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

Automated Analysis of Nocturnal Oximetry as Screening Tool for Childhood Obstructive Sleep Apnea-Hypopnea Syndrome

Producción CientíficaChildhood obstructive sleep apnea-hypopnea syndrome (OSAHS) is a highly prevalent condition that negatively affects health, performance and quality of life of infants and young children. Early detection and treatment improves neuropsychological and cognitive deficits linked with the disease. The aim of this study was to assess the performance of automated analysis of blood oxygen saturation (SpO2) recordings as a screening tool for OSAHS. As an initial step, statistical, spectral and nonlinear features were estimated to compose an initial feature set. Then, fast correlation-based filter (FCBF) was applied to search for the optimum subset. Finally, the discrimination power (OSAHS negative vs. OSAHS positive) of three pattern recognition algorithms was assessed: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA) and logistic regression (LR). Three clinical cutoff points commonly used in the literature for positive diagnosis of the disease were applied: apnea-hypopnea index (AHI) of 1, 3 and 5 events per hour (e/h). Our methodology reached 88.6% accuracy (71.4% sensitivity and 100.0% specificity, 100.0% positive predictive value, and 84.0% negative predictive value) in an independent test set using QDA for a clinical cut-off point of 5 e/h. These results suggest that SpO2 nocturnal recordings may be used to develop a reliable and efficient screening tool for childhood OSAHSJunta de Castilla y León (project VA059U13

Positive airway pressure and electrical stimulation methods for obstructive sleep apnea treatment: a patent review (2005-2014)

Producción CientíficaIntroduction. Obstructive sleep apnea-hypopnea syndrome (OSAHS) is a major health problem with significant negative effects on the health and quality of life. Continuous positive airway pressure (CPAP) is currently the primary treatment option and it is considered the most effective therapy for OSAHS. Nevertheless, comfort issues due to improper fit to patient’s changing needs and breathing gas leakage limit the patient’s adherence to treatment. Areas covered. The present patent review describes recent innovations in the treatment of OSAHS related to optimization of the positive pressure delivered to the patient, methods and systems for continuous self-adjusting pressure during inspiration and expiration phases, and techniques for electrical stimulation of nerves and muscles responsible for the airway patency. Expert opinion. In the last years, CPAP-related inventions have mainly focused on obtaining an optimal self-adjusting pressure according to patient’s needs. Despite intensive research carried out, treatment compliance is still a major issue. Hypoglossal electrical nerve stimulation could be an effective secondary treatment option when CPAP primary therapy fails. Several patents have been granted focused on selective stimulation techniques and parameter optimization of the stimulating pulse waveform. Nevertheless, there remain important issues to address, like effectiveness and adverse events due to improper stimulation.Ministerio de Economía y Competitividad (TEC2011-22987)Junta de Castilla y León (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

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

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

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

Statistical and Nonlinear Analysis of Oximetry from Respiratory Polygraphy to Assist in the Diagnosis of Sleep Apnea in Children

Producción CientíficaObstructive Sleep Apnea-Hypopnea Syndrome (OSAHS) is a sleep related breathing disorder that has important consequences in the health and development of infants and young children. To enhance the early detection of OSAHS, we propose a methodology based on automated analysis of nocturnal blood oxygen saturation (SpO2) from respiratory polygraphy (RP) at home. A database composed of 50 SpO2 recordings was analyzed. Three signal processing stages were carried out: (i) feature extraction, where statistical features and nonlinear measures were computed and combined with conventional oximetric indexes, (ii) feature selection using genetic algorithms (GAs), and (iii) feature classification through logistic regression (LR). Leave-one-out cross-validation (loo-cv) was applied to assess diagnostic performance. The proposed method reached 80.8% sensitivity, 79.2% specificity, 80.0% accuracy and 0.93 area under the ROC curve (AROC), which improved the performance of single conventional indexes. Our results suggest that automated analysis of SpO2 recordings from at-home RP provides essential and complementary information to assist in OSAHS diagnosis in children.Ministerio de Economía y Competitividad (TEC2011-22987)Fundación General CSIC (Proyecto Cero 2011 sobre Envejecimiento)Obra social de la Caixa y CSICJunta de Castilla y León (VA059U13

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

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