Classification of oximetry signals using Bayesian neural networks to assist in the detection of obstructive sleep apnoea syndrome

In the present study, multilayer perceptron (MLP) neural networks were applied to help in the diagnosis of obstructive sleep apnoea syndrome (OSAS). Oxygen saturation (SaO2) recordings from nocturnal pulse oximetry were used for this purpose. We performed time and spectral analysis of these signals to extract 14 features related to OSAS. The performance of two different MLP classifiers was compared: maximum likelihood (ML) and Bayesian (BY) MLP networks. A total of 187 subjects suspected of suffering from OSAS took part in the study. Their SaO2 signals were divided into a training set with 74 recordings and a test set with 113 recordings. BY-MLP networks achieved the best performance on the test set with 85.58% accuracy (87.76% sensitivity and 82.39% specificity). These results were substantially better than those provided by ML-MLP networks, which were affected by overfitting and achieved an accuracy of 76.81% (86.42% sensitivity and 62.83% specificity). Our results suggest that the Bayesian framework is preferred to implement our MLP classifiers. The proposed BY-MLP networks could be used for early OSAS detection. They could contribute to overcome the difficulties of nocturnal polysomnography (PSG) and thus reduce the demand for these studies

Assessment of Time and Frequency Domain Entropies to Detect Sleep Apnoea in Heart Rate Variability Recordings from Men and Women

Producción CientíficaHeart rate variability (HRV) provides useful information about heart dynamics both under healthy and pathological conditions. Entropy measures have shown their utility to characterize these dynamics. In this paper, we assess the ability of spectral entropy (SE) and multiscale entropy (MsE) to characterize the sleep apnoea-hypopnea syndrome (SAHS) in HRV recordings from 188 subjects. Additionally, we evaluate eventual differences in these analyses depending on the gender. We found that the SE computed from the very low frequency band and the low frequency band showed ability to characterize SAHS regardless the gender; and that MsE features may be able to distinguish gender specificities. SE and MsE showed complementarity to detect SAHS, since several features from both analyses were automatically selected by the forward-selection backward-elimination algorithm. Finally, SAHS was modelled through logistic regression (LR) by using optimum sets of selected features. Modelling SAHS by genders reached significant higher performance than doing it in a jointly way. The highest diagnostic ability was reached by modelling SAHS in women. The LR classifier achieved 85.2% accuracy (Acc) and 0.951 area under the ROC curve (AROC). LR for men reached 77.6% Acc and 0.895 AROC, whereas LR for the whole set reached 72.3% Acc and 0.885 AROC. Our results show the usefulness of the SE and MsE analyses of HRV to detect SAHS, as well as suggest that, when using HRV, SAHS may be more accurately modelled if data are separated by gender.Ministerio de Economía, Industria y Competitividad (TEC2011-22987)Junta de Castilla y León (programa de apoyo a proyectos de investigación - Ref. VA059U13

Multiscale entropy analysis of unattended oximetric recordings to assist in the screening of paediatric sleep apnoea at home

Producción CientíficaUntreated paediatric obstructive sleep apnoea syndrome (OSAS) can severely affect the development and quality of life of children. In-hospital polysomnography (PSG) is the gold standard for a definitive diagnosis though it is relatively unavailable and particularly intrusive. Nocturnal portable oximetry has emerged as a reliable technique for OSAS screening. Nevertheless, additional evidences are demanded. Our study is aimed at assessing the usefulness of multiscale entropy (MSE) to characterise oximetric recordings. We hypothesise that MSE could provide relevant information of blood oxygen saturation (SpO2) dynamics in the detection of childhood OSAS. In order to achieve this goal, a dataset composed of unattended SpO2 recordings from 50 children showing clinical suspicion of OSAS was analysed. SpO2 was parameterised by means of MSE and conventional oximetric indices. An optimum feature subset composed of five MSE-derived features and four conventional clinical indices were obtained using automated bidirectional stepwise feature selection. Logistic regression (LR) was used for classification. Our optimum LR model reached 83.5% accuracy (84.5% sensitivity and 83.0% specificity). Our results suggest that MSE provides relevant information from oximetry that is complementary to conventional approaches. Therefore, MSE may be useful to improve the diagnostic ability of unattended oximetry as a simplified screening test for childhood OSAS.Sociedad Española de Neumología y Cirugía Torácica (SEPAR) project 153/2015Junta de Castilla y León (Consejería de Educación) y el Fondo Europeo de Desarrollo Regional (FEDER), projects (RTC-2015-3446-1) y (TEC2014-53196-R)Ministerio de Economía y Competitividad (MINECO) y FEDER, y el proyecto POCTEP 0378_AD_EEGWA_2_P de la Comisión Europea. L.National Institutes of Health (NIH) grant 1R01HL130984-01Ministerio de Asuntos Económicos y Transformación Digital, grant IJCI-2014-2266

