Deep Neural Networks with Weighted Averaged Overnight Airflow Features for Sleep Apnea-Hypopnea Severity Classification

Dramatic raising of Deep Learning (DL) approach and its capability in biomedical applications lead us to explore the advantages of using DL for sleep Apnea-Hypopnea severity classification. To reduce the complexity of clinical diagnosis using Polysomnography (PSG), which is multiple sensing platform, we incorporates our proposed DL scheme into one single Airflow (AF) sensing signal (subset of PSG). Seventeen features have been extracted from AF and then fed into Deep Neural Networks to classify in two studies. First, we proposed a binary classifications which use the cutoff indices at AHI = 5, 15 and 30 events/hour. Second, the multiple Sleep Apnea-Hypopnea Syndrome (SAHS) severity classification was proposed to classify patients into 4 groups including no SAHS, mild SAHS, moderate SAHS, and severe SAHS. For methods evaluation, we used a higher number of patients than related works to accommodate more diversity which includes 520 AF records obtained from the MrOS sleep study (Visit 2) database. We then applied the 10-fold cross-validation technique to get the accuracy, sensitivity and specificity. Moreover, we compared the results from our main classifier with other two approaches which were used in previous researches including the Support Vector Machine (SVM) and the Adaboost-Classification and Regression Trees (AB-CART). From the binary classification, our proposed method provides significantly higher performance than other two approaches with the accuracy of 83.46 %, 85.39 % and 92.69 % in each cutoff, respectively. For the multiclass classification, it also returns a highest accuracy of all approaches with 63.70 %

Single Channel ECG for Obstructive Sleep Apnea Severity Detection using a Deep Learning Approach

Obstructive sleep apnea (OSA) is a common sleep disorder caused by abnormal breathing. The severity of OSA can lead to many symptoms such as sudden cardiac death (SCD). Polysomnography (PSG) is a gold standard for OSA diagnosis. It records many signals from the patient's body for at least one whole night and calculates the Apnea-Hypopnea Index (AHI) which is the number of apnea or hypopnea incidences per hour. This value is then used to classify patients into OSA severity levels. However, it has many disadvantages and limitations. Consequently, we proposed a novel methodology of OSA severity classification using a Deep Learning approach. We focused on the classification between normal subjects (AHI $$ 30). The 15-second raw ECG records with apnea or hypopnea events were used with a series of deep learning models. The main advantages of our proposed method include easier data acquisition, instantaneous OSA severity detection, and effective feature extraction without domain knowledge from expertise. To evaluate our proposed method, 545 subjects of which 364 were normal and 181 were severe OSA patients obtained from the MrOS sleep study (Visit 1) database were used with the k-fold cross-validation technique. The accuracy of 79.45\% for OSA severity classification with sensitivity, specificity, and F-score was achieved. This is significantly higher than the results from the SVM classifier with RR Intervals and ECG derived respiration (EDR) signal feature extraction. The promising result shows that this proposed method is a good start for the detection of OSA severity from a single channel ECG which can be obtained from wearable devices at home and can also be applied to near real-time alerting systems such as before SCD occurs

Non-invasive respiration monitoring by thermal imaging to detect sleep apnoea

Accurate airflow measurements are vital to diagnose apnoeas; respiratory pauses occurring during sleep that interrupt airflow to the lungs. Apnoea diagnosis usually requires an overnight polysomnography during which numerous vital signs are monitored, including respiratory rate and airflow. The current gold standard in respiration monitoring is a nasal pressure sensor which is placed inside the nostrils of the patient and through which the airflow is measured. Due to the contact nature of the sensor, it is often refused or removed during polysomnography, especially in the case of paediatric patients. We have found that around 50% of children refuse the use of nasal prongs due to its in-vasiveness, and of those that accepted it, 64% removed the sensor over the course of the polysomnography. We evaluated a non-contact method to monitor respiration by developing infrared thermal imaging, whereby temperature fluc-tuations associated with respiration are measured and correlated with airflow. A study was carried out on a sample of 11 healthy adult volunteers whose res-piratory signals were recorded over four simulated apnoea scenarios. The res-piratory signal obtained through thermal imaging was compared against the gold standard nasal pressure sensor. In 70% of cases, apnoea related events were well correlated with airflow sensor readings. In 16% of recordings the subject’s head position did not allow correct identification of the region of interest (i.e. the nostrils). For the remaining 14% of cases there was partial agreement between the thermal measurements and airflow sensor readings. These results indicate thermal imaging can be valuable as a detection tool for sleep apnoea, particularly in the case of paediatric patients

