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A treatment–response index from wearable sensors for quantifying Parkinson's disease motor states
Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.ORCID-id: 0000-0003-0403-338X
Högskolan Dalarna, Akademin Industri och samhälle, Statistik.ORCID-id: 0000-0002-3183-3756
Visa övriga samt affilieringar
2018 (Engelska)Ingår i: IEEE journal of biomedical and health informatics, ISSN 2168-2194, E-ISSN 2168-2208, Vol. 22, nr 5, s. 1341-1349Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

The goal of this study was to develop an algorithm that automatically quantifies motor states (off,on,dyskinesia) in Parkinson's disease (PD), based on accelerometry during a hand pronation-supination test. Clinician's ratings using the Treatment Response Scale (TRS), ranging from -3 (very Off) to 0 (On) to +3 (very dyskinetic), was used as target. For that purpose, 19 participants with advanced PD and 22 healthy persons were recruited in a single center open label clinical trial in Uppsala, Sweden. The trial consisted of single levodopa dose experiments for the people with PD (PwP), where participants were asked to perform standardized wrist rotation tests, using each hand, before and at pre-specified time points after the dose. The participants used wrist sensors containing a 3D accelerometer and gyroscope. Features to quantify the level, variation and asymmetry of the sensor signals, three-level Discrete Wavelet Transform features and approximate entropy measures were extracted from the sensors data. At the time of the tests, the PwP were video recorded. Three movement disorder specialists rated the participants’ state on the TRS scale. A Treatment Response Index from Sensors (TRIS) was constructed to quantify the motor states based on the wrist rotation tests. Different machine learning algorithms were evaluated to map the features derived from the sensor data to the ratings provided by the three specialists. Results from cross validation, both in 10-fold and a leave-one-individual out setting, showed good predictive power of a support vector machine model and high correlation to the TRS scale. Values at the end tails of the TRS scale were under and over predicted due to the lack of observations at those values but the model managed to accurately capture the dose - effect profiles of the patients. In addition, the TRIS had good test-retest reliability on the baseline levels of the PD participants (Intraclass correlation coefficient of 0.83) and reasonable sensitivity to levodopa treatment (0.33 for the TRIS). For a series of test occasions the proposed algorithms provided dose - effect time profiles for participants with PD, which could be useful during therapy individualization of people suffering from advanced PD

Ort, förlag, år, upplaga, sidor
2018. Vol. 22, nr 5, s. 1341-1349
Nyckelord [en]
Accelerometers, Accelerometry, Diseases, Feature extraction, Levodopa response, Machine learning, Parkinson's disease, Pattern recognition, Sensor phenomena and characterization, Signal processing, Wearable sensors, Wrist
Nationell ämneskategori
Data- och informationsvetenskap
Forskningsämne
Komplexa system - mikrodataanalys
Identifikatorer
URN: urn:nbn:se:du-26745DOI: 10.1109/JBHI.2017.2777926ISI: 000441795800002PubMedID: 29989996Scopus ID: 2-s2.0-85035809095OAI: oai:DiVA.org:du-26745DiVA, id: diva2:1164219
Tillgänglig från: 2017-12-11 Skapad: 2017-12-11 Senast uppdaterad: 2020-01-23Bibliografiskt granskad
Ingår i avhandling
1. Automating levodopa dosing schedules for Parkinson’s disease
Öppna denna publikation i ny flik eller fönster >>Automating levodopa dosing schedules for Parkinson’s disease
2019 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
Abstract [en]

Parkinson’s disease (PD) is the second most common neurodegenerative disease. Levodopa is mainly used to manage the motor symptoms of PD. However, disease progression and long-term use of levodopa cause reduced medication efficacy and side effects. When that happens, precise individualized dosing schedules are required.

This doctoral thesis in the field of Micro-data analysis introduces an end-to-end solution for the automation of the pharmacological management of PD with levodopa, and offers some insight on levodopa pharmacodynamics. For that purpose, an algorithm that derives objective ratings for the patients’ motor function through wearable sensors is introduced, a method to construct individual patient profiles is developed, and two dosing algorithms for oral and intestinal administration of levodopa are presented. Data from five different sources were used to develop the methods and evaluate the performance of the proposed algorithms.

The dose automation algorithms can work both with clinical and objective ratings (through wearable devices), and their application was evaluated against dosing adjustments from movement disorders experts. Both dosing algorithms showed promise and their dosing suggestions were similar to those of the clinicians.

The objective ratings algorithm had good test-retest reliability and its application during a clinical study was successful. Furthermore, the method of fitting individual patient models was robust and worked well with the objective ratings algorithm. Finally, a study was carried out that showed that about half the patients on levodopa treatment show reduced response during the afternoon hours, pointing to the need for more precise modelling of levodopa pharmacodynamics.

Ort, förlag, år, upplaga, sidor
Borlänge: Dalarna University, 2019
Serie
Dalarna Doctoral Dissertations ; 9
Nyckelord
Parkinson’s disease, levodopa, symptom assessment, symptom management, dosing algorithms, wearable sensors, microtablets, continuous infusion
Nationell ämneskategori
Datavetenskap (datalogi) Datorsystem Systemvetenskap, informationssystem och informatik
Forskningsämne
Komplexa system - mikrodataanalys, FLOAT - Flexibel levodopa-optimerings och individanpassningsteknik
Identifikatorer
urn:nbn:se:du-29435 (URN)978-91-85941-80-3 (ISBN)
Disputation
2019-04-05, sal Clas Ohlson, Borlänge, 13:00 (Engelska)
Opponent
Handledare
Tillgänglig från: 2019-03-11 Skapad: 2019-02-06 Senast uppdaterad: 2020-01-10Bibliografiskt granskad

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