Deficits in tapping accuracy and variability in tremor patients

Luft, Frauke; Sharifi, Sarvi; Mugge, Winfred; Schouten, Alfred C.; Bour, L.J.; Rootselaar, A.F. van; Veltink, Petrus H.; Heida, Tjitske

doi:10.1186/s12984-019-0528-6

Cited by 8 publications

(14 citation statements)

References 37 publications

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“…This study focused on motor symptoms of the upper limb using a flexible sensor on the hand, but other sensor placements may be relevant for monitoring global presentations of symptoms. Incorporating additional types of sensors may improve detection accuracy, such as utilizing EMG data in tremor classification [36]. Note that the optimal data collection strategy may vary for other symptoms of PD (e.g., freezing of gait, postural instability, muscle rigidity, dystonia).…”

Section: Discussionmentioning

confidence: 99%

Role of data measurement characteristics in the accurate detection of Parkinson’s disease symptoms using wearable sensors

Shawen

O’Brien

Venkatesan

et al. 2020

J NeuroEngineering Rehabil

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Background: Parkinson's disease (PD) is a progressive neurological disease, with characteristic motor symptoms such as tremor and bradykinesia. There is a growing interest to continuously monitor these and other symptoms through body-worn sensor technology. However, limited battery life and memory capacity hinder the potential for continuous, long-term monitoring with these devices. There is little information available on the relative value of adding sensors, increasing sampling rate, or computing complex signal features, all of which may improve accuracy of symptom detection at the expense of computational resources. Here we build on a previous study to investigate the relationship between data measurement characteristics and accuracy when using wearable sensor data to classify tremor and bradykinesia in patients with PD. Methods: Thirteen individuals with PD wore a flexible, skin-mounted sensor (collecting tri-axial accelerometer and gyroscope data) and a commercial smart watch (collecting tri-axial accelerometer data) on their predominantly affected hand. The participants performed a series of standardized motor tasks, during which a clinician scored the severity of tremor and bradykinesia in that limb. Machine learning models were trained on scored data to classify tremor and bradykinesia. Model performance was compared when using different types of sensors (accelerometer and/or gyroscope), different data sampling rates (up to 62.5 Hz), and different categories of pre-engineered features (up to 148 features). Performance was also compared between the flexible sensor and smart watch for each analysis. Results: First, there was no effect of device type for classifying tremor symptoms (p > 0.34), but bradykinesia models incorporating gyroscope data performed slightly better (up to 0.05 AUROC) than other models (p = 0.01). Second, model performance decreased with sampling frequency (p < 0.001) for tremor, but not bradykinesia (p > 0.47). Finally, model performance for both symptoms was maintained after substantially reducing the feature set.

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Section: Discussionmentioning

confidence: 99%

Role of data measurement characteristics in the accurate detection of Parkinson’s disease symptoms using wearable sensors

Shawen

O’Brien

Venkatesan

et al. 2020

J NeuroEngineering Rehabil

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“…The frontal area is mainly responsible for motor planning, organization and regulation and is involved in initiation, maintenance, coordination and planning of complex sequences of movements. In most cases the HC group had a significantly higher TRP in this region indicating that these areas are important for a high tapping accuracy [2]. In the posterior area, areas responsible for combining somatosensory, auditory and visual information and also areas that are involved in motor learning and bimanual manipulation are located.…”

Section: Intra-groups Analysis Healthy Controlsmentioning

confidence: 92%

“…The percentage of tremor presence during each task was determined for each subject by calculating the percentage of epochs in which kinetic tremor was detected. From the accelerometer data the tapping accuracy [2] was calculated for each task and tapping condition according to (4).…”

Section: Outcome Parametersmentioning

confidence: 99%

“…Several studies have investigated movement parameters and cortical and subcortical changes in patients and healthy controls during hand and finger movements. In a previous study [2] we showed that PD patients tap significantly less accurately during a 2 Hz tapping task and with a greater variability during a 4 Hz tapping task than ET patients and healthy controls (HC). Furthermore, ET patients tapped less accurately and with a greater variability than HC during a 4 Hz tapping task.…”

Section: Introductionmentioning

confidence: 96%

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Distinct cortical activity patterns in Parkinson’s disease and essential tremor during a bimanual tapping task