Multi-scale Order Recurrence Plot based deterministic analysis on Heart Rate Variability in Congestive Heart Failure Assessment

Congestive heart failure (CHF) is a cardiovascular disease associated with the abnormal autonomic nervous system (ANS). Heart rate variability analysis (HRV) is the main method for the quantitative evaluation of autonomic nervous function. Common analytical methods of HRV include time domain, frequency domain, and nonlinear methods. However, these methods generally ignore the short-term volatility of heart rate and autonomic ganglion law. Therefore, this study proposes a new parameter to analyze heart rate variability-determination of a multi-scale order recurrence plot (MSORP_DET). This method can analyze the HRV in patients with heart failure on multiple time scales. This study analyzed the R-R interval in 24-hour HRV data from 98 samples (54 normal subjects and 44 patients with CHF). The results showed that MSORP_DET could significantly distinguish CHF patients from normal subjects (p<0.001). Moreover, the accuracy rate of screening patients with CHF reached the maximum of 81.6% by using the combination of low frequency/high frequency (LF/HF) and MSORP_DET, compared with 78.6% when using LF/HF alone. Therefore, MSORP_DET can be used as a new index to screen patients with CHF and reveal that the rhythm of ANS in patients with heart failure is more complex than that in normal people

A Time-Series Approach to Predict Obstructive Sleep Apnea (OSA) Episodes

Abstract -Sleep apnea is a common respiratory disorder during sleep. It is characterized by pauses in breathing or shallow breathing during sleep for longer than 10 seconds. Except the fact that not having a proper sleep and being rested for the next day, in some cases the apnea period (not breathing interval) may last more than 30 seconds and this situation can even be fatal. 14% of men and 5% of women suffer from Obstructive Sleep Apnea (OSA) in United States. Patients may experience apnea for more than 300 times in a single night sleep. Polysomnography (PSG) is a multi-parametric recording of biophysiological changes, containing EEG, ECG, SpO2, Nasal Airflow signals, performed during overnight sleep. In this study, a fully automatic apnea detection algorithm is developed and an early warning system is proposed to predict OSA episodes by extracting time-series features of OSA periods and regular respiration using nasal airflow signal. Extracted features are then reduced to improve the performance of the prediction. Support vector machines (SVM), one of the commonly used classification algorithms in medical applications, is implemented for learning and prediction of the OSA episodes. The results show that OSA episodes are predicted with 87.6% of accuracy and 91.3% of sensitivity, 30 seconds before patient faces apnea. By this approach, apnea related health risks can be minimized by foreknowledge

Contributions to interoperability, scalability and formalization of personal health systems

