Oximetry use in obstructive sleep apnea

Producción CientíficaIntroduction. Overnight oximetry has been proposed as an accessible, simple, and reliable technique for obstructive sleep apnea syndrome (OSAS) diagnosis. From visual inspection to advanced signal processing, several studies have demonstrated the usefulness of oximetry as a screening tool. However, there is still controversy regarding the general application of oximetry as a single screening methodology for OSAS. Areas covered. Currently, high-resolution portable devices combined with pattern recognition-based applications are able to achieve high performance in the detection this disease. In this review, recent studies involving automated analysis of oximetry by means of advanced signal processing and machine learning algorithms are analyzed. Advantages and limitations are highlighted and novel research lines aimed at improving the screening ability of oximetry are proposed. Expert commentary. Oximetry is a cost-effective tool for OSAS screening in patients showing high pretest probability for the disease. Nevertheless, exhaustive analyses are still needed to further assess unattended oximetry monitoring as a single diagnostic test for sleep apnea, particularly in the pediatric population and in especial groups with significant comorbidities. In the following years, communication technologies and big data analysis will overcome current limitations of simplified sleep testing approaches, changing the detection and management of OSAS.This research has been partially supported by the projects DPI2017-84280-R and RTC-2015-3446-1 from Ministerio de Economía, Industria y Competitividad and European Regional Development Fund (FEDER), the project 66/2016 of the Sociedad Española de Neumología y Cirugía Torácica (SEPAR), and the project VA037U16 from the Consejería de Educación de la Junta de Castilla y León and FEDER. D. Álvarez was in receipt of a Juan de la Cierva grant IJCI-2014-22664 from the Ministerio de Economía y Competitividad

Vauvojen unen luokittelu patja-sensorilla ja EKG:lla

Infants spend the majority of their time asleep. Although extensive studies have been carried out, the role of sleep for infant cognitive, psychomotor, temperament and developmental outcomes is not clear. The current contradictory results may be due to the limited precision when monitoring infant sleep for prolonged periods of time, from weeks to even months. Sleep-wake cycle can be assessed with sleep questionnaires and actigraphy, but they cannot separate sleep stages. The gold standard for sleep state annotation is polysomnography (PSG), which consist of several signal modalities such as electroencephalogram, electrooculogram, electrocardiogram (ECG), electromyogram, respiration sensor and pulse oximetry. A sleep clinician manually assigns sleep stages for 30 sec epochs based on the visual observation of these signals. Because method is obtrusive and laborious it is not suitable for monitoring long periods. There is, therefore, a need for an automatic and unobtrusive sleep staging approach. In this work, a set of classifiers for infant sleep staging was created and evaluated. The cardiorespiratory and gross body movement signals were used as an input. The different classifiers aim to distinguish between two or more different sleep states. The classifiers were built on a clinical sleep polysomnography data set of 48 infants with ages ranging from 1 week to 18 weeks old (a median of 5 weeks). Respiration and gross body movements were observed using an electromechanical film bed mattress sensor manufactured by Emfit Ltd. ECG of the PSG setup was used for extracting cardiac activity. Signals were preprocessed to remove artefacts and an extensive set of features (N=81) were extracted on which the classifiers were trained. The NREM3 vs other states classifier provided the most accurate results. The median accuracy was 0.822 (IQR: 0.724-0.914). This is comparable to previously published studies on other sleep classifiers, as well as to the level of clinical interrater agreement. Classification methods were confounded by the lack of muscle atonia and amount of gross body movements in REM sleep. The proposed method could be readily applied for home monitoring, as well as for monitoring in neonatal intensive care units.Vauvat nukkuvat suurimman osan vuorokaudesta. Vaikkakin laajasti on tutkittu unen vaikutusta lapsen kognitioon, psykomotoriikkaan, temperamenttiin ja kehitykseen, selkeää kuvaa ja yhtenäistä konsensusta tiedeyhteisössä ei ole saavutettu. Yksi syy tähän on että ei ole olemassa menetelmää, joka soveltuisi jatkuva-aikaiseen ja pitkäkestoiseen unitilan monitorointiin. Vauvojen uni-valve- sykliä voidaan selvittää vanhemmille suunnatuilla kyselyillä ja aktigrafialla, mutta näillä ei voi havaita unitilojen rakennetta. Kliinisenä standardina unitilojen seurannassa on polysomnografia, jossa samanaikaisesti mitataan mm. potilaan elektroenkelografiaa, elektro-okulografiaa, elektrokardiografiaa, electromyografiaa, hengitysinduktiivisesta pletysmografiaa, happisaturaatiota ja hengitysvirtauksia. Kliinikko suorittaa univaiheluokittelun signaaleista näkyvien, vaiheille tyypillisten, hahmojen perusteella. Työläyden ja häiritsevän mittausasetelman takia menetelmä ei sovellu pitkäaikaiseen seurantaan. On tarvetta kehittää tarkoitukseen sopivia automaattisia ja huomaamattomia unenseurantamenetelmiä. Tässä työssä kehitettiin ja testattiin sydämen syke-, hengitys ja liikeanalyysiin perustuvia koneluokittimia vauvojen unitilojen havainnointiin. Luokittimet opetettiin kliinisessa polysomnografiassa kerätyllä datalla 48 vauvasta, joiden ikä vaihteli 1. viikosta 18. viikkoon (mediaani 5 viikkoa). Vauvojen hengitystä ja liikkeitä seurattiin Emfit Oy:n valmistamalla elektromekaaniseen filmiin pohjatuvalla patja-sensorilla. Lisäksi ECG:lla seurattiin sydäntä ja opetuksessa käytettiin lääkärin suorittamaa PSG-pohjaista luokitusta. Esikäsittelyn jälkeen signaaleista laskettiin suuri joukko piirrevektoreita (N=81), joihin luokittelu perustuu. NREM3-univaiheen tunnistus onnistui parhaiten 0.822 mediaani-tarkkuudella ja [0.724,0.914] kvartaaleilla. Tulos on yhtenevä kirjallisuudessa esitettyjen arvojen kanssa ja vastaa kliinikkojen välistä toistettavuutta. Muilla luokittimilla univaiheet sekoituivat keskenään, mikä on oletattavasti selitettävissä aikuisista poikeavalla REM-unen aikaisella lihasjäykkyydellä ja kehon liikkeillä. Työ osoittaa, että menetelmällä voi seurata vauvojen uniluokkien oskillaatiota. Järjestelmää voisi käyttää kotiseurannassa tai vastasyntyneiden teholla unenvalvontaan

