Real Time Fixed Point Adaptive Chaotic System Generator for Deep Brain Stimulation Using FPGA

• Published in: 2018 International Conference on Frontiers of Information Technology (FIT) pp. 182 - 187
• Main Authors: Shah, Syed Aamir Ali, Zhang, Lei, Bais, Abdul
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-12-2018
• Subjects: Artificial neural networks; Back propagation algorithm; Chaotic system; Deep brain stimulation; Diseases; Field programmable gate arrays; Fixed point implementation; FPGA; Medical diagnostic imaging; Neurons; Parkinson disease; Satellite broadcasting; Time series analysis
• DOI: 10.1109/FIT.2018.00039

Parkinson disease (PD) is a neurodegenerative disorder which is caused by the untimely death of dopamine generating neurons situated in substantia nigra pars compacta and striatum. The depletion of striatal dopamine induces the synchronization in brain activity in motor regions which results in tremors. Deep brain stimulation (DBS) is used as an obvious choice in therapy when the medication stops working. Traditional DBS systems are open loop systems in which stimulation parameters need to be adjusted manually from among a large pool of available combinations. This makes the whole process tedious for the doctors and traumatic for the patients. We have developed an FPGA based device which can monitor, learn and mimic the behavior of chaotic systems on the go. The purpose of this device is to learn the chaotic behavior of brain, when in normal state, and generate the chaotic stimulation signal during PD based tremors.