The ageing of the world's population combined with unhealthy lifestyles are contributing to a major prevalence of chronic diseases. This scenario poses the challenge of providing good healthcare services to that people affected by chronic illnesses, but without increasing its costs. A prominent way to face this challenge is through pervasive healthcare. Research in pervasive healthcare tries to shift the current centralized healthcare delivery model focused on the doctors, to a more distributed model focused on the patients. In this context Personal Health Systems (PHSs) consists on approaching sampling technologies into the hands of the patients, without disturbing its activities of the daily life, to monitor patient's physiological parameters and providing feedback on their state. The use of PHSs involves the patients in the management of their illness and in their own well being too. The development of PHSs has to face technological issues in order to be accepted by our society. Within them it is important to ensure interoperability between different systems in order to make them work together. Scalability it is also a concern, as their performance must not decrease when increasing the number of users. Another issue is how to formalize the medical knowledge for each patient, as different patients may have different target goals. Security and privacy are a must feature because of the sensitive nature of medical data. Other issues involve the the integration with legacy systems, and the usability of graphical user interfaces in order to encourage old people with the use these technologies. The aim of this PhD thesis is to contribute into the state-of-the-art of PHSs by tackling together different of the above-mentioned challenges. First, to achieve interoperability we use the CDA standard as a format to encode and exchange health data and alerts related with the status of the patient. We show how these documents can be generated automatically through the use of XML templates. Second, we address the scalability by distributing the computations needed to monitor the patients over their devices, rather than performing them in a centralized server. In this context we develop the MAGPIE agent platform, which runs on Android devices, as a framework able to provide intelligence to PHSs, and generate alerts that can be of interest for the patients and the medical doctors. Third, we focus on the formalization of PHSs by providing a tool for the practitioners where they can define, in a graphical way, monitoring rules related with chronic diseases that are integrated with the MAGPIE agent platform. The thesis also explores different ways to share the data collected with PHSs in order to improve the outcomes obtained with the use of this technology. Data is shared between individuals following a Distributed Event-Based System (DEBS) approach, where different people can subscribe to the alerts produced by the patient. Data is also shared between institutions with a network protocol called MOSAIC, and we focus on the security aspects of this protocol. The research in this PhD focuses in the use case of Diabetes Mellitus; and it has been developed in the context of the projects MONDAINE, MAGPIE, COMMODITY12 and TAMESIS.L'envelliment de la població mundial combinat amb uns estils de vida no saludables contribueixen a una major prevalença d'enfermetats cròniques. Aquest escenari presenta el repte de proporcionar uns bons serveis sanitaris a les persones afectades per aquestes enfermetats, sense incrementar-ne els costos. Una solució prometedora a aquest repte és mitjançant l'aplicació del que en anglès s'anomena "pervasive healthcare". L'investigació en aquesta camp tracta de canviar l'actual model centralitzat de serveis sanitaris enfocat en el personal sanitari, per un model de serveis distribuït enfocat en els pacients. En aquest context, els Personal Health Systems (PHSs) consisteixen en posar a l'abast dels pacients les tecnologies de monitorització, i proporcionar-los informació sobre el seu estat. L'ús de PHSs involucra els pacients en la gestió de la seva enfermetat i del seu propi benestar. L'acceptació dels PHSs per part de la societat implica certs reptes tecnològics en el seu desenvolupament. És important garantir la seva interoperabilitat per tal de que puguin treballar conjuntament. La seva escalabilitat també s'ha de tenir en compte, ja que el seu rendiment no s'ha de veure afectat al incrementar-ne el número d'usuaris. Un altre aspecte a considerar és com formalitzar el coneixement mèdic per cada pacient, ja que cada un d'ells pot tenir objectius diferents. La seguretat i privacitat són característiques desitjades degut a la naturalesa sensible de les dades mèdiques. Altres problemàtiques impliquen la integració amb sistemes heretats, i la usabilitat de les interfícies gràfiques per fomentar-ne el seu ús entre les persones grans. L'objectiu d'aquesta tesi és