Sleep Apnea Detection Using Multi-Error-Reduction Classification System with Multiple Bio-Signals.

INTRODUCTION: Obstructive sleep apnea (OSA) can cause serious health problems such as hypertension or cardiovascular disease. The manual detection of apnea is a time-consuming task, and automatic diagnosis is much more desirable. The contribution of this work is to detect OSA using a multi-error-reduction (MER) classification system with multi-domain features from bio-signals. METHODS: Time-domain, frequency-domain, and non-linear analysis features are extracted from oxygen saturation (SaO2), ECG, airflow, thoracic, and abdominal signals. To analyse the significance of each feature, we design a two-stage feature selection. Stage 1 is the statistical analysis stage, and Stage 2 is the final feature subset selection stage using machine learning methods. In Stage 1, two statistical analyses (the one-way analysis of variance (ANOVA) and the rank-sum test) provide a list of the significance level of each kind of feature. Then, in Stage 2, the support vector machine (SVM) algorithm is used to select a final feature subset based on the significance list. Next, an MER classification system is constructed, which applies a stacking with a structure that consists of base learners and an artificial neural network (ANN) meta-learner. RESULTS: The Sleep Heart Health Study (SHHS) database is used to provide bio-signals. A total of 66 features are extracted. In the experiment that involves a duration parameter, 19 features are selected as the final feature subset because they provide a better and more stable performance. The SVM model shows good performance (accuracy = 81.68%, sensitivity = 97.05%, and specificity = 66.54%). It is also found that classifiers have poor performance when they predict normal events in less than 60 s. In the next experiment stage, the time-window segmentation method with a length of 60s is used. After the above two-stage feature selection procedure, 48 features are selected as the final feature subset that give good performance (accuracy = 90.80%, sensitivity = 93.95%, and specificity = 83.82%). To conduct the classification, Gradient Boosting, CatBoost, Light GBM, and XGBoost are used as base learners, and the ANN is used as the meta-learner. The performance of this MER classification system has the accuracy of 94.66%, the sensitivity of 96.37%, and the specificity of 90.83%

Algoritmos avanzados para detección del síndrome de apnea-hipopnea obstructiva del sueño

El Síndrome de Apnea-Hipopnea Obstructiva del Sueño (SAHOS) es un trastorno del sueño muy prevalente en la población general y con afectación de múltiples órganos. Se estima que esta patología afecta entre el 3% y 5% de la población adulta en todo el mundo y aumenta con la edad. Si bien el SAHOS es más frecuente en adultos, afecta también a niños con una prevalencia cercana al 3%. Los eventos respiratorios asociados al SAHOS durante el sueño ocurren como consecuencia de una alteración anatómico-funcional de la vía aérea superior que producen su estrechamiento parcial (hipopnea) o su bloqueo total (apnea). Para establecer el grado de severidad del SAHOS, se define el Índice de Apnea-Hipopnea. Éste índice representa el número de eventos de apnea-hipopnea por hora de sueño. El estudio de referencia para el correcto diagnóstico del SAHOS es la Polisomnografía nocturna. Dado que este tipo de estudio requiere no solo de la medición simultánea de una gran cantidad de señales fisiológicas, sino también de una infraestructura especial y de personal calificado, es de muy difícil acceso y muy costosa en términos de tiempo y dinero.En esta tesis se aborda el diseño, desarrollo, implementación y evaluación de tres métodos para el reconocimiento automático de los eventos de apnea-hipopnea a partir del análisis y procesamiento de las señales de saturación de oxígeno en sangre (SaO2). En particular, se presentan dos métodos de selección de características denominados MDAS y MDCS, los cuales se basan en representaciones ralas de señales de SaO2 para el reconocimiento de eventos de apnea-hipopnea. Además, en esta tesis se introduce una nueva medida de discriminabilidad binaria denotada por DCAF, la cual es capaz de detectar átomos discriminativos en un diccionario. Asimismo, esta medida permite cuantificar eficientemente sus correspondientes grados de discriminabilidad, lo cual resulta útil a los efectos de la clasificación. Los métodos MDAS y MDCS hacen uso de la media DCAF para detectar los átomos más discriminativos de un diccionario dado y, a partir de ellos, realizan la selección de características. En particular, el método MDCS utiliza la medida DCAF para seleccionar los átomos más discriminativos y, a partir de ellos, construir un sub-diccionario. En base a los experimentos desarrollados en esta tesis, el desempeño de la nueva medida DCAF fue comparada con el de varias otras medidas de información del estado del arte. Los resultados muestran que DCAF logró un muy buen desempeño. Por otro lado, el nuevo método MDCS fue comparado con otros tres métodos del estado de arte, superando significativamente el desempeño de todos ellos.Esta tesis introduce además una extensión del problema de clasificación binaria a uno multi-clase. En este contexto, se propone una generalización de la medida DCAF (la cual tiene en cuenta solo dos clases en los datos) a más de dos clases. En particular, la nueva medida de discriminabilidad combinada no solo tiene en cuenta la probabilidad condicional de activación de los átomos en un diccionario dada la clase y el valor de su correspondiente coeficiente de activación, sino que también incorpora el efecto que éste tiene sobre el error total de representación. Asimismo, se presenta un nuevo método iterativo llamado DAS-KSVD para el aprendizaje de diccionarios estructurados en el contexto de problemas de clasificación multi-clase, que utiliza ésta medida. El nuevo método permite detectar los átomos más discriminativos para cada una de las clases. Utilizando una base de datos de dígitos manuscritos ampliamente utilizada en la literatura, se realizó un análisis del desempeño del método DAS-KSVD obteniéndose tasas de reconocimiento superiores a las obtenidas por técnicas semejantes del estado del arte. También se utilizó el nuevo método DAS-KSVD en un problema de clasificación multi-clase asociado al SAHOS. Los resultados obtenidos muestran que éste método tiene un muy buen desempeño en la detección de la patología.Fil: Rolon, Roman Emanuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentin

