Análisis de la saturación de oxígeno en sangre para la ayuda al diagnóstico del Síndrome de Apnea-hipopnea del Sueño en niños

El síndrome de apneas-hipopneas del sueño (SAHS)en la infancia es un trastorno respiratorio caracterizado por una obstrucción parcial prolongada de la vía aérea superior yju obstrucción intermitente completa que interrumpe la ventilación normal durante el sueño y los patrones normales del mismo. Elmétodo de diagnóstico estándar es la polisomnografía (PSG)nocturna en una unidad del sueño especializada. Sin embargo, esta prueba presenta numerosas limitaciones en cuanto a disponibilidad, complejidad, tiempo y coste. Palabras clave: Este trabajo tiene como principales objetivos la implementación y aplicación de diversos métodos de procesado de señal sobre los registros de Sp02 procedentes de la poligrafía domiciliaria (PLG)nocturna, con el fin de obtener información relevante sobre el efecto que producen los eventos de apnea en niños. Asimismo, se pretende estudiar el rendimiento diagnóstico de los algoritmos implementados en comparación con la metodología diagnóstica están dar basada en la PSG.Grado en Ingeniería de Tecnologías Específicas de Telecomunicació

Extracción, selección y clasificación automática de características de la señal de oximetría en la detección del síndrome de apnea-hipopnea del sueño en niños

El Síndrome de la Apnea Hipopnea del Sueño (SAHS) en la infancia es un trastorno respiratorio del sueño caracterizado por una obstrucción parcial y/o completa de la vía aérea superior. El SAHS tiene una prevalencia de entre el 1 y el 5% y puede originar múltiples consecuencias negativas para la salud y el desarrollo de los niños, como déficit neurocognitivo, retraso del crecimiento o disfunción cardiaca. La técnica diagnóstica de referencia es la polisomnografía (PSG), que es un método complejo, costoso, altamente intrusivo y de disponibilidad limitada. Estas limitaciones han favorecido la aparición de alternativas más sencillas enfocadas principalmente al análisis automático de un conjunto reducido de señales. Este trabajo se ha desarrollado bajo la hipótesis de que el análisis automático de la señal de SpO2 puede proporcionar información relevante en la ayuda al diagnóstico del SAHS infantil. En este trabajo se ha analizado la señal de saturación de oxígeno en sangre (SpO2) procedente de la oximetría nocturna con el objetivo de evaluar su capacidad diagnóstica. Para ello se ha contado con 981 registros (583 SAHS negativo y 398 SAHS positivo) procedentes del Comer Children's Hospital de la Universidad de Chicago. Estos registros pertenecen a niños de ambos sexos de 0 a 13 años con signos y síntomas indicativos de SAHS.Departamento de Teoría de la Señal y Comunicaciones e Ingeniería TelemáticaMáster en Ingeniería de Telecomunicació

The Different Facets of Heart Rate Variability in Obstructive Sleep Apnea

Obstructive sleep apnea (OSA), a heterogeneous and multifactorial sleep related breathing disorder with high prevalence, is a recognized risk factor for cardiovascular morbidity and mortality. Autonomic dysfunction leads to adverse cardiovascular outcomes in diverse pathways. Heart rate is a complex physiological process involving neurovisceral networks and relative regulatory mechanisms such as thermoregulation, renin-angiotensin-aldosterone mechanisms, and metabolic mechanisms. Heart rate variability (HRV) is considered as a reliable and non-invasive measure of autonomic modulation response and adaptation to endogenous and exogenous stimuli. HRV measures may add a new dimension to help understand the interplay between cardiac and nervous system involvement in OSA. The aim of this review is to introduce the various applications of HRV in different aspects of OSA to examine the impaired neuro-cardiac modulation. More specifically, the topics covered include: HRV time windows, sleep staging, arousal, sleepiness, hypoxia, mental illness, and mortality and morbidity. All of these aspects show pathways in the clinical implementation of HRV to screen, diagnose, classify, and predict patients as a reasonable and more convenient alternative to current measures.Peer Reviewe

Derivation and validation of a blood biomarker score for 2-day mortality prediction from prehospital care: a multicenter, cohort, EMS-based study

