Discriminating Progressive Supranuclear Palsy from Parkinson’s Disease using wearable technology and machine learning

Vos, Maarten De; Prince, John T.; Buchanan, Tim; FitzGerald, James J.; Antoniades, C

doi:10.1101/19006866

Cited by 3 publications

(16 citation statements)

References 19 publications

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“…The array of kinematic features was reduced by first excluding all those features that did not progress significantly with time at the group level. This first step removed 6 of the 10 features identified in our previous study 21 as being key to the differential diagnosis of PSP from PD patients and HCs. Of the remaining 4, none were in the group of three features that regressed most clearly with time (turn velocity, toe‐off angle, and variability of stride length), and thus none were included in the best‐performing progression models ( UPDRS LRA and PSPRS LRA ).…”

Section: Discussionmentioning

confidence: 99%

“…The software used in the current study (MobilityLab) automatically outputs more than 150 features for gait and postural sway tasks. A printed list of the exported features can be found elsewhere. 21 All analysis was performed using custom software written in Python (v3.8).…”

Section: Methodsmentioning

confidence: 99%

“…A printed list of the exported features can be found elsewhere. 21 All analysis was performed using custom software written in Python (v3.8).…”

Section: Data Reduction and Analysismentioning

confidence: 99%

“…The validity of wearable sensor outcomes is justified by the moderate/good correlation with the motor scores of the MDS‐UPDRS, Part III, in typical PD patients 19,20 . The ability of IMU‐derived data to classify PSP, PD patients, and healthy controls (HCs) has recently been demonstrated in a cross‐sectional analysis 21 …”

mentioning

confidence: 99%

“…19,20 The ability of IMU-derived data to classify PSP, PD patients, and healthy controls (HCs) has recently been demonstrated in a cross-sectional analysis. 21 Wearable devices introduce the prospect of identifying objective biomarkers to measure the onset, progression, and severity of neurodegenerative conditions; accelerating clinical trials; and facilitating the discovery of new therapeutics. Although a few studies have utilized sensors to monitor motor features longitudinally in typical PD, 22,23 studies focused on PSP patients are virtually nonexistent.…”

mentioning

confidence: 99%

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Longitudinal Monitoring of Progressive Supranuclear Palsy using Body‐Worn Movement Sensors

Sotirakis

Conway

et al. 2022

Movement Disorders

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Background We have previously shown that wearable technology and machine learning techniques can accurately discriminate between progressive supranuclear palsy (PSP), Parkinson's disease, and healthy controls. To date these techniques have not been applied in longitudinal studies of disease progression in PSP. Objectives We aimed to establish whether data collected by a body‐worn inertial measurement unit (IMU) network could predict clinical rating scale scores in PSP and whether it could be used to track disease progression. Methods We studied gait and postural stability in 17 participants with PSP over five visits at 3‐month intervals. Participants performed a 2‐minute walk and an assessment of postural stability by standing for 30 seconds with their eyes closed, while wearing an array of six IMUs. Results Thirty‐two gait and posture features were identified, which progressed significantly with time. A simple linear regression model incorporating the three features with the clearest progression pattern was able to detect statistically significant progression 3 months in advance of the clinical scores. A more complex linear regression and a random forest approach did not improve on this. Conclusions The reduced variability of the models, in comparison to clinical rating scales, allows a significant change in disease status from baseline to be observed at an earlier stage. The current study sheds light on the individual features that are important in tracking disease progression. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…A printed list of the exported features can be found elsewhere. 21 All analysis was performed using custom software written in Python (v3.8).…”

Section: Data Reduction and Analysismentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Longitudinal Monitoring of Progressive Supranuclear Palsy using Body‐Worn Movement Sensors

Sotirakis

Conway

et al. 2022

Movement Disorders

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Parkinsonism and Digital Measurement

Patel

Brzezicki²,

Su³

et al. 2022

Studies in Neuroscience, Psychology and Behavioral Economics

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The application of artificial intelligence and custom algorithms with inertial wearable devices for gait analysis and detection of gait-altering pathologies in adults: A scoping review of literature

et al. 2022

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Background The purpose of this scoping review was to explore the current applications of objective gait analysis using inertial measurement units, custom algorithms and artificial intelligence algorithms in detecting neurological and musculoskeletal gait altering pathologies from healthy gait patterns. Methods Literature searches were conducted of four electronic databases (Medline, PubMed, Embase and Web of Science) to identify studies that assessed the accuracy of these custom gait analysis models with inputs derived from wearable devices. Data was collected according to the preferred reporting items for systematic reviews and meta-analysis statement guidelines. Results A total of 23 eligible studies were identified for inclusion in the present review, including 10 custom algorithms articles and 13 artificial intelligence algorithms articles. Nine studies evaluated patients with Parkinson’s disease of varying severity and subtypes. Support vector machine was the commonest adopted artificial intelligence algorithm model, followed by random forest and neural networks. Overall classification accuracy was promising for articles that use artificial intelligence algorithms, with nine articles achieving more than 90% accuracy. Conclusions Current applications of artificial intelligence algorithms are reasonably effective discrimination between pathological and non-pathological gait. Of these, machine learning algorithms demonstrate the additional capacity to handle complicated data input, when compared to other custom algorithms. Notably, there has been increasing application of machine learning algorithms for conducting gait analysis. More studies are needed with unsupervised methods and in non-clinical settings to better reflect the community and home-based usage.

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Discriminating Progressive Supranuclear Palsy from Parkinson’s Disease using wearable technology and machine learning

Cited by 3 publications

References 19 publications

Longitudinal Monitoring of Progressive Supranuclear Palsy using Body‐Worn Movement Sensors

Longitudinal Monitoring of Progressive Supranuclear Palsy using Body‐Worn Movement Sensors

Parkinsonism and Digital Measurement

The application of artificial intelligence and custom algorithms with inertial wearable devices for gait analysis and detection of gait-altering pathologies in adults: A scoping review of literature

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