Recent Advances in Statistical Data and Signal Analysis: Application to Real World Diagnostics from Medical and Biological Signals

Medical and biological signals span almost the entire spectrum from EEG to X-rays and their sources range from molecular scales to large organs such as heart, brain, and muscles. Signal processing techniques (including image analysis) are constantly serving towards improving the state-of-the-art in medical and biological data analysis and interpretation. There is constant scientific endeavour to get better insight into the hidden information beneath the huge stack of medical data that we encounter. Consequently there has been a major shift towards quantitative analysis of medical data through various computational approaches. Computational approaches that have been hugely popular and found important applications include computational modeling, Bayesian and graphical models, machine learning, deep-learning, pattern recognition, optimization, spectral and pseudospectral analysis, stochastic modelling, iterative system model adaptation, and multiscale multiphysics analysis to name a few.