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    Sleep apnea detection from single-lead ECG using features based on ECG -derived respiration (EDR) signals

    , Article IRBM ; Volume 39, Issue 3 , 2018 , Pages 206-218 ; 19590318 (ISSN) Janbakhshi, P ; Shamsollahi, M. B ; Sharif University of Technology
    Elsevier Masson SAS  2018
    Background and objective: One of the important applications of non-invasive respiration monitoring using ECG signal is the detection of obstructive sleep apnea (OSA). ECG-derived respiratory (EDR) signals, contribute to useful information about apnea occurrence. In this paper, two EDR extraction methods are proposed, and their application in automatic OSA detection using single-lead ECG is investigated. Methods: EDR signals are extracted based on new respiration-related features in ECG beats morphology, such as ECG variance (EDRVar) and phase space reconstruction area (EDRPSR). After evaluating the EDRs by comparing them to a reference respiratory signal, they are used in an automatic OSA... 

    Extraction of Respiratory Information from ECG and Application on the
    Apnea Detection

    , M.Sc. Thesis Sharif University of Technology Janbakhshi, Parvaneh (Author) ; Shamsollahi, Mohammad Bagher (Supervisor)
    Respiration signal is one of the biological information required to monitor patient respiratory activities. Noninvasive respiratory monitoring is an extensive field of research, which has seen widespread interest for several years. It is well known that the respiratory activity influences electrocardiographic measurements (ECG) in various ways. Therefore, different signal processing techniques have been developed for extracting this respiratory information from the ECG, namely ECG derived respiratory (EDR). Potential advantages of such techniques are their low cost, high convenience and the ability to simultaneously monitor cardiac and respiratory activity. One of the aims of this thesis is... 

    Automatic detection of respiratory events during sleep from Polysomnography data using Layered Hidden Markov Model

    , Article Physiological Measurement ; Volume 43, Issue 1 , 2022 ; 09673334 (ISSN) Sadoughi, A ; Shamsollahi, M. B ; Fatemizadeh, E ; Sharif University of Technology
    IOP Publishing Ltd  2022
    Objective. Sleep apnea is a serious respiratory disorder, which is associated with increased risk factors for cardiovascular disease. Many studies in recent years have been focused on automatic detection of sleep apnea from polysomnography (PSG) recordings, however, detection of subtle respiratory events named Respiratory Event Related Arousals (RERAs) that do not meet the criteria for apnea or hypopnea is still challenging. The objective of this study was to develop automatic detection of sleep apnea based on Hidden Markov Models (HMMs) which are probabilistic models with the ability to learn different dynamics of the real time-series such as clinical recordings. Approach. In this study, a... 

    Automatic Detection of Sleep Arousal from EEG Signal Using Respiratory Information

    , M.Sc. Thesis Sharif University of Technology Aghdaei, Elnaz (Author) ; Shamsollahi, Mohammad Bagher (Supervisor)
    Sleep is vital for physical and mental health, affecting neurocognitive, physiological, and psychopathology functions and performance. Arousals are linked with sleep and interrupt the sleep states, forming a sleep/arousal loop. Spontaneous arousals are part of a normal sleep/wake cycle. There are also different clinical conditions causing sleep fragmentation and arousals, including sleep apnea (obstructive, central, and mixed apnea), hypopnea, and non-apnea such as respiratory effort-related arousals (RERA), snoring, teeth grinding, and periodic leg movement.This research introduced a novel approach for automatic arousal detection inspired by extracting respiratory information from EEG... 

    Evaluation and Analysis of Fault Tolerant Cooperative WBAN

    , M.Sc. Thesis Sharif University of Technology Falati, Diba (Author) ; Jahed, Mehran (Supervisor)
    Wireless area body networks (WBANs) are a set of sensors designed to monitor an individual’s health remotely. These networks are required to transfer the data properly to the healthcare professional. Since there is no guarantee that no faults might occur, these networks should have fault tolerance capability such that they can maintain their main function properly. In this thesis, a method for fault tolerance in WBANs is proposed.In this research, to test the proposed method, a case study on sleep apnea disorder is selected for monitoring patients who experience sleep apnea. To this end, the Apnea-ECG dataset from the PhysioNet repository was considered which contains five different types of... 

    , M.Sc. Thesis Sharif University of Technology Masoudi, Samira (Author) ; Shamsollahi, Mohammad Bagher (Supervisor)
    Apnea-bradycardia is a medical term for prolonged respiratory pause accompanied with a heart rate reduction which is a common event among preterm infants. Repetition of apnea-bradycardia episodescompromises oxygenation and tissue perfusion and may lead to neurological impairment or even short-term morbi-mortality. Main solution to this breathing-related disorder is continues monitoring of infants in neonatal intensive care units in order to detect apnea-bradycardia event, generate an alarm and warn available nurse or physician to initiate quick nursing actions. Various studies have been done in this area and different methods are proposed which mainly focus on cardiac signal processing. This... 

