| 1. |
- Aghanavesi, Somayeh, 1981-, et al.
(författare)
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A multiple motion sensors index for motor state quantification in Parkinson's disease
- 2020
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Ingår i: Computer Methods and Programs in Biomedicine. - : Elsevier BV. - 0169-2607 .- 1872-7565. ; 189
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Tidskriftsartikel (refereegranskat)abstract
- 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. © 2019
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| 2. |
- Aghanavesi, Somayeh, 1981-, et al.
(författare)
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A review of Parkinson’s disease cardinal and dyskinetic motor symptoms assessment methods using sensor systems
- 2016
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 3. |
- Aghanavesi, Somayeh, et al.
(författare)
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Measuring temporal irregularity in spiral drawings of patients with Parkinson’s disease
- 2017
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Ingår i: Abstracts of the 21<sup>st</sup> International Congress of Parkinson's Disease and Movement Disorders. - : John Wiley & Sons. ; , s. s252-s252
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Konferensbidrag (populärvet., debatt m.m.)abstract
- Objective: The aim of this work is to evaluate clinimetric properties of a method for measuring Parkinson’s disease (PD) upper limb temporal irregularities during spiral drawing tasks.Background: Basal ganglia fluctuations of PD patients are associated with motor symptoms and relating them to objective sensor-based measures may facilitate the assessment of temporal irregularities, which could be difficult to be assessed visually. The present study investigated the upper limb temporal irregularity of patients at different stages of PD and medication time points.Methods: 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 the videos of patients' performance according to six items of UPDRS-III, dyskinesia (Dys), and Treatment Response Scale (TRS). A temporal irregularity score (TIS) was developed using approximate entropy (ApEn) method. Differences in mean TIS between two groups of patients and healthy subjects, and also across four subject groups: early, intermediate, advanced patients and, healthy subjects were assessed. The relative ability of TIS to detect changes from baseline (no medication) to later time points when patients were on medication was assessed. Correlations between TIS and clinical rating scales were assessed by Pearson correlation coefficients and test-retest reliability of TIS was measured by intra-class correlation coefficients (ICC).Results: The mean TIS was significantly different between healthy subjects and patients (P<0.0001). When assessing the changes in relation to treatment, clinical-based scores (TRS and Dys) had better responsiveness than TIS. However, the TIS was able to capture changes from Off to On, and the wearing off effects. Correlations between TIS and clinical scales were low indicating poor validity. Test-retest reliability correlation coefficient of the mean TIS was good (ICC=0.67).Conclusions: Our study found that TIS was able to differentiate spiral drawings drawn by patients from those drawn by healthy subjects. In addition, TIS could capture changes throughout the levodopa cycle.TIS was weakly correlated to clinical ratings indicating that TIS measures high frequency upper limb temporal irregularities that could be difficult to be detected during clinical observations.
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| 4. |
- Aghanavesi, Somayeh, 1981-
(författare)
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Smartphone-based Parkinson’s disease symptom assessment
- 2017
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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| 5. |
- Aghanavesi, Somayeh, 1981-, et al.
(författare)
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Verification of a Method for Measuring Parkinson's Disease Related Temporal Irregularity in Spiral Drawings
- 2017
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Ingår i: Sensors. - Basel : MDPI AG. - 1424-8220. ; 17:10
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Tidskriftsartikel (refereegranskat)abstract
- -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.
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| 6. |
- Ahmed, Mobyen Uddin, et al.
(författare)
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A fuzzy rule-based decision support system for Duodopa treatment in Parkinson
- 2006
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Ingår i: 23rd annual workshop of the Swedish Artificial Intelligence Society. - Umeå.
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Konferensbidrag (refereegranskat)abstract
- A decision support system (DSS) was implemented based on a fuzzy logic inference system (FIS) to provide assistance in dose alteration of Duodopa infusion in patients with advanced Parkinson’s disease, using data from motor state assessments and dosage. Three-tier architecture with an object oriented approach was used. The DSS has a web enabled graphical user interface that presents alerts indicating non optimal dosage and states, new recommendations, namely typical advice with typical dose and statistical measurements. One data set was used for design and tuning of the FIS and another data set was used for evaluating performance compared with actual given dose. Overall goodness-of-fit for the new patients (design data) was 0.65 and for the ongoing patients (evaluation data) 0.98. User evaluation is now ongoing. The system could work as an assistant to clinical staff for Duodopa treatment in advanced Parkinson’s disease.
