Unbiased and Mobile Gait Analysis Detects Motor Impairment in Parkinson's Disease

<div><p>Motor impairments are the prerequisite for the diagnosis in Parkinson's disease (PD). The cardinal symptoms (bradykinesia, rigor, tremor, and postural instability) are used for disease staging and assessment of progression. They serve as primary outcome measures for clinical studies aiming at symptomatic and disease modifying interventions. One major caveat of clinical scores such as the Unified Parkinson Disease Rating Scale (UPDRS) or Hoehn&Yahr (H&Y) staging is its rater and time-of-assessment dependency. Thus, we aimed to objectively and automatically classify specific stages and motor signs in PD using a mobile, biosensor based Embedded Gait Analysis using Intelligent Technology (eGaIT). eGaIT consist of accelerometers and gyroscopes attached to shoes that record motion signals during standardized gait and leg function. From sensor signals 694 features were calculated and pattern recognition algorithms were applied to classify PD, H&Y stages, and motor signs correlating to the UPDRS-III motor score in a training cohort of 50 PD patients and 42 age matched controls. Classification results were confirmed in a second independent validation cohort (42 patients, 39 controls). eGaIT was able to successfully distinguish PD patients from controls with an overall classification rate of 81%. Classification accuracy increased with higher levels of motor impairment (91% for more severely affected patients) or more advanced stages of PD (91% for H&Y III patients compared to controls), supporting the PD-specific type of analysis by eGaIT. In addition, eGaIT was able to classify different H&Y stages, or different levels of motor impairment (UPDRS-III). In conclusion, eGaIT as an unbiased, mobile, and automated assessment tool is able to identify PD patients and characterize their motor impairment. It may serve as a complementary mean for the daily clinical workup and support therapeutic decisions throughout the course of the disease.</p> </div

Feature characteristics.

<p>Overview of individual features extracted from eGaIT based gait analysis. Feature were extracted from both shoes during defined tasks using raw sensor data from gyroscope (G) and/or accelerometer (A) using designated axes (for complete list of feature see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0056956#pone.0056956.s003" target="_blank">table S3</a>).</p

Classification within patient subgroups.

<p>Subgroups of PD patients defined by either <i>H&Y I, II, III</i>, or UPDRS-III based levels of motor impairment (<i>UPDRS low</i>: 0–12, <i>mild</i>: 13–22; <i>high</i>: 23–50) were classified by two additional pattern recognition algorithms (“A_H&Y, A_UDPRS”) and cross-validated from all PD patients. Best classification results were obtained with classifier listed, resulting in the algorithms using selected features from specific tasks (A:10-meter walk, B: heel-toe tapping, C: circling).</p

Single feature changes of gait characteristics in PD.

<p>Individual feature show significant differences between PD and controls (representative examples A, two-feature blots B), but groups overlap substantially (*: p≤0.001, T-test).</p

Single features derived from pattern recognition paradigms.

<p>Selected features from derived from pattern recognition algorithm “A_PD” that show the highest difference (p<0.00001) if tested for differences between PD patients and controls (Student's T-test). Only low sensitivity and specificity is reached by single feature classification (AdaBoost). Description of feature includes the task and the sensor type/axis.</p

Clinical characteristics of correct and false negative classified PD patients.

<p>Clinical characteristics of false negative classified PD patients at early stages (<i>H&Y I</i>) or only mild motor impairment (<i>UPDRS-low</i>; <12 UPDRS-III motor score) by the algorithm “A_PD” compared to correctly classified patients revealed age as significantly reduced in the false negative patient groups (Student T-test, *: p<0.05).</p

Classification of PD patients and controls.

<p>The pattern recognition algorithm “A_PD” identified an optimal classifier and feature combination to reveal balanced classification rates, sensitivity, specificity, and positive predictive value (PPV) for the experiment PD patients vs. controls using the AdaBoost classifier and cross-validation in the training sample. This algorithm was validated using an independent validation sample. Features used for this algorithm include feature no.: 2, 5, 6, 8, 10, 11, 12, 13, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29 derived from the 10-meter walk, heel-toe tapping, and circling test (for detailed description see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0056956#pone.0056956.s003" target="_blank">table S3</a>).</p

Characteristics of the study population.

*<p>: p<0.001 Student's T-test.</p