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Aghanavesi, S., Westin, J., Bergquist, F., Nyholm, D., Askmark, H., Aquilonius, S. M., . . . Memedi, M. (2020). A multiple motion sensors index for motor state quantification in Parkinson's disease. Computer Methods and Programs in Biomedicine, 189, Article ID 105309.
Open this publication in new window or tab >>A multiple motion sensors index for motor state quantification in Parkinson's disease
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2020 (English)In: Computer Methods and Programs in Biomedicine, ISSN 0169-2607, E-ISSN 1872-7565, Vol. 189, article id 105309Article in journal (Refereed) 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.

National Category
Clinical Medicine
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-31738 (URN)10.1016/j.cmpb.2019.105309 (DOI)31982667 (PubMedID)2-s2.0-85078170133 (Scopus ID)
Available from: 2020-01-29 Created: 2020-01-29 Last updated: 2020-03-12Bibliographically approved
Aghanavesi, S., Fleyeh, H. & Dougherty, M. (2020). Feasibility of Using Dynamic Time Warping to Measure Motor States in Parkinson’s Disease. Journal of Sensors, 2020(3265795)
Open this publication in new window or tab >>Feasibility of Using Dynamic Time Warping to Measure Motor States in Parkinson’s Disease
2020 (English)In: Journal of Sensors, ISSN 1687-725X, E-ISSN 1687-7268, Vol. 2020, no 3265795Article in journal (Refereed) Epub ahead of print
Abstract [en]

The aim of this paper is to investigate the feasibility of using the Dynamic Time Warping (DTW) method to measure motor states in advanced Parkinson’s disease (PD). Data were collected from 19 PD patients who experimented leg agility motor tests with motion sensors on their ankles once before and multiple times after an administration of 150% of their normal daily dose of medication. Experiments of 22 healthy controls were included. Three movement disorder specialists rated the motor states of the patients according to Treatment Response Scale (TRS) using recorded videos of the experiments. A DTW-based motor state distance score (DDS) was constructed using the acceleration and gyroscope signals collected during leg agility motor tests. Mean DDS showed similar trends to mean TRS scores across the test occasions. Mean DDS was able to differentiate between PD patients at Off and On motor states. DDS was able to classify the motor state changes with good accuracy (82%). The PD patients who showed more response to medication were selected using the TRS scale, and the most related DTW-based features to their TRS scores were investigated. There were individual DTW-based features identified for each patient. In conclusion, the DTW method can provide information about motor states of advanced PD patients which can be used in the development of methods for automatic motor scoring of PD.

Keywords
Dynamic Time Warping, Parkinson's disease, signal processing
National Category
Medical Engineering
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-31909 (URN)10.1155/2020/3265795 (DOI)
Available from: 2020-02-16 Created: 2020-02-16 Last updated: 2020-02-26Bibliographically approved
Aghanavesi, S., Bergquist, F., Nyholm, D., Senek, M. & Memedi, M. (2020). Motion sensor-based assessment of Parkinson’s disease motor symptoms during leg agility tests: results from levodopa challenge. IEEE journal of biomedical and health informatics, 24(1), 111-119, Article ID 8637809.
Open this publication in new window or tab >>Motion sensor-based assessment of Parkinson’s disease motor symptoms during leg agility tests: results from levodopa challenge
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2020 (English)In: IEEE journal of biomedical and health informatics, ISSN 2168-2194, E-ISSN 2168-2208, Vol. 24, no 1, p. 111-119, article id 8637809Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
IEEE, 2020
National Category
Physiotherapy Other Medical Engineering
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-29542 (URN)10.1109/JBHI.2019.2898332 (DOI)000506642000012 ()2-s2.0-85077669455 (Scopus ID)
Available from: 2019-02-21 Created: 2019-02-21 Last updated: 2020-03-18Bibliographically approved
Aghanavesi, S., Fleyeh, H., Memedi, M. & Dougherty, M. (2019). Feasibility of using smartphones for quantification of Parkinson’s disease motor states during hand rotation tests. In: : . Paper presented at 41st International Engineering in Medicine and Biology Conference, Berlin, Germany, July 23–27, 2019.
Open this publication in new window or tab >>Feasibility of using smartphones for quantification of Parkinson’s disease motor states during hand rotation tests
2019 (English)Conference paper, Published paper (Refereed)
National Category
Medical Engineering
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-29543 (URN)
Conference
41st International Engineering in Medicine and Biology Conference, Berlin, Germany, July 23–27, 2019
Available from: 2019-02-21 Created: 2019-02-21 Last updated: 2019-06-05Bibliographically approved
Aghanavesi, S., Bergquist, F., Nyholm, D., Senek, M. & Memedi, M. (2019). Treatment response index from a multi-modal sensor fusion platform for assessment of motor states in Parkinson's disease.
Open this publication in new window or tab >>Treatment response index from a multi-modal sensor fusion platform for assessment of motor states in Parkinson's disease
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2019 (English)Manuscript (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.