Luft

Sharifi

Mugge

et al. 2020

J NeuroEngineering Rehabil

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Background: Parkinson's disease (PD) and essential tremor (ET) are neurodegenerative diseases characterized by movement deficits. Especially in PD, maintaining cyclic movement can be significantly disturbed due to pathological changes in the basal ganglia and the cerebellum. Providing external cues improves timing of these movements in PD and also affects ET. The aim of this study is to determine differences in cortical activation patterns in PD and ET patients during externally and internally cued movements. Methods: Eleven PD patients, twelve ET patients, OFF tremor suppressing medication, and nineteen age-matched healthy controls (HC) were included and asked to perform a bimanual tapping task at two predefined cue frequencies. The auditory cue, a metronome sound presented at 2 or 4 Hz, was alternately switched on and off every 30 s. Tapping at two different frequencies were used since it is expected that different brain networks are involved at different frequencies as has been shown in previous studies. Cortical activity was recorded using a 64channel EEG cap. To establish the cortical activation pattern in each group, the task related power (TRP) was calculated for each subject. For inter-groups analysis, EEG electrodes for divided into 5 different areas. Results: Inter-group analysis revealed significant differences in areas responsible for motor planning, organization and regulation and involved in initiation, maintenance, coordination and planning of complex sequences of movements. Within the area of the primary motor cortex the ET group showed a significantly lower TRP than the HC group. In the area responsible for combining somatosensory, auditory and visual information both patient groups had a higher TRP than the HC group. Conclusions: Different neurological networks are involved during cued and non-cued movements in ET, PD and HC. Distinct cortical activation patterns were revealed using task related power calculations. Different activation patterns were revealed during the 2 and 4 Hz tapping task indicating different strategies to execute movements at these rates. The results suggest that a including a cued/non-cued tapping task during clinical decision making could be a valuable tool in an objective diagnostic protocol.

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“…Several studies have investigated movement parameters and cortical and subcortical changes in patients and healthy controls during hand and finger movements. In a previous study [89] we showed that PD patients tap significantly less accurately during a 2 Hz tapping task and with a greater variability during a 4 Hz tapping task than ET patients and healthy controls (HC). Furthermore, ET patients tapped less accurately and with a greater variability than HC during a 4 Hz tapping task.…”

Section: Introductionmentioning

confidence: 96%

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Luft¹

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Movement disorders, such as essential tremor (ET) and Parkinson's disease (PD), are disabling and lower the quality of life of the affected patients. Despite their pathological differences, PD and ET can be difficult to distinguish from each other, due to overlapping symptoms, such as tremor or deficits during motor task performance. Common diagnostic tools, such as polymyography, movement disorder rating scales or single photon emission computed tomography (SPECT) scans are either invasive (SPECT), time consuming, subjective (rating scales), expensive and/or not widely available. Therefore, this thesis focusses on finding objective parameters to differentiate PD from ET that can be measured with commonly available tools.The first objective, quantifying tremor occurrence in ET and PD subjects and identifying corresponding cortical activity, is the topic of chapters 2 and 3. The second objective, quantifying timing deficits of ET and PD subjects during voluntary movement under different conditions and identifying corresponding neuronal networks, is the topic of chapters 4 to 6.In chapters 2 through 5 movement was recorded using accelerometers and muscle activation by surface EMG electrodes. Cortical activity was recorded using EEG in chapters 3 and 5. In chapter 6 brain activation was also measured using functional magnetic resonance imaging (fMRI). Movement was recorded using 3D MRI compatible accelerometers.Chapter 2 describes a new objective quantitative method to split surface electromyography and 3D accelerometer data into tremor and non-tremor windows. Afterwards, the tremor stability index was determined to indicate the advantage of detecting tremor windows prior to analysis. Subjects performed a resting, postural and movement task. Data was split into threesecond windows and the power spectral density was calculated for each window. The relative power around the peak frequency with respect to the power in the tremor band was used to classify the windows as either tremor or non-tremor. The method yielded a specificity of 96%, sensitivity of 85% and accuracy of 91% of tremor classification. During tremor significant differences were found between groups in the tremor stability index. The results suggest that the introduced method could be used to determine

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Deficits in tapping accuracy and variability in tremor patients

Cited by 8 publications

References 37 publications

Role of data measurement characteristics in the accurate detection of Parkinson’s disease symptoms using wearable sensors

Role of data measurement characteristics in the accurate detection of Parkinson’s disease symptoms using wearable sensors

Distinct cortical activity patterns in Parkinson’s disease and essential tremor during a bimanual tapping task

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