    Development of an Integrated Detail Model of Human Respiratory System for Control

    , M.Sc. Thesis Sharif University of Technology Arianpour, Mehrdad (Author) ; Bozorgmehri, Ramin (Supervisor)
    In this thesis, the latest human respiratory system is extended such that it addresses the effect of blood acidity on the performance of the human respiratory system. Since the model does not take into account any controller, two PI controllers proposed by Bental have been used to simulate the closed loop behavior of the respiratory system. Since, in the original model blood pH is considered as a constant value, the extended model has been used for the case where the blood pH pumped by heart is assumed to be and the obtained results have been compared with those of the original model. The similarity of the results shows the extended model to be valid for the normal case. Model since t he... 

    Sleep Apnea Detection Using Wearable Devices

    , M.Sc. Thesis Sharif University of Technology Rahimi, Hamid Reza (Author) ; Fotowat Ahmady, Ali (Supervisor) ; Akbar, Fatemeh (Supervisor)
    Approximately 1.36 billion people worldwide suffer from sleep apnea, necessitating accurate diagnosis and treatment. Traditional sleep clinics rely on Polysomnography (PSG)-based monitoring devices. However, these devices are not only voluminous but also prohibitively expensive, requiring numerous sensors and wires that disrupt sleep and cause discomfort for patients. To address these challenges, we have developed a wireless wearable system. This system comprises three sensor blocks, each capable of capturing vital signs from the body, resulting in a total of eight signals. The first two sensor blocks capture signals from the body, which are subsequently modulated and transmitted to a user's... 

    Evaluating the Effect of CPAP Pressure in Patient with Obstructive Sleep Apnea Using Heart Rate Variability

    , M.Sc. Thesis Sharif University of Technology Ahmadi Mousavi, Mohammad (Author) ; Vosoughi, Gholamreza (Supervisor) ; Arjmand, Navid (Supervisor)
    Continuous positive airway pressure (CPAP) is a standard treatment for patients with obstructive sleep apnea (OSA), a sleep-related breathing disorder characterized by full or partial occlusion of the upper airway during sleep. CPAP pressure adjust by a sleep technologist during attended laboratory polysomnography (PSG) to eliminate obstructive respiratory-related events (apneas, hypopneas). Because of changes in environment and lifestyle patients needed new adjustments for the device that brings discomfort and extra money for treatment. In recent, lots of methods have been proposed to replace PSG and minimized the number of biological signals to detect apnea, best results came from deep... 

    Switching kalman filter based methods for apnea bradycardia detection from ECG signals

    , Article Physiological Measurement ; Volume 36, Issue 9 , 2015 , Pages 1763-1783 ; 09673334 (ISSN) Ghahjaverestan, N. M ; Shamsollahi, M. B ; Ge, D ; Hernandez, A. I ; Sharif University of Technology
    Apnea bradycardia (AB) is an outcome of apnea occurrence in preterm infants and is an observable phenomenon in cardiovascular signals. Early detection of apnea in infants under monitoring is a critical challenge for the early intervention of nurses. In this paper, we introduce two switching Kalman filter (SKF) based methods for AB detection using electrocardiogram (ECG) signal. The first SKF model uses McSharry's ECG dynamical model integrated in two Kalman filter (KF) models trained for normal and AB intervals. Whereas the second SKF model is established by using only the RR sequence extracted from ECG and two AR models to be fitted in normal and AB intervals. In both SKF approaches, a... 

    Coupled hidden markov model-based method for apnea bradycardia detection

    , Article IEEE Journal of Biomedical and Health Informatics ; Volume 20, Issue 2 , 2016 , Pages 527-538 ; 21682194 (ISSN) Montazeri Ghahjaverestan, N ; Masoudi, S ; Shamsollahi, M. B ; Beuchée, A ; Pladys, P ; Ge, D ; Hernández, A. I ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc 
    In this paper, we present a novel framework for the coupled hidden Markov model (CHMM), based on the forward and backward recursions and conditional probabilities, given a multidimensional observation. In the proposed framework, the interdependencies of states networks are modeled with Markovian-like transition laws that influence the evolution of hidden states in all channels. Moreover, an offline inference approach by maximum likelihood estimation is proposed for the learning procedure of model parameters. To evaluate its performance, we first apply the CHMM model to classify and detect disturbances using synthetic data generated by the FitzHugh-Nagumo model. The average sensitivity and... 

    Apnea bradycardia detection based on new coupled hidden semi Markov model

    , Article Medical and Biological Engineering and Computing ; 12 November , 2020 Montazeri Ghahjaverestan, N ; Shamsollahi, M. B ; Ge, D ; Beuchee, A ; Hernandez, A. I ; Sharif University of Technology
    Springer Science and Business Media Deutschland GmbH  2020
    In this paper, a method for apnea bradycardia detection in preterm infants is presented based on coupled hidden semi Markov model (CHSMM). CHSMM is a generalization of hidden Markov models (HMM) used for modeling mutual interactions among different observations of a stochastic process through using finite number of hidden states with corresponding resting time. We introduce a new set of equations for CHSMM to be integrated in a detection algorithm. The detection algorithm was evaluated on a simulated data to detect a specific dynamic and on a clinical dataset of electrocardiogram signals collected from preterm infants for early detection of apnea bradycardia episodes. For simulated data, the...