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Wrist Rigidity Assessment During Deep Brain Stimulation Surgery

Title
Wrist Rigidity Assessment During Deep Brain Stimulation Surgery
Type
Article in International Conference Proceedings Book
Year
2015
Authors
Costa, P
(Author)
Other
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Rosas, MJ
(Author)
Other
The person does not belong to the institution. The person does not belong to the institution. The person does not belong to the institution. Without AUTHENTICUS Without ORCID
Vaz, R
(Author)
Other
The person does not belong to the institution. The person does not belong to the institution. The person does not belong to the institution. Without AUTHENTICUS Without ORCID
Conference proceedings International
Pages: 3423-3426
37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Milan, ITALY, AUG 25-29, 2015
Other information
Authenticus ID: P-00K-2MC
Abstract (EN): Parkinson's Disease (PD) patients often need Deep Brain Stimulation (DBS) surgery when they become intolerant to drugs or these lose efficiency. A stimulation electrode is implanted in the basal ganglia to promote the functional control of the deregulated dopaminergic motor pathways. The stimulation target is defined by medical imaging, followed by electrophysiological inspection for fine electrode position trimming and electrical stimulation tuning. Intra-operative stimulation of the target and the evaluation of wrist rigidity allows to choose the stimulation parameters which best alleviate PD symptoms without side effects. Neurologists impose a passive wrist flexion movement and qualitatively describe the perceived decrease in rigidity under different voltages, based on its experience and with subjectivity. We designed a novel, comfortable and wireless wearable motion sensor to classify the wrist rigidity by deriving a robust signal descriptor from angular speed values and a polynomial mathematical model to classify signals using a quantitative continuous scale. The descriptor significantly (p < 0.05) distinguished between non-rigid and rigid states, and the classification model labelled correctly 83.9 % of the evaluated signals against the blind-agreement of two specialists. Additionally, we developed a methodology to detect cogwheel rigidity from the angular speed signal with high sensitivity (0.93). Our system provides a reliable evaluation of wrist rigidity, improving upon the inherent subjective clinical evaluation while using small, simple and easy to use motion sensor.
Language: English
Type (Professor's evaluation): Scientific
No. of pages: 4
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