Automatic identification of sweet spots from MERs for electrodes implantation in STN-DBS

Abstract

Purpose

Microelectrode recordings (MERs) are a significant clinical indicator for sweet spots identification of implanted electrodes during deep brain stimulation of the subthalamic nucleus (STN) surgery. As 1D MERs signals have the unboundedness, large-range, large-amount and time-dependent characteristics, the purpose of this study is to propose an automatic and precise identification method of sweet spots from MERs, reducing the time-consuming and labor-intensive human annotations.

Methods

We propose an automatic identification method of sweet spots from MERs for electrodes implantation in STN-DBS. To better imitate the surgeons’ observation and obtain more intuitive contextual information, we first employ the 2D Gramian angular summation field (GASF) images generated from MERs data to perform the sweet spots determination for electrodes implantation. Then, we introduce the convolutional block attention module into convolutional neural network (CNN) to identify the 2D GASF images of sweet spots for electrodes implantation.

Results

Experimental results illustrate that the identification result of our method is consistent with the result of doctor’s decision, while our method can achieve the accuracy and precision of 96.72% and 98.97%, respectively, which outperforms state-of-the-art for intraoperative sweet spots determination.

Conclusions

The proposed method is the first time to automatically and accurately identify sweet spots from MERs for electrodes implantation by the combination an advanced time series-to-image encoding way with CBAM-enhanced networks model. Our method can assist neurosurgeons in automatically detecting the most likely locations of sweet spots for electrodes implantation, which can provide an important indicator for target selection while it reduces the localization error of the target during STN-DBS surgery.

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Acknowledgements

We gratefully thank the reviewers for their constructive comments. This work was supported in part by Key-Area Research and Development Program of Guangdong Province, China (2020B010165004), in part by Shenzhen Fundamental Research Program (JCYJ20200109110208764, JCYJ20200109110420626), in part by National Natural Science Foundation of China (U1813204, 61802385, 62072468), in part by Natural Science Foundation of Guangdong (2021A1515012604) and in part by the Fundamental Research Funds for the Central Universities, China University of Petroleum (East China) (No.18CX06060A). Weixin Si and Xiaodong Cai are both the corresponding authors of this article. Linxia Xiao and Caizi Li contributed equally to this work.

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Xiao, L., Li, C., Wang, Y. et al. Automatic identification of sweet spots from MERs for electrodes implantation in STN-DBS. Int J CARS 16, 809–818 (2021). https://doi.org/10.1007/s11548-021-02377-2

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Keywords

  • Sweet spots of STN-DBS
  • MERs
  • Gramian angular summation field
  • Convolutional block attention module