Deep Brain Stimulation (DBS) is a prominent treatment of Parkinson’s disease (PD) that sends stimulation signals into the brain. The Closed-Loop DBS (CL-DBS) is an adaptive DBS system sending different patterns of stimulation signals in accordance with the PD symptoms. The CL-DBS system is a wearable device carried by the patients, which thus requires advanced intelligent and energy-efficient hardware so that it can send the optimized stimulus maintaining a 24/7 real-time operation. The state-of-the-art CL-DBS systems are implemented with the traditional integrated circuits that cannot meet these demands. In this paper, we design a novel energy-efficient beta oscillation detector of the CL-DBS system using Spiking Neural Networks (SNNs) and memristive synapses. The proposed SNN-based beta oscillation detector is trained with PD model data and evaluated using experimental data from the rats. The improvement of our SNN-based CL-DBS system is evaluated with the architecture level simulator NeuroSIM. The reductions of the proposed system on the chip area, latency, and energy are 67.3%, 41.9%, and 11.7% by using memristive synapses comparing to the traditional SRAM.