Pain management based on spinal cord dorsal horn system response identification using artificial neural networks

Abstract

Pain, depending on its severity, is an uncomfortable and individual sensation for sending a signal being sensed by brain about body harms. The spinal cord and nerves provide the pathway for these messages to travel to and from the brain and other parts of the body. Although most of the patients such as cancerous patients may have pain for a variety of reasons, there is still no common way of controlling the pain. Pain identification mechanisms in the nerve system and modeling its artificial neural network (i.e. ANN) system is required to access the best way of clinical cure. Up to now, no practical model has been presented that is capable of identifying the dorsal horn of spinal cord response modes, memory role and regulating other senses' effects on these modes. In this paper, by using the bifurcation methodology and nonlinear dynamic behavior feature extraction of the pain data transmission system along with its supporting clinical database, an ANN model is presented which is able to identify the dorsal horn of spinal cord neuron responses, memory role, other senses' input effect and descending input effects from the high level of the nervous system. The results showed that the ANN model can accurately follow the clinical data based on electrical and thermal stimulations. Moreover, the ANN model simulates pain management while using both electrical and thermal stimulations. In conclusion, it is deduced that the proposed ANN model is efficient in pain management of severe painful patients. ط¢آ© 2014 National Taiwan University.