Advanced Signal Processing for Cardiovascular and Neurological Diseases

Advanced signal processing and computing techniques have been consistently playing a signifcant role in the feld of biomedical engineering research. Tis special issue focused on the use and elaboration of latest advanced techniques for biomedical data analysis, including but not limited to deep machine learning, compressed sensing, and nonlinear dynamical approaches. Nine out of twenty-one submitted manuscripts in response to this special issue were fnally accepted for publication, ranging from (i) noise suppression and removal in EEG and arterial photoplethysmography (PPG) signals; (ii) nonlinear dynamical approaches and multivariate and multiscale techniques for cardiovascular and neurophysiological imaging and signal processing; (iii) machine learning and deep neural network applications of cognitive outcome prediction for Alzheimer’s diseases and Parkinson’s diseases diagnosis; (iv) advanced signal processing to improve decision-making in brain-computer interface (BCI); and (v) acquisition and analysis of respiratory signals and rates using smartphones.