Modeling of pain using artificial neural networks.
In dealing with human nervous system, the sensation of pain is as sophisticated as other physiological phenomena. To obtain an acceptable model of the pain, physiology of the pain has been analysed in the present paper. Pain mechanisms are explained in block diagram representation form. Because of the nonlinear interactions existing among different sections in the diagram, artificial neural networks (ANNs) have been exploited. The basic patterns associated with chronic and acute pain have been collected and then used to obtain proper features for training the neural networks. Both static and dynamic representations of the ANNs were used in this regard. The trained networks then were employed to predict response of the body when it is exposed to special excitations. These excitations have not been used in the training phase and their behavior is interesting from the physiological view. Some of these predictions can be inferred from clinical experimentations. However, more clinical tests have to be accomplished for some of the predictions.
Haeri
M
M
Electrical Engineering Department, Sharif University of Technology, Azadi Avenue, P.O. Box 11365-9363, Tehran, Iran. haeri@sina.sharif.ac.ir
Asemani
D
D
Gharibzadeh
Sh
Sh
D000208:Acute Disease; D002908:Chronic Disease; D006801:Humans; D008959:Models, Neurological; D016571:Neural Networks, Computer; D010146:Pain / Q000503:physiopathology*