Computerized identification of motor complications in Parkinson's disease

Objective: To investigate whether spirography-based objective measures are able to effectively characterize the severity of unwanted symptom states (Off and dyskinesia) and discriminate them from motor state of healthy elderly subjects. Background: Sixty-five patients with advanced Parkinson’s disease (PD) and 10 healthy elderly (HE) subjects performed repeated assessments of spirography, using a touch screen telemetry device in their home environments. On inclusion, the patients were either treated with levodopa-carbidopa intestinal gel or were candidates for switching to this treatment. On each test occasion, the subjects were asked trace a pre-drawn Archimedes spiral shown on the screen, using an ergonomic pen stylus. The test was repeated three times and was performed using dominant hand. A clinician used a web interface which animated the spiral drawings, allowing him to observe different kinematic features, like accelerations and spatial changes, during the drawing process and to rate different motor impairments. Initially, the motor impairments of drawing speed, irregularity and hesitation were rated on a 0 (normal) to 4 (extremely severe) scales followed by marking the momentary motor state of the patient into 2 categories that is Off and Dyskinesia. A sample of spirals drawn by HE subjects was randomly selected and used in subsequent analysis. Methods: The raw spiral data, consisting of stylus position and timestamp, were processed using time series analysis techniques like discrete wavelet transform, approximate entropy and dynamic time warping in order to extract 13 quantitative measures for representing meaningful motor impairment information. A principal component analysis (PCA) was used to reduce the dimensions of the quantitative measures into 4 principal components (PC). In order to classify the motor states into 3 categories that is Off, HE and dyskinesia, a logistic regression model was used as a classifier to map the 4 PCs to the corresponding clinically assigned motor state categories. A stratified 10-fold cross-validation (also known as rotation estimation) was applied to assess the generalization ability of the logistic regression classifier to future independent data sets. To investigate mean differences of the 4 PCs across the three categories, a one-way ANOVA test followed by Tukey multiple comparisons was used. Results: The agreements between computed and clinician ratings were very good with a weighted area under the receiver operating characteristic curve (AUC) coefficient of 0.91. The mean PC scores were different across the three motor state categories, only at different levels. The first 2 PCs were good at discriminating between the motor states whereas the PC3 was good at discriminating between HE subjects and PD patients. The mean scores of PC4 showed a trend across the three states but without significant differences. The Spearman’s rank correlations between the first 2 PCs and clinically assessed motor impairments were as follows: drawing speed (PC1, 0.34; PC2, 0.83), irregularity (PC1, 0.17; PC2, 0.17), and hesitation (PC1, 0.27; PC2, 0.77). Conclusions: These findings suggest that spirography-based objective measures are valid measures of spatial- and time-dependent deficits and can be used to distinguish drug-related motor dysfunctions between Off and dyskinesia in PD. These measures can be potentially useful during clinical evaluation of individualized drug-related complications such as over- and under-medications thus maximizing the amount of time the patients spend in the On state.

Spiral drawing during self-rated dyskinesia is more impaired than during self-rated off

Background: A mobile device test battery, consisting of a patient diary collection section with disease-related questions and a fine motor test section (including spiral drawing tasks), was used by 65 patients with advanced Parkinson's disease (PD)(treated with intraduodenal levodopa/carbidopa gel infusion, Duodopa®, or candidates for this treatment) on 10439 test occasions in their home environments. On each occasion, patients traced three pre-drawn Archimedes spirals using an ergonomic stylus and self-assessed their motor function on a global Treatment Response Scale (TRS) ranging from -3 = very 'off' to 0 = 'on' to +3 = very dyskinetic. The spirals were processed by a computer-based method that generates a "spiral score" representing the PD-related drawing impairment. The scale for the score was based on a modified Bain & Findley rating scale in the range from 0 = no impairment to 5 = moderate impairment to 10 = extremely severe impairment. Objective: To analyze the test battery data for the purpose to find differences in spiral drawing performance of PD patients in relation to their self-assessments of motor function. Methods: Three motor states were used in the analysis; OFF state (including moderate and very 'off'), ON state ('on') and a dyskinetic (DYS) state (moderate and very dyskinetic). In order to avoid the problem of multiple test occasions per patient, 200 random samples of single test occasions per patient were drawn. One-way analysis of variance, ANOVA, test followed by Tukey multiple comparisons test was used to test if mean values of spiral test parameters, i.e. the spiral score and drawing completion times (in seconds), were different among the three motor states. Statistical significance was set at p<0.05. To investigate changes in the spiral score over the time-of-day test sessions for the three motor states, plots of statistical summaries were inspected. Results: The mean spiral score differed significantly across the three self-assessed motor states (p<0.001, ANOVA test). Tukey post-hoc comparisons indicate that the mean spiral score (mean ± SD; [95% CI for mean]) in DYS state (5.2 ± 1.8; [5.12, 5.28]) was higher than the mean spiral score in OFF (4.3 ± 1.7; [4.22, 4.37]) and ON (4.2 ± 1.7; [4.17, 4.29]) states. The mean spiral score was also significantly different among individual TRS values of slightly 'off' (4.02 ± 1.63), 'on' (4.07 ± 1.65) and slightly dyskinetic (4.6 ± 1.71), (p<0.001). There were no differences in drawing completion times among the three motor states (p=0.509). In the OFF and ON states, patients drew slightly more impaired spirals in the afternoon whereas in the DYS state the spiral drawing performance was more impaired in the morning. Conclusion: It was found that when patients considered themselves as being dyskinetic spiral drawing was more impaired (nearly one unit change in a 0-10 scale) compared to when they considered themselves as being 'off' and 'on'. The spiral drawing at patients that self-assessed their motor state as dyskinetic was slightly more impaired in the morning hours, between 8 and 12 o'clock, a situation possibly caused by the morning dose effect.

