Multi-Class AdaBoost to Detect Sleep Apnea-Hypopnea Syndrome Severity from Oximetry Recordings Obtained at Home

Producción CientíficaThis paper aims at evaluating a novel multi-class methodology to establish Sleep Apnea-Hypopnea Syndrome (SAHS) severity by the use of single-channel at-home oximetry recordings. The study involved 320 participants derived to a specialized sleep unit due to SAHS suspicion. These were assigned to one out of the four SAHS severity degrees according to the apnea-hypopnea index (AHI): no-SAHS (AHI<5 events/hour), mild-SAHS (5≤AHI<15 e/h), moderate-SAHS (15≤AHI<30 e/h), and severe-SAHS (AHI≥30 e/h). A set of statistical, spectral, and non-linear features were extracted from blood oxygen saturation (SpO2) signals to characterize SAHS. Then, an optimum set among these features were automatically selected based on relevancy and redundancy analyses. Finally, a multi-class AdaBoost model, built with the optimum set of features, was obtained from a training set (60%) and evaluated in an independent test set (40%). Our AdaBoost model reached 0.386 Cohen’s kappa in the four-class classification task. Additionally, it reached accuracies of 89.8%, 85.8%, and 74.8% when evaluating the AHI thresholds 5 e/h, 15 e/h, and 30 e/h, respectively, outperforming the classic oxygen desaturation index. Our results suggest that SpO2 obtained at home, along with multi-class AdaBoost, are useful to detect SAHS severity.Junta de Castilla y León (project VA059U13)Pneumology and Thoracic Surgery Spanish Society (265/2012

Evaluation of Machine-Learning Approaches to Estimate Sleep Apnea Severity from at-Home Oximetry Recordings

Producción CientíficaComplexity, costs, and waiting lists issues demand a simplified alternative for sleep apnea-hypopnea syndrome (SAHS) diagnosis. The blood oxygen saturation signal (SpO2) carries useful information about SAHS and can be easily acquired from overnight oximetry. In this study, SpO2 single-channel recordings from 320 subjects were obtained at patients’ home. They were used to automatically obtain statistical, spectral, non-linear, and clinical SAHS-related information. Relevant and non-redundant data from these analyses were subsequently used to train and validate four machine-learning methods with ability to classify SpO2 signals into one out of the four SAHS-severity degrees (no-SAHS, mild, moderate, and severe). All the models trained (linear discriminant analysis, 1-vs-all logistic regression, Bayesian multi-layer perceptron, and AdaBoost), outperformed the diagnostic ability of the conventionally-used 3% oxygen desaturation index. An AdaBoost model built with linear discriminants as base classifiers reached the highest figures. It achieved 0.479 Cohen’s in the SAHS severity classification, as well as 92.9%, 87.4%, and 78.7% accuracies in binary classification tasks using increasing severity thresholds (apnea-hypopnea index: 5, 15, and 30 events/hour, respectively). These results suggest that machine learning can be used along with SpO2 information acquired at patients’ home to help in SAHS diagnosis simplification.This research has been supported by the project VA037U16 from the Consejería de Educación de la Junta de Castilla y León, the project 265/2012 of the Sociedad Española de Neumología y Cirugía Torácica (SEPAR), the projects RTC-2015-3446-1 and TEC2014-53196-R from the Ministerio de Economía y Competitividad, and the European Regional Development Fund (FEDER). D. Álvarez was in receipt of a Juan de la Cierva grant from the Ministerio de Economía y Competitivida

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

Usefulness of Artificial Neural Networks in the Diagnosis and Treatment of Sleep Apnea-Hypopnea Syndrome

Sleep apnea-hypopnea syndrome (SAHS) is a chronic and highly prevalent disease considered a major health problem in industrialized countries. The gold standard diagnostic methodology is in-laboratory nocturnal polysomnography (PSG), which is complex, costly, and time consuming. In order to overcome these limitations, novel and simplified diagnostic alternatives are demanded. Sleep scientists carried out an exhaustive research during the last decades focused on the design of automated expert systems derived from artificial intelligence able to help sleep specialists in their daily practice. Among automated pattern recognition techniques, artificial neural networks (ANNs) have demonstrated to be efficient and accurate algorithms in order to implement computer-aided diagnosis systems aimed at assisting physicians in the management of SAHS. In this regard, several applications of ANNs have been developed, such as classification of patients suspected of suffering from SAHS, apnea-hypopnea index (AHI) prediction, detection and quantification of respiratory events, apneic events classification, automated sleep staging and arousal detection, alertness monitoring systems, and airflow pressure optimization in positive airway pressure (PAP) devices to fit patients’ needs. In the present research, current applications of ANNs in the framework of SAHS management are thoroughly reviewed

Automated Prediction of the Apnea-Hypopnea Index from Nocturnal Oximetry Recordings