Thermal imaging developments for respiratory airflow measurement to diagnose apnoea

Sleep-disordered breathing is a sleep disorder that manifests itself as intermittent pauses (apnoeas) in breathing during sleep. The condition disturbs the sleep and can results in a variety of health problems. Its diagnosis is complex and involves multiple sensors attached to the person to measure electroencephalogram (EEG), electrocardiogram (ECG), blood oxygen saturation (pulse oximetry, S

Pattern recognition applied to airflow recordings to help in sleep Apnea-Hypopnea Syndrome diagnosis

El Síndrome de la Apnea Hipopnea del Sueño (SAHS) es un trastorno caracterizado por pausas respiratorias durante el sueño. Se considera un grave problema de salud que afecta muy negativamente a la calidad de vida y está relacionada con las principales causas de mortalidad, como los accidentes cardiovasculares y cerebrovasculares. A pesar de su elevada prevalencia (2–7%) se considera una enfermedad infradiagnosticada. El diagnóstico estándar se realiza mediante polisomnografía (PSG) nocturna, que es un método complejo y de alto coste. Estas limitaciones han originado largas listas de espera. Esta Tesis Doctoral tiene como principal objetivo simplificar la metodología de diagnóstico del SAHS . Para ello, se propone el análisis exhaustivo de la señal de flujo aéreo monocanal. La metodología propuesta se basa en tres fases (i) extracción de características, (ii) selección de características, y (iii) procesado de la señal mediante métodos de reconocimiento de patrones. Los resultados obtenidos muestran un alto rendimiento diagnóstico de la propuesta tanto en la detección como en la determinación del grado de severidad del SAHS. Por ello, la principal conclusión de la Tesis Doctoral es que los métodos de reconocimiento automático de patrones aplicados sobre la señal de flujo aéreo monocanal resultan de utilidad para reducir la complejidad del proceso de diagnóstico del SAHS.Departamento de Teoría de la Señal y Comunicaciones e Ingeniería Telemátic

Diagnosis of pediatric obstructive sleep apnea: Preliminary findingsusing automatic analysis of airflow and oximetry recordings obtainedat patients’ home

Producción CientíficaThe obstructive sleep apnea syndrome (OSAS) greatly affects both the health and the quality of life of chil-dren. Therefore, an early diagnosis is crucial to avoid their severe consequences. However, the standarddiagnostic test (polysomnography, PSG) is time-demanding, complex, and costly. We aim at assessinga new methodology for the pediatric OSAS diagnosis to reduce these drawbacks. Airflow (AF) and oxy-gen saturation (SpO2) at-home recordings from 50 children were automatically processed. Informationfrom the spectrum of AF was evaluated, as well as combined with 3% oxygen desaturation index (ODI3)through a logistic regression model. A bootstrap methodology was conducted to validate the results.OSAS significantly increased the spectral content of AF at two abnormal frequency bands below (BW1)and above (BW2) the normal respiratory range. These novel bands are consistent with the occurrenceof apneic events and the posterior respiratory overexertion, respectively. The spectral information fromBW1 and BW2 showed complementarity both between them and with ODI3. A logistic regression modelbuilt with 3 AF spectral features (2 from BW1 and 1 from BW2) and ODI3 achieved (mean and 95% confi-dence interval): 85.9% sensitivity [64.5–98.7]; 87.4% specificity [70.2–98.6]; 86.3% accuracy [74.9–95.4];0.947 area under the receiver-operating characteristics curve [0.826–1]; 88.4% positive predictive value[72.3–98.5]; and 85.8% negative predictive value [65.8–98.5]. The combination of the spectral informationfrom two novel AF bands with the ODI3 from SpO2is useful for the diagnosis of OSAS in children.Ministerio de Economía y Competitividad (project TEC2011-22987)Junta de Castilla y León (project VA059U13