Sleep stage and obstructive apneaic epoch classification using single-lead ECG

<p>Abstract</p> <p>Background</p> <p>Polysomnography (PSG) is used to define physiological sleep and different physiological sleep stages, to assess sleep quality and diagnose many types of sleep disorders such as obstructive sleep apnea. However, PSG requires not only the connection of various sensors and electrodes to the subject but also spending the night in a bed that is different from the subject's own bed. This study is designed to investigate the feasibility of automatic classification of sleep stages and obstructive apneaic epochs using only the features derived from a single-lead electrocardiography (ECG) signal.</p> <p>Methods</p> <p>For this purpose, PSG recordings (ECG included) were obtained during the night's sleep (mean duration 7 hours) of 17 subjects (5 men) with ages between 26 and 67. Based on these recordings, sleep experts performed sleep scoring for each subject. This study consisted of the following steps: (1) Visual inspection of ECG data corresponding to each 30-second epoch, and selection of epochs with relatively clean signals, (2) beat-to-beat interval (RR interval) computation using an R-peak detection algorithm, (3) feature extraction from RR interval values, and (4) classification of sleep stages (or obstructive apneaic periods) using one-versus-rest approach. The features used in the study were the median value, the difference between the 75 and 25 percentile values, and mean absolute deviations of the RR intervals computed for each epoch. The k-nearest-neighbor (kNN), quadratic discriminant analysis (QDA), and support vector machines (SVM) methods were used as the classification tools. In the testing procedure 10-fold cross-validation was employed.</p> <p>Results</p> <p>QDA and SVM performed similarly well and significantly better than kNN for both sleep stage and apneaic epoch classification studies. The classification accuracy rates were between 80 and 90% for the stages other than non-rapid-eye-movement stage 2. The accuracies were 60 or 70% for that specific stage. In five obstructive sleep apnea (OSA) patients, the accurate apneaic epoch detection rates were over 89% for QDA and SVM.</p> <p>Conclusion</p> <p>This study, in general, showed that RR-interval based classification, which requires only single-lead ECG, is feasible for sleep stage and apneaic epoch determination and can pave the road for a simple automatic classification system suitable for home-use.</p

Survey of Machine Learning Techniques in the Analysis of EEG Signals for Parkinson’s Disease: A Systematic Review.

