member-academic-staff

Joachim Behar

Joachim Behar
Assistant Professor
Silver 345
  • CV

    1. PhD, Biomedical Engineering, Oxford, UK
    2. MSc, Biomedical Engineering, Oxford, UK
    3. MEng, Ecole des Mines de Saint-Étienne, France

  • Selected Publications

    1. Behar Joachim, Oster Julien, Qiao Li, Clifford Gari D. Signal Quality During Arrhythmia and its Application to False Alarm Reduction. IEEE Transaction on Biomedical Engineering. 60.6 (2013): 1660-6.
    2. Behar Joachim, Roebuck Aoife, Shahid Mohammed, Daly Jonathan, Miranda Pureza Andre Hallack, Niclas Palmius, Stradling John, Clifford Gari D. SleepAp: An Automated Obstructive Sleep Apnoea Screening Application for Smartphones. IEEE Journal of Biomedical Health Informatics. 19.1 (2015): 325-31.
    3. Oster Julien, Behar Joachim, Johnson Alistair, Sayadi Omid, Nemati Shamim, Clifford Gari D. Semisupervised ECG ventricular beat classification with novelty detection based on switching Kalman filters. IEEE Transactions on Biomedical Engineering 62.9 (2015): 2125-34.
    4. Behar Joachim, Andreotti Fernando, Zaunseder Sebastian, Li Qiao, Oster Julien, Clifford Gari D. An ECG simulator for generating maternal-foetal activity mixtures on abdominal ECG recordings. Physiological Measurement. 35.8 (2014): 1537-50.
    5. Behar Joachim, Ganesan Ambhighainath, Zhang Jin, Yaniv Yael. The Autonomic Nervous System Regulates the Heart Rate through cAMP-PKA Dependent and Independent Coupled-Clock Pacemaker Cell Mechanisms. Frontiers in Physiology. 7 (2016): 419.
    6. Behar Joachim, Oster Julien, Clifford Gari D. Combining and benchmarking methods of foetal ECG extraction without maternal or scalp electrode data. Physiological Measurement. 35.8 (2014): 1569-89.

    Winning entry of the MIT-Computing in Cardiology Challenge 2013.
    Google Scholar
     

  • Main Research Interests

    The AIM Lab researches innovative algorithms that can exploit the information encrypted within ‘big databases’ of physiological time series and other medical information, with the AIM to elaborate new pattern recognition algorithms that can be used for the purpose of intelligent remote patient monitoring i.e. supporting the monitoring of patients outside the traditional hospital setting.

  • Research Topics

    Keywords: machine learning, statistical signal processing, remote monitoring, computational physiology, sleep disorders, fetal monitoring, digital phenotyping.