Obstructive Sleep Apnea Detection Methods Based on Heart Rate Variability Analysis: Opportunities for a Future Cinc Challenge

[EN] The effects of sleep-related disorders, such as obstructive sleep apnea (OSA), can be devastating either in children or adults. Misdiagnosis may lead to severe cardiovascular diseases. Besides, OSA consequences are often related to bad job performance, and road accidents. Nowadays, polysomnography (PSG) is still considered the gold standard for OSA diagnosis, but the required facilities are extremely high, thus reducing availability worldwide. For this reason, simpler and cost-effective diagnosing methods have been proposed in the late years. In this regard, the heart rate variability (HRV) has been demonstrated to strongly reflect apnea episodes during sleep. Hence, this work reviews the latest advances in the evaluation of OSA from the HRV perspective to consider its potentialities for a future revisited CinC Challenge.This research has been supported by grants DPI201783952-C3 from MINECO/AEI/FEDER EU, SBPLY/17/180501/000411 from Junta de Comunidades de Castilla-la Mancha and AICO/2019/036 from Generalitat Valenciana. Moreover, Daniele Padovano has held graduate research scholarships from Escuela Polit ' ecnica de Cuenca and Instituto de Tecnolog ' ias Audiovisuales, University of CastillaLa ManchaPadovano, D.; Martinez-Rodrigo, A.; Pastor, JM.; Rieta, JJ.; Alcaraz, R. (2020). Obstructive Sleep Apnea Detection Methods Based on Heart Rate Variability Analysis: Opportunities for a Future Cinc Challenge. IEEE. 1-4. https://doi.org/10.22489/CinC.2020.400S1

On the Generalization of Sleep Apnea Detection Methods Based on Heart Rate Variability and Machine Learning

[EN] Obstructive sleep apnea (OSA) is a respiratory disorder highly correlated with severe cardiovascular diseases that has unleashed the interest of hundreds of experts aiming to overcome the elevated requirements of polysomnography, the gold standard for its detection. In this regard, a variety of algorithms based on heart rate variability (HRV) features and machine learning (ML) classifiers have been recently proposed for epoch-wise OSA detection from the surface electrocardiogram signal. Many researchers have employed freely available databases to assess their methods in a reproducible way, but most were purely tested with cross-validation approaches and even some using solely a single database for training and testing procedures. Hence, although promising values of diagnostic accuracy have been reported by some of these methods, they are suspected to be overestimated and the present work aims to analyze the actual generalization ability of several epoch-wise OSA detectors obtained through a common ML pipeline and typical HRV features. Precisely, the performance of the generated OSA detectors has been compared on two validation approaches, i.e., the widely used epoch-wise, k-fold cross-validation and the highly recommended external validation, both considering different combinations of well-known public databases. Regardless of the used ML classifiers and the selected HRV-based features, the external validation results have been 20 to 40% lower than those obtained with cross-validation in terms of accuracy, sensitivity, and specificity. Consequently, these results suggest that ML-based OSA detectors trained with public databases are still not sufficiently general to be employed in clinical practice, as well as that larger, more representative public datasets and the use of external validation are mandatory to improve the generalization ability and to obtain reliable assessment of the true predictive power of these algorithms, respectively.This research has received financial support from public grants PID2021-00X128525-IV0 and PID2021-123804OB-I00 of the Spanish Government 10.13039/501100011033 jointly with the European Regional Development Fund, SBPLY/17/180501/000411 and SBPLY/21/180501/000186 from Junta de Comunidades de Castilla-La Mancha, and AICO/2021/286 from Generalitat Valenciana. Moreover, Daniele Padovano holds a predoctoral scholarship 2022-PRED-20642, which is cofinanced by the operating program of European Social Fund (ESF) 2014-2020 of Castilla-La Mancha.Padovano, D.; Martínez-Rodrigo, A.; Pastor, JM.; Rieta, JJ.; Alcaraz, R. (2022). On the Generalization of Sleep Apnea Detection Methods Based on Heart Rate Variability and Machine Learning. IEEE Access. 10:92710-92725. https://doi.org/10.1109/ACCESS.2022.320191192710927251

Peripheral ameloblastoma of the upper gingiva: Report of a case and literature review

