Application of neural network and SVM to classify movement of rat in medical science
Journal Title: International Research Journal of Applied and Basic Sciences - Year 2013, Vol 4, Issue 3
Abstract
Identifying mammalian movement states has recently become an important topic in biological science research. Accurate assessment and analysis of movement is a fundamental requirement. Rodents are often used as models in the sleep field due to their ready availability and the similarities of their movement to human movement. The goal of intra-cortical brain computer interface (BCI) is to restore the lost functionalities in disabled patients suffering from severely impaired movements. The project aim was to develop a decoding method based on a rat model. Previously recorded data and an already develop pre-processing method were used. The experimental design was developed starting from intra-cortical (IC) signal recorded in the rat primary motor cortex (M1). The data pre-processing included denoising with wavelet technique, spike detection, and feature extraction. After the firing rates of intra-cortical neurons were extracted, artificial neural network (ANN) and support vector machine (SVM) were applied to classify the rat movements into two possible classes, Hit or No Hit.
Authors and Affiliations
Farahnaz Sadoughi| Department of Health Information Management, School of Health Management and Information Sciences, Tehran University of Medical Sciences, Tehran, Islamic Republic of Iran, Taha Samad Soltani| Department of Health Information Management, School of Health Management and Information Sciences, Tehran University of Medical Sciences, Tehran, Islamic Republic of Iran,t.ssoltany@razi.tums.ac.ir, Mostafa Shanbe Zadeh| Department of Health Information Management, School of Health Management and Information Sciences, Tehran University of Medical Sciences, Tehran, Islamic Republic of Iran
Keywords
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