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A method for measuring Parkinson's disease related temporal irregularity in spiral drawings
Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.ORCID-id: 0000-0002-2372-4226
Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.ORCID-id: 0000-0002-1548-5077
Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.ORCID-id: 0000-0003-0403-338X
2016 (engelsk)Inngår i: 2016 IEEE International Conference on Biomedical and Health Informatics, 2016, s. 410-413Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
2016. s. 410-413
HSV kategori
Forskningsprogram
Komplexa system - mikrodataanalys, FLOAT - Flexibel levodopa-optimerings och individanpassningsteknik
Identifikatorer
URN: urn:nbn:se:du-21183DOI: 10.1109/BHI.2016.7455921ISI: 000381398000102ISBN: 978-1-5090-2455-1 (digital)OAI: oai:DiVA.org:du-21183DiVA, id: diva2:908008
Konferanse
Biomedical and Health Informatics 2016, Las Vegas, 24-27 February
Tilgjengelig fra: 2016-03-01 Laget: 2016-03-01 Sist oppdatert: 2020-02-26bibliografisk kontrollert
Inngår i avhandling
1. Smartphone-based Parkinson’s disease symptom assessment
Åpne denne publikasjonen i ny fane eller vindu >>Smartphone-based Parkinson’s disease symptom assessment
2017 (engelsk)Licentiatavhandling, med artikler (Annet vitenskapelig)
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. 

sted, utgiver, år, opplag, sider
Borlänge: Dalarna University, 2017. s. 67
Serie
Dalarna Licentiate Theses ; 6
Emneord
Parkinson’s disease; symptom assessment; spiral; tapping; smartphone; temporal irregularity; timing variability; approximate entropy;
HSV kategori
Forskningsprogram
Komplexa system - mikrodataanalys, FLOAT - Flexibel levodopa-optimerings och individanpassningsteknik
Identifikatorer
urn:nbn:se:du-24925 (URN)978-91-85941-99-5 (ISBN)
Presentation
2017-06-02, Clas Ohlson, Borlänge, 11:43 (engelsk)
Veileder
Tilgjengelig fra: 2017-05-15 Laget: 2017-05-12 Sist oppdatert: 2020-01-23bibliografisk kontrollert
2. Sensor-based knowledge- and data-driven methods: A case of Parkinson’s disease motor symptoms quantification
Åpne denne publikasjonen i ny fane eller vindu >>Sensor-based knowledge- and data-driven methods: A case of Parkinson’s disease motor symptoms quantification
2020 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
Borlänge: Dalarna University, 2020
Serie
Dalarna Doctoral Dissertations ; 12
Emneord
Parkinson’s disease, motion sensors, motor symptoms, smartphone, microdata, multivariate analysis, data-driven, knowledge-driven, support vector machine stepwise regression, predictive models
HSV kategori
Forskningsprogram
Komplexa system - mikrodataanalys
Identifikatorer
urn:nbn:se:du-32065 (URN)978-91-88679-00-0 (ISBN)
Disputas
2020-05-08, Clas Ohlson, Borlänge, 13:00 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2020-04-15 Laget: 2020-02-26 Sist oppdatert: 2020-04-15bibliografisk kontrollert

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