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A novel approach to assess sleep-related rhythmic movement disorder in children using automatic 3D analysis

A novel approach to assess sleep-related rhythmic movement disorder in children using automatic 3D analysis
A novel approach to assess sleep-related rhythmic movement disorder in children using automatic 3D analysis

Background: Unlike other episodic sleep disorders in childhood, there are no agreed severity indices for rhythmic movement disorder. While movements can be characterized in detail by polysomnography, in our experience most children inhibit rhythmic movement during polysomnography. Actigraphy and home video allow assessment in the child’s own environment, but both have limitations. Standard actigraphy analysis algorithms fail to differentiate rhythmic movements from other movements. Manual annotation of 2D video is time consuming. We aimed to develop a sensitive, reliable method to detect and quantify rhythmic movements using marker free and automatic 3D video analysis.

Method: Patients with rhythmic movement disorder (n = 6, 4 male) between age 5 and 14 years (M: 9.0 years, SD: 4.2 years) spent three nights in the sleep laboratory as part of a feasibility study (https://clinicaltrials.gov/ct2/show/NCT03528096). 2D and 3D video data recorded during the adaptation and baseline nights were analyzed. One ceiling-mounted camera captured 3D depth images, while another recorded 2D video. We developed algorithms to analyze the characteristics of rhythmic movements and built a classifier to distinguish between rhythmic and non-rhythmic movements based on 3D video data alone. Data from 3D automated analysis were compared to manual 2D video annotations to assess algorithm performance. Novel indices were developed, specifically the rhythmic movement index, frequency index, and duration index, to better characterize severity of rhythmic movement disorder in children.

Result: Automatic 3D video analysis demonstrated high levels of agreement with the manual approach indicated by a Cohen’s kappa >0.9 and F1-score >0.9. We also demonstrated how rhythmic movement assessment can be improved using newly introduced indices illustrated with plots for ease of visualization.

Conclusion: 3D video technology is widely available and can be readily integrated into sleep laboratory settings. Our automatic 3D video analysis algorithm yields reliable quantitative information about rhythmic movements, reducing the burden of manual scoring. Furthermore, we propose novel rhythmic movement disorder severity indices that offer a means to standardize measurement of this disorder in both clinical and research practice. The significance of the results is limited due to the nature of a feasibility study and its small number of samples. A larger follow up study is needed to confirm presented results.

1664-0640
1-10
Gall, Markus
3f816846-ad18-45cb-9b43-63bd8d817bb7
Kohn, Bernhard
61a8bd3b-54d4-4f4b-bc29-8ae22cdb3369
Wiesmeyr, Christoph
5485f23a-ca4b-4f8b-abf7-0b0049aa23c3
van Sluijs, Rachel M.
4c3792fa-db42-40ee-a4ab-81259e7a0883
Wilhelm, Elisabeth
f47b84a9-b52e-4b71-810f-79106374484b
Rondei, Quincy
12c55922-d7ac-4943-9ebe-b9201d5b3169
Jäger, Lukas
e7e6538c-6b2d-4b76-ad7a-ac09a45111f2
Achermann, Peter
422723f0-03d4-41b9-84f9-06de6b7c3642
Landolt, Hans-Peter
b18ee0f2-bb4a-4007-a304-2db063ffbcb1
Jenni, Oskar G.
810b3f7b-9317-4b64-9190-11571810926e
Riener, Robert
7491aca1-7871-4cdc-8be3-bf5e65676f84
Hill, Catherine
867cd0a0-dabc-4152-b4bf-8e9fbc0edf8d
Garn, Heinrich
5d014add-8e70-40bb-b4d7-c3156e72b7c3
Gall, Markus
3f816846-ad18-45cb-9b43-63bd8d817bb7
Kohn, Bernhard
61a8bd3b-54d4-4f4b-bc29-8ae22cdb3369
Wiesmeyr, Christoph
5485f23a-ca4b-4f8b-abf7-0b0049aa23c3
van Sluijs, Rachel M.
4c3792fa-db42-40ee-a4ab-81259e7a0883
Wilhelm, Elisabeth
f47b84a9-b52e-4b71-810f-79106374484b
Rondei, Quincy
12c55922-d7ac-4943-9ebe-b9201d5b3169
Jäger, Lukas
e7e6538c-6b2d-4b76-ad7a-ac09a45111f2
Achermann, Peter
422723f0-03d4-41b9-84f9-06de6b7c3642
Landolt, Hans-Peter
b18ee0f2-bb4a-4007-a304-2db063ffbcb1
Jenni, Oskar G.
810b3f7b-9317-4b64-9190-11571810926e
Riener, Robert
7491aca1-7871-4cdc-8be3-bf5e65676f84
Hill, Catherine
867cd0a0-dabc-4152-b4bf-8e9fbc0edf8d
Garn, Heinrich
5d014add-8e70-40bb-b4d7-c3156e72b7c3