According to the 2005 histological classification of odontogenic neoplasms by the World Health Organization, ameloblastoma is a benign, locally invasive epithelial odontogenic tumor of putative enamel organ origin. There are four distinct subgroups in which this neoplasm can be gathered: the solid/multicystic type, the unicystic type, the desmoplastic and the peripheral type. Peripheral ameloblastoma is believed to be the rarest subgroup, making up for 2 to 10% of all ameloblastomas. From its first description by Kuru in 1911 to date, less than 200 cases of PA have been described in literature. PAs commonly affect the mandible, in the maxilla the most common location is the soft palatal tissue of the tuberosity area. The present report discusses a rare case of PA aroused in the gingiva of upper jaw in a 64-year-old woman. The treatment of the lesion and its immunohistochemical phenotype are described. A review of the literature is also performed, focusing on the epidemiological and pathological aspects of the lesions and their implications on the therapy

Métodos modernos de aprendizaje automático para la detección de apnea del sueño en registros de electrocardiograma

La apnea obstructiva del sueño es un trastorno respiratorio estrechamente relacionado con múltiples enfermedades cardiovasculares. Los costes asociados a la polisomnografía, el método estándar para la detección de apnea, limitan considerablemente su aplicación a nivel mundial. Ante la creciente incidencia de esta enfermedad y la baja tasa de su diagnóstico, los métodos de detección de apnea basados en el análisis del electrocardiograma han ganado popularidad en los últimos años, especialmente los fundamentados en técnicas de aprendizaje automático. En el presente trabajo, se han reproducido los métodos de detección más relevantes del estado del arte para someterlos bajo análisis, y además, se propone un modelo de aprendizaje profundo capaz de identificar episodios de apnea a partir de una novedosa forma de procesar la variabilidad del ritmo cardíaco. Los resultados obtenidos sugieren la existencia de un sesgo considerable en los métodos tradicionales de aprendizaje automático, particularmente en aquellos entrenados y validados sobre una misma base de datos por métodos de validación cruzada. Por otra parte, el modelo de aprendizaje profundo propuesto no solo lleva asociado un coste computacional bajo, sino que ha superado en rendimiento a la mayoría de trabajos anteriormente publicados, obteniendo valores de exactitud superiores al 90% sobre observaciones totalmente ajenas a la base de datos de entrenamiento

GITT. Detección automática de la Apnea Obstructiva del sueño, basada en el análisis de la variabilidad del ritmo cardíaco

El síndrome de la apnea obstructiva del sueño es un trastorno respiratorio de considerable incidencia en la población general. Los síntomas asociados a este trastorno pueden manifestarse en somnolencia y falta de coordinación, pudiendo ocasionar problemas de diversa índole, tales como accidentes de tráfico, ansiedad crónica y depresión. La polisomnografía (PSG), es el método estándar de diagnóstico, pero su elevado coste y exigencia técnica limita significativamente su cobertura global. En consecuencia, han surgido nuevos métodos alternativos basados en fotopletismografía, la señal electrocardiográfica (ECG) y la respiración derivada del ECG. Estos métodos han demostrado que la variabilidad del ritmo cardíaco (VRC) constituye un predictor robusto de los episodios de apnea. En este trabajo, se ha diseñado un método para comprender la eficacia del diagnóstico basado en la VRC. Para ello, se han realizado dos investigaciones bibliográficas: una en el contexto de las metodologías existentes y otra en el de las bases de datos disponibles. Además, se han establecido los criterios generales de exactitud siguiendo las directrices más comunes en la literatura científica. También, se han extraído 35 señales de ECG con anotaciones de apnea por minuto de la base de datos de Apnea-ECG (la cual está disponible en Physionet), y sobre ellas se han aplicado técnicas de aprendizaje automático supervisado. A este último respecto, se propone el empleo de técnicas alternativas de análisis espectral junto a herramientas novedosas para la extracción de características no lineales. En concreto, se pone a prueba el periodograma de Lomb-Scargle (PLS) y tres variedades distintas de entropía: la entropía muestral, la de Rényi y la de Tsallis. En el mejor de los casos, los resultados obtenidos presentan una precisión del 82.1 %, frente al 90% de la literatura general. La contribución del PLS ha destacado notablemente frente al resto de predictores, no siendo así para los extraídos con métodos de diagnóstico de apnea no lineales. Estos resultados permiten concluir en que el empleo de métodos alternativos a la PSG continúa siendo afectado por la nohomogeneidad de las bases de datos. Sin embargo, este hecho constituye un amplio abanico de oportunidades, existiendo un sinfín de combinaciones posibles aún por explorar en la detección de esta condición. Por todo lo anterior, este trabajo pretende servir de contribución a la comunidad científica aportando nuevas visiones sobre el ámbito en la detección de apnea durante el sueño

