چكيده به لاتين
chronic pain is an annoying issue that has a great impact on the mental state of the patient, his/her quality of life and daily activities. Unfortunately, the high cost of different therapies, in addition to their side effects, fails to treat or reduce the pain of the patient.
Using the electrical stimulation is applied to the spinal cord, cortex or depth of the brain, or peripheral nerves for chronic pain relief. Electrical stimulation on demand and stimulation parameter alteration proportional to the pain severity are the most important objectives that researchers within the field of chronic pain management have been addressed. The first step to designing a closed-loop system and adoption of appropriate control methods is the continuous measurement of pain. Previous studies have shown that study of wide dynamic range (WDR) neurons activity can be very helpful for chronic pain measurement.
In this study effect of sciatic nerve injury so-called spared nerve injury (SNI) model of six sham, after injury and six neuropathic pain model rats in activity on WDR neurons were compared in different days. Three different feautures including firing rate of the action potential, inter-spike intervals (ISIs) of fired action potentials and local field potentials (LFP) were used to measure the activity of WDR neurons. Results show an increase in the response of the firing rate of WDR neuron and a decrease in interspike intervals in the rat model that suffering from neuropathic pain. Wavelet decomposition was used for feature extraction from continuous firing rate, interpolated interspike interval and LFP signals. For feature selection from a component of wavelet decomposition the analysis of variance was used. We obtain a result of classification for thirty trials of each sham, post operation and SNI model rat groups.
Result of classification accuracy of Wavelet decomposition is more than original signals. Results shown that the best classification accuracy on different levels of neuropathic pain obtained by using wavelet decomposition of continuous firing rate signal as neural network input.