Danilo P. Mandic

Danilo P. Mandic

Danilo P. Mandic is currently a Professor of Signal Processing with Imperial College London, London, U.K., where he has been involved in the area of nonlinear adaptive and biomedical signal processing. He has been a Guest Professor with Katholieke Universiteit Leuven, Leuven, Belgium and a Frontier Researcher with RIKEN, Tokyo. His publication record includes two research monographs entitled Recurrent Neural Networks for Prediction (West Sussex, U.K.: Wiley, 2001) and Complex-Valued Nonlinear Adaptive Filters: Noncircularity, Widely Linear and Neural Models (West Sussex, U.K.: Wiley, 2009), an edited book entitled Signal Processing for Information Fusion (New York: Springer, 2008), and more than 200 publications in signal and image processing. He has produced award winning papers and products from his collaboration with the industry, and has received the Presidents Award for excellence in postgraduate supervision at Imperial College. He is a member of the London Mathematical Society.


Contributions

  • Pain Prediction from ECG in Vascular Surgery
    Pain Prediction from ECG in Vascular Surgery

    Part of the Special Issue NIH-IEEE POCT 2016
    Varicose vein surgeries are routine outpatient procedures, which are often performed under local anaesthesia. The use of local anaesthesia both minimises the risk to patients and is cost effective, however, a number of patients still experience pain during surgery. Surgical teams must therefore decide to administer either a general or local anaesthetic based on their subjective qualitative assessment of patient anxiety and sensitivity to pain, without any means to objectively validate their decision…

  • Automatic Sleep Monitoring Using Ear-EEG

    The monitoring of sleep patterns without patient’s inconvenience or involvement of a medical specialist is a clinical question of significant importance. To this end, we propose an automatic sleep stage monitoring system based on an affordable, unobtrusive, discreet, and long-term wearable in-ear sensor for recording the electroencephalogram (ear-EEG). The selected features for sleep pattern classification from a single ear-EEG channel include the spectral edge frequency and multi-scale fuzzy entropy, a structural complexity feature. In this preliminary study, the manually scored hypnograms from simultaneous scalp-EEG and ear-EEG recordings of four subjects are used as labels for two analysis scenarios.

  • Smart Helmet: Wearable Multichannel ECG & EEG
    Smart Helmet: Wearable Multichannel ECG & EEG

    Modern wearable technologies have enabled continuous recording of vital signs, however, for activities such as cycling, motor-racing, or military engagement, a helmet with embedded sensors would provide maximum convenience and the opportunity to monitor simultaneously both the vital signs and the electroencephalogram (EEG). To this end, we investigate the feasibility of recording the electrocardiogram (ECG), respiration, and EEG from face-lead locations, by embedding multiple electrodes within a standard helmet.

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