A Novel Glove Monitoring System Used to Quantify Neurological Symptoms During Deep-Brain Stimulation Surgery

Quantitative assessment of parkinsonian tremor based on inertial sensors can provide reliable feedback on the effect of medication. In this regard, the features of parkinsonian tremor and its unique properties such as motor fluctuations and dyskinesia are taken into account. Least-square-estimation models are used to assess the severities of rest, postural, and action tremors. In addition, a time-frequency signal analysis algorithm for tremor state detection was also included in the tremor assessment method. This inertial sensor-based method was verified through comparison with an electromagnetic motion tracking system. Seven Parkinson’s disease (PD) patients were tested using this tremor assessment system. The measured tremor amplitudes correlated well with the judgments of a neurologist (r = 0.98). The systematic analysis of sensor-based tremor quantification and the corresponding experiments could be of great help in monitoring the severity of parkinsonian tremor.

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“…A portable parkinsonian tremor assessment system was used in this study [ 14 , 15 , 16 , 17 ]. Its system diagram is shown in Figure 2 .…”

Section: Quantitative Assessment Of Parkinsonian Tremormentioning

confidence: 99%

“…To quantify neurological symptoms during deep-brain surgery, we have presented a wearable system for tremor quantitative assessment [ 14 , 15 , 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

Quantitative Assessment of Parkinsonian Tremor Based on an Inertial Measurement Unit

Dai

Zhang

Lueth

2015

Sensors

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126

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“…In order to capture patterns of movements and objectively be able to analyze PD motor behaviors, wearable devices with built-in sensors are presented in several studies [2, 4, 5, 34–37]. Most of the devices consist of wearable systems containing inertial sensors, such as gyroscopes and accelerometers and others use electromyographic sensors in order to collect electrical activity from the muscles of the affected limb.…”

Section: Introductionmentioning

confidence: 99%

Feature visualization and classification for the discrimination between individuals with Parkinson’s disease under levodopa and DBS treatments

et al. 2016

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BackgroundOver the years, a number of distinct treatments have been adopted for the management of the motor symptoms of Parkinson’s disease (PD), including pharmacologic therapies and deep brain stimulation (DBS). Efficacy is most often evaluated by subjective assessments, which are prone to error and dependent on the experience of the examiner. Our goal was to identify an objective means of assessing response to therapy.MethodsIn this study, we employed objective analyses in order to visualize and identify differences between three groups: healthy control (N = 10), subjects with PD treated with DBS (N = 12), and subjects with PD treated with levodopa (N = 16). Subjects were assessed during execution of three dynamic tasks (finger taps, finger to nose, supination and pronation) and a static task (extended arm with no active movement). Measurements were acquired with two pairs of inertial and electromyographic sensors. Feature extraction was applied to estimate the relevant information from the data after which the high-dimensional feature space was reduced to a two-dimensional space using the nonlinear Sammon’s map. Non-parametric analysis of variance was employed for the verification of relevant statistical differences among the groups (p < 0.05). In addition, K-fold cross-validation for discriminant analysis based on Gaussian Finite Mixture Modeling was employed for data classification.ResultsThe results showed visual and statistical differences for all groups and conditions (i.e., static and dynamic tasks). The employed methods were successful for the discrimination of the groups. Classification accuracy was 81 ± 6% (mean ± standard deviation) and 71 ± 8%, for training and test groups respectively.ConclusionsThis research showed the discrimination between healthy and diseased groups conditions. The methods were also able to discriminate individuals with PD treated with DBS and levodopa. These methods enable objective characterization and visualization of features extracted from inertial and electromyographic sensors for different groups.

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“… As there are no previous studies about the correlation between age and the quantitative parameters of parkinsonian bradykinesia, the repeatability of bradykinesia parameters and effects of the subject’s age and physical status (both mental and physical factors) on the bradykinesia parameters should be investigated [ 28 ]. Effects of PD’s other symptoms, such as tremor, akinesia, and motor fluctuations [ 29 , 30 ], on the bradykinesia parameters. Correlations between the features were taken into consideration together with the clinical evaluations of bradykinesia provided by several neurologists, in order to assess the robustness of the method proposed.…”

Section: Discussionmentioning

confidence: 99%

“…Effects of PD’s other symptoms, such as tremor, akinesia, and motor fluctuations [ 29 , 30 ], on the bradykinesia parameters.…”

Section: Discussionmentioning

confidence: 99%

Quantitative assessment of parkinsonian bradykinesia based on an inertial measurement unit

2015

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BackgroundAs the most characteristic feature of Parkinson’s disease (PD), bradykinesia (slowness of movement) affects all patients with Parkinson’s disease and interferes with their daily activities. This study introduces a wearable bradykinesia assessment system whose core component is composed of an inertial measurement unit.MethodsThe system diagram and assessment task were defined in accordance with clinical requirements from neurologists. Based on hand grasping actions, calculations of hand grasping ranges and statistical methods of quantitatively assessing parkinsonian bradykinesia were presented. Seven control subjects and eight patients were tested with this system.ResultsExperimental results show that a calculated bradykinesia parameter (modified mean range, instead of mean and standard deviation of the grasp ranges) correlated well with the evaluations of a neurologist (Pearson’s correlation coefficient r = −0.83, p < 0.001).ConclusionsThe bradykinesia assessment system was tested on both health subjects and PD patients. The results show that this system has greater correlation with the evaluations by neurologists than other parkinsonian bradykinesia assessment systems. The modified mean range was verified as the major bradykinesia parameter (key indicator). This study is helpful to those who want to use consumer-grade inertial sensors for quantitative assessment of motor symptoms during treatment.

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A Novel Glove Monitoring System Used to Quantify Neurological Symptoms During Deep-Brain Stimulation Surgery

Cited by 29 publications

References 31 publications

Quantitative Assessment of Parkinsonian Tremor Based on an Inertial Measurement Unit

Quantitative Assessment of Parkinsonian Tremor Based on an Inertial Measurement Unit

Feature visualization and classification for the discrimination between individuals with Parkinson’s disease under levodopa and DBS treatments

Quantitative assessment of parkinsonian bradykinesia based on an inertial measurement unit

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