Screening the risk of obstructive sleep apnea by utilizing supervised learning techniques based on anthropometric features and snoring events

OBJECTIVES: Obstructive sleep apnea (OSA) is typically diagnosed by polysomnography (PSG). However, PSG is time-consuming and has some clinical limitations. This study thus aimed to establish machine learning models to screen for the risk of having moderate-to-severe and severe OSA based on easily acquired features. METHODS: We collected PSG data on 3529 patients from Taiwan and further derived the number of snoring events. Their baseline characteristics and anthropometric measures were obtained, and correlations among the collected variables were investigated. Next, six common supervised machine learning techniques were utilized, including random forest (RF), extreme gradient boosting (XGBoost), k-nearest neighbor (kNN), support vector machine (SVM), logistic regression (LR), and naïve Bayes (NB). First, data were independently separated into a training and validation dataset (80%) and a test dataset (20%). The approach with the highest accuracy in the training and validation phase was employed to classify the test dataset. Next, feature importance was investigated by calculating the Shapley value of every factor, which represented the impact on OSA risk screening. RESULTS: The RF produced the highest accuracy (of >70%) in the training and validation phase in screening for both OSA severities. Hence, we employed the RF to classify the test dataset, and results showed a 79.32% accuracy for moderate-to-severe OSA and 74.37% accuracy for severe OSA. Snoring events and the visceral fat level were the most and second most essential features of screening for OSA risk. CONCLUSIONS: The established model can be considered for screening for the risk of having moderate-to-severe or severe OSA

Snoring and arousals in full-night polysomnographic studies from sleep apnea-hypopnea syndrome patients

SAHS (Sleep Apnea-Hypopnea Syndrome) is recognized to be a serious disorder with high prevalence in the population. The main clinical triad for SAHS is made up of 3 symptoms: apneas and hypopneas, chronic snoring and excessive daytime sleepiness (EDS). The gold standard for diagnosing SAHS is an overnight polysomnographic study performed at the hospital, a laborious, expensive and time-consuming procedure in which multiple biosignals are recorded. In this thesis we offer improvements to the current approaches to diagnosis and assessment of patients with SAHS. We demonstrate that snoring and arousals, while recognized key markers of SAHS, should be fully appreciated as essential tools for SAHS diagnosis. With respect to snoring analysis (applied to a 34 subjects¿ database with a total of 74439 snores), as an alternative to acoustic analysis, we have used less complex approaches mostly based on time domain parameters. We concluded that key information on SAHS severity can be extracted from the analysis of the time interval between successive snores. For that, we built a new methodology which consists on applying an adaptive threshold to the whole night sequence of time intervals between successive snores. This threshold enables to identify regular and non-regular snores. Finally, we were able to correlate the variability of time interval between successive snores in short 15 minute segments and throughout the whole night with the subject¿s SAHS severity. Severe SAHS subjects show a shorter time interval between regular snores (p=0.0036, AHI cp(cut-point): 30h-1) and less dispersion on the time interval features during all sleep. Conversely, lower intra-segment variability (p=0.006, AHI cp: 30h-1) is seen for less severe SAHS subjects. Also, we have shown successful in classifying the subjects according to their SAHS severity using the features derived from the time interval between regular snores. Classification accuracy values of 88.2% (with 90% sensitivity, 75% specificity) and 94.1% (with 94.4% sensitivity, 93.8% specificity) for AHI cut-points of severity of 5 and 30h-1, respectively. In what concerns the arousal study, our work is focused on respiratory and spontaneous arousals (45 subjects with a total of 2018 respiratory and 2001 spontaneous arousals). Current beliefs suggest that the former are the main cause for sleep fragmentation. Accordingly, sleep clinicians assign an important role to respiratory arousals when providing a final diagnosis on SAHS. Provided that the two types of arousals are triggered by different mechanisms we hypothesized that there might exist differences between their EEG content. After characterizing our arousal database through spectral analysis, results showed that the content of respiratory arousals on a mild SAHS subject is similar to that of a severe one (p>>0.05). Similar results were obtained for spontaneous arousals. Our findings also revealed that no differences are observed between the features of these two kinds of arousals on a same subject (r=0.8, p<0.01 and concordance with Bland-Altman analysis). As a result, we verified that each subject has almost like a fingerprint or signature for his arousals¿ content and is similar for both types of arousals. In addition, this signature has no correlation with SAHS severity and this is confirmed for the three EEG tracings (C3A2, C4A1 and O1A2). Although the trigger mechanisms of the two arousals are known to be different, our results showed that the brain response is fairly the same for both of them. The impact that respiratory arousals have in the sleep of SAHS patients is unquestionable but our findings suggest that the impact of spontaneous arousals should not be underestimated

