Karthick P A, Wan Kai Rui, An Qi Angela See, Dauwels Justin, King Nicolas Kon Kam
Department of Instrumentation and Control Engineering, National Institute of Technology, Tiruchirappalli, India; School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Ave, 639798, Singapore.
Department of Neurosurgery, National Neuroscience Institute, Singapore; Department of Neurosurgery, Singapore General Hospital, Singapore.
J Neurosci Methods. 2020 Sep 1;343:108826. doi: 10.1016/j.jneumeth.2020.108826. Epub 2020 Jul 2.
Deep brain stimulation (DBS) to the subthalamic nucleus (STN) is an effective neurosurgery that overcomes the motor system alternations of patients with advanced Parkinson's disease. The most challenging aspect of DBS surgery is the accurate identification of STN and its borders. In general, it is performed manually by a neurophysiologist using the microelectrode recordings (MERs). This process is subjective, and tedious and further, interpretation of MERs is difficult because of its inherent nonstationary variations.
In this work, the wavelet-packet based features are proposed to automatically localize the STN and its subcortical structures using microelectrode recorded signals during DBS surgery. The study analyses 2904 MERs of 26 PD patients who underwent DBS implantation. The low and high order statistical parameters are extracted from the wavelet packet coefficients of MERs and used in the classifications, namely, non-STN vs. STN, pre-STN vs. STN and STN vs. post-STN.
Most of the features are significantly different in STN and its subcortical regions, namely, pre-STN and post-STN. The proposed features achieve an average accuracy of 85 % in non-STN vs. STN, 87.2 % in pre-STN vs. STN and 77.7 % in STN vs. post-STN. The accuracy is improved by around 10 % in non-STN vs. STN and STN vs. post-STN when the transition error is 1 mm.
The proposed features are found to be better than the wavelet features.
The proposed approach could be a potential useful adjunct for the real-time rapid intraoperative identification of STN and its anatomical borders.
对丘脑底核（STN）进行深部脑刺激（DBS）是一种有效的神经外科手术，可克服晚期帕金森病患者的运动系统改变。DBS手术最具挑战性的方面是准确识别STN及其边界。一般来说，这是由神经生理学家使用微电极记录（MERs）手动完成的。这个过程主观且繁琐，此外，由于MERs固有的非平稳变化，对其进行解读也很困难。
在这项工作中，提出了基于小波包的特征，以在DBS手术期间使用微电极记录的信号自动定位STN及其皮质下结构。该研究分析了26例接受DBS植入的帕金森病患者的2904次MERs。从MERs的小波包系数中提取低阶和高阶统计参数，并用于分类，即非STN与STN、STN前与STN以及STN与STN后。
大多数特征在STN及其皮质下区域，即STN前和STN后有显著差异。所提出的特征在非STN与STN分类中平均准确率达到85%，在STN前与STN分类中为87.2%，在STN与STN后分类中为77.7%。当过渡误差为1毫米时，非STN与STN以及STN与STN后分类的准确率提高了约10%。
发现所提出的特征优于小波特征。
所提出的方法可能是实时快速术中识别STN及其解剖边界的潜在有用辅助方法。