Digital spiral analysis for objective assessment of fine motor timing variability in Parkinson's disease

OBJECTIVES: To develop a method for objective assessment of fine motor timing variability in Parkinson’s disease (PD) patients, using digital spiral data gathered by a touch screen device. BACKGROUND: A retrospective analysis was conducted on data from 105 subjects including65 patients with advanced PD (group A), 15 intermediate patients experiencing motor fluctuations (group I), 15 early stage patients (group S), and 10 healthy elderly subjects (HE) were examined. The subjects were asked to perform repeated upper limb motor tasks by tracing a pre-drawn Archimedes spiral as shown on the screen of the device. The spiral tracing test was performed using an ergonomic pen stylus, using dominant hand. The test was repeated three times per test occasion and the subjects were instructed to complete it within 10 seconds. Digital spiral data including stylus position (x-ycoordinates) and timestamps (milliseconds) were collected and used in subsequent analysis. The total number of observations with the test battery were as follows: Swedish group (n=10079), Italian I group (n=822), Italian S group (n = 811), and HE (n=299). METHODS: The raw spiral data were processed with three data processing methods. To quantify motor timing variability during spiral drawing tasks Approximate Entropy (APEN) method was applied on digitized spiral data. APEN is designed to capture the amount of irregularity or complexity in time series. APEN requires determination of two parameters, namely, the window size and similarity measure. In our work and after experimentation, window size was set to 4 and similarity measure to 0.2 (20% of the standard deviation of the time series). The final score obtained by APEN was normalized by total drawing completion time and used in subsequent analysis. The score generated by this method is hence on denoted APEN. In addition, two more methods were applied on digital spiral data and their scores were used in subsequent analysis. The first method was based on Digital Wavelet Transform and Principal Component Analysis and generated a score representing spiral drawing impairment. The score generated by this method is hence on denoted WAV. The second method was based on standard deviation of frequency filtered drawing velocity. The score generated by this method is hence on denoted SDDV. Linear mixed-effects (LME) models were used to evaluate mean differences of the spiral scores of the three methods across the four subject groups. Test-retest reliability of the three scores was assessed after taking mean of the three possible correlations (Spearman’s rank coefficients) between the three test trials. Internal consistency of the methods was assessed by calculating correlations between their scores. RESULTS: When comparing mean spiral scores between the four subject groups, the APEN scores were different between HE subjects and three patient groups (P=0.626 for S group with 9.9% mean value difference, P=0.089 for I group with 30.2%, and P=0.0019 for A group with 44.1%). However, there were no significant differences in mean scores of the other two methods, except for the WAV between the HE and A groups (P<0.001). WAV and SDDV were highly and significantly correlated to each other with a coefficient of 0.69. However, APEN was not correlated to neither WAV nor SDDV with coefficients of 0.11 and 0.12, respectively. Test-retest reliability coefficients of the three scores were as follows: APEN (0.9), WAV(0.83) and SD-DV (0.55). CONCLUSIONS: The results show that the digital spiral analysis-based objective APEN measure is able to significantly differentiate the healthy subjects from patients at advanced level. In contrast to the other two methods (WAV and SDDV) that are designed to quantify dyskinesias (over-medications), this method can be useful for characterizing Off symptoms in PD. The APEN was not correlated to none of the other two methods indicating that it measures a different construct of upper limb motor function in PD patients than WAV and SDDV. The APEN also had a better test-retest reliability indicating that it is more stable and consistent over time than WAV and SDDV.