Deep brain simulation wearable IoT sensor device based Parkinson brain disorder detection using heuristic tubu optimized sequence modular neural network

Abstract

Abstract Now-a-days most of the people affected by Parkinson’s disease it is one of the degenerative brain disorder occurred due to the midbrain cell death. The Parkinson disease affects people speech, expressions and movement gradually. This disease is identified by using several automatic computer detection systems but they are unable to predict in earlier stage. Also, the traditional methods fails to predict the disease with maximum accuracy also increase the false classification accuracy. Due to this issue, wearable IoT mental health sensor device such as deep brain simulation (DBS) is used to collect patient brain activities, cell condition for predicting brain functionality changes. The collected information is processed by heuristic tubu optimized sequence modular neural network (HTSMNN). The method examines gathered information continuously and independent manner to predict the changes present in the brain successfully. The introduces method recognize the changes of brain function with high speed that reduces the prediction delay which helps to improve the Parkinson disease seriousness in future direction. Then the efficiency of the system is evaluated using MATLAB based experimental results and discussions.