National Category
Medical Engineering
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-29546 (URN)
Available from: 2019-02-21 Created: 2019-02-21 Last updated: 2019-06-05Bibliographically approved
Matic, T., Aghanavesi, S., Memedi, M., Nyholm, D., Bergquist, F., Groznik, V., . . . Sadikov, A. (2019). Unsupervised learning from motion sensor data to assess the condition of patients with parkinson's disease. In: : . Paper presented at 17th Conference on Artificial Intelligence in Medicine, AIME 2019; Poznan; Poland; 26-29 June 2019 (pp. 420-424). , 11526
Open this publication in new window or tab >>Unsupervised learning from motion sensor data to assess the condition of patients with parkinson's disease
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2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Parkinson’s disease (PD) is a chronic neurodegenerative disorder that predominantly affects the patient’s motor system, resulting in muscle rigidity, bradykinesia, tremor, and postural instability. As the disease slowly progresses, the symptoms worsen, and regular monitoring is required to adjust the treatment accordingly. The objective evaluation of the patient’s condition is sometimes rather difficult and automated systems based on various sensors could be helpful to the physicians. The data in this paper come from a clinical study of 19 advanced PD patients with motor fluctuations. The measurements used come from the motion sensors the patients wore during the study. The paper presents an unsupervised learning approach applied on this data with the aim of checking whether sensor data alone can indicate the patient’s motor state. The rationale for the unsupervised approach is that there was significant inter-physician disagreement on the patient’s condition (target value for supervised machine learning). The input to clustering came from sensor data alone. The resulting clusters were matched against the physicians’ estimates showing relatively good agreement. 

Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 11526
Keywords
Bradykinesia, Dyskinesia, Motion sensor, Objective evaluation, Parkinson’s disease, Patient monitoring, Unsupervised learning
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-30569 (URN)10.1007/978-3-030-21642-9_52 (DOI)000495606500052 ()2-s2.0-85068314091 (Scopus ID)9783030216412 (ISBN)
Conference
17th Conference on Artificial Intelligence in Medicine, AIME 2019; Poznan; Poland; 26-29 June 2019
Available from: 2019-07-22 Created: 2019-07-22 Last updated: 2019-12-09Bibliographically approved
Aghanavesi, S., Filip, B., Nyholm, D., Senek, M. & Memedi, M. (2018). Feasibility of a multi-sensor data fusion method for assessment of Parkinson’s disease motor symptoms. In: : . Paper presented at International Congress of Parkinson’s Disease and Movement Disorders (MDS), Hong Kong, 5-9 October 2018.
Open this publication in new window or tab >>Feasibility of a multi-sensor data fusion method for assessment of Parkinson’s disease motor symptoms
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2018 (English)Conference paper, Poster (with or without abstract) (Refereed)
Abstract [en]

Title: Feasibility of a multi-sensor data fusion method for assessment of Parkinson’s disease motor symptoms

Objective: To assess the feasibility of measuring Parkinson’s disease (PD) motor symptoms with a multi-sensor data fusion method. More specifically, the aim is to assess validity, reliability and sensitivity to treatment of the methods.

