A non-contact system for intraoperative quantitative assessment of bradykinesia in deep brain stimulation surgery. (October 2022)

• Record Type: Journal Article
• Authors: Yu, Ningbo; Yu, Yang; Lin, Jianeng; Yang, Yuchen; Wu, Jingchao; Liang, Siquan; Wu, Jialing; Han, Jianda
• Is Part Of: Computer methods and programs in biomedicine. Volume 225(2022)
• Publication Date: 2022-10
• Subjects: Deep brain stimulation -- Intraoperative test -- Quantitative assessment -- Non-contact measurement -- Bradykinesia; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2022.107005
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A compact optical sensor measures patient voluntary movement without interfering. A software monitors, analyzes and displays the movement and bradykinesia features. The system was technically validated, and applied for 20 patients in DBS surgery. This brings an effective solution for DBS intraoperative assessment of bradykinesia. Abstract: Background and Objective: Deep brain stimulation (DBS) is an effective treatment for a number of neurological diseases, especially for the advanced stage of Parkinson's disease (PD). Objective assessment of patients' motor symptoms is crucial for accurate electrode targeting and treatment. Existing approaches suffer from subjective variability or interference with voluntary motion. This work is aimed to establish an objective assessment system to quantify bradykinesia in DBS surgery. Methods: Based on the analysis of the requirements for intraoperative assessment, we developed a system with non-contact measurement, online movement feature extraction, and interactive data analysis and visualization. An optical sensor, Leap Motion Controller (LMC), was taken to detect hand movement in three clinical tasks. A graphic user interface was designed to process, compare and visualize the collected data and assessment results online. Quantified movement features include amplitude, frequency, velocity, their decrement and variability, etc. Technical validation of the system was performed with a motion capture system (Mocap), with respect to data-level and feature-level accuracy and reliability. Clinical validation was conducted with 20 PD patients for intraoperative assessments in DBS surgery. Treatment responses with respect to the bradykinesia movement features were analyzed. Single case analysis and group statistical analysis were performed to examine the differences between preoperative and intraoperative performance, and the correlation between the clinical ratings and the quantified assessment was analyzed. Results: For the movements measured by LMC and Mocap, the average Pearson's correlation coefficient was 0.986, and the mean amplitude difference was 2.11 mm. No significant difference was found for all movement features quantified by LMC and Mocap. For the clinical tests, key movement features showed significant differences between the preoperative baseline and intraoperative performance when the brain stimulation was ON. The assessment results were significantly correlated with the MDS-UPDRS clinical ratings. Conclusions: The proposed non-contact system has established itself as an objective intraoperative assessment, analysis, and visualization tool for DBS treatment of Parkinson's disease.