was decreased from 101 to 74 IU/kg/week; and the mean dose of epoetin beta could be reduced from 148 to 131 IU/kg/week at the end of treatment. Conclusions. A regular 50 mg weekly dosing schedule of iron sucrose maintains stable iron stores and haemoglobin levels in haemodialysed patients and allows considerable dose reductions for epoetins. Low-dose intravenous iron therapy may represent an optimal approach to treat the continuous loss of iron in dialysis patient

PTX3 Polymorphisms and Invasive Mold Infections After Solid Organ Transplant

Donor PTX3 polymorphisms were shown to influence the risk of invasive aspergillosis among hematopoietic stem cell transplant recipients. Here, we show that PTX3 polymorphisms are independent risk factors for invasive mold infections among 1101 solid organ transplant recipients, thereby strengthening their role in mold infection pathogenesis and patients' risk stratificatio

Automatic sleep stage scoring with single-channel EEG using convolutional neural networks

We used convolutional neural networks (CNNs) for automatic sleep stage scoring based on single-channel electroencephalography (EEG) to learn task-specific filters for classification without using prior domain knowledge. We used an openly available dataset from 20 healthy young adults for evaluation and applied 20-fold cross-validation. We used class balanced random sampling within the stochastic gradient descent (SGD) optimization of the CNN to avoid skewed performance in favor of the most represented sleep stages. We achieved high mean F1-score (81%, range 79-83%), mean accuracy across individual sleep stages (82%, range 80-84%) and overall accuracy (74%, range 71-76%) over all subjects. By analyzing and visualizing the filters that our CNN learns, we found that rules learned by the filters correspond to sleep scoring criteria in the American Academy of Sleep Medicine (AASM) manual that human experts follow. Our method's performance is balanced across classes and our results are comparable to state-of-the-art methods with hand-engineered features. We show that, without using prior domain knowledge, a CNN can automatically learn to distinguish among different normal sleep stages