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Smartphone-based Parkinson’s disease symptom assessment
Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.ORCID-id: 0000-0002-1548-5077
2017 (Engelska)Licentiatavhandling, sammanläggning (Övrigt vetenskapligt)
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. 

Ort, förlag, år, upplaga, sidor
Borlänge: Högskolan Dalarna, 2017. , 67 s.
Serie
Dalarna Licentiate Theses, 6
Nyckelord [en]
Parkinson’s disease; symptom assessment; spiral; tapping; smartphone; temporal irregularity; timing variability; approximate entropy;
Nationell ämneskategori
Datorsystem
Forskningsämne
Komplexa system - mikrodataanalys, FLOAT - Flexibel levodopa-optimerings och individanpassningsteknik
Identifikatorer
URN: urn:nbn:se:du-24925ISBN: 978-91-85941-99-5 (tryckt)OAI: oai:DiVA.org:du-24925DiVA: diva2:1095172
Presentation
2017-06-02, Clas Ohlson, Borlänge, 11:43 (Engelska)
Handledare
Tillgänglig från: 2017-05-15 Skapad: 2017-05-12 Senast uppdaterad: 2017-05-22Bibliografiskt granskad
Delarbeten
1. A review of Parkinson’s disease cardinal and dyskinetic motor symptoms assessment methods using sensor systems
Öppna denna publikation i ny flik eller fönster >>A review of Parkinson’s disease cardinal and dyskinetic motor symptoms assessment methods using sensor systems
2016 (Engelska)Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

This paper is reviewing objective assessments of Parkinson’s disease(PD) motor symptoms, cardinal, and dyskinesia, using sensor systems. It surveys the manifestation of PD symptoms, sensors that were used for their detection, types of signals (measures) as well as their signal processing (data analysis) methods. A summary of this review’s finding is represented in a table including devices (sensors), measures and methods that were used in each reviewed motor symptom assessment study. In the gathered studies among sensors, accelerometers and touch screen devices are the most widely used to detect PD symptoms and among symptoms, bradykinesia and tremor were found to be mostly evaluated. In general, machine learning methods are potentially promising for this. PD is a complex disease that requires continuous monitoring and multidimensional symptom analysis. Combining existing technologies to develop new sensor platforms may assist in assessing the overall symptom profile more accurately to develop useful tools towards supporting better treatment process.

Serie
Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, ISSN 1867-8211
Nyckelord
Parkinson’s disease; sensors; objective assessment; motor symptoms; machine learning; dyskinesia; bradykinesia; Rigidity; tremor
Nationell ämneskategori
Datorsystem
Forskningsämne
Komplexa system - mikrodataanalys, FLOAT - Flexibel levodopa-optimerings och individanpassningsteknik
Identifikatorer
urn:nbn:se:du-23271 (URN)10.1007/978-3-319-51234-1_8 (DOI)9783319512334 (ISBN)
Konferens
The 3rd EAI International Conference on IoT Technologies for HealthCare, October 18–19, 2016, Västerås, Sweden
Forskningsfinansiär
VINNOVAKK-stiftelsen
Tillgänglig från: 2016-10-24 Skapad: 2016-10-24 Senast uppdaterad: 2017-05-15Bibliografiskt granskad
2. A smartphone-based system to quantify dexterity in Parkinson's disease patients
Öppna denna publikation i ny flik eller fönster >>A smartphone-based system to quantify dexterity in Parkinson's disease patients
Visa övriga...
2017 (Engelska)Ingår i: Informatics in Medicine Unlocked, ISSN 2352-9148, Vol. 9, 11-17 s.Artikel i tidskrift (Refereegranskat) 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.

Nyckelord
Parkinson's disease; motor assessment; spiral tests; tapping tests; smartphone; dyskinesia; bradykinesia; objective measures; telemedicine
Nationell ämneskategori
Elektroteknik och elektronik
Forskningsämne
Komplexa system - mikrodataanalys
Identifikatorer
urn:nbn:se:du-25034 (URN)10.1016/j.imu.2017.05.005 (DOI)
Forskningsfinansiär
KK-stiftelsen
Tillgänglig från: 2017-05-19 Skapad: 2017-05-19 Senast uppdaterad: 2017-06-05Bibliografiskt granskad
3. A method for measuring Parkinson's disease related temporal irregularity in spiral drawings
Öppna denna publikation i ny flik eller fönster >>A method for measuring Parkinson's disease related temporal irregularity in spiral drawings
2016 (Engelska)Ingår i: 2016 IEEE International Conference on Biomedical and Health Informatics, 2016, 410-413 s.Konferensbidrag, Publicerat paper (Refereegranskat)
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.

Nationell ämneskategori
Datorsystem Signalbehandling
Forskningsämne
Komplexa system - mikrodataanalys, FLOAT - Flexibel levodopa-optimerings och individanpassningsteknik
Identifikatorer
urn:nbn:se:du-21183 (URN)10.1109/BHI.2016.7455921 (DOI)000381398000102 ()978-1-5090-2455-1 (ISBN)
Konferens
Biomedical and Health Informatics 2016, Las Vegas, 24-27 February
Tillgänglig från: 2016-03-01 Skapad: 2016-03-01 Senast uppdaterad: 2017-05-15Bibliografiskt granskad

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