Producción CientíficaIdentifying potentially life-threatening diseases is a key challenge for emergency medical services. This study aims at examining the role of different prehospital biomarkers from point-of-care testing to derive and validate a score to detect 2-day in-hospital mortality. We conducted a prospective, observational, prehospital, ongoing, and derivation—validation study in three Spanish provinces, in adults evacuated by ambulance and admitted to the emergency department. A total of 23 ambulance-based biomarkers were collected from each patient. A biomarker score based on logistic regression was fitted to predict 2-day mortality from an optimum subset of variables from prehospital blood analysis, obtained through an automated feature selection stage. 2806 cases were analyzed, with a median age of 68 (interquartile range 51–81), 42.3% of women, and a 2-day mortality rate of 5.5% (154 non-survivors). The blood biomarker score was constituted by the partial pressure of carbon dioxide, lactate, and creatinine. The score fitted with logistic regression using these biomarkers reached a high performance to predict 2-day mortality, with an AUC of 0.933 (95% CI 0.841–0.973). The following risk levels for 2-day mortality were identified from the score: low risk (score < 1), where only 8.2% of non-survivors were assigned to; medium risk (1 ≤ score < 4); and high risk (score ≥ 4), where the 2-day mortality rate was 57.6%. The novel blood biomarker score provides an excellent association with 2-day in-hospital mortality, as well as real-time feedback on the metabolic-respiratory patient status. Thus, this score can help in the decision-making process at critical moments in life-threatening situations.Junta de Castilla y León (Gerencia Regional de Salud - grant number GRS 1903/A/19 and GRS 2131/A/20)Ministerio de Ciencia e Innovación/Agencia Estatal de Investigación/10.13039/501100011033/’, ERDF A way of making Europe, and Next GenerationEU/PRTR (under projects PID2020-115468RB-I00 and PDC2021-120775-I00)CIBER -Consorcio Centro de Investigación Biomédica en Red (Instituto de Salud Carlos III) (CB19/01/00012)Publicación en abierto financiada por el Consorcio de Bibliotecas Universitarias de Castilla y León (BUCLE), con cargo al Programa Operativo 2014ES16RFOP009 FEDER 2014-2020 DE CASTILLA Y LEÓN, Actuación:20007-CL - Apoyo Consorcio BUCL

Wavelet analysis of overnight airflow to detect obstructive sleep apnea in children

Producción CientíficaThis study focused on the automatic analysis of the airflow signal (AF) to aid in the diagnosis of pediatric obstructive sleep apnea (OSA). Thus, our aims were: (i) to characterize the overnight AF characteristics using discrete wavelet transform (DWT) approach, (ii) to evaluate its diagnostic utility, and (iii) to assess its complementarity with the 3% oxygen desaturation index (ODI3). In order to reach these goals, we analyzed 946 overnight pediatric AF recordings in three stages: (i) DWT-derived feature extraction, (ii) feature selection, and (iii) pattern recognition. AF recordings from OSA patients showed both lower detail coefficients and decreased activity associated with the normal breathing band. Wavelet analysis also revealed that OSA disturbed the frequency and energy distribution of the AF signal, increasing its irregularity. Moreover, the information obtained from the wavelet analysis was complementary to ODI3. In this regard, the combination of both wavelet information and ODI3 achieved high diagnostic accuracy using the common OSA-positive cutoffs: 77.97%, 81.91%, and 90.99% (AdaBoost.M2), and 81.96%, 82.14%, and 90.69% (Bayesian multi-layer perceptron) for 1, 5, and 10 apneic events/hour, respectively. Hence, these findings suggest that DWT properly characterizes OSA-related severity as embedded in nocturnal AF, and could simplify the diagnosis of pediatric OSA.Ministerio de Ciencia, Innovación y Universidades, Agencia Estatal de Investigación y Fondo Europeo de Desarrollo Regional (FEDER) - (Projects DPI2017-84280-R and RTC-2017-6516-1)Comisión Europea y Fondo Europeo de Desarrollo Regional (FEDER) - (POCTEP 0702_MIGRAINEE_2_E)Instituto de Salud Carlos III y Fondo Europeo de Desarrollo Regional (FEDER) - (CIBER-BBN)Ministerio de Ciencia e Innovación, Agencia Estatal de Investigación y Fondo Social Europeo - (grant RYC2019- 028566-I)Ministerio de Educación, Cultura y Deporte - (grant FPU16/02938)Institutes of Health - (grants HL130984, HL140548, and AG061824

