Measurements of biosignals from the human body has a huge and ever widening range of applications. The mathematical description of the processes generating these biosignals provides a deeper understanding of their functioning and thus opens new perspectives to a variety of applications, ranging from health diagnosis to assessment of athletic performance. Different health conditions are characterized by different patterns in the signals, which may be captured by the parameters of a dynamic model. Identification of DD models for the human ventilatory system under different health and fitness conditions, using classical model structures (AR, NAR, NARMA, NARMAX, etc) and/or artificial neural networks (ANNs), is the aim of this MSc project.
The student will follow courses on Linear Systems, Instrumentation and System Identification. Her/his work will include measuring lung sounds from patients; treating these data and then using them for the identification of linear and nonlinear DD models; validating the models and comparing their performance in the assessment of the lung’s health condition.