Apneic Events Detection Using Different Features of Airflow Signals

Apneic-event based sleep disorders are very common and affect greatly the daily life of people. However, diagnosis of these disorders by detecting apneic events are very difficult. Studies show that analyzes of airflow signals are effective in diagnosis of apneic-event based sleep disorders. According to these studies, diagnosis can be performed by detecting the apneic episodes of the airflow signals. This work deals with detection of apneic episodes on airflow signals belonging to Apnea-ECG (Electrocardiogram) and MIT (Massachusetts Institute of Technology) BIH (Bastons’s Beth Isreal Hospital) databases. In order to accomplish this task, three representative feature sets namely classic feature set, amplitude feature set and descriptive model feature set were created. The performance of these feature sets were evaluated individually and in combination with the aid of the random forest classifier to detect apneic episodes. Moreover, effective features were selected by OneR Attribute Eval Feature Selection Algorithm to obtain higher performance. Selected 28 features for Apnea-ECG database and 31 features for MITBIH database from 54 features were applied to classifier to compare achievements. As a result, the highest classification accuracies were obtained with the usage of effective features as 96.21% for Apnea-ECG database and 92.23% for MIT-BIH database. Kappa values are also quite good (91.80 and 81.96%) and support the classification accuracies for both databases, too. The results of the study are quite promising for determining apneic events on a minute-by-minute basis

Characterization and processing of novel neck photoplethysmography signals for cardiorespiratory monitoring

Epilepsy is a neurological disorder causing serious brain seizures that severely affect the patients' quality of life. Sudden unexpected death in epilepsy (SUDEP), for which no evident decease reason is found after post-mortem examination, is a common cause of mortality. The mechanisms leading to SUDEP are uncertain, but, centrally mediated apneic respiratory dysfunction, inducing dangerous hypoxemia, plays a key role. Continuous physiological monitoring appears as the only reliable solution for SUDEP prevention. However, current seizure-detection systems do not show enough sensitivity and present a high number of intolerable false alarms. A wearable system capable of measuring several physiological signals from the same body location, could efficiently overcome these limitations. In this framework, a neck wearable apnea detection device (WADD), sensing airflow through tracheal sounds, was designed. Despite the promising performance, it is still necessary to integrate an oximeter sensor into the system, to measure oxygen saturation in blood (SpO2) from neck photoplethysmography (PPG) signals, and hence, support the apnea detection decision. The neck is a novel PPG measurement site that has not yet been thoroughly explored, due to numerous challenges. This research work aims to characterize neck PPG signals, in order to fully exploit this alternative pulse oximetry location, for precise cardiorespiratory biomarkers monitoring. In this thesis, neck PPG signals were recorded, for the first time in literature, in a series of experiments under different artifacts and respiratory conditions. Morphological and spectral characteristics were analyzed in order to identify potential singularities of the signals. The most common neck PPG artifacts critically corrupting the signal quality, and other breathing states of interest, were thoroughly characterized in terms of the most discriminative features. An algorithm was further developed to differentiate artifacts from clean PPG signals. Both, the proposed characterization and classification model can be useful tools for researchers to denoise neck PPG signals and exploit them in a variety of clinical contexts. In addition to that, it was demonstrated that the neck also offered the possibility, unlike other body parts, to extract the Jugular Venous Pulse (JVP) non-invasively. Overall, the thesis showed how the neck could be an optimum location for multi-modal monitoring in the context of diseases affecting respiration, since it not only allows the sensing of airflow related signals, but also, the breathing frequency component of the PPG appeared more prominent than in the standard finger location. In this context, this property enabled the extraction of relevant features to develop a promising algorithm for apnea detection in near-real time. These findings could be of great importance for SUDEP prevention, facilitating the investigation of the mechanisms and risk factors associated to it, and ultimately reduce epilepsy mortality.Open Acces