Models and Analysis of Vocal Emissions for Biomedical Applications

The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies

Sleep Medicine and the Evolution of Contemporary Sleep Pharmacotherapy

Sleep is a fundamental physiological feature experienced by all known mammalian, and most non-mammalian, species. Underscoring its importance is the wide array of neural and cellular processes that have evolved to govern when and how it occurs, its duration, sequence of phases, and the influence it exerts on numerous other brain functions. This book takes up the growing prevalence of sleep disorders affecting these processes and the panorama of pharmaceutical tools that have evolved for their medical care. Its wide-ranging discussion promises not only recent updates on their clinical management but a contemporary window into sleep’s cross-cutting relevance for the many neurological dysfunctions now known to associate with sleep disturbances

Evaluación de la oximetría nocturna portátil como método simplificado de ayuda al diagnóstico del síndrome de apnea-hipopnea del sueño en pacientes con enfermedad pulmonar obstructiva crónica

La Enfermedad Pulmonar Obstructiva Crónica (EPOC) es una enfermedad frecuente, prevenible y tratable, caracterizada por una limitación persistente al flujo aéreo, que usualmente es progresiva y se asocia con una respuesta anormal inflamatoria en las vías aéreas y en los pulmones a partículas nocivas o gases. Los trastornos del sueño son frecuentes en pacientes con EPOC, principalmente el Síndrome de Apnea-Hipopnea del Sueño (SAHS), el insomnio y el movimiento periódico de piernas. Tanto el SAHS como la EPOC son dos enfermedades muy prevalentes en la práctica clínica. Ambas presentan una elevada morbilidad y su asociación conlleva importantes consecuencias sociosanitarias, especialmente en el ámbito de las enfermedades cardiovasculares, así como un aumento del coste anual de la enfermedad. Por todo ello, es esencial un diagnóstico precoz que permita instaurar un tratamiento y disminuir la morbimortalidad de estos pacientes. El método de diagnóstico estándar del SAHS es la polisomnografía (PSG) nocturna en una unidad del sueño especializada. Aunque la PSG es una prueba efectiva, presenta numerosas limitaciones en cuanto a disponibilidad, complejidad, tiempo y coste. Estos inconvenientes han generado grandes listas de espera que retrasan significativamente el diagnóstico y tratamiento de la enfermedad. Esta situación ha puesto de manifiesto la necesidad de nuevas metodologías diagnósticas que permitan reducir la complejidad en el proceso de detección de esta patología. En este sentido, las redes neuronales artificiales (RN) han demostrado una gran utilidad en numerosas aplicaciones dentro de la medicina en general y del contexto del diagnóstico del SAHS en particular. Sin embargo, no hay estudios que hayan validado exhaustivamente el rendimiento de técnicas automáticas aplicadas sobre señales adquiridas en el domicilio de pacientes con EPOC y sospecha de SAHS. Esta necesidad justifica el diseño y evaluación de técnicas automáticas de procesado de la señal de oximetría domiciliaria para la detección de SAHS en pacientes con EPOC. La presente Tesis Doctoral se ha desarrollado bajo la hipótesis de que el empleo de una RN basada en la información procedente de la oximetría nocturna portátil puede ser de utilidad para el diagnóstico del SAHS independientemente de la presencia de EPOC. Por ello, el objetivo principal de este estudio consiste en analizar la utilidad de una RN de detección de SAHS basada en la oximetría nocturna portátil, evaluando exhaustivamente cómo influye en su rendimiento diagnóstico la presencia de una EPOC asociada.Departamento de Biología Celular, Histología y FarmacologíaDoctorado en Investigación en Ciencias de la Salu

Advances in Electrocardiograms

Electrocardiograms have become one of the most important, and widely used medical tools for diagnosing diseases such as cardiac arrhythmias, conduction disorders, electrolyte imbalances, hypertension, coronary artery disease and myocardial infarction. This book reviews recent advancements in electrocardiography. The four sections of this volume, Cardiac Arrhythmias, Myocardial Infarction, Autonomic Dysregulation and Cardiotoxicology, provide comprehensive reviews of advancements in the clinical applications of electrocardiograms. This book is replete with diagrams, recordings, flow diagrams and algorithms which demonstrate the possible future direction for applying electrocardiography to evaluating the development and progression of cardiac diseases. The chapters in this book describe a number of unique features of electrocardiograms in adult and pediatric patient populations with predilections for cardiac arrhythmias and other electrical abnormalities associated with hypertension, coronary artery disease, myocardial infarction, sleep apnea syndromes, pericarditides, cardiomyopathies and cardiotoxicities, as well as innovative interpretations of electrocardiograms during exercise testing and electrical pacing

Behavioural sleep medicine conceptualisations and associated treatment of clinical insomnia disorder in adults

This thesis summarises a selection of forty-two studies [1-42], published by the author during the period 2000-2012, investigating the conceptual basis of Insomnia Disorder, and its evaluation and treatment, principally using cognitive and behavioural interventions. The work reflects a range of research methodologies including experimental, psychometric, qualitative and population-based studies, and randomised controlled trials. Important theoretical contributions to the literature published in this period are also included and reference is made to major textbooks, position papers, and influential chapter contribution