Search for: clinical-data
0.005 seconds

    Paroxysmal atrial fibrillation prediction using Kalman filter

    , Article ACM International Conference Proceeding Series, 26 October 2011 through 29 October 2011, Barcelona ; 2011 ; 9781450309134 (ISBN) Montazeri, N ; Shamsollahi, M. B ; Carrault, G ; Hernández, A. I ; Sharif University of Technology
    In this paper, we proposed a method based on Kalman Filter for predicting the onset of paroxysmal atrial fibrillation (PAF) from the electrocardiogram (ECG) using clinical data available from the Computers in Cardiology (CinC) Challenge 2001. To predict PAF, we developed an algorithm based upon the number of atrial premature complexes (APCs) in the ECG. The algorithm detects classical isolated APCs by monitoring fidelity signals, which is defined here as a function of the innovation signal of Kalman filter, in vicinity of premature heartbeats and decides whether one beat is APC or not then predicts PAF, based on the number of APC. The challenge database consists of 56 pairs of 30-minute ECG... 

    Epileptic seizure detection based on video and EEG recordings

    , Article 2017 IEEE Biomedical Circuits and Systems Conference, BioCAS 2017 - Proceedings, 19 October 2017 ; Volume 2018-January , 2018 , Pages 1-4 ; 9781509058037 (ISBN) Aghaei, H ; Kiani, M. M ; Aghajan, H ; IEEE Circuits and Systems Society (CAS); IEEE Engineering in Medicine and Biology Society (EMBS); SSCS ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2018
    Clinical data from epileptic patients reveal important information about the characteristics of the particular type of epilepsy. Such data is often acquired in a bimodal fashion, e.g. video recordings are collected with the standard Electroencephalogram (EEG) data, in order to help the specialists validate their assessment based on one modality with the other. Manual annotation of the onset of seizures across several days' worth of data is time consuming. This paper proposes an automated epilepsy seizure detection method based on a combination of features from EEG and video data, and compares it against detectors using either modality alone. © 2017 IEEE  

    A mathematical description of physician decision making

    , Article 2nd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2008, Shanghai, 16 May 2008 through 18 May 2008 ; 2008 , Pages 281-284 ; 9781424417483 (ISBN) Niknam, K ; Niknam, S ; Sharif University of Technology
    IEEE Computer Society  2008
    This paper presents a mathematical model describing how the physicians actually make a diagnostic decision. Next to a description of diagnostic decision making as done by the physicians, this paper shows that how can we design and develop a new approach to medical diagnosis based on the human principles of decision making. The model was challenged to diagnose a series of actual patients. Real clinical data was entered into the model and the system produced a ranked list of possible diagnoses for each case. The results indicated good performance when compared with internist's diagnosis. The proposed method is effective and can be applied to describe medical reasoning as done by the... 

    The utilization of patients’ information to improve the performance of radiotherapy centers: A data-driven approach

    , Article Computers and Industrial Engineering ; Volume 172 , 2022 ; 03608352 (ISSN) Moradi, S ; Najafi, M ; Mesgari, S ; Zolfagharinia, H ; Sharif University of Technology
    Elsevier Ltd  2022
    The high demand for radiotherapy services, combined with the limited capacity of available resources, patient unpunctuality, and series of appointments, makes Patient Appointment Scheduling (PAS) in radiotherapy centers very challenging. Although most centers use a First-Come-First-Serve (FCFS) policy for appointment scheduling, this approach does not consider patients’ behaviors, and consequently, it performs poorly. This type of inappropriate scheduling usually leads to inefficiency at the center and/or patient dissatisfaction. This study provides a data-driven approach to patient appointment scheduling to deal with the challenges mentioned above, and it considers patients’ histories of... 

    Mathematical modeling of CSF pulsatile hydrodynamics based on fluid-solid interaction

    , Article IEEE Transactions on Biomedical Engineering ; Volume 57, Issue 6 , 2010 , Pages 1255-1263 ; 00189294 (ISSN) Masoumi, N ; Bastani, D ; Najarian, S ; Ganji, F ; Farmanzad, F ; Seddighi, A. S ; Sharif University of Technology
    Intracranial pressure (ICP) is derived from cerebral blood pressure and cerebrospinal fluid (CSF) circulatory dynamics and can be affected in the course of many diseases. Computer analysis of the ICP time pattern plays a crucial role in the diagnosis and treatment of those diseases. This study proposes the application of Linninger et al.s [IEEE Trans. Biomed. Eng. , vol. 52, no. 4, pp. 557565, Apr. 2005] fluidsolid interaction model of CSF hydrodynamic in ventricular system based on our clinical data from a group of patients with brain parenchyma tumor. The clinical experiments include the arterial blood pressure (ABP), venous blood pressure, and ICP in the subarachnoid space (SAS). These... 

    Joint edge detection and motion estimation of cardiac MR image sequence by a phase field method

    , Article Computers in Biology and Medicine ; Volume 40, Issue 1 , 2010 , Pages 21-28 ; 00104825 (ISSN) Eslami, A ; Jahed, M ; Preusser, T ; Sharif University of Technology
    In this paper a variational framework for joint segmentation and motion estimation is employed for inspecting heart in Cine MRI sequences. A functional including Mumford-Shah segmentation and optical flow based dense motion estimation is approximated using the phase-field technique. The minimizer of the functional provides an optimum motion field and edge set by considering both spatial and temporal discontinuities. Exploiting calculus of variation principles, multiple partial differential equations associated with the Euler-Lagrange equations of the functional are extracted, first. Next, the finite element method is used to discretize the resulting PDEs for numerical solution. Several... 

    A hierarchical machine learning model based on Glioblastoma patients' clinical, biomedical, and image data to analyze their treatment plans

    , Article Computers in Biology and Medicine ; Volume 150 , 2022 ; 00104825 (ISSN) Ershadi, M. M ; Rahimi Rise, Z ; Akhavan Niaki, S. T ; Sharif University of Technology
    Elsevier Ltd  2022
    Aim of study: Glioblastoma Multiforme (GBM) is an aggressive brain cancer in adults that kills most patients in the first year due to ineffective treatment. Different clinical, biomedical, and image data features are needed to analyze GBM, increasing complexities. Besides, they lead to weak performances for machine learning models due to ignoring physicians' knowledge. Therefore, this paper proposes a hierarchical model based on Fuzzy C-mean (FCM) clustering, Wrapper feature selection, and twelve classifiers to analyze treatment plans. Methodology/Approach: The proposed method finds the effectiveness of previous and current treatment plans, hierarchically determining the best decision for...