An Experimental Review on Obstructive Sleep Apnea Detection Based on Heart Rate Variability and Machine Learning Techniques

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.[EN] Obstructive sleep apnea (OSA) is a respiratory syndrome of high incidence in the general population and correlated with some cardiovascular diseases. Several techniques have been proposed in the last decades to find a surrogate method to polysomnography (PSG), the gold standard for the diagnosis of OSA. The present study comprises an experimental review on the state-of-the-art methods for OSA detection through the public Apnea-ECG database, which is available at PhysioNet. Precisely, traditional time-frequency domain features were extracted from the heart rate variability (HRV) signal, together with some common complexity measures. Given their ability to deal with real-world time series, two additional entropy-based measures were also tested, i.e., Rènyi and Tsallis entropies. Moreover, univariate and multivariate classifiers were applied, including diagnostic test, support vectors machine, and k-nearest neighbors. Ultimately, two sequential feature selection (SFS) algorithms were employed to reduce the computational cost of the resulting discriminant models. The major findings reported that multivariate classifiers reached similar results to those found in the literature. Moreover, univariate classification results suggested that the frequency domain features provided the best OSA detection, although a well-known entropy index also obtained a good performance.This research has been supported by grants DPI2017-83952- C3 from MINECO/AEI/FEDER EU, SBPLY/17/180501/ 000411 from Junta de Comunidades de Castilla-la Mancha and AICO/2019/036 from Generalitat Valenciana. Moreover, Daniele Padovano has held graduate research scholarships from Escuela Politecnica de Cuenca and Instituto de Tecnolog ¿ ¿¿as Audiovisuales, University of Castilla-La ManchaPadovano, D.; Martinez-Rodrigo, A.; Pastor, JM.; Rieta, JJ.; Alcaraz, R. (2020). An Experimental Review on Obstructive Sleep Apnea Detection Based on Heart Rate Variability and Machine Learning Techniques. IEEE. 1-4. https://doi.org/10.1109/EHB50910.2020.9280302S1

Cardiopulmonary exercise test predicts right heart catheterization

Right heart catheterization (RHC) is usually required to confirm the diagnosis of pulmonary artery hypertension (PAH). As an invasive test, RHC may be associated with possible complications, so noninvasive parameters able to predict PAH at RHC would be extremely useful

Hospitalization cost reduction with sacubitril-valsartan implementation in a cohort of patients from the Daunia Heart Failure Registry

Introduction: Aim of this study was to assess the impact of the introduction of new class of drugs (ARNI: angiotensin receptor-neprilysin inhibitor) on hospital related costs in a real world cohort of patients with chronic heart failure (CHF). Methods: Seventy-three consecutive patients with CHF and systolic dysfunction eligible for the treatment with ARNIs from the Daunia Heart Failure Registry were enrolled. Incidence of hospitalizations before and after treatment with ARNI, costs for drug and hospitalization for HF were recorded, indexed per year and compared. Results: Indexed mean number of hospitalizations per year was 0.93 ± 1.70 before and 0.19 ± 0.70 after introduction of ARNI (p < 0.001, −80%), 2.26 ± 1.95 before and 0.38 ± 1.2 after ARNI in the subgroup of patients with at least one hospitalization for HF in the year before treatment with ARNI (p < 0.001, −83%).Mean indexed cost for hospitalization was 2067 ± 3715 euros before and 1847 ± 1549 after ARNI (p n.s., −11%); in the subgroup with at least one hospitalization for HF 5175 ± 4345 before and 2311 ± 2308 after ARNI (p < 0.001, −55%). Cost reduction increased with the number of indexed hospitalization per year before ARNI from 11% to 66%. Conclusion: In a real world scenario, treatment with ARNI is associated with lower indexed rates of hospitalizations and hospitalization related costs. Cost reduction increases with at least one indexed hospitalization for heart failure before treatment with ARNI. Keywords: Chronic heart failure, Angiotensin receptor blockers, Sacubitril, Neprilysin inhibition, ARNI, Cost analysi