Background: Data from 19 advanced PD patients (Gender: 14 males and 5 females, mean age: 71.4, mean years with PD: 9.7, mean years with levodopa: 9.5) were collected in a single center, open label, single dose clinical trial in Sweden [1].

Methods: The patients performed leg agility and 2-5 meter straight walking tests while wearing motion sensors on their limbs. They performed the tests at baseline, at the time they received the morning dose, and at pre-specified time points until the medication wore off. While performing the tests the patients were video recorded. The videos were observed by three movement disorder specialists who rated the symptoms using a treatment response scale (TRS), ranging from -3 (very off) to 3 (very dyskinetic). The sensor data consisted of lower limb data during leg agility, upper limb data during walking, and lower limb data during walking. Time series analysis was performed on the raw sensor data extracted from 17 patients to derive a set of quantitative measures, which were then used during machine learning to be mapped to mean ratings of the three raters on the TRS scale. Combinations of data were tested during the machine learning procedure.

Results: Using data from both tests, the Support Vector Machines (SVM) could predict the motor states of the patients on the TRS scale with a good agreement in relation to the mean ratings of the three raters (correlation coefficient = 0.92, root mean square error = 0.42, p<0.001). Additionally, there was good test-retest reliability of the SVM scores during baseline and second tests with intraclass-correlation coefficient of 0.84. Sensitivity to treatment for SVM was good (Figure 1), indicating its ability to detect changes in motor symptoms. The upper limb data during walking was more informative than lower limb data during walking since SVMs had higher correlation coefficient to mean ratings.  

Conclusions: The methodology demonstrates good validity, reliability, and sensitivity to treatment. This indicates that it could be useful for individualized optimization of treatments among PD patients, leading to an improvement in health-related quality of life.

National Category
Signal Processing
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-28773 (URN)
Conference
International Congress of Parkinson’s Disease and Movement Disorders (MDS), Hong Kong, 5-9 October 2018
Available from: 2018-10-18 Created: 2018-10-18 Last updated: 2018-10-22Bibliographically approved
Aghanavesi, S., Bergquist, F., Nyholm, D., Senek, M. & Memedi, M. (2018). Objective assessment of Parkinson’s disease motor symptoms during leg agility test using motion sensors. In: : . Paper presented at International Congress of Parkinson’s Disease and Movement Disorders (MDS), Hong Kong, 5-9 October 2018.
Open this publication in new window or tab >>Objective assessment of Parkinson’s disease motor symptoms during leg agility test using motion sensors
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2018 (English)Conference paper, Poster (with or without abstract) (Refereed)
Abstract [en]

Title: Objective assessment of Parkinson’s disease motor symptoms during leg agility test using motion sensors

Objective: To develop and evaluate machine learning methods for assessment of Parkinson’s disease (PD) motor symptoms using leg agility (LA) data collected with motion sensors during a single dose experiment.

Background: Nineteen advanced PD patients (Gender: 14 males and 5 females, mean age: 71.4, mean years with PD: 9.7, mean years with levodopa: 9.5) were recruited in a single center, open label, single dose clinical trial in Sweden [1].

Methods: The patients performed up to 15 LA tasks while wearing motions sensors on their foot ankle. They performed tests at pre-defined time points starting from baseline, at the time they received a morning dose (150% of their levodopa equivalent morning dose), and at follow-up time points until the medication wore off. The patients were video recorded while performing the motor tasks. and three movement disorder experts rated the observed motor symptoms using 4 items from the Unified PD Rating Scale (UPDRS) motor section including UPDRS #26 (leg agility), UPDRS #27 (Arising from chair), UPDRS #29 (Gait), UPDRS #31 (Body Bradykinesia and Hypokinesia), and dyskinesia scale. In addition, they rated the overall mobility of the patients using Treatment Response Scale (TRS), ranging from -3 (very off) to 3 (very dyskinetic). Sensors data were processed and their quantitative measures were used to develop machine learning methods, which mapped them to the mean ratings of the three raters. The quality of measurements of the machine learning methods was assessed by convergence validity, test-retest reliability and sensitivity to treatment.

