du.sePublications
Change search
Refine search result
1 - 10 of 10
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • chicago-author-date
  • chicago-note-bibliography
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1.
    Aghanavesi, Somayeh
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Bergquist, Filip
    Nyholm, Dag
    Senek, Marina
    Memedi, Mevludin
    Motion sensor-based assessment of Parkinson’s disease motor symptoms during leg agility tests: results from levodopa challenge2019In: IEEE journal of biomedical and health informatics, ISSN 2168-2194, E-ISSN 2168-2208Article in journal (Refereed)
    Abstract [en]

    Parkinson’s disease (PD) is a degenerative, progressive disorder of the central nervous system that mainly affects motor control. The aim of this study was to develop data-driven methods and test their clinimetric properties to detect and quantify PD motor states using motion sensor data from leg agility tests. Nineteen PD patients were recruited in a levodopa single dose challenge study. PD patients performed leg agility tasks while wearing motion sensors on their lower extremities. Clinical evaluation of video recordings was performed by three movement disorder specialists who used four items from the motor section of the Unified PD Rating Scale (UPDRS), the treatment response scale (TRS) and a dyskinesia score. Using the sensor data, spatiotemporal features were calculated and relevant features were selected by feature selection. Machine learning methods like support vector machines (SVM), decision trees and linear regression, using 10-fold cross validation were trained to predict motor states of the patients. SVM showed the best convergence validity with correlation coefficients of 0.81 to TRS, 0.83 to UPDRS #31 (body bradykinesia and hypokinesia), 0.78 to SUMUPDRS (the sum of the UPDRS items: #26-leg agility, #27-arising from chair and #29-gait), and 0.67 to dyskinesia. Additionally, the SVM-based scores had similar test-retest reliability in relation to clinical ratings. The SVM-based scores were less responsive to treatment effects than the clinical scores, particularly with regards to dyskinesia. In conclusion, the results from this study indicate that using motion sensors during leg agility tests may lead to valid and reliable objective measures of PD motor symptoms.

  • 2.
    Aghanavesi, Somayeh
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Bergquist, Filip
    Nyholm, Dag
    Senek, Marina
    Memedi, Mevludin
    Treatment response index from a multi-modal sensor fusion platform for assessment of motor states in Parkinson's disease2019Manuscript (preprint) (Other academic)
    Abstract [en]

    The aim of this paper is to develop and evaluate a multi-sensor data fusion platform for quantifying Parkinson’s disease (PD) motor states. More specifically, the aim is to evaluate the clinimetric properties (validity, reliability, and responsiveness to treatment) of the method, using data from motion sensors during lower- and upper-limb tests.

    Methods: Nineteen PD patients and 22 healthy controls were recruited in a single center study. Subjects performed standardized motor tasks of Unified PD Rating Scale (UPDRS), including leg agility, hand rotation, and walking after wearing motion sensors on ankles and wrists. PD patients received a single levodopa dose before and at follow-up time points after the dose administration. Patients were video recorded and their motor symptoms were rated by three movement disorder experts. Experts rated each and every test occasions based on the six items of UPDRS-III (motor section), the treatment response scale (TRS) and the dyskinesia score. Spatiotemporal features were extracted from the sensor data. Features from lower limbs and upper limbs were fused. Feature selection methods of stepwise regression (SR), Lasso regression and principle component analysis (PCA) were used to select the most important features. Different machine learning methods of linear regression (LR), decision trees, and support vector machines were examined and their clinimetric properties were assessed.

    Results: Treatment response index from multimodal motion sensors (TRIMMS) scores obtained from the most valid method of LR when using data from all tests. Features were selected by SR, and this method resulted in r=0.95 to TRS. The test-retest reliability of TRIMMS was good with intra-class correlation coefficient of 0.82. Responsiveness of the TRIMMS to levodopa treatment was similar to the responsiveness of TRS.

