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Automated detection of subthalamic nucleus in deep brain stimulation surgery for Parkinson's disease using microelectrode recordings and wavelet packet features.
Journal of Neuroscience Methods ( IF 3 ) Pub Date : 2020-07-02 , DOI: 10.1016/j.jneumeth.2020.108826
P A Karthick 1 , Kai Rui Wan 2 , Angela See An Qi 2 , Justin Dauwels 3 , Nicolas Kon Kam King 4
Affiliation  

Background

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.

New methods

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.

Results

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.

Comparison with existing methods

The proposed features are found to be better than the wavelet features.

Conclusions

The proposed approach could be a potential useful adjunct for the real-time rapid intraoperative identification of STN and its anatomical borders.



中文翻译:

利用微电极记录和小波包特征,在帕金森氏病深层脑刺激手术中自动检测丘脑下核。

背景

丘脑底核(STN)的深部脑刺激(DBS)是一种有效的神经外科手术,可克服晚期帕金森氏病患者的运动系统交替。DBS手术最具挑战性的方面是准确识别STN及其边界。通常,它是由神经生理学家使用微电极记录(MER)手动执行的。这个过程是主观的,乏味的,而且,由于其固有的非平稳变化,对MERs的解释非常困难。

新方法

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及其皮层下结构。该研究分析了26名接受DBS植入的PD患者的2904名MER。从MER的小波包系数中提取低阶和高阶统计参数,并将其用于分类中,即非STN与STN,STN之前与STN以及STN与STN之后。

结果

在STN及其皮层下区域,即STN之前和STN后,大多数功能都存在显着差异。所提出的功能在非STN与STN中达到85%的平均准确度,在STN之前与STN中达到87.2%的平均准确度,在STN与之后的STN中达到77.7%的平均准确度。当过渡误差为1 mm时,非STN相对于STN和STN相对于后STN的精度提高了约10%。

与现有方法的比较

发现所提出的特征比小波特征更好。

结论

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手段。

更新日期:2020-07-08
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