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      Automated Stage Discrimination of Parkinson’s Disease

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          Background: Treatment plans for Parkinson’s disease (PD) are based on a disease stage scale, which is generally determined using a manual, observational procedure. Automated, sensor-based discrimination saves labor and costs in clinical settings and may offer augmented stage determination accuracy. Previous automated devices were either cumbersome or costly and were not suitable for individuals who cannot walk without support.

          Methods: Since 2017, a device has been available that successfully detects PD and operates for people who cannot walk without support. In the present study, the suitability of this device for automated discrimination of PD stages was tested. The device consists of a walking frame fitted with sensors to simultaneously support walking and monitor patient gait. Sixty-five PD patients in Hoehn and Yahr (HY) stages 1 to 4 and 24 healthy controls were subjected to supported Timed Up and Go (TUG) tests, while using the walking frame. The walking trajectory, velocity, acceleration and force were recorded by the device throughout the tests. These physical parameters were converted into symptomatic spatiotemporal quantities that are conventionally used in PD gait assessment.

          Results: An analysis of variance (ANOVA) test extended by a confidence interval (CI) analysis indicated statistically significant separability between HY stages for the following spatiotemporal quantities: TUG time (p < 0.001), straight line walking time (p < 0.001), turning time (p < 0.001), and step count (p < 0.001). A negative correlation was obtained for mean step velocity (p < 0.001) and mean step length (p < 0.001). Moreover, correlations were established between these, as well as additional spatiotemporal quantities, and disease duration, L-dihydroxyphenylalanine-(L-DOPA) dose, motor fluctuation, dyskinesia and the mobile part of the Unified Parkinson Disease Rating Scale (UPDRS).

          Conclusions: We have proven that stage discrimination of PD can be automated, even to patients who cannot support themselves. A similar method might be successfully applied to other gait disorders.

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          Most cited references 24

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          Gait assessment in Parkinson's disease: toward an ambulatory system for long-term monitoring.

          An ambulatory gait analysis method using body-attached gyroscopes to estimate spatio-temporal parameters of gait has been proposed and validated against a reference system for normal and pathologic gait. Later, ten Parkinson's disease (PD) patients with subthalamic nucleus deep brain stimulation (STN-DBS) implantation participated in gait measurements using our device. They walked one to three times on a 20-m walkway. Patients did the test twice: once STN-DBS was ON and once 180 min after turning it OFF. A group of ten age-matched normal subjects were also measured as controls. For each gait cycle, spatio-temporal parameters such as stride length (SL), stride velocity (SV), stance (ST), double support (DS), and gait cycle time (GC) were calculated. We found that PD patients had significantly different gait parameters comparing to controls. They had 52% less SV, 60% less SL, and 40% longer GC. Also they had significantly longer ST and DS (11% and 59% more, respectively) than controls. STN-DBS significantly improved gait parameters. During the stim ON period, PD patients had 31% faster SV, 26% longer SL, 6% shorter ST, and 26% shorter DS. GC, however, was not significantly different. Some of the gait parameters had high correlation with Unified Parkinson's Disease Rating Scale (UPDRS) subscores including SL with a significant correlation (r = -0.90) with UPDRS gait subscore. We concluded that our method provides a simple yet effective way of ambulatory gait analysis in PD patients with results confirming those obtained from much more complex and expensive methods used in gait labs.
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            Inference by eye: reading the overlap of independent confidence intervals.

             Geoff Cumming (2009)
            When 95 per cent confidence intervals (CIs) on independent means do not overlap, the two-tailed p-value is less than 0.05 and there is a statistically significant difference between the means. However, p for non-overlapping 95 per cent CIs is actually considerably smaller than 0.05: If the two CIs just touch, p is about 0.01, and the intervals can overlap by as much as about half the length of one CI arm before p becomes as large as 0.05. Keeping in mind this rule-that overlap of half the length of one arm corresponds approximately to statistical significance at p = 0.05-can be helpful for a quick appreciation of figures that display CIs, especially if precise p-values are not reported. The author investigated the robustness of this and similar rules, and found them sufficiently accurate when sample sizes are at least 10, and the two intervals do not differ in width by more than a factor of 2. The author reviewed previous discussions of CI overlap and extended the investigation to p-values other than 0.05 and 0.01. He also studied 95 per cent CIs on two proportions, and on two Pearson correlations, and found similar rules apply to overlap of these asymmetric CIs, for a very broad range of cases. Wider use of figures with 95 per cent CIs is desirable, and these rules may assist easy and appropriate understanding of such figures. Copyright (c) 2008 John Wiley & Sons, Ltd.
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              Dyskinesias and motor fluctuations in Parkinson's disease. A community-based study.

              We investigated the prevalence of dyskinesias and motor fluctuations, and the factors determining their occurrence, in a community-based population of patients with Parkinson's disease. Among 124 patients with Parkinson's disease, 87 (70%) had received a levodopa preparation. Among these 87 patients, 28% were experiencing treatment-induced dyskinesias and 40% response fluctuations. The prevalence of motor fluctuations was best predicted by disease duration and dose of levodopa, whereas dyskinesias could be best predicted by duration of treatment. Patients with a shorter time from symptom onset to initiation of levodopa and younger patients had developed motor complications earlier, and patients who had started treatment with a dopamine agonist had developed these treatment complications later. Although a satisfactory response to medication was associated with higher rates of motor complications, poor or moderate response was associated with lower quality of life in patients with a disease duration of /=10 years. We conclude that motor fluctuations are most strongly related to disease duration and dose of levodopa, and dyskinesias to duration of levodopa treatment. However, poorer quality of life associated with inadequate dosage of levodopa may be the price for a low rate of motor complications in patients with Parkinson's disease.

                Author and article information

                BIO Integration
                Compuscript (Ireland )
                01 September 2020
                24 June 2020
                : 1
                : 2
                : 55-63
                1School of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg, South Africa
                2Department of Medical Engineering, Afeka, Tel-Aviv Academic College of Engineering, Israel
                3The Movement Disorders Institute, Department of Neurology and Sagol Neuroscience Center, Chaim Sheba Medical Center, Tel-Hashomer, Israel
                4Sackler Faculty of Medicine, Tel Aviv University, Israel
                Author notes
                Correspondence to: Prof. Vered Aharonson, School of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg, South Africa. E-mail: vered.aharonson@ 123456wits.ac.za
                Copyright © 2020 Bio Integration

                This is an open access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/). See https://bio-integration.org/copyright-and-permissions/

                Self URI (journal-page): https://bio-integration.org/
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