A 2D convolutional neural network to detect sleep apnea in children using airflow and oximetry

Producción CientíficaThe gold standard approach to diagnose obstructive sleep apnea (OSA) in children is overnight in-lab polysomnography (PSG), which is labor-intensive for clinicians and onerous to healthcare systems and families. Simplification of PSG should enhance availability and comfort, and reduce complexity and waitlists. Airflow (AF) and oximetry (SpO2) signals summarize most of the information needed to detect apneas and hypopneas, but automatic analysis of these signals using deep-learning algorithms has not been extensively investigated in the pediatric context. The aim of this study was to evaluate a convolutional neural network (CNN) architecture based on these two signals to estimate the severity of pediatric OSA. PSG-derived AF and SpO2 signals from the Childhood Adenotonsillectomy Trial (CHAT) database (1638 recordings), as well as from a clinical database (974 recordings), were analyzed. A 2D CNN fed with AF and SpO2 signals was implemented to estimate the number of apneic events, and the total apnea-hypopnea index (AHI) was estimated. A training-validation-test strategy was used to train the CNN, adjust the hyperparameters, and assess the diagnostic ability of the algorithm, respectively. Classification into four OSA severity levels (no OSA, mild, moderate, or severe) reached 4-class accuracy and Cohen's Kappa of 72.55% and 0.6011 in the CHAT test set, and 61.79% and 0.4469 in the clinical dataset, respectively. Binary classification accuracy using AHI cutoffs 1, 5 and 10 events/h ranged between 84.64% and 94.44% in CHAT, and 84.10%–90.26% in the clinical database. The proposed CNN-based architecture achieved high diagnostic ability in two independent databases, outperforming previous approaches that employed SpO2 signals alone, or other classical feature-engineering approaches. Therefore, analysis of AF and SpO2 signals using deep learning can be useful to deploy reliable computer-aided diagnostic tools for childhood OSA.Ministerio de Ciencia, Innovación y Universidades - Agencia Estatal de Investigación (project 10.13039/501100011033)Fondo Europeo de Desarrollo Regional - Unión Europea (projects PID2020-115468RB-I00 and PDC2021-120775-I00)Sociedad Española de Neumología y Cirugía Torácica (project 649/2018)Sociedad Española de Sueño (project Beca de Investigación SES 2019)Consorcio Centro de Investigación Biomédica en Red - Instituto de Salud Carlos III - Ministerio de Ciencia, Innovación y Universidades (project CB19/01/00012)National Institutes of Health (projects HL083075, HL083129, UL1-RR-024134 and UL1 RR024989)National Heart, Lung, and Blood Institute (projects R24 HL114473 and 75N92019R002)Ministerio de Educación, Cultura y Deporte (grant FPU16/02938)Ministerio de Ciencia, Innovación y Universidades - Agencia Estatal de Investigación - Fondo Social Europeo (grant RYC2019-028566-I)National Institutes of Health (grants HL130984, HL140548, and AG061824

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

Libro de Actas de la "I Jornada para Alumnos de Trabajo Fin de Grado y Trabajo Fin de Máster: Uso Efectivo de Herramientas TIC"

Innovación EducativaEste libro de resúmenes engloba los trabajos presentados en la “I Jornada para alumnos de Trabajo Fin de Grado (TFG) y Trabajo Fin de Máster (TFM). Uso efectivo de herramientas TIC”, celebrada en Valladolid el 21 de marzo de 2019. Los trabajos aceptados tienen como autores a alumnos de TFG o TFM que han presentado su tema de estudio de TFG/TFM y los principales resultados obtenidos hasta el momento de acuerdo con los requisitos formales de la Jornada. Estos resúmenes cubren un amplio rango de temáticas, incluyendo el procesado de señales e imágenes o el diseño de dispositivos y redes de comunicaciones, entre otros. Todos los trabajos han seguido un proceso de revisión riguroso, siendo evaluados en profundidad por los miembros del Comité Organizador. Los editores del libro de resúmenes de la “I Jornada para alumnos de Trabajo Fin de Grado (TFG) y Trabajo Fin de Máster (TFM). Uso efectivo de herramientas TIC” agradecen enormemente a todos los alumnos, profesores y ponentes su participación en la Jornada, ya que su contribución ha sido imprescindible para la celebración de este evento.Vicerrectorado de Docencia de la Universidad de Valladolid (PID Nº 55: “Nuevas propuestas en la tutorización de Trabajos Fin de Grado y Trabajos Fin de Máster con el apoyo de entornos virtuales de aprendizaje colaborativo"

