Authors:
Tuan D. Pham
1
;
Truong Cong Thang
1
;
Mayumi Oyama-Higa
2
;
Hoc X. Nguyen
1
;
Hameed Saji
1
and
Masahide Sugiyama
1
Affiliations:
1
The University of Aizu, Japan
;
2
Chaos Technology Research Laboratory, Japan
Keyword(s):
Chaos, Lyapunov Exponents, Nonlinear Dynamical Analysis, Sample Entropy, Photoplethysmograph, Depression Detection, Biosignal Classification.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Data Manipulation
;
Detection and Identification
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Physiological Processes and Bio-Signal Modeling, Non-Linear Dynamics
;
Sensor Networks
;
Soft Computing
Abstract:
Depressive disorders are mental illnesses that can severely affect one’s health and well-being. If depression is not early detected and left untreated, it can consequently lead to suicide. This paper presents for the first time a novel combination of chaos theory and nonlinear dynamical analysis of signal complexity of photoplethysmography waveforms for detection of depression. Experimental results obtained from the analysis of mentally disordered and control subjects suggest the potential application of the proposed approach.