    Conclusions: The results from this study indicate that fusing motion sensors data gathered during standardized motor tasks leads to valid, reliable and sensitive objective measurements of PD motor symptoms. These measurements could be further utilized in studies for individualized optimization of treatments in PD.

  • 3.
    Aghanavesi, Somayeh
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Fleyeh, Hasan
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Memedi, Mevludin
    Dougherty, Mark
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Feasibility of using smartphones for quantification of Parkinson’s disease motor states during hand rotation tests2019Conference paper (Refereed)
  • 4.
    Aghanavesi, Somayeh
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Memedi, Mevludin
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Dougherty, Mark
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Nyholm, Dag
    Westin, Jerker
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Verification of a method for measuring Parkinson’s disease related temporal irregularity in spiral drawings2017In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 17, no 10, article id 2341Article in journal (Refereed)
    Abstract [en]

    Parkinson's disease (PD) is a progressive movement disorder caused by the death of dopamine-producing cells in the midbrain. There is a need for frequent symptom assessment, since the treatment needs to be individualized as the disease progresses. The aim of this paper was to verify and further investigate the clinimetric properties of an entropy-based method for measuring PD-related upper limb temporal irregularities during spiral drawing tasks. More specifically, properties of a temporal irregularity score (TIS) for patients at different stages of PD, and medication time points were investigated. Nineteen PD patients and 22 healthy controls performed repeated spiral drawing tasks on a smartphone. Patients performed the tests before a single levodopa dose and at specific time intervals after the dose was given. Three movement disorder specialists rated videos of the patients based on the unified PD rating scale (UPDRS) and the Dyskinesia scale. Differences in mean TIS between the groups of patients and healthy subjects were assessed. Test-retest reliability of the TIS was measured. The ability of TIS to detect changes from baseline (before medication) to later time points was investigated. Correlations between TIS and clinical rating scores were assessed. The mean TIS was significantly different between healthy subjects and patients in advanced groups (p-value = 0.02). Test-retest reliability of TIS was good with Intra-class Correlation Coefficient of 0.81. When assessing changes in relation to treatment, TIS contained some information to capture changes from Off to On and wearing off effects. However, the correlations between TIS and clinical scores (UPDRS and Dyskinesia) were weak. TIS was able to differentiate spiral drawings drawn by patients in an advanced stage from those drawn by healthy subjects, and TIS had good test-retest reliability. TIS was somewhat responsive to single-dose levodopa treatment. Since TIS is an upper limb high-frequency-based measure, it cannot be detected during clinical assessment.

  • 5.
    Fleyeh, Hasan
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Westin, Jerker
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Extracting Body Landmarks from Videos for Parkinson Gait Analysis2019Conference paper (Refereed)
    Abstract [en]

    Patients with Parkinson disease (PD) exhibit a gait disorder called festinating gait which is caused by deficiency of dopamine in the basal ganglia. To analyze gait of patients with PD, different spatiotemporal parameters such as stride length, cadence, and walking speed should be calculated. This paper aims to present a method to extract useful information represented by the positions of certain landmarks on the human body that can be used for analysis of PD patients’ gait. This method is tested using 132 videos collected from 7 PD patients and 7 healthy controls. The positions of 4 body landmarks, namely body’s center of gravity (COG), the position of the head, and the position of the feet, was computed using a total of more than 41000 of video frames. Results of object’s movement plots show high level of accuracy in the calculation of the body landmarks.

  • 6.
    Javed, Farrukh
    et al.
    Business School, Örebro University.
    Thomas, Ilias
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Memedi, Mevludin
    Business School, Örebro University.
    A comparison of feature selection methods when using motion sensors data: a case study in Parkinson’s disease2018In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2018, p. 5426-5429Conference paper (Refereed)
    Abstract [en]

