Decentralized Data-Privacy Preserving Deep-Learning Approaches for Enhancing Inter-Database Generalization in Automatic Sleep Staging.

Automatic sleep staging has been an active field of development. Despite multiple efforts, the area remains a focus of research interest. Indeed, while promising results have reported in past literature, uptake of automatic sleep scoring in the clinical setting remains low.

Challenges of Applying Automated Polysomnography Scoring at Scale.

Automatic polysomnography analysis can be leveraged to shorten scoring times, reduce associated costs, and ultimately improve the overall diagnosis of sleep disorders. Multiple and diverse strategies have been attempted for implementation of this technology at scale in the routine workflow of sleep centers. The field, however, is complex and presents unsolved challenges in a number of areas.

Quality of Sleep Data Validation From the Xiaomi Mi Band 5 Against Polysomnography: Comparison Study.

Background: Polysomnography is the gold standard for measuring and detecting sleep patterns. In recent years, activity wristbands have become popular because they record continuous data in real time. Hence, comprehensive validation studies are needed to analyze the performance and reliability of these devices in the recording of sleep parameters.

Computer-assisted analysis of polysomnographic recordings improves inter-scorer associated agreement and scoring times.

Study Objectives: To investigate inter-scorer agreement and scoring time differences associated with visual and computer-assisted analysis of polysomnographic (PSG) recordings.

Methods: A group of 12 expert scorers reviewed 5 PSGs that were independently selected in the context of each of the following tasks: (i) sleep staging, (ii) scoring of leg movements, (iii) detection of respiratory (apneic-related) events, and (iv) of electroencephalographic (EEG) arousals. All scorers independently reviewed the same recordings, hence resulting in 20 scoring exercises per scorer from an equal amount of different subjects.

Inter-database validation of a deep learning approach for automatic sleep scoring.

Study Objectives: Development of inter-database generalizable sleep staging algorithms represents a challenge due to increased data variability across different datasets. Sharing data between different centers is also a problem due to potential restrictions due to patient privacy protection. In this work, we describe a new deep learning approach for automatic sleep staging, and address its generalization capabilities on a wide range of public sleep staging databases.

Sleep-related leg movements in obstructive sleep apnea: definitions, determinants, and clinical consequences.

Study Objectives: To investigate (1) the effect of different scoring rules on leg movement (LM) classification in patients with obstructive sleep apnea (OSA); (2) determinants of respiratory event related leg movements (rLM); and (3) to relate LM parameters to clinical outcomes.

Methods: (1) LM classification was compared between the World Association of Sleep Medicine (WASM) 2006 and the WASM 2016 rules in 336 participants with apnea hypopnea index (AHI) ≥ 5; (2) determinants and features of rLM were investigated with logistic mixed regression in 172 participants with AHI ≥ 10 and respiratory disturbance index (RDI) ≥ 15, and (3) LM parameters were compared for patients with and without cardiovascular events and related to continuous positive airway pressure (CPAP) adherence.

Results: WASM-2016 scoring significantly reduced periodic limb movements of sleep (PLMS) frequency in OSA participants even when only considering the new periodicity criteria.

Addressing database variability in learning from medical data: An ensemble-based approach using convolutional neural networks and a case of study applied to automatic sleep scoring.

In this work we examine some of the problems associated with the development of machine learning models with the objective to achieve robust generalization capabilities on common-task multiple-database scenarios. Referred to as the "database variability problem", we focus on a specific medical domain (sleep staging in sleep medicine) to show the non-triviality of translating the estimated model's local generalization capabilities into independent external databases. We analyze some of the scalability problems when multiple-database data are used as inputs to train a single learning model.

Large-scale validation of an automatic EEG arousal detection algorithm using different heterogeneous databases.

Objective: To assess the validity of an automatic EEG arousal detection algorithm using large patient samples and different heterogeneous databases.

Methods: Automatic scorings were confronted with results from human expert scorers on a total of 2768 full-night PSG recordings obtained from two different databases. Of them, 472 recordings were obtained during a clinical routine at our sleep center and were subdivided into two subgroups of 220 (HMC-S) and 252 (HMC-M) recordings each, according to the procedure followed by the clinical expert during the visual review (semi-automatic or purely manual, respectively).