Automated Analysis of Unattended Portable Oximetry by means of Bayesian Neural Networks to Assist in the Diagnosis of Sleep Apnea

Producción CientíficaSleep apnea-hypopnea syndrome (SAHS) is a chronic sleep-related breathing disorder, which is currently considered a major health problem. In-lab nocturnal polysomnography (NPSG) is the gold standard diagnostic technique though it is complex and relatively unavailable. On the other hand, the analysis of blood oxygen saturation (SpO2) from nocturnal pulse oximetry (NPO) is a simple, noninvasive, highly available and effective alternative. This study focused on the design and assessment of a neural network (NN) aimed at detecting SAHS using information from at-home unsupervised portable SpO2 recordings. A Bayesian multilayer perceptron NN (MLP-NN) was proposed, fed with complementary oximetric features properly selected. A dataset composed of 320 unattended SpO2 recordings was analyzed (60% for training and 40% for validation). The proposed Bayesian MLP-NN achieved 94.2% sensitivity, 69.6% specificity, and 89.8% accuracy in the test set. Our results suggest that automated analysis of at-home portable NPO recordings by means of Bayesian MLP-NN could be an effective and highly available technique in the context of SAHS diagnosis.Junta de Castilla y León (project VA059U13)Pneumology and Thoracic Surgery Spanish Society (265/2012

An explainable deep-learning architecture for pediatric sleep apnea identification from overnight airflow and oximetry signals

Producción CientíficaDeep-learning algorithms have been proposed to analyze overnight airflow (AF) and oximetry (SpO2) signals to simplify the diagnosis of pediatric obstructive sleep apnea (OSA), but current algorithms are hardly interpretable. Explainable artificial intelligence (XAI) algorithms can clarify the models-derived predictions on these signals, enhancing their diagnostic trustworthiness. Here, we assess an explainable architecture that combines convolutional and recurrent neural networks (CNN + RNN) to detect pediatric OSA and its severity. AF and SpO2 were obtained from the Childhood Adenotonsillectomy Trial (CHAT) public database (n = 1,638) and a proprietary database (n = 974). These signals were arranged in 30-min segments and processed by the CNN + RNN architecture to derive the number of apneic events per segment. The apnea-hypopnea index (AHI) was computed from the CNN + RNN-derived estimates and grouped into four OSA severity levels. The Gradient-weighted Class Activation Mapping (Grad-CAM) XAI algorithm was used to identify and interpret novel OSA-related patterns of interest. The AHI regression reached very high agreement (intraclass correlation coefficient > 0.9), while OSA severity classification achieved 4-class accuracies 74.51% and 62.31%, and 4-class Cohen’s Kappa 0.6231 and 0.4495, in CHAT and the private datasets, respectively. All diagnostic accuracies on increasing AHI cutoffs (1, 5 and 10 events/h) surpassed 84%. The Grad-CAM heatmaps revealed that the model focuses on sudden AF cessations and SpO2 drops to detect apneas and hypopneas with desaturations, and often discards patterns of hypopneas linked to arousals. Therefore, an interpretable CNN + RNN model to analyze AF and SpO2 can be helpful as a diagnostic alternative in symptomatic children at risk of OSA.Ministerio de Ciencia e Innovación /AEI/10.13039/501100011033/ FEDER (grants PID2020-115468RB-I00 and PDC2021-120775-I00)CIBER -Consorcio Centro de Investigación Biomédica en Red- (CB19/01/00012), Instituto de Salud Carlos IIINational Institutes of Health (HL083075, HL083129, UL1-RR-024134, UL1 RR024989)National Heart, Lung, and Blood Institute (R24 HL114473, 75N92019R002)Ministerio de Ciencia e Innovación - Agencia Estatal de Investigación- “Ramón y Cajal” grant (RYC2019-028566-I