Multimodal Signal Processing for Diagnosis of Cardiorespiratory Disorders

This thesis addresses the use of multimodal signal processing to develop algorithms for the automated processing of two cardiorespiratory disorders. The aim of the first application of this thesis was to reduce false alarm rate in an intensive care unit. The goal was to detect five critical arrhythmias using processing of multimodal signals including photoplethysmography, arterial blood pressure, Lead II and augmented right arm electrocardiogram (ECG). A hierarchical approach was used to process the signals as well as a custom signal processing technique for each arrhythmia type. Sleep disorders are a prevalent health issue, currently costly and inconvenient to diagnose, as they normally require an overnight hospital stay by the patient. In the second application of this project, we designed automated signal processing algorithms for the diagnosis of sleep apnoea with a main focus on the ECG signal processing. We estimated the ECG-derived respiratory (EDR) signal using different methods: QRS-complex area, principal component analysis (PCA) and kernel PCA. We proposed two algorithms (segmented PCA and approximated PCA) for EDR estimation to enable applying the PCA method to overnight recordings and rectify the computational issues and memory requirement. We compared the EDR information against the chest respiratory effort signals. The performance was evaluated using three automated machine learning algorithms of linear discriminant analysis (LDA), extreme learning machine (ELM) and support vector machine (SVM) on two databases: the MIT PhysioNet database and the St. Vincent’s database. The results showed that the QRS area method for EDR estimation combined with the LDA classifier was the highest performing method and the EDR signals contain respiratory information useful for discriminating sleep apnoea. As a final step, heart rate variability (HRV) and cardiopulmonary coupling (CPC) features were extracted and combined with the EDR features and temporal optimisation techniques were applied. The cross-validation results of the minute-by-minute apnoea classification achieved an accuracy of 89%, a sensitivity of 90%, a specificity of 88%, and an AUC of 0.95 which is comparable to the best results reported in the literature

Snoring and arousals in full-night polysomnographic studies from sleep apnea-hypopnea syndrome patients