Gall, Markus, Kohn, Bernhard, Wiesmeyr, Christoph, van Sluijs, Rachel M., Wilhelm, Elisabeth, Rondei, Quincy, Jäger, Lukas, Achermann, Peter, Landolt, Hans-Peter, Jenni, Oskar G., Riener, Robert, Hill, Catherine and Garn, Heinrich (2019) A novel approach to assess sleep-related rhythmic movement disorder in children using automatic 3D analysis. Frontiers in Psychiatry, 10 (709), 1-10. (doi:10.3389/fpsyt.2019.00709).

Record type: Article

Abstract

Background: Unlike other episodic sleep disorders in childhood, there are no agreed severity indices for rhythmic movement disorder. While movements can be characterized in detail by polysomnography, in our experience most children inhibit rhythmic movement during polysomnography. Actigraphy and home video allow assessment in the child’s own environment, but both have limitations. Standard actigraphy analysis algorithms fail to differentiate rhythmic movements from other movements. Manual annotation of 2D video is time consuming. We aimed to develop a sensitive, reliable method to detect and quantify rhythmic movements using marker free and automatic 3D video analysis.

Method: Patients with rhythmic movement disorder (n = 6, 4 male) between age 5 and 14 years (M: 9.0 years, SD: 4.2 years) spent three nights in the sleep laboratory as part of a feasibility study (https://clinicaltrials.gov/ct2/show/NCT03528096). 2D and 3D video data recorded during the adaptation and baseline nights were analyzed. One ceiling-mounted camera captured 3D depth images, while another recorded 2D video. We developed algorithms to analyze the characteristics of rhythmic movements and built a classifier to distinguish between rhythmic and non-rhythmic movements based on 3D video data alone. Data from 3D automated analysis were compared to manual 2D video annotations to assess algorithm performance. Novel indices were developed, specifically the rhythmic movement index, frequency index, and duration index, to better characterize severity of rhythmic movement disorder in children.

Result: Automatic 3D video analysis demonstrated high levels of agreement with the manual approach indicated by a Cohen’s kappa >0.9 and F1-score >0.9. We also demonstrated how rhythmic movement assessment can be improved using newly introduced indices illustrated with plots for ease of visualization.

Conclusion: 3D video technology is widely available and can be readily integrated into sleep laboratory settings. Our automatic 3D video analysis algorithm yields reliable quantitative information about rhythmic movements, reducing the burden of manual scoring. Furthermore, we propose novel rhythmic movement disorder severity indices that offer a means to standardize measurement of this disorder in both clinical and research practice. The significance of the results is limited due to the nature of a feasibility study and its small number of samples. A larger follow up study is needed to confirm presented results.

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Accepted/In Press date: 4 September 2019
Published date: 16 October 2019

Identifiers

Local EPrints ID: 437378
URI: http://eprints.soton.ac.uk/id/eprint/437378
ISSN: 1664-0640
PURE UUID: 7ef1ab1b-aecd-499d-beef-9af162c4a567
ORCID for Catherine Hill: ORCID iD orcid.org/0000-0003-2372-5904

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Date deposited: 29 Jan 2020 17:32
Last modified: 17 Mar 2024 02:48

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Contributors

Author: Markus Gall
Author: Bernhard Kohn
Author: Christoph Wiesmeyr
Author: Rachel M. van Sluijs
Author: Elisabeth Wilhelm
Author: Quincy Rondei
Author: Lukas Jäger
Author: Peter Achermann
Author: Hans-Peter Landolt
Author: Oskar G. Jenni
Author: Robert Riener
Author: Catherine Hill ORCID iD
Author: Heinrich Garn

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