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| 7. |
- Ashfaq, Awais, 1990-
(författare)
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Predicting clinical outcomes via machine learning on electronic health records
- 2019
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The rising complexity in healthcare, exacerbated by an ageing population, results in ineffective decision-making leading to detrimental effects on care quality and escalates care costs. Consequently, there is a need for smart decision support systems that can empower clinician's to make better informed care decisions. Decisions, which are not only based on general clinical knowledge and personal experience, but also rest on personalised and precise insights about future patient outcomes. A promising approach is to leverage the ongoing digitization of healthcare that generates unprecedented amounts of clinical data stored in Electronic Health Records (EHRs) and couple it with modern Machine Learning (ML) toolset for clinical decision support, and simultaneously, expand the evidence base of medicine. As promising as it sounds, assimilating complete clinical data that provides a rich perspective of the patient's health state comes with a multitude of data-science challenges that impede efficient learning of ML models. This thesis primarily focuses on learning comprehensive patient representations from EHRs. The key challenges of heterogeneity and temporality in EHR data are addressed using human-derived features appended to contextual embeddings of clinical concepts and Long-Short-Term-Memory networks, respectively. The developed models are empirically evaluated in the context of predicting adverse clinical outcomes such as mortality or hospital readmissions. We also present evidence that, surprisingly, different ML models primarily designed for non-EHR analysis (like language processing and time-series prediction) can be combined and adapted into a single framework to efficiently represent EHR data and predict patient outcomes.
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| 8. |
- Begum, Shahina, et al.
(författare)
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Induction of an Adaptive Neuro-Fuzzy Inference System for Investigating Fluctuation in Parkinson´s Disease : The 23rd Annual Workshop of the Swedish Artificial Intelligence Society Umeå, May 10-12, 2006
- 2006
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Ingår i: Proceedings of SAIS 2006. ; , s. 67-72
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Konferensbidrag (refereegranskat)abstract
- This paper presents a methodology to formulate natural language rules for an adaptive neuro-fuzzy system based on discovered knowledge, supported by prior knowledge and statistical modeling. These rules could be improved using statistical methods and neural nets. This gives clinicians a valuable tool to explore the importance of different variables and their relations in a disease and could aid treatment selection. A prototype using the proposed methodology has been used to induce an Adaptive Neuro Fuzzy Inference Model that has been used to "discover" relationships between fluctuation, treatment and disease severity in Parkinson. Preliminary results from this project are promising and show that Neuro-fuzzy techniques in combination with statistical methods may offer medical research and medical applications a useful combination of methods.
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| 9. |
- Begum, Shahina, et al.
(författare)
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Induction of an Adaptive Neuro-Fuzzy Inference System for investing fluctuation in Parkinson’s disease
- 2006
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Ingår i: 23rd annual workshop of the Swedish Artificial Intelligence Society. - Umeå.
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Konferensbidrag (refereegranskat)abstract
- This paper presents a methodology to formulate natural language rules for an adaptive neuro-fuzzy system based on discovered knowledge, supported by prior knowledge and statistical modeling. Relationships between disease related variables and fluctuations in Parkinson’s disease is often complex. Experts have simplified but mostly reliable “fuzzy” rules based on experience. These rules could be improved using statistical methods and neural nets. This gives clinicians a valuable tool to explore the importance of different variables and their relations in a disease and could aid treatment selection. A prototype using the proposed methodology has been used to induce an Adaptive Neuro Fuzzy Inference Model that has been used to “discover” relationships between fluctuation, treatment and disease severity. More data is needed to confirm these findings. The project shows that artificial intelligence techniques and methods in combination with statistical methods offer medical research and applications valuable opportunities.
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| 10. |
- Blom, Hans, et al.
(författare)
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Spatial Distribution of DARPP-32 in Dendritic Spines
- 2013
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Ingår i: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 8:9, s. e75155-
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Tidskriftsartikel (refereegranskat)abstract
- The phosphoprotein DARPP-32 (dopamine and cyclic adenosine 3́, 5́-monophosphate-regulated phosphoprotein, 32 kDa) is an important component in the molecular regulation of postsynaptic signaling in neostriatum. Despite the importance of this phosphoprotein, there is as yet little known about the nanoscale distribution of DARPP-32. In this study we applied superresolution stimulated emission depletion microscopy (STED) to assess the expression and distribution of DARPP-32 in striatal neurons. Primary culture of striatal neurons were immunofluorescently labeled for DARPP-32 with Alexa-594 and for the dopamine D1 receptor (D1R) with atto-647N. Dual-color STED microscopy revealed discrete localizations of DARPP-32 and D1R in the spine structure, with clustered distributions in both head and neck. Dissected spine structures reveal that the DARPP-32 signal rarely overlapped with the D1R signal. The D1R receptor is positioned in an "aggregated" manner primarily in the spine head and to some extent in the neck, while DARPP-32 forms several neighboring small nanoclusters spanning the whole spine structure. The DARPP-32 clusters have a mean size of 52 +/- 6 nm, which is close to the resolution limit of the microscope and corresponds to the physical size of a few individual phosphoprotein immunocomplexes. Dissection of synaptic proteins using superresolution microscopy gives possibilities to reveal in better detail biologically relevant information, as compared to diffraction-limited microscopy. In this work, the dissected postsynaptic topology of the DARPP-32 phosphoprotein provides strong evidence for a compartmentalized and confined distribution in dendritic spines. The protein topology and the relatively low copy number of phosphoprotein provides a conception of DARPP-32's possibilities to fine-tune the regulation of synaptic signaling, which should have an impact on the performance of the neuronal circuits in which it is expressed.
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