Quantifying Levodopa-Induced Dyskinesia Using Depth Camera

Maria Dyshel; David Arkadir; Hagai Bergman; Daphna Weinshall

doi:10.1109/ICCVW.2015.73

Quantifying Levodopa-Induced Dyskinesia Using Depth Camera

Maria Dyshel, David Arkadir, Hagai Bergman, Daphna Weinshall

The Hebrew University of Jerusalem

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

We present a novel method to detect and assess the severity of Levodopa-Induced Dyskinesia (LID) in Parkinson's Disease (PD) patients, based on Microsoft Kinect recordings of the patients. Dyskinesia denotes involuntary movements induced by chronic treatment with levodopa in patients with PD. Detection and objective quantification of dyskinesia is essential for optimizing the medication regime and developing novel treatments for PD. We used Microsoft Kinect sensor to track limb and neck movements of a patient performing two motor tasks. Using a new motion segmentation algorithm, kinematic features were extracted from the videos and classified using Support Vector Machines (SVMs). The method was tested on 25 recordings of 9 PD patients, and achieved sensitivity of 0.82 at EER in overall dyskinesia detection. Moreover, it provided a numerical overall score for the severity of dyskinesia, which showed high correlation with the neurologist's assessment of the patient's state. The study shows that depth camera recordings can be used to monitor and grade the severity of levodopa-induced dyskinesia, and therefore can potentially provide valuable aid to clinicians and researchers.

Original language	English
Title of host publication	Proceedings - 2015 IEEE International Conference on Computer Vision Workshops, ICCVW 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	511-518
Number of pages	8
ISBN (Electronic)	9781467383905
DOIs	https://doi.org/10.1109/ICCVW.2015.73
State	Published - 11 Feb 2016
Event	15th IEEE International Conference on Computer Vision Workshops, ICCVW 2015 - Santiago, Chile Duration: 11 Dec 2015 → 18 Dec 2015

Publication series

Name	Proceedings of the IEEE International Conference on Computer Vision
Volume	2015-February

Conference

Conference	15th IEEE International Conference on Computer Vision Workshops, ICCVW 2015
Country/Territory	Chile
City	Santiago
Period	11/12/15 → 18/12/15

Keywords

Biomedical monitoring
Cameras
Feature extraction
Medical diagnostic imaging
Monitoring
Motion segmentation
Videos

All Science Journal Classification (ASJC) codes

Software
Computer Vision and Pattern Recognition

Access to Document

10.1109/ICCVW.2015.73

Cite this

Dyshel, M., Arkadir, D., Bergman, H., & Weinshall, D. (2016). Quantifying Levodopa-Induced Dyskinesia Using Depth Camera. In Proceedings - 2015 IEEE International Conference on Computer Vision Workshops, ICCVW 2015 (pp. 511-518). Article 7406422 (Proceedings of the IEEE International Conference on Computer Vision; Vol. 2015-February). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCVW.2015.73

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title = "Quantifying Levodopa-Induced Dyskinesia Using Depth Camera",

abstract = "We present a novel method to detect and assess the severity of Levodopa-Induced Dyskinesia (LID) in Parkinson's Disease (PD) patients, based on Microsoft Kinect recordings of the patients. Dyskinesia denotes involuntary movements induced by chronic treatment with levodopa in patients with PD. Detection and objective quantification of dyskinesia is essential for optimizing the medication regime and developing novel treatments for PD. We used Microsoft Kinect sensor to track limb and neck movements of a patient performing two motor tasks. Using a new motion segmentation algorithm, kinematic features were extracted from the videos and classified using Support Vector Machines (SVMs). The method was tested on 25 recordings of 9 PD patients, and achieved sensitivity of 0.82 at EER in overall dyskinesia detection. Moreover, it provided a numerical overall score for the severity of dyskinesia, which showed high correlation with the neurologist's assessment of the patient's state. The study shows that depth camera recordings can be used to monitor and grade the severity of levodopa-induced dyskinesia, and therefore can potentially provide valuable aid to clinicians and researchers.",

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author = "Maria Dyshel and David Arkadir and Hagai Bergman and Daphna Weinshall",

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Dyshel, M, Arkadir, D, Bergman, H & Weinshall, D 2016, Quantifying Levodopa-Induced Dyskinesia Using Depth Camera. in Proceedings - 2015 IEEE International Conference on Computer Vision Workshops, ICCVW 2015., 7406422, Proceedings of the IEEE International Conference on Computer Vision, vol. 2015-February, Institute of Electrical and Electronics Engineers Inc., pp. 511-518, 15th IEEE International Conference on Computer Vision Workshops, ICCVW 2015, Santiago, Chile, 11/12/15. https://doi.org/10.1109/ICCVW.2015.73

Quantifying Levodopa-Induced Dyskinesia Using Depth Camera. / Dyshel, Maria; Arkadir, David; Bergman, Hagai et al.
Proceedings - 2015 IEEE International Conference on Computer Vision Workshops, ICCVW 2015. Institute of Electrical and Electronics Engineers Inc., 2016. p. 511-518 7406422 (Proceedings of the IEEE International Conference on Computer Vision; Vol. 2015-February).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - We present a novel method to detect and assess the severity of Levodopa-Induced Dyskinesia (LID) in Parkinson's Disease (PD) patients, based on Microsoft Kinect recordings of the patients. Dyskinesia denotes involuntary movements induced by chronic treatment with levodopa in patients with PD. Detection and objective quantification of dyskinesia is essential for optimizing the medication regime and developing novel treatments for PD. We used Microsoft Kinect sensor to track limb and neck movements of a patient performing two motor tasks. Using a new motion segmentation algorithm, kinematic features were extracted from the videos and classified using Support Vector Machines (SVMs). The method was tested on 25 recordings of 9 PD patients, and achieved sensitivity of 0.82 at EER in overall dyskinesia detection. Moreover, it provided a numerical overall score for the severity of dyskinesia, which showed high correlation with the neurologist's assessment of the patient's state. The study shows that depth camera recordings can be used to monitor and grade the severity of levodopa-induced dyskinesia, and therefore can potentially provide valuable aid to clinicians and researchers.

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KW - Monitoring

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M3 - منشور من مؤتمر

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EP - 518

BT - Proceedings - 2015 IEEE International Conference on Computer Vision Workshops, ICCVW 2015

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 15th IEEE International Conference on Computer Vision Workshops, ICCVW 2015

Y2 - 11 December 2015 through 18 December 2015

ER -

Dyshel M, Arkadir D, Bergman H , Weinshall D. Quantifying Levodopa-Induced Dyskinesia Using Depth Camera. In Proceedings - 2015 IEEE International Conference on Computer Vision Workshops, ICCVW 2015. Institute of Electrical and Electronics Engineers Inc. 2016. p. 511-518. 7406422. (Proceedings of the IEEE International Conference on Computer Vision). doi: 10.1109/ICCVW.2015.73

Quantifying Levodopa-Induced Dyskinesia Using Depth Camera

Abstract

Publication series

Conference

Keywords

All Science Journal Classification (ASJC) codes

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