[en] 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.

[es] 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.