    The objective of this study is to investigate the effects of feature selection methods on the performance of machine learning methods for quantifying motor symptoms of Parkinson's disease (PD) patients. Different feature selection methods including step-wise regression, Lasso regression and Principal Component Analysis (PCA) were applied on 88 spatiotemporal features that were extracted from motion sensors during hand rotation tests. The selected features were then used in support vector machines (SVM), decision trees (DT), linear regression, and random forests models to calculate a so-called treatment-response index (TRIS). The validity, testretest reliability and sensitivity to treatment were assessed for each combination (feature selection method plus machine learning method). There were improvements in correlation coefficients and root mean squared error (RMSE) for all the machine learning methods, except DTs, when using the selected features from step-wise regression inputs. Using step-wise regression and SVM was found to have better sensitivity to treatment and higher correlation to clinical ratings on the Unified PD Rating Scale as compared to the combination of PCA and SVM. When assessing the ability of the machine learning methods to discriminate between tests performed by PD patients and healthy controls the results were mixed. These results suggest that the choice of feature selection methods is crucial when working with data-driven modelling. Based on our findings the step-wise regression can be considered as the method with the best performance.

  • 7. Johansson, D.
    et al.
    Ericsson, A.
    Johansson, A.
    Medvedev, A.
    Nyholm, D.
    Ohlsson, F.
    Senek, M.
    Spira, J.
    Thomas, Ilias
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Westin, Jerker
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Individualization of levodopa treatment using a microtablet dispenser and ambulatory accelerometry2018In: CNS Neuroscience & Therapeutics, ISSN 1755-5930, E-ISSN 1755-5949, Vol. 24, no 5, p. 439-447Article in journal (Refereed)
    Abstract [en]

    Aim

    This 4‐week open‐label observational study describes the effect of introducing a microtablet dose dispenser and adjusting doses based on objective free‐living motor symptom monitoring in individuals with Parkinson's disease (PD).

    Methods

    Twenty‐eight outpatients with PD on stable levodopa treatment with dose intervals of ≤4 hour had their daytime doses of levodopa replaced with levodopa/carbidopa microtablets, 5/1.25 mg (LC‐5) delivered from a dose dispenser device with programmable reminders. After 2 weeks, doses were adjusted based on ambulatory accelerometry and clinical monitoring.

    Results

    Twenty‐four participants completed the study per protocol. The daily levodopa dose was increased by 15% (112 mg, < 0.001) from period 1 to 2, and the dose interval was reduced by 12% (22 minutes, P = 0.003). The treatment adherence to LC‐5 was high in both periods. The MDS‐UPDRS parts II and III, disease‐specific quality of life (PDQ‐8), wearing‐off symptoms (WOQ‐19), and nonmotor symptoms (NMS Quest) improved after dose titration, but the generic quality‐of‐life measure EQ‐5D‐5L did not. Blinded expert evaluation of accelerometry results demonstrated improvement in 60% of subjects and worsening in 25%.

    Conclusions

    The introduction of a levodopa microtablet dispenser and accelerometry aided dose adjustments improve PD symptoms and quality of life in the short term.

  • 8. Johansson, Dongni
    et al.
    Thomas, Ilias
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Ericsson, Anders
    Johansson, Anders
    Medvedev, Alexander
    Memedi, Mevludin
    Nyholm, Dag
    Ohlsson, Fredrik
    Westin, Jerker
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Bergquist, Filip
    Evaluation of a sensor algorithm for motor state rating in Parkinson's disease2019In: Parkinsonism & Related Disorders, ISSN 1353-8020, E-ISSN 1873-5126Article in journal (Refereed)
    Abstract [en]

    INTRODUCTION: A treatment response objective index (TRIS) was previously developed based on sensor data from pronation-supination tests. This study aimed to examine the performance of TRIS for medication effects in a new population sample with Parkinson's disease (PD) and its usefulness for constructing individual dose-response models.

    METHODS: Twenty-five patients with PD performed a series of tasks throughout a levodopa challenge while wearing sensors. TRIS was used to determine motor changes in pronation-supination tests following a single levodopa dose, and was compared to clinical ratings including the Treatment Response Scale (TRS) and six sub-items of the UPDRS part III.