Background: Parkinson’s disease (PD) affects 7–10 million people worldwide. Its diagnosis is clinical and can be supported by image-based tests, which are expensive and not always accessible. Electroencephalograms (EEG) are non-invasive, widely accessible, low-cost tests. However, the signals obtained are difficult to analyze visually, so advanced techniques, such as Machine Learning (ML), need to be used. In this article, we review those studies that consider ML techniques to study the EEG of patients with PD. Methods: The review process was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, which are used to provide quality standards for the objective evaluation of various studies. All publications before February 2022 were included, and their main characteristics and results were evaluated and documented through three key points associated with the development of ML techniques: dataset quality, data preprocessing, and model evaluation. Results: 59 studies were included. The predominating models were Support Vector Machine (SVM) and Artificial Neural Networks (ANNs). In total, 31 articles diagnosed PD with a mean accuracy of 97.35 ± 3.46%. There was no standard cleaning protocol for EEG and a great heterogeneity in EEG characteristics was shown, although spectral features predominated by 88.37%. Conclusions: Neither the cleaning protocol nor the number of EEG channels influenced the classification results. A baseline value was provided for the PD diagnostic problem, although recent studies focus on the identification of cognitive impairment.post-print1392 K

Intelligent Biosignal Analysis Methods

This book describes recent efforts in improving intelligent systems for automatic biosignal analysis. It focuses on machine learning and deep learning methods used for classification of different organism states and disorders based on biomedical signals such as EEG, ECG, HRV, and others

Obstructive Sleep Apnea Detection Methods Based on Heart Rate Variability Analysis: Opportunities for a Future Cinc Challenge

[EN] The effects of sleep-related disorders, such as obstructive sleep apnea (OSA), can be devastating either in children or adults. Misdiagnosis may lead to severe cardiovascular diseases. Besides, OSA consequences are often related to bad job performance, and road accidents. Nowadays, polysomnography (PSG) is still considered the gold standard for OSA diagnosis, but the required facilities are extremely high, thus reducing availability worldwide. For this reason, simpler and cost-effective diagnosing methods have been proposed in the late years. In this regard, the heart rate variability (HRV) has been demonstrated to strongly reflect apnea episodes during sleep. Hence, this work reviews the latest advances in the evaluation of OSA from the HRV perspective to consider its potentialities for a future revisited CinC Challenge.This research has been supported by grants DPI201783952-C3 from MINECO/AEI/FEDER EU, SBPLY/17/180501/000411 from Junta de Comunidades de Castilla-la Mancha and AICO/2019/036 from Generalitat Valenciana. Moreover, Daniele Padovano has held graduate research scholarships from Escuela Polit ' ecnica de Cuenca and Instituto de Tecnolog ' ias Audiovisuales, University of CastillaLa ManchaPadovano, D.; Martinez-Rodrigo, A.; Pastor, JM.; Rieta, JJ.; Alcaraz, R. (2020). Obstructive Sleep Apnea Detection Methods Based on Heart Rate Variability Analysis: Opportunities for a Future Cinc Challenge. IEEE. 1-4. https://doi.org/10.22489/CinC.2020.400S1

Unobtrusive monitoring of behavior and movement patterns to detect clinical depression severity level via smartphone

The number of individuals with mental disorders is increasing and they are commonly found among individuals who avoid social interaction and like to live alone. Amongst such mental health disorders is depression which is both common and serious. The present paper introduces a method to assess the depression level of an individual using a smartphone by monitoring their daily activities. The time domain characteristics from a smartphone acceleration sensor were used alongside a vector machine algorithm to classify physical activities. Additionally, the geographical location information was clustered using a smartphone GPS sensor to simplify movement patterns. A total of 12 features were extracted from individuals’ physical activity and movement patterns and were analyzed alongside their weekly depression scores using the nine-item Patient Health Questionnaire. Using a wrapper feature selection method, a subset of features was selected and applied to a linear regression model to estimate the depression score. The support vector machine algorithm was then used to classify the depression severity level among individuals (absence, moderate, severe) and had an accuracy of 87.2% in severe depression cases which outperformed other classification models including the k-nearest neighbor and artificial neural network. This method of identifying depression is a cost-effective solution for long-term use and can monitor individuals for depression without invading their personal space or creating other day-to-day disturbances