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A multiple motion sensors index for motor state quantification in Parkinson's disease
Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.ORCID-id: 0000-0002-1548-5077
Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.ORCID-id: 0000-0003-0403-338X
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2020 (Engelska)Ingår i: Computer Methods and Programs in Biomedicine, ISSN 0169-2607, E-ISSN 1872-7565, Vol. 189, artikel-id 105309Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

AIM: To construct a Treatment Response Index from Multiple Sensors (TRIMS) for quantification of motor state in patients with Parkinson's disease (PD) during a single levodopa dose. Another aim was to compare TRIMS to sensor indexes derived from individual motor tasks.

METHOD: Nineteen PD patients performed three motor tests including leg agility, pronation-supination movement of hands, and walking in a clinic while wearing inertial measurement unit sensors on their wrists and ankles. They performed the tests repeatedly before and after taking 150% of their individual oral levodopa-carbidopa equivalent morning dose.Three neurologists blinded to treatment status, viewed patients' videos and rated their motor symptoms, dyskinesia, overall motor state based on selected items of Unified PD Rating Scale (UPDRS) part III, Dyskinesia scale, and Treatment Response Scale (TRS). To build TRIMS, out of initially 178 extracted features from upper- and lower-limbs data, 39 features were selected by stepwise regression method and were used as input to support vector machines to be mapped to mean reference TRS scores using 10-fold cross-validation method. Test-retest reliability, responsiveness to medication, and correlation to TRS as well as other UPDRS items were evaluated for TRIMS.

RESULTS: The correlation of TRIMS with TRS was 0.93. TRIMS had good test-retest reliability (ICC = 0.83). Responsiveness of the TRIMS to medication was good compared to TRS indicating its power in capturing the treatment effects. TRIMS was highly correlated to dyskinesia (R = 0.85), bradykinesia (R = 0.84) and gait (R = 0.79) UPDRS items. Correlation of sensor index from the upper-limb to TRS was 0.89.

CONCLUSION: Using the fusion of upper- and lower-limbs sensor data to construct TRIMS provided accurate PD motor states estimation and responsive to treatment. In addition, quantification of upper-limb sensor data during walking test provided strong results.

Ort, förlag, år, upplaga, sidor
2020. Vol. 189, artikel-id 105309
Nationell ämneskategori
Klinisk medicin
Forskningsämne
Komplexa system - mikrodataanalys
Identifikatorer
URN: urn:nbn:se:du-31738DOI: 10.1016/j.cmpb.2019.105309PubMedID: 31982667Scopus ID: 2-s2.0-85078170133OAI: oai:DiVA.org:du-31738DiVA, id: diva2:1389208
Tillgänglig från: 2020-01-29 Skapad: 2020-01-29 Senast uppdaterad: 2020-03-12Bibliografiskt granskad
Ingår i avhandling
1. Sensor-based knowledge- and data-driven methods: A case of Parkinson’s disease motor symptoms quantification
Öppna denna publikation i ny flik eller fönster >>Sensor-based knowledge- and data-driven methods: A case of Parkinson’s disease motor symptoms quantification
2020 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
Abstract [en]

The overall aim of this thesis was to develop and evaluate new knowledge- and data-driven methods for supporting treatment and providing information for better assessment of Parkinson’s disease (PD).

PD is complex and progressive. There is a large amount of inter- and intravariability in motor symptoms of patients with PD (PwPD). The current evaluation of motor symptoms that are done at clinics by using clinical rating scales is limited and provides only part of the health status of PwPD. An accurate and clinically approved assessment of PD is required using frequent evaluation of symptoms.

To investigate the problem areas, the thesis adopted the microdata analysis approach including the stages of data collection, data processing, data analysis, and data interpretation. Sensor systems including smartphone and tri-axial motion sensors were used to collect data from advanced PwPD experimenting with repeated tests during a day. The experiments were rated by clinical experts. The data from sensors and the clinical evaluations were processed and used in subsequent analysis.

The first three papers in this thesis report the results from the investigation, verification, and development of knowledge- and data-driven methods for quantifying the dexterity in PD. The smartphone-based data collected from spiral drawing and alternate tapping tests were used for the analysis. The results from the development of a smartphone-based data-driven method can be used for measuring treatment-related changes in PwPD. Results from investigation and verification of an approximate entropy-based method showed good responsiveness and test-retest reliability indicating that this method is useful in measuring upper limb temporal irregularity.

The next two papers, report the results from the investigation and development of motion sensor-based knowledge- and data-driven methods for quantification of the motor states in PD. The motion data were collected from experiments such as leg agility, walking, and rapid alternating movements of hands. High convergence validity resulted from using motion sensors during leg agility tests. The results of the fusion of sensor data gathered during multiple motor tests were promising and led to valid, reliable and responsive objective measures of PD motor symptoms.

Results in the last paper investigating the feasibility of using the Dynamic Time-Warping method for assessment of PD motor states showed it is feasible to use this method for extracting features to be used in automatic scoring of PD motor states.

The findings from the knowledge- and data-driven methodology in this thesis can be used in the development of systems for follow up of the effects of treatment and individualized treatments in PD.

Ort, förlag, år, upplaga, sidor
Borlänge: Dalarna University, 2020
Serie
Dalarna Doctoral Dissertations ; 12
Nyckelord
Parkinson’s disease, motion sensors, motor symptoms, smartphone, microdata, multivariate analysis, data-driven, knowledge-driven, support vector machine stepwise regression, predictive models
Nationell ämneskategori
Datorsystem Datorteknik
Forskningsämne
Komplexa system - mikrodataanalys
Identifikatorer
urn:nbn:se:du-32065 (URN)978-91-88679-00-0 (ISBN)
Disputation
2020-05-08, Clas Ohlson, Borlänge, 13:00 (Engelska)
Opponent
Handledare
Tillgänglig från: 2020-04-15 Skapad: 2020-02-26 Senast uppdaterad: 2020-04-15Bibliografiskt granskad

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