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Kais Gadhoumi, PhD

Title(s)Assistant Researcher, Physiological Nursing
SchoolSchool of Nursing
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    Collapse Biography 
    Collapse Education and Training
    McGill University, CanadaPhD03/2015Biomedical Engineering
    University of Sherbrooke, CanadaMSc04/2000Electrical Engineering
    Collapse Awards and Honors
    McGill University2015John. F. Davis Award
    University of Melbourne2015IWSP Award
    Montreal Neurological Institute2015Simon Groom Award
    American Epilepsy Society 2014Fellows Award
    American Epilepsy Society2012Grass Foundation Young Investigator Award
    Canadian League Against Epilepsy2012Top Research Award
    McGill University2009Provost's Graduate Fellowship
    Canadian International Development Agency1998  - 2001International Scholarship

    Collapse Overview 
    Collapse Overview
    I am interested into the development of predictive analytics for health care applications. I use signal processing, statistical and machine learning approaches to analyse physiological data and develop diagnostic and prognostic models. My current research focuses on three development axes: 1 New methods for robust detection of treatable arrythmias in the electrocardiogram and pulsatile signals, 2 Multimodal approaches for the prediction of clinical deterioration and 3 Diagnostic and prognostic tools in neurocritical care.

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    Collapse Bibliographic 
    Collapse Publications
    Publications listed below are automatically derived from MEDLINE/PubMed and other sources, which might result in incorrect or missing publications. Researchers can login to make corrections and additions, or contact us for help.
    List All   |   Timeline
    1. Pereira T, Ding C, Gadhoumi K, Tran N, Colorado RA, Meisel K, Hu X. Deep learning approaches for plethysmography signal quality assessment in the presence of atrial fibrillation. Physiol Meas. 2019 Nov 25. PMID: 31766037.
      View in: PubMed
    2. Pereira T, Gadhoumi K, Ma M, Xiuyun L, Xiao R, Colorado RA, Keenan KJ, Meisel K, Hu X. A Supervised Approach to Robust Photoplethysmography Quality Assessment. IEEE J Biomed Health Inform. 2019 Apr 03. PMID: 30951482.
      View in: PubMed
    3. Liu X, Gadhoumi K, Xiao R, Tran N, Smielewski P, Czosnyka M, Hetts SW, Ko N, Hu X. Continuous monitoring of cerebrovascular reactivity through pulse transit time and intracranial pressure. Physiol Meas. 2019 01 23; 40(1):01LT01. PMID: 30577032.
      View in: PubMed
    4. Gadhoumi K, Do D, Badilini F, Pelter MM, Hu X. Wavelet leader multifractal analysis of heart rate variability in atrial fibrillation. J Electrocardiol. 2018 Nov - Dec; 51(6S):S83-S87. PMID: 30177367.
      View in: PubMed
    5. Gadhoumi K, Lina JM, Mormann F, Gotman J. Seizure prediction for therapeutic devices: A review. J Neurosci Methods. 2016 Feb 15; 260:270-82. PMID: 26099549.
      View in: PubMed
    6. Gadhoumi K, Gotman J, Lina JM. Scale invariance properties of intracerebral EEG improve seizure prediction in mesial temporal lobe epilepsy. PLoS One. 2015; 10(4):e0121182. PMID: 25867083.
      View in: PubMed
    7. Gadhoumi K, Lina JM, Gotman J. Seizure prediction in patients with mesial temporal lobe epilepsy using EEG measures of state similarity. Clin Neurophysiol. 2013 Sep; 124(9):1745-54. PMID: 23643577.
      View in: PubMed
    8. Kais Gadhoumi, Jean-Marc Lina, Jean Gotman. Corrigendum to “Discriminating preictal and interictal states in patients with temporal lobe epilepsy using wavelet analysis of intracerebral EEG” [Clinical Neurophysiology 123 (2012) 1906–1916]. Clinical Neurophysiology. 2013 Feb 1; 124(2):428.
      View in: Publisher Site
    9. Gadhoumi K, Lina JM, Gotman J. Discriminating preictal and interictal states in patients with temporal lobe epilepsy using wavelet analysis of intracerebral EEG. Clin Neurophysiol. 2012 Oct; 123(10):1906-16. PMID: 22480601.
      View in: PubMed