Results: Results from the 10-fold cross validation showed good convergent validity of the machine learning methods (Support Vector Machines, SVM) with correlation coefficients of 0.81 for TRS, 0.78 for UPDRS #26, 0.69 for UPDRS #27, 0.78 for UPDRS #29, 0.83 for UPDRS #31, and 0.67 for dyskinesia scale (P<0.001). There were good correlations between scores produced by the methods during the first (baseline) and second tests with coefficients ranging from 0.58 to 0.96, indicating good test-retest reliability. The machine learning methods had lower sensitivity than mean clinical ratings (Figure. 1).

Conclusions: The presented methodology was able to assess motor symptoms in PD well, comparable to movement disorder experts. The leg agility test did not reflect treatment related changes.

National Category
Signal Processing
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-28772 (URN)
Conference
International Congress of Parkinson’s Disease and Movement Disorders (MDS), Hong Kong, 5-9 October 2018
Funder
VINNOVA
Available from: 2018-10-18 Created: 2018-10-18 Last updated: 2018-10-22Bibliographically approved
Aghanavesi, S., Nyholm, D., Marina, S., Bergquist, F. & Memedi, M. (2017). A smartphone-based system to quantify dexterity in Parkinson's disease patients. Informatics in Medicine Unlocked, 9, 11-17
Open this publication in new window or tab >>A smartphone-based system to quantify dexterity in Parkinson's disease patients
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2017 (English)In: Informatics in Medicine Unlocked, ISSN 2352-9148, Vol. 9, p. 11-17Article in journal (Refereed) Published
Abstract [en]

Objectives: The aim of this paper is to investigate whether a smartphone-based system can be used to quantify dexterity in Parkinson’s disease (PD). More specifically, the aim was to develop data-driven methods to quantify and characterize dexterity in PD. Methods: Nineteen advanced PD patients and 22 healthy controls participated in a clinical trial in Uppsala, Sweden. The subjects were asked to perform tapping and spiral drawing tests using a smartphone. Patients performed the tests before, and at pre-specified time points after they received 150% of their usual levodopa morning dose. Patients were video recorded and their motor symptoms were assessed by three movement disorder specialists using three Unified PD Rating Scale (UPDRS) motor items from part III, the dyskinesia scoring and the treatment response scale (TRS). The raw tapping and spiral data were processed and analyzed with time series analysis techniques to extract 37 spatiotemporal features. For each of the five scales, separate machine learning models were built and tested by using principal components of the features as predictors and mean ratings of the three specialists as target variables. Results: There were weak to moderate correlations between smartphone-based scores and mean ratings of UPDRS item #23 (0.52; finger tapping), UPDRS #25 (0.47; rapid alternating movements of hands), UPDRS #31 (0.57; body bradykinesia and hypokinesia), sum of the three UPDRS items (0.46), dyskinesia (0.64), and TRS (0.59). When assessing the test-retest reliability of the scores it was found that, in general, the clinical scores had better test-retest reliability than the smartphone-based scores. Only the smartphone-based predicted scores on the TRS and dyskinesia scales had good repeatability with intra-class correlation coefficients of 0.51 and 0.84, respectively. Clinician-based scores had higher effect sizes than smartphone-based scores indicating a better responsiveness in detecting changes in relation to treatment interventions. However, the first principal component of the 37 features was able to capture changes throughout the levodopa cycle and had trends similar to the clinical TRS and dyskinesia scales. Smartphone-based scores differed significantly between patients and healthy controls. Conclusions: Quantifying PD motor symptoms via instrumented, dexterity tests employed in a smartphone is feasible and data from such tests can also be used for measuring treatment-related changes in patients.