Sleep-Related Leg Movements in Patients with Transient Ischemic Attack and Controls.

Background: Periodic leg movements during sleep (PLMS) have been associated with an increased risk for cardiovascular diseases and there is a high prevalence of PLMS found in patients with obstructive sleep apnea syndrome (OSAS). We evaluated patients with transient ischemic attack (TIA) for PLMS and respiratory related leg movements (RRLM), versus a control group without TIA.

Methods: Twenty-five patients with TIA and 34 patients with no vascular diagnosis were referred for polysomnography.

A simple and robust method for the automatic scoring of EEG arousals in polysomnographic recordings.

Background: Clinical diagnosis of sleep disorders relies on the polysomnographic test to examine the neurophysiological markers of the sleep process. In this test, the recording of the electroencephalographic activity and the submental electromyogram is the source of the analysis for the detection of electroencephalographic arousals. The identification of these events is important for the evaluation of the sleep continuity because they cause the fragmentation of the normal sleep process.

Stroke and other cardiovascular events in patients with obstructive sleep apnea and the effect of continuous positive airway pressure.

Obstructive sleep apnea (OSA) is a known-risk factor for cardiovascular diseases. There are indications that treatment with continuous positive airway pressure (CPAP) reduces the risk of new cardiovascular events. In this study, we analyzed the incidence of cardiovascular events in patients with OSA and compared for the impact of CPAP therapy.

Spectral Heart Rate Variability analysis using the heart timing signal for the screening of the Sleep Apnea-Hypopnea Syndrome.

Some approaches have been published in the past using Heart Rate Variability (HRV) spectral features for the screening of Sleep Apnea-Hypopnea Syndrome (SAHS) patients. However there is a big variability among these methods regarding the selection of the source signal and the specific spectral components relevant to the analysis. In this study we investigate the use of the Heart Timing (HT) as the source signal in comparison to the classical approaches of Heart Rate (HR) and Heart Period (HP).

Computer-assisted diagnosis of the Sleep Apnea-Hypopnea Syndrome: An overview of different approaches.

Automatic diagnosis of the Sleep Apnea-Hypopnea Syndrome (SAHS) has become an important area of research due to the growing interest in the field of sleep medicine, and the costs associated to its manual diagnosis. The increment and heterogeneity of the different techniques, however, makes somewhat difficult to adequately follow recent developments. In this paper an overview within the area of computer-assisted diagnosis of SAHS has been performed.

Computer-Assisted Diagnosis of the Sleep Apnea-Hypopnea Syndrome: A Review.

Automatic diagnosis of the Sleep Apnea-Hypopnea Syndrome (SAHS) has become an important area of research due to the growing interest in the field of sleep medicine and the costs associated with its manual diagnosis. The increment and heterogeneity of the different techniques, however, make it somewhat difficult to adequately follow the recent developments. A literature review within the area of computer-assisted diagnosis of SAHS has been performed comprising the last 15 years of research in the field.

Improving detection of apneic events by learning from examples and treatment of missing data.

This paper presents a comparative study over the respiratory pattern classification task involving three missing data imputation techniques, and four different machine learning algorithms. The main goal was to find a classifier that achieves the best accuracy results using a scalable imputation method in comparison to the method used in a previous work of the authors. The results obtained show that the Self-organization maps imputation method allows any classifier to achieve improvements over the rest of the imputation methods, and that the Feedforward neural network classifier offers the best performance regardless the imputation method used.

Intelligent approach for analysis of respiratory signals and oxygen saturation in the sleep apnea/hypopnea syndrome.

This work deals with the development of an intelligent approach for clinical decision making in the diagnosis of the Sleep Apnea/Hypopnea Syndrome, SAHS, from the analysis of respiratory signals and oxygen saturation in arterial blood, SaO2. In order to accomplish the task the proposed approach makes use of different artificial intelligence techniques and reasoning processes being able to deal with imprecise data. These reasoning processes are based on fuzzy logic and on temporal analysis of the information.