DeepSleepNet-Lite: A Simplified Automatic Sleep Stage Scoring Model with Uncertainty Estimates

Deep learning is widely used in the most recent automatic sleep scoring algorithms. Its popularity stems from its excellent performance and from its ability to process raw signals and to learn feature directly from the data. Most of the existing scoring algorithms exploit very computationally demanding architectures, due to their high number of training parameters, and process lengthy time sequences in input (up to 12 minutes). Only few of these architectures provide an estimate of the model uncertainty. In this study we propose DeepSleepNet-Lite, a simplified and lightweight scoring architecture, processing only 90-seconds EEG input sequences. We exploit, for the first time in sleep scoring, the Monte Carlo dropout technique to enhance the performance of the architecture and to also detect the uncertain instances. The evaluation is performed on a single-channel EEG Fpz-Cz from the open source Sleep-EDF expanded database. DeepSleepNet-Lite achieves slightly lower performance, if not on par, compared to the existing state-of-the-art architectures, in overall accuracy, macro F1-score and Cohen’s kappa (on Sleep-EDF v1-2013 ±30mins: 84.0%, 78.0%, 0.78; on Sleep-EDF v2-2018 ±30mins: 80.3%, 75.2%, 0.73). Monte Carlo dropout enables the estimate of the uncertain predictions. By rejecting the uncertain instances, the model achieves higher performance on both versions of the database (on Sleep-EDF v1-2013 ±30mins: 86.1.0%, 79.6%, 0.81; on Sleep-EDF v2-2018 ±30mins: 82.3%, 76.7%, 0.76). Our lighter sleep scoring approach paves the way to the application of scoring algorithms for sleep analysis in realtime

Multi-Scored Sleep Databases: How to Exploit the Multiple-Labels in Automated Sleep Scoring.

STUDY OBJECTIVES Inter-scorer variability in scoring polysomnograms is a well-known problem. Most of the existing automated sleep scoring systems are trained using labels annotated by a single scorer, whose subjective evaluation is transferred to the model. When annotations from two or more scorers are available, the scoring models are usually trained on the scorer consensus. The averaged scorer's subjectivity is transferred into the model, losing information about the internal variability among different scorers. In this study, we aim to insert the multiple-knowledge of the different physicians into the training procedure. The goal is to optimize a model training, exploiting the full information that can be extracted from the consensus of a group of scorers. METHODS We train two lightweight deep learning based models on three different multi-scored databases. We exploit the label smoothing technique together with a soft-consensus (LSSC) distribution to insert the multiple-knowledge in the training procedure of the model. We introduce the averaged cosine similarity metric (ACS) to quantify the similarity between the hypnodensity-graph generated by the models with-LSSC and the hypnodensity-graph generated by the scorer consensus. RESULTS The performance of the models improves on all the databases when we train the models with our LSSC. We found an increase in ACS (up to 6.4%) between the hypnodensity-graph generated by the models trained with-LSSC and the hypnodensity-graph generated by the consensus. CONCLUSION Our approach definitely enables a model to better adapt to the consensus of the group of scorers. Future work will focus on further investigations on different scoring architectures and hopefully large-scale-heterogeneous multi-scored datasets

Deep Generative Models: The winning key for large and easily accessible ECG datasets?

Large high-quality datasets are essential for building powerful artificial intelligence (AI) algorithms capable of supporting advancement in cardiac clinical research. However, researchers working with electrocardiogram (ECG) signals struggle to get access and/or to build one. The aim of the present work is to shed light on a potential solution to address the lack of large and easily accessible ECG datasets. Firstly, the main causes of such a lack are identified and examined. Afterward, the potentials and limitations of cardiac data generation via deep generative models (DGMs) are deeply analyzed. These very promising algorithms have been found capable not only of generating large quantities of ECG signals but also of supporting data anonymization processes, to simplify data sharing while respecting patients' privacy. Their application could help research progress and cooperation in the name of open science. However several aspects, such as a standardized synthetic data quality evaluation and algorithm stability, need to be further explored

A novel interaction mechanism accounting for different acylphosphatase effects on cardiac and fast twitch skeletal muscle sarcoplasmic reticulum calcium pumps

AbstractIn cardiac and skeletal muscle Ca2+ translocation from cytoplasm into sarcoplasmic reticulum (SR) is accomplished by different Ca2+-ATPases whose functioning involves the formation and decomposition of an acylphosphorylated phosphoenzyme intermediate (EP). In this study we found that acylphosphatase, an enzyme well represented in muscular tissues and which actively hydrolyzes EP, had different effects on heart (SERCA2a) and fast twitch skeletal muscle SR Ca2+-ATPase (SERCA1). With physiological acylphosphatase concentrations SERCA2a exhibited a parallel increase in the rates of both ATP hydrolysis and Ca2+ transport; in contrast, SERCA1 appeared to be uncoupled since the stimulation of ATP hydrolysis matched an inhibition of Ca2+ pump. These different effects probably depend on phospholamban, which is associated with SERCA2a but not SERCA1. Consistent with this view, the present study suggests that acylphosphatase-induced stimulation of SERCA2a, in addition to an enhanced EP hydrolysis, may be due to a displacement of phospholamban, thus to a removal of its inhibitory effect