Producción CientíficaNocturnal polysomnography (PSG) is the goldstandard for sleep apnea-hypopnea syndrome (SAHS) diagnosis. It provides the value of the apnea-hypopnea index (AHI), which is used to evaluate SAHS severity. However, PSG is costly, complex, and time-consuming. We present a novel approach for automatic estimation of the AHI from nocturnal oxygen saturation (SaO2 ) recordings and the results of an assessment study designed to characterize its performance. A set of 240 SaO2 signals was available for the assessment study. The data were divided into training (96 signals) and test (144 signals) sets for model optimization and validation, respectively. Fourteen time-domain and frequency-domain features were used to quantify the effect of SAHS on SaO2 recordings. Regression analysis was performed to estimate the functional relationship between the extracted features and the AHI. Multiple linear regression (MLR) and multilayer perceptron (MLP) neural networks were evaluated. The MLP algorithm achieved the highest performance with an intraclass correlation coefficient (ICC) of 0.91. The proposed MLP-based method could be used as an accurate and cost-effective procedure for SAHS diagnosis in the absence of PSG.This work was supported in part by the Ministerio de Ciencia e Innovación and FEDER under Grant TEC 2008-02241, and in part by the grant project from the Consejería de Sanidad de la Junta de Castilla y León GRS 337/A/09

Utilidad de las señales de oximetría y flujo aéreo en el diagnóstico simplificado de la apnea obstructiva del sueño. Diseño de un test automático domiciliario

Obstructive Sleep Apnea (OSA) is a respiratory disorder characterized by recurrent episodes of total (apnea) or partial (hypopnea) absence of airflow during sleep. Untreated OSA produces a significant decrease in quality of life and is associated with the main causes of mortality in industrialized countries.However, OSA is considered an underdiagnosed chronic disease. Continuous positive airway pressure (CPAP) is the most common therapeutic option. Nocturnal polysomnography (PSG) in a specialized sleep unit is the reference diagnostic method, although it has low availability and accessibility. Consequently, in recent years there has been a significant demand for abbreviated methods, most of them at home, to reduce waiting lists. The fundamental hypothesis that the use of automatic processing techniques based on machine learning tools could allow maximizing the diagnostic accuracy of a reduced set of combined biomedical signals: overnight oximetry and airflow recorded at patient&#8217;s home. The main objective was to evaluate whether the joint analysis by means of machine learning algorithms of unsupervised SpO2 and AF signals acquired at patient's home leads to a significant increase in diagnostic performance compared to single-channel approaches. A prospective observational study was carried out in which a population referred consecutively to the Sleep Unit showing moderate-to-high clinical suspicion of having OSA was analyzed.All patients underwent an unsupervised PSG at home(gold standard) from which the SpO2 and AF signals were extracted, which were subsequently processed offline.The apnea-hypopnea index(AHI) derived from the PSG was used to confirm or rule out the presence of the disease.Three different approaches for screening patients with suspected OSA were assessed in terms of the source of information used: single-channel based on SpO2, single-channel based on AF, and two-channel combining information from both SpO2 and AF.The automatic processing of the SpO2 and AF signals was developed in 4 stages: preprocessing, feature extraction, feature selection, and pattern recognition. Unsupervised SpO2 and AF recordings were parameterized using the fast correlation-based filter(FCBF)algorithm.The following machine learning methods were used: linear regression(MLR), multilayer perceptron neural networks(MLP) and support vector machines(SVM). The population was divided into independent training and test groups. Agreement between the estimated and the actual AHIderived from at-home PSG was assessed, and typical OSA cutoff points(5, 15, and 30 events/h) were applied. A total of 299 unattended PSGs were performed at home, with a validity percentage of 85.6%. The highest agreement between the estimated AHI and the PSG AHI was reached by the SVMSpO2+AF model, with an CCI 0.93 and a 4-class kappa index 0.71, as well as with an overall accuracy for the 4 OSA severity categories equal to 81.25%, significantly higher than the individual analysis of the SpO2 signal and the airflow signal.The SVMSpO2+AF model achieved the highest diagnostic performance of all algorithms for the detection of severe OSA, with an accuracy of 95.83% and AUC ROC 0.98. In addition, the AUC ROC of the dual-channel models was significantly higher (p<0.01) than that achieved by all the single-channel approaches for the cutoff of 15events/h. The proposed methodology based on the joint automatic analysis of the SpO2 and AF signals acquired at home showed a high complementarity that led to a remarkable increase in diagnostic performance compared to single-channel approaches. The automatic models outperformed the conventional indices(desaturation and airflow-derived indexes) both in terms of correlation and concordance with the AHI from PSG, as well as in terms of overall diagnostic accuracy, providing a moderate increase in diagnostic performance, particularly in the detection of moderate-to-severe OSA.Our findings suggest that the joint analysis of oximetry and airflow signals by means of machine learning methods allows a simplified as well as accurate screening of OSA at patient's home.La Apnea Obstructiva del Sueño (AOS) es un trastorno respiratorio crónico infradiagnosticado caracterizado por la repetición recurrente de episodios de ausencia total (apnea) o parcial (hipopnea) del flujo aéreo (FA) durante el sueño, que disminuye la calidad de vida y aumenta la mortalidad. La CPAP es el tratamiento más habitual, no invasivo, eficaz y coste-efectivo, por lo que favorecer el proceso de diagnóstico es fundamental. La PSG nocturna es el método diagnóstico de referencia, presentando baja disponibilidad y accesibilidad, lo que ha contribuido a desbordar los recursos disponibles, retrasando el diagnóstico y el tratamiento. En contexto de la simplificación diagnóstica portátil, en auge, el uso de únicamente una (monocanal) o dos (bi-canal) señales, como las de SpO2 y FA ha sido ampliamente explorado, aunque la mayoría en entornos hospitalarios controlados. La hipótesis se fundamenta en que las técnicas de procesado automático basadas en machine learning podrían maximizar la precisión diagnóstica de un conjunto reducido de señales combinadas. El objetivo consistió en evaluar si el análisis conjunto mediante algoritmos de aprendizaje automático de las señales de SpO2 y FA no supervisadas adquiridas en el domicilio aumenta el rendimiento diagnóstico en comparación con los enfoques de un solo canal. Se llevó a cabo un estudio observacional prospectivo en pacientes con sospecha moderada-alta de AOS. Se realizó una PSG no supervisada en su domicilio (gold standard de referencia), de la que se extrajeron las señales de SpO2 y FA, procesadas offline posteriormente. El índice de apnea-hipopnea (IAH) derivado de la PSG se empleó para confirmar o descartar la presencia de la enfermedad. Se implementaron y compararon 3 metodologías de screening en función de la fuente de información empleada: (1) monocanal basado en SpO2, (2) monocanal basado en FA, (3) bi-canal combinando SpO2 y FA. El procesado automático de las señales de SpO2 y FA se desarrolló en 4 etapas: preprocesado, extracción de características, selección de características (mediante fast correlation-based filter, FCBF) y reconocimiento de patrones. Cada enfoque de screening se empleó para estimar automáticamente el IAH utilizando los siguientes métodos de machine learning: (1) regresión lineal múltiple (MLR), (2) redes neuronales perceptrón multicapa (MLP) y (3) máquinas vector soporte (SVM). La población se dividió en grupos independientes de entrenamiento (60%) y test (40%). Se realizaron un total de 299 PSGs domiciliarias. Los modelos de enfoque combinado bi-canal alcanzaron valores de concordancia entre el IAH estimado y el IAH de la PSG domiciliaria y de rendimiento diagnóstico para todos los puntos de corte típicos de AOS (5, 15 y 30 e/h) superiores al enfoque monocanal. La mayor concordancia fue alcanzada por el modelo SVMSpO2+FA (CCI 0.93, kappa4 clases 0.71, precisión global 81.25%), significativamente superior a los análisis individuales. El modelo SVMSpO2+FA alcanzó el mayor rendimiento diagnóstico de todos los algoritmos para la detección de AOS grave (precisión 95.83% y AUC ROC 0.98). Además, el AUC ROC de los modelos bi-canal fue superior (p <0.01) al de los enfoques monocanal para el punto de corte de 15 e/h. La metodología propuesta basada en el análisis automático conjunto de las señales de SpO2 y FA adquiridas en el domicilio mostró una alta complementariedad y un notable aumento del rendimiento diagnóstico en comparación con los enfoques monocanal. Los modelos automáticos superaron globalmente a los índices clásicos (de desaturación y de eventos de flujo aéreo), aportando un incremento moderado del rendimiento diagnóstico particularmente en la detección de AOS moderado-grave. Los resultados obtenidos indican que el análisis conjunto de las señales de oximetría y flujo mediante métodos de aprendizaje automático permite un screening simplificado a la vez que preciso de la AOS en el domicilio del paciente.Escuela de DoctoradoDoctorado en Investigación en Ciencias de la Salu

