In:
PLOS ONE, Public Library of Science (PLoS), Vol. 16, No. 1 ( 2021-1-26), p. e0244133-
Abstract:
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.
Type of Medium:
Online Resource
ISSN:
1932-6203
DOI:
10.1371/journal.pone.0244133
DOI:
10.1371/journal.pone.0244133.g001
DOI:
10.1371/journal.pone.0244133.g002
DOI:
10.1371/journal.pone.0244133.g003
DOI:
10.1371/journal.pone.0244133.g004
DOI:
10.1371/journal.pone.0244133.g005
DOI:
10.1371/journal.pone.0244133.g006
DOI:
10.1371/journal.pone.0244133.g007
DOI:
10.1371/journal.pone.0244133.g008
DOI:
10.1371/journal.pone.0244133.t001
DOI:
10.1371/journal.pone.0244133.t002
DOI:
10.1371/journal.pone.0244133.s001
DOI:
10.1371/journal.pone.0244133.s002
DOI:
10.1371/journal.pone.0244133.s003
DOI:
10.1371/journal.pone.0244133.r001
DOI:
10.1371/journal.pone.0244133.r002
DOI:
10.1371/journal.pone.0244133.r003
DOI:
10.1371/journal.pone.0244133.r004
DOI:
10.1371/journal.pone.0244133.r005
DOI:
10.1371/journal.pone.0244133.r006
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2021
detail.hit.zdb_id:
2267670-3
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