Discriminating progressive supranuclear palsy from Parkinson's disease using wearable technology and machine learning
Background Progressive supranuclear palsy (PSP), a neurodegenerative conditions may be difficult to discriminate clinically from idiopathic Parkinson’s disease (PD). It is critical that we are able to do this accurately and as early as possible in order that future disease modifying therapies for PS...
| Main Authors: | , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Elsevier
2020
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| _version_ | 1826298923519574016 |
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| author | De Vos, M Prince, J Buchanan, T FitzGerald, JJ Antoniades, CA |
| author_facet | De Vos, M Prince, J Buchanan, T FitzGerald, JJ Antoniades, CA |
| author_sort | De Vos, M |
| collection | OXFORD |
| description | Background
Progressive supranuclear palsy (PSP), a neurodegenerative conditions may be difficult to discriminate clinically from idiopathic Parkinson’s disease (PD). It is critical that we are able to do this accurately and as early as possible in order that future disease modifying therapies for PSP may be deployed at a stage when they are likely to have maximal benefit. Analysis of gait and related tasks is one possible means of discrimination.
Research Question
Here we investigate a wearable sensor array coupled with machine learning approaches as a means of disease classification.
Methods
21 participants with PSP, 20 with PD, and 39 healthy control (HC) subjects performed a two minute walk, static sway test, and timed up-and-go task, while wearing an array of six inertial measurement units. The data were analysed to determine what features discriminated PSP from PD and PSP from HC. Two machine learning algorithms were applied, Logistic Regression (LR) and Random Forest (RF).
Results
17 features were identified in the combined dataset that contained independent information. The RF classifier outperformed the LR classifier, and allowed discrimination of PSP from PD with 86 % sensitivity and 90 % specificity, and PSP from HC with 90 % sensitivity and 97 % specificity. Using data from the single lumbar sensor only resulted in only a modest reduction in classification accuracy, which could be restored using 3 sensors (lumbar, right arm and foot). However for maximum specificity the full six sensor array was needed.
Significance
A wearable sensor array coupled with machine learning methods can accurately discriminate PSP from PD. Choice of array complexity depends on context; for diagnostic purposes a high specificity is needed suggesting the more complete array is advantageous, while for subsequent disease tracking a simpler system may suffice. |
| first_indexed | 2024-03-07T04:54:08Z |
| format | Journal article |
| id | oxford-uuid:d602c480-4f69-4d5e-ad0f-0127ec387e2b |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T04:54:08Z |
| publishDate | 2020 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | oxford-uuid:d602c480-4f69-4d5e-ad0f-0127ec387e2b2022-03-27T08:30:11ZDiscriminating progressive supranuclear palsy from Parkinson's disease using wearable technology and machine learningJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:d602c480-4f69-4d5e-ad0f-0127ec387e2bEnglishSymplectic ElementsElsevier2020De Vos, MPrince, JBuchanan, TFitzGerald, JJAntoniades, CABackground Progressive supranuclear palsy (PSP), a neurodegenerative conditions may be difficult to discriminate clinically from idiopathic Parkinson’s disease (PD). It is critical that we are able to do this accurately and as early as possible in order that future disease modifying therapies for PSP may be deployed at a stage when they are likely to have maximal benefit. Analysis of gait and related tasks is one possible means of discrimination. Research Question Here we investigate a wearable sensor array coupled with machine learning approaches as a means of disease classification. Methods 21 participants with PSP, 20 with PD, and 39 healthy control (HC) subjects performed a two minute walk, static sway test, and timed up-and-go task, while wearing an array of six inertial measurement units. The data were analysed to determine what features discriminated PSP from PD and PSP from HC. Two machine learning algorithms were applied, Logistic Regression (LR) and Random Forest (RF). Results 17 features were identified in the combined dataset that contained independent information. The RF classifier outperformed the LR classifier, and allowed discrimination of PSP from PD with 86 % sensitivity and 90 % specificity, and PSP from HC with 90 % sensitivity and 97 % specificity. Using data from the single lumbar sensor only resulted in only a modest reduction in classification accuracy, which could be restored using 3 sensors (lumbar, right arm and foot). However for maximum specificity the full six sensor array was needed. Significance A wearable sensor array coupled with machine learning methods can accurately discriminate PSP from PD. Choice of array complexity depends on context; for diagnostic purposes a high specificity is needed suggesting the more complete array is advantageous, while for subsequent disease tracking a simpler system may suffice. |
| spellingShingle | De Vos, M Prince, J Buchanan, T FitzGerald, JJ Antoniades, CA Discriminating progressive supranuclear palsy from Parkinson's disease using wearable technology and machine learning |
| title | Discriminating progressive supranuclear palsy from Parkinson's disease using wearable technology and machine learning |
| title_full | Discriminating progressive supranuclear palsy from Parkinson's disease using wearable technology and machine learning |
| title_fullStr | Discriminating progressive supranuclear palsy from Parkinson's disease using wearable technology and machine learning |
| title_full_unstemmed | Discriminating progressive supranuclear palsy from Parkinson's disease using wearable technology and machine learning |
| title_short | Discriminating progressive supranuclear palsy from Parkinson's disease using wearable technology and machine learning |
| title_sort | discriminating progressive supranuclear palsy from parkinson s disease using wearable technology and machine learning |
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