Kwang Hyon Park1, Sukkyu Sun2, Yong Hoon Lim1, Hye Ran Park3, Jae Meen Lee4, Kawngwoo Park5, Beomseok Jeon6, Hee-Pyoung Park7, Hee Chan Kim2,8,9, Sun Ha Paek1,10,11. 1. Department of Neurosurgery, Seoul National University Hospital, Seoul, Korea. 2. Interdisciplinary Program in Bioengineering, Graduate School, Seoul National University, Seoul, Korea. 3. Department of Neurosurgery, Soonchunhyang University Seoul Hospital, Seoul, Korea. 4. Department of Neurosurgery, Pusan National University Hospital, Busan, Korea. 5. Department of Neurosurgery, Gachon University Gil Medical Center, Incheon, Korea. 6. Department of Neurology, Seoul National University Hospital, Seoul, Korea. 7. Department of Anesthesiology and Pain Medicine, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea. 8. Department of Biomedical Engineering College of Medicine, Seoul National University, Seoul, Korea. 9. Institute of Medical & Biological Engineering, Medical Research Center, Seoul National University, Seoul, Korea. 10. Ischemia Hypoxia Disease Institute, Seoul National University College of Medicine, Seoul, Korea. 11. Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea.
Abstract
BACKGROUND: Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment for improving the motor symptoms of advanced Parkinson's disease (PD). Accurate positioning of the stimulation electrodes is necessary for better clinical outcomes. OBJECTIVE: We applied deep learning techniques to microelectrode recording (MER) signals to better predict motor function improvement, represented by the UPDRS part III scores, after bilateral STN DBS in patients with advanced PD. If we find the optimal stimulation point with MER by deep learning, we can improve the clinical outcome of STN DBS even under restrictions such as general anesthesia or non-cooperation of the patients. METHODS: In total, 696 4-second left-side MER segments from 34 patients with advanced PD who underwent bilateral STN DBS surgery under general anesthesia were included. We transformed the original signal into three wavelets of 1-50 Hz, 50-500 Hz, and 500-5,000 Hz. The wavelet-transformed MER was used for input data of the deep learning. The patients were divided into two groups, good response and moderate response groups, according to DBS on to off ratio of UPDRS part III score for the off-medication state, 6 months postoperatively. The ratio were used for output data in deep learning. The Visual Geometry Group (VGG)-16 model with a multitask learning algorithm was used to estimate the bilateral effect of DBS. Different ratios of the loss function in the task-specific layer were applied considering that DBS affects both sides differently. RESULTS: When we divided the MER signals according to the frequency, the maximal accuracy was higher in the 50-500 Hz group than in the 1-50 Hz and 500-5,000 Hz groups. In addition, when the multitask learning method was applied, the stability of the model was improved in comparison with single task learning. The maximal accuracy (80.21%) occurred when the right-to-left loss ratio was 5:1 or 6:1. The area under the curve (AUC) was 0.88 in the receiver operating characteristic (ROC) curve. CONCLUSION: Clinical improvements in PD patients who underwent bilateral STN DBS could be predicted based on a multitask deep learning-based MER analysis.
BACKGROUND: Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment for improving the motor symptoms of advanced Parkinson's disease (PD). Accurate positioning of the stimulation electrodes is necessary for better clinical outcomes. OBJECTIVE: We applied deep learning techniques to microelectrode recording (MER) signals to better predict motor function improvement, represented by the UPDRS part III scores, after bilateral STN DBS in patients with advanced PD. If we find the optimal stimulation point with MER by deep learning, we can improve the clinical outcome of STN DBS even under restrictions such as general anesthesia or non-cooperation of the patients. METHODS: In total, 696 4-second left-side MER segments from 34 patients with advanced PD who underwent bilateral STN DBS surgery under general anesthesia were included. We transformed the original signal into three wavelets of 1-50 Hz, 50-500 Hz, and 500-5,000 Hz. The wavelet-transformed MER was used for input data of the deep learning. The patients were divided into two groups, good response and moderate response groups, according to DBS on to off ratio of UPDRS part III score for the off-medication state, 6 months postoperatively. The ratio were used for output data in deep learning. The Visual Geometry Group (VGG)-16 model with a multitask learning algorithm was used to estimate the bilateral effect of DBS. Different ratios of the loss function in the task-specific layer were applied considering that DBS affects both sides differently. RESULTS: When we divided the MER signals according to the frequency, the maximal accuracy was higher in the 50-500 Hz group than in the 1-50 Hz and 500-5,000 Hz groups. In addition, when the multitask learning method was applied, the stability of the model was improved in comparison with single task learning. The maximal accuracy (80.21%) occurred when the right-to-left loss ratio was 5:1 or 6:1. The area under the curve (AUC) was 0.88 in the receiver operating characteristic (ROC) curve. CONCLUSION: Clinical improvements in PDpatients who underwent bilateral STN DBS could be predicted based on a multitask deep learning-based MER analysis.
Authors: Michal Sobstyl; Miroslaw Zabek; Artur Zaczynski; Wojciech Gorecki; Zbigniew Mossakowski; Grazyna Brzuszkiewicz-Kuzmicka Journal: Turk Neurosurg Date: 2017 Impact factor: 1.003
Authors: Holger R Roth; Le Lu; Ari Seff; Kevin M Cherry; Joanne Hoffman; Shijun Wang; Jiamin Liu; Evrim Turkbey; Ronald M Summers Journal: Med Image Comput Comput Assist Interv Date: 2014
Authors: Srikant Vallabhajosula; Ihtsham U Haq; Nelson Hwynn; Genko Oyama; Michael Okun; Mark D Tillman; Chris J Hass Journal: Brain Stimul Date: 2014-10-28 Impact factor: 8.955
Authors: Wonki Kim; In Ho Song; Yong Hoon Lim; Mi-Ryoung Kim; Young Eun Kim; Jae Ha Hwang; In Keyoung Kim; Sang Woo Song; Jin Wook Kim; Woong-Woo Lee; Han-Joon Kim; Cheolyoung Kim; Hee Chan Kim; In Young Kim; Hee Pyoung Park; Dong Gyu Kim; Beom Seok Jeon; Sun Ha Paek Journal: J Korean Med Sci Date: 2014-09-02 Impact factor: 2.153