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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... 

    On the control of unknown continuous time chaotic systems by applying takens embedding theory

    , Article Chaos, Solitons and Fractals ; Volume 109 , April , 2018 , Pages 53-57 ; 09600779 (ISSN) Kaveh, H ; Salarieh, H ; Hajiloo, R ; Sharif University of Technology
    Elsevier Ltd  2018
    In this paper, a new approach to control continuous time chaotic systems with an unknown governing equation and limitation on the measurement of states, has been investigated. In many chaotic systems, disability to measure all of the states is a usual limitation, like in some economical, biological and many other engineering systems. Takens showed that a chaotic attractor has an astonishing feature in which it can embed to a mathematically similar attractor by using time series of one of the states. The new embedded attractor saves much information from the original attractor. This phenomenon has been deployed to present a new way to control continuous time chaotic systems, when only one of... 

    Detection of change to SSVEPs using analysis of phase space topological : a novel approach

    , Article Neurophysiology ; Volume 51, Issue 3 , 2019 , Pages 180-190 ; 00902977 (ISSN) Soroush, M. Z ; Maghooli, K ; Pisheh, N. F ; Mohammadi, M ; Soroush, P. Z ; Tahvilian, P ; Sharif University of Technology
    Springer New York LLC  2019
    A novel method based on EEG nonlinear analysis and analysis of steady-state visual evoked potentials (SSVEPs) has been processed. The EEG phase space is reconstructed, and some new geometrical features are extracted. Statistical analysis is carried out based on ANOVA, and most significant features are selected and then fed into a multi-class support vector machine (MSVM). Both offline and online phases are considered to fully address SSVEP detection. In the offline mode, the whole design evaluation, feature selection, and classifier training are performed. In the online scenario, the proposed method is evaluated and the detection rate is reported for both phases. Subject-dependent and... 

    A new framework based on recurrence quantification analysis for epileptic seizure detection

    , Article IEEE Journal of Biomedical and Health Informatics ; Volume 17, Issue 3 , 2013 , Pages 572-578 ; 21682194 (ISSN) Niknazar, M ; Mousavi, S. R ; Vosoughi Vahdat, B ; Sayyah, M ; Sharif University of Technology
    This study presents applying recurrence quantification analysis (RQA) on EEG recordings and their subbands: delta, theta, alpha, beta, and gamma for epileptic seizure detection. RQA is adopted since it does not require assumptions about stationarity, length of signal, and noise. The decomposition of the original EEG into its five constituent subbands helps better identification of the dynamical system of EEG signal. This leads to better classification of the database into three groups: Healthy subjects, epileptic subjects during a seizure-free interval (Interictal) and epileptic subjects during a seizure course (Ictal). The proposed algorithm is applied to an epileptic EEG dataset provided... 

    ECG-derived respiration estimation from single-lead ECG using gaussian process and phase space reconstruction methods

    , Article Biomedical Signal Processing and Control ; Volume 45 , 2018 , Pages 80-90 ; 17468094 (ISSN) Janbakhshi, P ; Shamsollahi, M. B ; Sharif University of Technology
    Elsevier Ltd  2018
    Respiratory activity influences electrocardiographic measurements (ECG) in various ways. Therefore, extraction of respiratory information from ECG, namely ECG-derived respiratory (EDR), can be used as a promising noninvasive method to monitor respiration activity. In this paper, an automatic EDR extraction system using single-lead ECG is proposed. Respiration effects on ECG are categorized into two different models: additive and multiplicative based models. After selection of a proper model for each subject using a proposed criterion, gaussian process (GP) and phase space reconstruction area (PSRArea) are introduced as new methods of EDR extraction for additive and multiplicative models,... 

    Chaos control in delayed phase space constructed by the Takens embedding theory

    , Article Communications in Nonlinear Science and Numerical Simulation ; Volume 54 , 2018 , Pages 453-465 ; 10075704 (ISSN) Hajiloo, R ; Salarieh, H ; Alasty, A ; Sharif University of Technology
    Elsevier B.V  2018
    In this paper, the problem of chaos control in discrete-time chaotic systems with unknown governing equations and limited measurable states is investigated. Using the time-series of only one measurable state, an algorithm is proposed to stabilize unstable fixed points. The approach consists of three steps: first, using Takens embedding theory, a delayed phase space preserving the topological characteristics of the unknown system is reconstructed. Second, a dynamic model is identified by recursive least squares method to estimate the time-series data in the delayed phase space. Finally, based on the reconstructed model, an appropriate linear delayed feedback controller is obtained for...