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Mahdi Marsousi
Academic Affiliation:
Multimedia Processing Lab, 
Electrical and Computer Eng. Dept., 
University of Toronto,
Toronto, ON, Canada
Industrial Affiliation:
Research Engineer,
Research and Development Department,
Magna Electronics Inc.,
1 Kenview Blvd, Suite 200,
Brampton, ON, L6T 5E6, Canada
Contact Information:
Academic email:
Work email: 
Email: marsousi@gmail.com
Cell-phone: (647) 967-1585

Physiological signal simulator:

Physiological signal simulator has been always required in both industry and academia for three reasons, including (a) testing and maintenance of biomedical equipment, (b) evaluating biomedical algorithms, (c) training and educational purposes. Although it is ideal to use real physiological signals for the three above-mentioned requirements, there are some issues which restricts the applicability of real physiological signals, including (a) privacy of information and ethical constraints, (b) non-reproducible abnormalities, (c) physical constraints of patients. Firstly, it is obvious that the use of patient’s medical records are strictly prohibited, unless the patient’s consent and approval is achieved. Secondly, many biomedical devices are designed to detect patient’s medical condition, and their testing and evaluation need abnormal signals to be reproduced. Some of the abnormality cases rarely happen. In addition, it is unsafe to take physiological signals of unstable abnormal patients for training and evaluation purposes. Therefore, physiological signal simulators are highly required in academic research and clinical environment.

Physiological signal simulators have been developed to be used in particular applications. They are mostly application specific, such ECG and EEG signal simulators. However, none of the existing solutions provides an affordable solution to model variety of physiological signals.

In this line of research, I developed a cost-efficient, flexible, and powerful framework to model any physiological signal of interest, to be applied in both academia and clinical environments. The simulator framework consists of a software GUI to model physiological signal of interest, and an electronic board to receive the model from the GUI and to generate electrical signals of interest. The components of physiological signal modeling are (1) B-Spline interpolation function, (2) wide-band noise modeling, (3) low frequency artefact signals, (4) irregular pattern generation to model abnormalities of interest. The framework allows to import a physiological signal of interest, and automatically extract the signal pattern. This helps to easily reproduce a short-period recorded signals for a long while, and also to add irregular patterns of interest to the physiological signal.

Steps of simulating a physiological signal of interest
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