Next Article in Journal
Clinical Value of Information Entropy Compared with Deep Learning for Ultrasound Grading of Hepatic Steatosis
Previous Article in Journal
A-DVM: A Self-Adaptive Variable Matrix Decision Variable Selection Scheme for Multimodal Problems
Previous Article in Special Issue
fNIRS Complexity Analysis for the Assessment of Motor Imagery and Mental Arithmetic Tasks
Open AccessEditorial

Assessing Complexity in Physiological Systems through Biomedical Signals Analysis

1
IRCCS Fondazione Don Carlo Gnocchi, 20148 Milan, Italy
2
Department of Engineering, University of Palermo, 90128 Palermo, Italy
3
Department of Information Engineering and Research Center “E. Piaggio”, University of Pisa, 56122 Pisa, Italy
*
Author to whom correspondence should be addressed.
Entropy 2020, 22(9), 1005; https://doi.org/10.3390/e22091005
Received: 7 September 2020 / Accepted: 8 September 2020 / Published: 9 September 2020
Note: In lieu of an abstract, this is an excerpt from the first page.

The idea that most physiological systems are complex has become increasingly popular in recent decades [...] View Full-Text
Keywords: entropy; multifractality; multiscale; cardiovascular system; brain; information flow entropy; multifractality; multiscale; cardiovascular system; brain; information flow
MDPI and ACS Style

Castiglioni, P.; Faes, L.; Valenza, G. Assessing Complexity in Physiological Systems through Biomedical Signals Analysis. Entropy 2020, 22, 1005.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop