부산대학교 도서관

Deep brain stimulation (DBS) has demonstrated therapeutic efficacy in the treatment of neurological and psychiatric disorders. Currently, DBS devices employ an ‘open-loop’ configuration, requiring manual adjustment of electrical stimulation to address patient needs. For this reason, closed-loop DBS is being developed, delivering appropriate treatment on-demand based on internal signal monitoring. A key challenge in current research is the complexity of interpreting the measured signals and delivering appropriate interventions, currently no miniaturised closed-loop DBS device has on-board artificial intelligence (AI) to meet this need. This paper presents a new miniaturised device, named SmartStim, that uses AI to monitor dynamically changing brain biomarkers. In addition, the AI decides if the output stimulator is required for treatment. This device has two key components: the hardware module (neural sensor unit, processor, and neurostimulator) and a software module (data processing, AI, and firmware). The neural sensor unit is comprised of two subcomponents. The first is a potentiostat that can perform impedance analysis, and the second is a dedicated fast scan cyclic voltammetry (FSCV) front-end that can perform scan rates up to 1000 V/s. This device can output current-controlled stimulation waveforms in a frequency range of 5 Hz – 200 Hz, a current range of $1~\mu \text{A}$ to 10 mA, with active charge balancing. Five experiments were conducted to validate SmartStim: static resistive load test, emulated brain resistance test, static electrochemical cell test, impedance test, and dynamic serotonin test. These experiments confirm the potential for SmartStim to identify neurochemical patterns in a mouse brain using AI.