Keywords
Parkinson's disease; motor assessment; spiral tests; tapping tests; smartphone; dyskinesia; bradykinesia; objective measures; telemedicine
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-25034 (URN)10.1016/j.imu.2017.05.005 (DOI)
Funder
Knowledge Foundation
Available from: 2017-05-19 Created: 2017-05-19 Last updated: 2020-02-26Bibliographically approved
Aghanavesi, S. (2017). Smartphone-based Parkinson’s disease symptom assessment. (Licentiate dissertation). Borlänge: Dalarna University
Open this publication in new window or tab >>Smartphone-based Parkinson’s disease symptom assessment
2017 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis consists of four research papers presenting a microdata analysis approach to assess and evaluate the Parkinson’s disease (PD) motor symptoms using smartphone-based systems. PD is a progressive neurological disorder that is characterized by motor symptoms. It is a complex disease that requires continuous monitoring and multidimensional symptom analysis. Both patients’ perception regarding common symptom and their motor function need to be related to the repeated and time-stamped assessment; with this, the full extent of patient’s condition could be revealed. The smartphone enables and facilitates the remote, long-term and repeated assessment of PD symptoms. Two types of collected data from smartphone were used, one during a three year, and another during one-day clinical study. The data were collected from series of tests consisting of tapping and spiral motor tests. During the second time scale data collection, along smartphone-based measurements patients were video recorded while performing standardized motor tasks according to Unified Parkinson’s disease rating scales (UPDRS).

At first, the objective of this thesis was to elaborate the state of the art, sensor systems, and measures that were used to detect, assess and quantify the four cardinal and dyskinetic motor symptoms. This was done through a review study. The review showed that smartphones as the new generation of sensing devices are preferred since they are considered as part of patients’ daily accessories, they are available and they include high-resolution activity data. Smartphones can capture important measures such as forces, acceleration and radial displacements that are useful for assessing PD motor symptoms.

Through the obtained insights from the review study, the second objective of this thesis was to investigate whether a combination of tapping and spiral drawing tests could be useful to quantify dexterity in PD. More specifically, the aim was to develop data-driven methods to quantify and characterize dexterity in PD. The results from this study showed that tapping and spiral drawing tests that were collected by smartphone can detect movements reasonably well related to under- and over-medication.

The thesis continued by developing an Approximate Entropy (ApEn)-based method, which aimed to measure the amount of temporal irregularity during spiral drawing tests. One of the disabilities associated with PD is the impaired ability to accurately time movements. The increase in timing variability among patients when compared to healthy subjects, suggests that the Basal Ganglia (BG) has a role in interval timing. ApEn method was used to measure temporal irregularity score (TIS) which could significantly differentiate the healthy subjects and patients at different stages of the disease. This method was compared to two other methods which were used to measure the overall drawing impairment and shakiness. TIS had better reliability and responsiveness compared to the other methods. However, in contrast to other methods, the mean scores of the ApEn-based method improved significantly during a 3-year clinical study, indicating a possible impact of pathological BG oscillations in temporal control during spiral drawing tasks. In addition, due to the data collection scheme, the study was limited to have no gold standard for validating the TIS. However, the study continued to further investigate the findings using another screen resolution, new dataset, new patient groups, and for shorter term measurements. The new dataset included the clinical assessments of patients while they performed tests according to UPDRS. The results of this study confirmed the findings in the previous study. Further investigation when assessing the correlation of TIS to clinical ratings showed the amount of temporal irregularity present in the spiral drawing cannot be detected during clinical assessment since TIS is an upper limb high frequency-based measure. 

Place, publisher, year, edition, pages
Borlänge: Dalarna University, 2017. p. 67
Series
Dalarna Licentiate Theses ; 6
Keywords
Parkinson’s disease; symptom assessment; spiral; tapping; smartphone; temporal irregularity; timing variability; approximate entropy;
National Category
Computer Systems
Research subject
Complex Systems – Microdata Analysis, FLOAT - Flexible Levodopa Optimizing Assistive Technology
Identifiers
urn:nbn:se:du-24925 (URN)978-91-85941-99-5 (ISBN)
Presentation
2017-06-02, Clas Ohlson, Borlänge, 11:43 (English)
Supervisors
Available from: 2017-05-15 Created: 2017-05-12 Last updated: 2020-01-23Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-1548-5077

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