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    Design and implementation of an ultralow-power Ecg patch and smart cloud-based platform

    , Article IEEE Transactions on Instrumentation and Measurement ; Volume 71 , 2022 ; 00189456 (ISSN) Baraeinejad, B ; Shayan, M. F ; Vazifeh, A. R ; Rashidi, D ; Hamedani, M. S ; Tavolinejad, H ; Gorji, P ; Razmara, P ; Vaziri, K ; Vashaee, D ; Fakharzadeh, M ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2022
    Abstract
    This article reports the development of a new smart electrocardiogram (ECG) monitoring system, consisting of the related hardware, firmware, and Internet of Things (IoT)-based web service for artificial intelligence (AI)-assisted arrhythmia detection and a complementary Android application for data streaming. The hardware aspect of this article proposes an ultralow power patch sampling ECG data at 256 samples/s with 16-bit resolution. The battery life of the device is two weeks per charging, which alongside the flexible and slim (193.7 mm times62.4 mm times8.6 mm) and lightweight (43 g) allows the user to continue real-life activities while the real-time monitoring is being done without... 

    Human identification using ECG feature extracted from innovation signal of Extended Kalman Filter

    , Article 2012 5th International Conference on Biomedical Engineering and Informatics, BMEI 2012 ; 2012 , Pages 545-549 ; 9781467311816 (ISBN) Naraghi, M. E ; Almasi, A ; Shamsollahi, M. B ; Sharif University of Technology
    2012
    Abstract
    Electrocardiogram is one of the most prominent cardiac signals being capable to be utilized for medical uses such as arrhythmia detection. Over the years, the feasibility of using this signal for human identification issue has been investigated, and some methods have been proposed. In this Paper a novel approach is proposed for electrocardiogram (ECG) based human identification using Extended Kalman Filter (EKF). The innovation signal of EKF has been considered as feature which is used to classify different subjects. In this paper a general issue, human identification, is summarized to a classification problem in which the proposed features of each subject is calculated, and the... 

    A hybrid deep model for automatic arrhythmia classification based on LSTM recurrent networks

    , Article 15th IEEE International Symposium on Medical Measurements and Applications, MeMeA 2020, 1 June 2020 through 3 June 2020 ; 2020 Bitarafan, A ; Amini, A ; Baghshah, M. S ; Khodajou Chokami, H ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2020
    Abstract
    Electrocardiogram (ECG) recording of electrical heart activities has a vital diagnostic role in heart diseases. We propose to tackle the problem of arrhythmia detection from ECG signals totally by a deep model that does not need any hand-designed feature or heuristic segmentation (e.g., ad-hoc R-peak detection). In this work, we first segment ECG signals by detecting R-peaks automatically via a convolutional network, including dilated convolutions and residual connections. Next, all beats are aligned around their R-peaks as the most informative section of the heartbeat in detecting arrhythmia. After that, a deep learning model, including both dilated convolution layers and a Long-Short Term... 

    Life-threatening arrhythmia verification in ICU patients using the joint cardiovascular dynamical model and a bayesian filter

    , Article IEEE Transactions on Biomedical Engineering ; Volume 58, Issue 10 PART 1 , 2011 , Pages 2748-2757 ; 00189294 (ISSN) Sayadi, O ; Shamsollahi, M. B ; Sharif University of Technology
    Abstract
    In this paper, a novel nonlinear joint dynamical model is presented, which is based on a set of coupled ordinary differential equations of motion and a Gaussian mixture model representation of pulsatile cardiovascular (CV) signals. In the proposed framework, the joint interdependences of CV signals are incorporated by assuming a unique angular frequency that controls the limit cycle of the heart rate. Moreover, the time consequence of CV signals is controlled by the same phase parameter that results in the space dimensionality reduction. These joint equations together with linear assignments to observation are further used in the Kalman filter structure for estimation and tracking. Moreover,...