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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). 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
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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2019 (English)In: IEEE journal of biomedical and health informatics, ISSN 2168-2194, E-ISSN 2168-2208Article in journal (Refereed) Epub ahead of print
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, 2019
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)
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)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: 2019-07-22Bibliographically 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: 2019-06-05Bibliographically 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 in Microdata Analysis ; 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: 2019-06-17Bibliographically approved
Aghanavesi, S., Memedi, M., Dougherty, M., Nyholm, D. & Westin, J. (2017). Verification of a method for measuring Parkinson’s disease related temporal irregularity in spiral drawings. Sensors, 17(10), Article ID 2341.
Open this publication in new window or tab >>Verification of a method for measuring Parkinson’s disease related temporal irregularity in spiral drawings
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2017 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 17, no 10, article id 2341Article in journal (Refereed) Published
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.

Keywords
Parkinson's disease; smartphone; spiral tests; temporal irregularity; timing variability; motor assessment; approximate entropy; complexity
National Category
Other Medical Engineering Probability Theory and Statistics
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-29318 (URN)10.3390/s17102341 (DOI)29027941 (PubMedID)2-s2.0-85032855199 (Scopus ID)
Available from: 2019-01-21 Created: 2019-01-21 Last updated: 2019-06-05Bibliographically approved
Memedi, M., Aghanavesi, S. & Westin, J. (2016). A method for measuring Parkinson's disease related temporal irregularity in spiral drawings. In: 2016 IEEE International Conference on Biomedical and Health Informatics: . Paper presented at Biomedical and Health Informatics 2016, Las Vegas, 24-27 February (pp. 410-413).
Open this publication in new window or tab >>A method for measuring Parkinson's disease related temporal irregularity in spiral drawings
2016 (English)In: 2016 IEEE International Conference on Biomedical and Health Informatics, 2016, p. 410-413Conference paper, Published paper (Refereed)
Abstract [en]

The objective of this paper was to develop and evaluate clinimetric properties of a method for measuring Parkinson's disease (PD)-related temporal irregularities using digital spiral analysis. In total, 108 (98 patients in different stages of PD and 10 healthy elderly subjects) performed repeated spiral drawing tasks in their home environments using a touch screen device. A score was developed for representing the amount of temporal irregularity during spiral drawing tasks, using Approximate Entropy (ApEn) technique. In addition, two previously published spiral scoring methods were adapted and their scores were analyzed. The mean temporal irregularity score differed significantly between healthy elderly subjects and advanced PD patients (P<0.005). The ApEn-based method had a better responsiveness and test-retest reliability when compared to the other two methods. In contrast to the other methods, the mean scores of the ApEn-based method improved significantly during a 3 year clinical study, indicating a possible impact of pathological basal ganglia oscillations in temporal control during spiral drawing tasks. In conclusion, the ApEn-based method could be used for differentiating between patients in different stages of PD and healthy subjects. The responsiveness and test-retest reliability were good for the ApEn-based method indicating that this method is useful for measuring upper limb temporal irregularity at a micro-level.

National Category
Computer Systems Signal Processing
Research subject
Complex Systems – Microdata Analysis, FLOAT - Flexible Levodopa Optimizing Assistive Technology
Identifiers
urn:nbn:se:du-21183 (URN)10.1109/BHI.2016.7455921 (DOI)000381398000102 ()978-1-5090-2455-1 (ISBN)
Conference
Biomedical and Health Informatics 2016, Las Vegas, 24-27 February
Available from: 2016-03-01 Created: 2016-03-01 Last updated: 2019-06-05Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-1548-5077

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