A Heterogeneous Sensing Suite for Multisymptom Quantification of Parkinson’s Disease

Huo, Weiguang; Angeles, Paolo; Tai, Yen F.; Pavese, Nicola; Wilson, Samuel; Hu, Michèle; Vaidyanathan, Ravi

doi:10.1109/tnsre.2020.2978197

Cited by 47 publications

(48 citation statements)

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“…This indicates that our method of motor dysfunction assessment can approximate the severity of a patient's condition when compared to the standardized assessments of trained clinicians. While other tools developed for quantification of motor dysfunction correlate more strongly with MDS-UPDRS-III ( 37 ), we did not specifically design our task to optimize this relationship. Rather, our goal was to achieve maximal differentiation between normal and abnormal goal-directed movement.…”

Section: Discussionmentioning

confidence: 99%

Multi-Dimensional, Short-Timescale Quantification of Parkinson's Disease and Essential Tremor Motor Dysfunction

Sanderson

Liu

et al. 2020

Front. Neurol.

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Introduction: Parkinson's disease (PD) is a progressive movement disorder characterized by heterogenous motor dysfunction with fluctuations in severity. Objective, short-timescale characterization of this dysfunction is necessary as therapies become increasingly adaptive. Objectives: This study aims to characterize a novel, naturalistic, and goal-directed tablet-based task and complementary analysis protocol designed to characterize the motor features of PD. Methods: A total of 26 patients with PD and without deep brain stimulation (DBS), 20 control subjects, and eight patients with PD and with DBS completed the task. Eight metrics, each designed to capture an aspect of motor dysfunction in PD, were calculated from 1-second, non-overlapping epochs of the raw positional and pressure data captured during task completion. These metrics were used to generate a classifier using a support vector machine (SVM) model to produce a unifying, scalar "motor error score" (MES). The data generated from these patients with PD were compared to same-day standard clinical assessments. Additionally, these data were compared to analogous data generated from a separate group of 12 patients with essential tremor (ET) to assess the task's specificity for different movement disorders. Finally, an SVM model was generated for each of the eight patients with PD and with DBS to differentiate between their motor dysfunction in the "DBS On" and "DBS Off" stimulation states. Results: The eight metrics calculated from the raw positional and force data captured during task completion were non-redundant. MES generated by the SVM analysis protocol showed a strong correlation with MDS-UPDRS-III scores assigned by movement disorder specialists. Analysis of the relative contributions of each of the eight metrics showed a significant difference between the motor dysfunction of PD and ET. Much of this difference was attributable to the homogenous, tremor-dominant phenotype Sanderson et al. Quantifying Movement Disorders on a Tablet of ET motor dysfunction. Finally, in individual patients with PD with DBS, task performance and subsequent SVM classification effectively differentiated between the "DBS On" and "DBS Off" stimulation states. Conclusion: This tablet-based task and analysis protocol correlated strongly with expert clinical assessments of PD motor dysfunction. Additionally, the task showed specificity for PD when compared to ET, another common movement disorder. This specificity was driven by the relative heterogeneity of motor dysfunction of PD compared to ET. Finally, the task was able to distinguish between the "DBS On" and "DBS Off" states within single patients with PD. This task provides temporally-precise and specific information about motor dysfunction in at least two movement disorders that could feasibly correlate to neural activity.

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Section: Discussionmentioning

confidence: 99%

Multi-Dimensional, Short-Timescale Quantification of Parkinson's Disease and Essential Tremor Motor Dysfunction

Sanderson

Liu

et al. 2020

Front. Neurol.

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“…We are also extending this work in a similar vein for Parkinson's disease (PD), where a neurologist uses movement to assess patient state and prescribe medication dose [39], [40] An HPE system could automate this process, however, existing systems only use acceleration data due to challenges in fusing data for full orientation, which our system overcomes. Serg Technologies 2 is leading this work to commercialize a home TH PD system.…”

Section: Implementation: Wearable Human Motion Tracking Rehabilitmentioning

confidence: 99%

An Extended Complementary Filter for Full-Body MARG Orientation Estimation

Madgwick¹,

Wilson

Turk

et al. 2020

IEEE/ASME Trans. Mechatron.

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Inertial sensing suites now permeate all forms of smart automation, yet a plateau exists in the real-world derivation of global orientation. Magnetic field fluctuations and inefficient sensor fusion still inhibit deployment. In this article, we introduce a new algorithm, an extended complementary filter (ECF), to derive 3-D rigid body orientation from inertial sensing suites addressing these challenges. The ECF combines computational efficiency of classic complementary filters with improved accuracy compared to popular optimization filters. We present a complete formulation of the algorithm, including an extension to address the challenge of orientation accuracy in the presence of fluctuating magnetic fields. Performance is tested under a variety of conditions and benchmarked against the commonly used gradient decent inertial sensor fusion algorithm. Results demonstrate improved efficiency, with the ECF achieving convergence 30% faster than standard alternatives. We further demonstrate an improved robustness to sources of magnetic interference in pitch and roll and to fast changes of orientation in the yaw direction. The ECF has been implemented at the core of a wearable rehabilitation system tracking movement of stroke patients for home telehealth. The ECF and accompanying magnetic disturbance rejection algorithm enables previously unachievable real-time patient movement feedback in the form of a full virtual human (avatar), even in the presence of magnetic Manuscript

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“…Several studies have deciphered motor classifiers by comparing certain motor response differences between healthy and Parkinson's subjects [37][38][39][40]. Kuhner et al [37,38] used RF algorithms to stratify 14 PD patients with (DBS-ON) or without (DBS-OFF) stimulation from 26 healthy subjects using a variety of motor control tasks.…”

Section: Predictive Motor Biomarkersmentioning

confidence: 99%

“…Such classifying features could then be applied to a large-scale study of gait analysis. Similarly, Huo et al [39] developed a unique multi-sensor motor response test and utilized KNN to classify 33 subjects as either having PD or being healthy with a 96.6% accuracy.…”

Section: Predictive Motor Biomarkersmentioning

confidence: 99%

Machine Learning’s Application in Deep Brain Stimulation for Parkinson’s Disease: A Review

et al. 2020

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Deep brain stimulation (DBS) is a surgical treatment for advanced Parkinson’s disease (PD) that has undergone technological evolution that parallels an expansion in clinical phenotyping, neurophysiology, and neuroimaging of the disease state. Machine learning (ML) has been successfully used in a wide range of healthcare problems, including DBS. As computational power increases and more data become available, the application of ML in DBS is expected to grow. We review the literature of ML in DBS and discuss future opportunities for such applications. Specifically, we perform a comprehensive review of the literature from PubMed, the Institute for Scientific Information’s Web of Science, Cochrane Database of Systematic Reviews, and Institute of Electrical and Electronics Engineers’ (IEEE) Xplore Digital Library for ML applications in DBS. These studies are broadly placed in the following categories: (1) DBS candidate selection; (2) programming optimization; (3) surgical targeting; and (4) insights into DBS mechanisms. For each category, we provide and contextualize the current body of research and discuss potential future directions for the application of ML in DBS.

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A Heterogeneous Sensing Suite for Multisymptom Quantification of Parkinson’s Disease

Cited by 47 publications

References 50 publications

Multi-Dimensional, Short-Timescale Quantification of Parkinson's Disease and Essential Tremor Motor Dysfunction

Multi-Dimensional, Short-Timescale Quantification of Parkinson's Disease and Essential Tremor Motor Dysfunction

An Extended Complementary Filter for Full-Body MARG Orientation Estimation

Machine Learning’s Application in Deep Brain Stimulation for Parkinson’s Disease: A Review

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