Utility of AdaBoost to Detect Sleep Apnea-Hypopnea Syndrome From Single-Channel Airflow

Producción CientíficaThe purpose of this study is to evaluate the usefulness of the boosting algorithm AdaBoost (AB) in the context of the sleep apnea-hypopnea syndrome (SAHS) diagnosis. Methods: We characterize SAHS in single-channel airflow (AF) signals from 317 subjects by the extraction of spectral and non-linear features. Relevancy and redundancy analyses are conducted through the fast correlation-based filter (FCBF) to derive the optimum set of features among them. These are used to feed classifiers based on linear discriminant analysis (LDA) and classification and regression trees (CART). LDA and CART models are sequentially obtained through AB, which combines their performances to reach higher diagnostic ability than each of them separately. Results: Our AB-LDA and AB-CART approaches showed high diagnostic performance when determining SAHS and its severity. The assessment of different apnea-hypopnea index cutoffs using an independent test set derived into high accuracy: 86.5% (5 events/h), 86.5% (10 events/h), 81.0% (15 events/h), and 83.3% (30 events/h). These results widely outperformed those from logistic regression and a conventional event-detection algorithm applied to the same database. Conclusion: Our results suggest that AB applied to data from single-channel AF can be useful to determine SAHS and its severity. Significance: SAHS detection might be simplified through the only use of single-channel AF data.Ministerio de Economía y Competitividad (project TEC2011-22987)Junta de Castilla y León (project VA059U13

Exploring the Spectral Information of Airflow Recordings to Help in Pediatric Obstructive Sleep Apnea-Hypopnea Syndrome Diagnosis

Producción CientíficaThis work aims at studying the usefulness of the spectral information contained in airflow (AF) recordings in the context of Obstructive Sleep Apnea-Hypopnea Syndrome (OSAHS) in children. To achieve this goal, we defined two spectral bands of interest related to the occurrence of apneas and hypopneas. We characterized these bands by extracting six common spectral features from each one. Two out of the 12 features reached higher diagnostic ability than the 3% oxygen desaturation index (ODI3), a clinical parameter commonly used as screener for OSAHS. Additionally, the stepwise logistic regression (SLR) featureselection algorithm showed that the information contained in the two bands was complementary, both between them and with ODI3. Finally, the logistic regression method involving spectral features from the two bands, as well as ODI3, achieved high diagnostic performance after a bootstrap validation procedure (84.6±9.6 sensitivity, 87.2±9.1 specificity, 85.8±5.2 accuracy, and 0.969±0.03 area under ROC curve). These results suggest that the spectral information from AF is helpful to detect OSAHS in childrenMinisterio de Economía y Competitividad (TEC2011-22987)Junta de Castilla y León (VA059U13

Automatic analysis of overnight airflow to help in the diagnosis of pediatric obstructive sleep apnea

La apnea obstructiva del sueño (AOS) pediátrica es una enfermedad respiratoria altamente prevalente e infradiagnosticada que puede afectar negativamente a las funciones fisiológicas y cognitivas de los niños, causándoles graves deficiencias neurocognitivas, cardiometabólicas y endocrinas. El método estándar para su diagnóstico es la polisomnografía nocturna, una prueba compleja, de elevado coste, altamente intrusiva y poco accesible, lo que genera largas listas de espera y retrasos en el diagnóstico. Por ello, es necesario desarrollar pruebas diagnósticas más sencillas. Una de estas alternativas es el análisis automático de señales cardiorrespiratorias. Así, esta tesis doctoral presenta un compendio de cuatro publicaciones que proponen el uso de novedosos métodos de procesado de señal (no lineal, espectral, bispectral, gráficos de recurrencia y wavelet) que permiten caracterizar exhaustivamente el comportamiento del flujo aéreo nocturno de los niños y simplificar el diagnóstico de la apnea obstructiva del sueño pediátrica.Departamento de Teoría de la Señal y Comunicaciones e Ingeniería TelemáticaDoctorado en Tecnologías de la Información y las Telecomunicacione

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