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.

Associated articles

JTEHM, Articles, Published Articles
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... Read more
JTEHM, Articles, Published Articles
Pain Prediction from ECG in Vascular Surgery
      This Article is Featured in 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,... Read more
JTEHM, Articles, Published Articles
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... Read more