    RESULTS: As expected, correlations between TRIS and clinical ratings were lower in the new population than in the initial study. TRIS was still significantly correlated to TRS (rs = 0.23, P < 0.001) with a root mean square error (RMSE) of 1.33. For the patients (n = 17) with a good levodopa response and clear motor fluctuations, a stronger correlation was found (rs = 0.38, RMSE = 1.29, P < 0.001). The mean TRIS increased significantly when patients went from the practically defined off to their best on state (P = 0.024). Individual dose-response models could be fitted for more participants when TRIS was used for modelling than when TRS ratings were used.

    CONCLUSION: The objective sensor index shows promise for constructing individual dose-response models, but further evaluations and retraining of the TRIS algorithm are desirable to improve its performance and to ensure its clinical effectiveness.

  • 9.
    Memedi, Mevludin
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Westin, Jerker
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Nyholm, Dag
    Dougherty, Mark
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Groth, Torgny
    A web application for follow-up of results from a mobile device test battery for Parkinson's disease patients2011In: Computer Methods and Programs in Biomedicine, ISSN 0169-2607, E-ISSN 1872-7565, Vol. 104, no 2, p. 219-226Article in journal (Refereed)
    Abstract [en]

    This paper describes a web-based system for enabling remote monitoring of patients with Parkinson's disease (PD) and supporting clinicians in treating their patients. The system consists of a patient node for subjective and objective data collection based on a handheld computer, a service node for data storage and processing, and a web application for data presentation. Using statistical and machine learning methods, time series of raw data are summarized into scores for conceptual symptom dimensions and an "overall test score" providing a comprehensive profile of patient's health during a test period of about one week. The handheld unit was used quarterly or biannually by 65 patients with advanced PD for up to four years at nine clinics in Sweden. The IBM Computer System Usability Questionnaire was administered to assess nurses' satisfaction with the web application. Results showed that a majority of the nurses were quite satisfied with the usability although a sizeable minority were not. Our findings support that this system can become an efficient tool to easily access relevant symptom information from the home environment of PD patients. (C) 2011 Elsevier Ireland Ltd. All rights reserved.

  • 10.
    Thomas, Ilias
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Memedi, Mevludin
    Westin, Jerker
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Nyholm, Dag
    The effect of continuous levodopa treatment during the afternoon hours2019In: Acta Neurologica Scandinavica, ISSN 0001-6314, E-ISSN 1600-0404, Vol. 139, no 1, p. 70-75Article in journal (Refereed)
    Abstract [en]

    OBJECTIVE: The aim of this retrospective study was to investigate if patients with PD, who are treated with levodopa-carbidopa intestinal gel (LCIG), clinically worsen during the afternoon hours and if so, to evaluate whether this occurs in all LCIG-treated patients or in a sub-group of patients.

    METHODS: Three published studies were identified and included in the analysis. All studies provided individual response data assessed on the treatment response scale (TRS) and patients were treated with continuous LCIG. Ninety-eight patients from the three studies fulfilled the criteria. T-tests were performed to find differences on the TRS values between the morning and the afternoon hours, linear mixed effect models were fitted on the afternoon hours' evaluations to find trends of wearing-off, and patients were classified into three TRS categories (meaningful increase in TRS, meaningful decrease in TRS, non -meaningful increase or decrease).

    RESULTS: In all three studies significant statistical differences were found between the morning TRS values and the afternoon TRS values (p-value <= 0.001 in all studies). The linear mixed effect models had significant negative coefficients for time in two studies, and 48 out of 98 patients (49%) showed a meaningful decrease of TRS during the afternoon hours.

    CONCLUSION: The results from all studies were consistent, both in the proportion of patients in the three groups and the value of TRS decrease in the afternoon hours. Based on these findings there seems to be a group of patients with predictable "off" behavior in the later parts of the day. This article is protected by copyright. All rights reserved.

1 - 10 of 10
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • chicago-author-date
  • chicago-note-bibliography
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf