Abnormally-synchronized, high-voltage spindles (HVSs) are associated with motor deficits in 6-hydroxydopamine-lesioned parkinsonian rats. The non-stationary, spike-and-wave HVSs (5-13 Hz) represent the cardinal parkinsonian state in the local field potentials (LFPs). Although deep brain stimulation (DBS) is an effective treatment for the Parkinson’s disease, continuous stimulation results in cognitive and neuropsychiatric side effects. Therefore, an adaptive stimulator able to stimulate the brain only upon the occurrence of HVSs is demanded. This paper proposes an algorithm not only able to detect the HVSs with low latency but also friendly for hardware realization of an adaptive stimulator. The algorithm is based on autoregressive modeling at interval, whose parameters are learnt online by an adaptive Kalman filter. In the LFPs containing 1131 HVS episodes from different brain regions of four parkinsonian rats, the algorithm detects all HVSs with 100% sensitivity. The algorithm also achieves higher precision (96%) and lower latency (61 ms), while requiring less computation time than the continuous wavelet transform method. As the latency is much shorter than the mean duration of an HVS episode (4.3 s), the proposed algorithm is suitable for realization of a smart neuromodulator for mitigating HVSs effectively by closed-loop DBS.

异常同步的高压纺锤体 (HVS) 与 6-羟基多巴胺损伤的帕金森病大鼠的运动缺陷有关。非平稳的尖峰波 HVS (5-13 Hz) 代表局部场电位 (LFP) 中的主要帕金森病状态。尽管深部脑刺激 (DBS) 是治疗帕金森病的有效方法，但持续刺激会导致认知和神经精神方面的副作用。因此，需要一种仅在发生 HVS 时才能够刺激大脑的适应性刺激器。本文提出了一种算法，不仅能够以低延迟检测 HVS，而且对自适应刺激器的硬件实现也很友好。该算法基于间隔自回归建模，其参数由自适应卡尔曼滤波器在线学习。在包含来自四只帕金森病大鼠不同大脑区域的 1131 个 HVS 事件的 LFP 中，该算法以 100% 的灵敏度检测到所有 HVS。该算法还实现了更高的精度 (96%) 和更低的延迟 (61 ms)，同时比连续小波变换方法需要更少的计算时间。由于延迟远短于 HVS 发作的平均持续时间（4.3 s），所提出的算法适用于实现智能神经调节器，以通过闭环 DBS 有效减轻 HVS。