contribuir a l'estat de l'art dels PHSs tractant de manera conjunta varis dels reptes mencionats. Per abordar l'interoperabilitat s'utilitza l'estàndard CDA com a format per codificar les dades mèdiques i alertes relacionades amb el pacient. A més es mostra com aquests documents poden generar-se de forma automàtica mitjançant l' ús de plantilles XML. Per tractar l'escalabilitat es distribueixen les computacions per monitoritzar els pacients entre els seus terminals mòbils, en comptes de realitzar-les en un servidor central. En aquest context es desenvolupa la plataforma d'agents MAGPIE com a framework per proporcionar intelligència als PHSs i generar alertes d'interès per al metge i el pacient. La formalització s'aborda mitjançant una eina que permet als metges definir de manera gràfica regles de monitorització relacionades amb enfermetats cròniques, que a més estan integrades amb la plataforma d'agents MAGPIE. La tesi també explora diferents maneres de compartir les dades recol·lectades amb un PHS, amb l'objectiu de millorar els resultats obtinguts amb aquesta tecnologia. Les dades es comparteixen entre individus seguint un enfoc de sistemes distribuïts basats en events (DEBS), on diferents usuaris poden subscriure's a les alertes produïdes per el pacient. Les dades també es comparteixen entre institucions mitjançant un protocol de xarxa anomenat MOSAIC. A la tesi es desenvolupen els aspectes de seguretat d'aquest protocol. La test es centra en la Diabetis Mellitus com a cas d'ús, i s'ha realitzat en el context dels projectes MONDAINE, MAGPIE, COMMODITY12 i TAMESIS.El envejecimiento de la población mundial combinado con unos estilos de vida no saludables contribuyen a una mayor prevalencia de enfermedades crónicas. Este escenario presenta el reto de proporcionar unos buenos servicios sanitarios a las personas afectadas por estas enfermedades, sin incrementar sus costes. Una solución prometedora a este reto es mediante la aplicación de lo que en inglés se denomina "pervasive healthcare". La investigación en este campo trata de cambiar el actual modelo centralizado de servicios sanitarios enfocado hacia el personal sanitario, por un modelo distribuido enfocado hacia los pacientes. En este contexto, los Personal Health Systems (PHSs) consisten en poner al alcance de los pacientes las tecnologías de monitorización, y proporcionarles información sobre su estado. El uso de PHSs involucra a los pacientes en la gestión de su enfermedad y en su propio bienestar. La aceptación de los PHSs por parte de la sociedad implica ciertos retos tecnológicos en su desarrollo. Es importante garantizar su interoperabilidad para que puedan trabajar conjuntamente. Su escalabilidad también se debe tener en cuenta, ya que su rendimiento no tiene que verse afectado al incrementar su número de usuarios. Otro aspecto a considerar es cómo formalizar el conocimiento médico para cada paciente, ya que cada uno puede tener objetivos distintos. La seguridad y privacidad son características deseadas debido a la naturaleza sensible de los datos médicos. Otras problemáticas implican la integración con sistemas heredados, y la usabilidad de las interfaces gráficas para fomentar su uso entre las personas mayores. El objetivo de esta tesis es contribuir al estado del arte de los PHSs tratando de manera conjunta varios de los retos mencionados. Para abordar la interoperabilidad se usa el estándar CDA como formato para codificar los datos médicos y alertas relacionados con el paciente. Además se muestra como estros documentos pueden generarse de forma automática mediante el uso de plantillas XML. Para tratar la escalabilidad se distribuye la computación para monitorizar a los pacientes en sus terminales móbiles, en lugar de realizarla en un servidor central. En este contexto se desarrolla la plataforma de agentes MAGPIE como framework para proporcionar inteligencia a los PHSs y generar alertas de interés para el médico y el paciente. La formalización se aborda mediante una herramienta que permite a los médicos definir de manera gráfica reglas de monitorización relacionadas con enfermedades crónicas, que ademas están integradas con la plataforma de agentes MAGPIE. La tesis también explora distintas formas de compartir los datos recolectados con un PHS, con el fin de mejorar los resultados obtenidos mediante esta tecnología. Los datos se comparten entre individuos siguiendo un enfoque de sistemas distribuidos basados en eventos (DEBS), donde distintos usuarios pueden suscribirse a las alertas producidas por el paciente. Los datos también se comparten entre instituciones mediante un protocolo dered llamado MOSAIC. En la tesis se desarrollan los aspectos de seguridad de este protocolo. La tesis se centra en la Diabetes Mellitus como caso de uso, y se ha realizado en el contexto de los proyectos MONDAINE, MAGPIE, COMMODITY12 y TAMESIS.Postprint (published version

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