SAHS (Sleep Apnea-Hypopnea Syndrome) is recognized to be a serious disorder with high prevalence in the population. The main clinical triad for SAHS is made up of 3 symptoms: apneas and hypopneas, chronic snoring and excessive daytime sleepiness (EDS). The gold standard for diagnosing SAHS is an overnight polysomnographic study performed at the hospital, a laborious, expensive and time-consuming procedure in which multiple biosignals are recorded. In this thesis we offer improvements to the current approaches to diagnosis and assessment of patients with SAHS. We demonstrate that snoring and arousals, while recognized key markers of SAHS, should be fully appreciated as essential tools for SAHS diagnosis. With respect to snoring analysis (applied to a 34 subjects¿ database with a total of 74439 snores), as an alternative to acoustic analysis, we have used less complex approaches mostly based on time domain parameters. We concluded that key information on SAHS severity can be extracted from the analysis of the time interval between successive snores. For that, we built a new methodology which consists on applying an adaptive threshold to the whole night sequence of time intervals between successive snores. This threshold enables to identify regular and non-regular snores. Finally, we were able to correlate the variability of time interval between successive snores in short 15 minute segments and throughout the whole night with the subject¿s SAHS severity. Severe SAHS subjects show a shorter time interval between regular snores (p=0.0036, AHI cp(cut-point): 30h-1) and less dispersion on the time interval features during all sleep. Conversely, lower intra-segment variability (p=0.006, AHI cp: 30h-1) is seen for less severe SAHS subjects. Also, we have shown successful in classifying the subjects according to their SAHS severity using the features derived from the time interval between regular snores. Classification accuracy values of 88.2% (with 90% sensitivity, 75% specificity) and 94.1% (with 94.4% sensitivity, 93.8% specificity) for AHI cut-points of severity of 5 and 30h-1, respectively. In what concerns the arousal study, our work is focused on respiratory and spontaneous arousals (45 subjects with a total of 2018 respiratory and 2001 spontaneous arousals). Current beliefs suggest that the former are the main cause for sleep fragmentation. Accordingly, sleep clinicians assign an important role to respiratory arousals when providing a final diagnosis on SAHS. Provided that the two types of arousals are triggered by different mechanisms we hypothesized that there might exist differences between their EEG content. After characterizing our arousal database through spectral analysis, results showed that the content of respiratory arousals on a mild SAHS subject is similar to that of a severe one (p>>0.05). Similar results were obtained for spontaneous arousals. Our findings also revealed that no differences are observed between the features of these two kinds of arousals on a same subject (r=0.8, p<0.01 and concordance with Bland-Altman analysis). As a result, we verified that each subject has almost like a fingerprint or signature for his arousals¿ content and is similar for both types of arousals. In addition, this signature has no correlation with SAHS severity and this is confirmed for the three EEG tracings (C3A2, C4A1 and O1A2). Although the trigger mechanisms of the two arousals are known to be different, our results showed that the brain response is fairly the same for both of them. The impact that respiratory arousals have in the sleep of SAHS patients is unquestionable but our findings suggest that the impact of spontaneous arousals should not be underestimated

Consumer sleep technology for the screening of obstructive sleep apnea and snoring : current status and a protocol for a systematic review and meta-analysis of diagnostic test accuracy

Funding Information: This work has received research funding from the European Union's Horizon 2020 research and innovation programme under grant agreement no. 965417. Timo Leppänen reports additional funding from NordForsk (NordSleep project 90458) via Business Finland (5133/31/2018), the Academy of Finland (323536), and the Research Committee of the Kuopio University Hospital Catchment Area for the State Research Funding (5041794). Erna Sif Arnardóttir and Anna Sigridur Islind report additional funding from NordForsk (NordSleep project 90458) via the Icelandic Research Fund. Funding Information: European Union's Horizon 2020 research and innovation program, Grant/Award Number: 965417; Academy of Finland, Grant/Award Number: 323536; Kuopio University Hospital Catchment Area for the State Research Funding, Grant/Award Number: 5041794; NordForsk, Grant/Award Number: 90458 Funding information Publisher Copyright: © 2023 The Authors. Journal of Sleep Research published by John Wiley & Sons Ltd on behalf of European Sleep Research Society.There are concerns about the validation and accuracy of currently available consumer sleep technology for sleep-disordered breathing. The present report provides a background review of existing consumer sleep technologies and discloses the methods and procedures for a systematic review and meta-analysis of diagnostic test accuracy of these devices and apps for the detection of obstructive sleep apnea and snoring in comparison with polysomnography. The search will be performed in four databases (PubMed, Scopus, Web of Science, and the Cochrane Library). Studies will be selected in two steps, first by an analysis of abstracts followed by full-text analysis, and two independent reviewers will perform both phases. Primary outcomes include apnea–hypopnea index, respiratory disturbance index, respiratory event index, oxygen desaturation index, and snoring duration for both index and reference tests, as well as the number of true positives, false positives, true negatives, and false negatives for each threshold, as well as for epoch-by-epoch and event-by-event results, which will be considered for the calculation of surrogate measures (including sensitivity, specificity, and accuracy). Diagnostic test accuracy meta-analyses will be performed using the Chu and Cole bivariate binomial model. Mean difference meta-analysis will be performed for continuous outcomes using the DerSimonian and Laird random-effects model. Analyses will be performed independently for each outcome. Subgroup and sensitivity analyses will evaluate the effects of the types (wearables, nearables, bed sensors, smartphone applications), technologies (e.g., oximeter, microphone, arterial tonometry, accelerometer), the role of manufacturers, and the representativeness of the samples.Peer reviewe