Diseño y evaluación de metodologías de análisis automático de la oximetría nocturna como método simplificado de detección del síndrome de apnea-hipopnea obstructiva del sueño en niños. Validación en el hospital y en el domicilio.

El síndrome de apnea-hipopnea obstructiva del sueño (SAHOS) es una enfermedad de alta prevalencia en la población infantil, con una importante morbilidad y elevado impacto sociosanitario, en la que la detección precoz es esencial para iniciar un adecuado tratamiento, el cual debe ser siempre individualizado. El SAHOS es una alteración fisiopatológica compleja y multifactorial, en la que no sólo influye una susceptibilidad genética e individual (factores anatómicos y dinámicos), sino también de estilo de vida. Los factores de riesgo más frecuentes son la hipertrofia adenoamigdalar y la obesidad. Los síntomas en los niños son escasos, son principalmente nocturnos y requieren un alto nivel de sospecha. El SAHOS no diagnosticado o no tratado se relaciona con diferentes consecuencias metabólicas, cardiovasculares, neurocognitivas, inflamatorias, conductuales y falta de desarrollo estaturoponderal, lo que conduce a un empeoramiento del estado de salud en términos generales y disminución de calidad de vida.Departamento de Anatomía y RadiologíaDoctorado en Investigación en Ciencias de la Salu