Dual, HLA-B27 Subtype-dependent Conformation of a Self-peptide

The products of the human leukocyte antigen subtypes HLA-B*2705 and HLA-B*2709 differ only in residue 116 (Asp vs. His) within the peptide binding groove but are differentially associated with the autoimmune disease ankylosing spondylitis (AS); HLA-B*2705 occurs in AS-patients, whereas HLA-B*2709 does not. The subtypes also generate differential T cell repertoires as exemplified by distinct T cell responses against the self-peptide pVIPR (RRKWRRWHL). The crystal structures described here show that pVIPR binds in an unprecedented dual conformation only to HLA-B*2705 molecules. In one binding mode, peptide pArg5 forms a salt bridge to Asp116, connected with drastically different interactions between peptide and heavy chain, contrasting with the second, conventional conformation, which is exclusively found in the case of B*2709. These subtype-dependent differences in pVIPR binding link the emergence of dissimilar T cell repertoires in individuals with HLA-B*2705 or HLA-B*2709 to the buried Asp116/His116 polymorphism and provide novel insights into peptide presentation by major histocompatibility antigens

HLA-E gene polymorphism associates with ankylosing spondylitis in Sardinia

Abstract INTRODUCTION: Ankylosing spondylitis (AS) is a severe, chronic inflammatory disease strongly associated with HLA-B27. The presence of additional HLA risk factors has been suggested by several studies. The aim of the current study is to assess the occurrence of an additional HLA susceptibility locus in the region between HLA-E and HLA-C in the Sardinian population. METHODS: 200 random controls, 120 patients with AS and 175 HLA-B27 positive controls were genotyped for six single nucleotide polymorphisms (SNPs) spanning the HLA region between HLA-E and HLA-C loci previously shown to harbour an additional susceptibility locus for AS. Allele, genotype and haplotype frequencies were compared. RESULTS: The data confirm our previous finding of a significant increase in patients with AS of allele A at SNP rs1264457 encoding for an Arg at the functional HLA-E polymorphism (Arg128/Gly128). This was due to a remarkable increase in the frequency of genotype A/A in patients vs HLA-B27-matched controls (51% vs 29%; P for trend: 5 x 10-5). Genotype distribution of three other SNPs mapping in genes (GNL1, PRR3 and ABCF-1) close to HLA-E and showing high LD with it, was also significantly skewed. Accordingly, haplotype distribution was also remarkably different. The frequency of the haplotype AAGA, is 42% in random controls, increases to 53% in the HLA-B27-positive controls, and reaches 68% in patients with AS (P values: 2 x 10-11 vs random and 3 x 10-4 vs HLA-B27 controls). CONCLUSIONS: There is a strong association between the presence of a haplotype in genes mapping between HLA-E and HLA-C and AS due to an increase of homozygous markers in patients. The strongest association however, is with the HLA-E functional polymorphism rs1264457. Since HLA-E is the ligand for the NKG2A receptor, these data point to the natural killer (NK) activity as possible player in the pathogenesis of AS

A khorasan wheat-based replacement diet improves risk profile of patients with type 2 diabetes mellitus (T2DM): a randomized crossover trial

PURPOSES: The aim of the present study was to examine whether a replacement diet with products made with organic ancient khorasan wheat could provide additive protective effects in reducing glucose, insulin, lipid and inflammatory risk factors, and in restoring blood redox balance in type 2 diabetes mellitus (T2DM) patients compared to diet with product made with modern organic wheat. METHODS: We conducted a randomized, double-blinded crossover trial with two intervention phases on 21 T2DM patients (14 females, 7 males). The participants were assigned to consume products (bread, pasta, crackers and biscuits) made using semi-whole flour from organic wheat that was either from ancient khorasan wheat or modern control wheat for 8 weeks in a random order. An 8-week washout period was implemented between the interventions. Laboratory analyses were performed both at the beginning and at the end of each intervention phase. RESULTS: The metabolic risk profile improved only after the khorasan intervention period, as measured by a reduction in total and LDL cholesterol (mean reduction: −3.7 and −3.4 %, respectively), insulin (−16.3 %) and blood glucose (−9.1 %). Similarly, there was a significant reduction in circulating levels of reactive oxygen species (ROS), vascular endothelial growth factor (VEGF) and interleukin-1ra, and a significant increase of total antioxidant capacity (+6.3 %). No significant differences from baseline were noted after the modern control wheat intervention phase. The change (from pre- to post-intervention) between the two intervention arms was significantly different (p < 0.05) for total and LDL-c, insulin and HOMA index. CONCLUSIONS: A replacement diet with ancient khorasan wheat consumption provided additive protection in reducing total and LDL cholesterol, insulin, blood glucose, ROS production, and some inflammatory risk factors, which are all key factors warranting of control in secondary prevention of T2DM compared to a diet with products made with modern wheat

U-Sleep's resilience to AASM guidelines

AASM guidelines are the result of decades of efforts aiming at standardizing sleep scoring procedure, with the final goal of sharing a worldwide common methodology. The guidelines cover several aspects from the technical/digital specifications,e.g., recommended EEG derivations, to detailed sleep scoring rules accordingly to age. Automated sleep scoring systems have always largely exploited the standards as fundamental guidelines. In this context, deep learning has demonstrated better performance compared to classical machine learning. Our present work shows that a deep learning based sleep scoring algorithm may not need to fully exploit the clinical knowledge or to strictly adhere to the AASM guidelines. Specifically, we demonstrate that U-Sleep, a state-of-the-art sleep scoring algorithm, can be strong enough to solve the scoring task even using clinically non-recommended or non-conventional derivations, and with no need to exploit information about the chronological age of the subjects. We finally strengthen a well-known finding that using data from multiple data centers always results in a better performing model compared with training on a single cohort. Indeed, we show that this latter statement is still valid even by increasing the size and the heterogeneity of the single data cohort. In all our experiments we used 28528 polysomnography studies from 13 different clinical studies

Effects of clinical decision topic on patients’ involvement in and satisfaction with decisions and their subsequent implementation

Clinical decision-making is the vehicle for mental health care delivery, and predictors of decision-making experience and adherence are under-researched. The aim was to investigate the relationship between decision topic and kind of involvement in the decision, satisfaction and subsequent implementation, from both staff and patient perspectives