Task-Specific Tremor Quantification in a Clinical Setting for Parkinson’s Disease

Andrade, Adriano O.; Paixão, Ana Paula Sousa; Cabral, Ariana Moura; Rabelo, Amanda Gomes; Luiz, Luiza Maire David; Dionisio, Valdeci Carlos; Vieira, Marcus Fraga; Pereira, Janser Moura; Rueda, Alice; Krishnan, Sridhar; Pereira, Adriano Alves

doi:10.1007/s40846-020-00576-x

Cited by 13 publications

(17 citation statements)

References 43 publications

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“…Some efficient and accessible non-invasive biomarkers such as tremor signals including tremor acceleration and surface electromyogram (sEMG) have been investigated for the differentiation between PD and ET ( Meigal et al, 2013 ; Barrantes et al, 2017 ). And a series of statistical characteristics of tremor signals including the dominant frequency and peak value were extracted and studied for distinguishing PD and ET ( Hossen et al, 2010 ; Thanawattano et al, 2015 ; De Oliveira Andrade et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Identification and Classification of Parkinsonian and Essential Tremors for Diagnosis Using Machine Learning Algorithms

Xing

Luo

et al. 2022

Front. Neurosci.

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Due to overlapping tremor features, the medical diagnosis of Parkinson’s disease (PD) and essential tremor (ET) mainly relies on the clinical experience of doctors, which often leads to misdiagnosis. Seven predictive models using machine learning algorithms including random forest (RF), eXtreme Gradient Boosting (XGBoost), support vector machine (SVM), logistic regression (LR), ridge classification (Ridge), backpropagation neural network (BP), and convolutional neural network (CNN) were evaluated and compared aiming to better differentiate between PD and ET by using accessible demographics and tremor information of the upper limbs. The tremor information including tremor acceleration and surface electromyogram (sEMG) signals were collected from 398 patients (PD = 257, ET = 141) and then were used to train the established models to separate PD and ET. The performance of the models was evaluated by indices of accuracy and area under the curve (AUC), which indicated the ensemble learning models including RF and XGBoost showed the best overall predictive ability with accuracy above 0.84 and AUC above 0.90. Furthermore, the relative importance of sex, age, four postures, and five tremor features was analyzed and ranked showing that the dominant frequency of sEMG of flexors, the average amplitude of sEMG of flexors, resting posture, and winging posture had a greater impact on the diagnosis of PD, whereas sex and age were less important. These results provide a reference for the intelligent diagnosis of PD and show promise for use in wearable tremor suppression devices.

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Section: Introductionmentioning

confidence: 99%

Identification and Classification of Parkinsonian and Essential Tremors for Diagnosis Using Machine Learning Algorithms

Xing

Luo

et al. 2022

Front. Neurosci.

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Section: Discussionmentioning

confidence: 99%

“…For objective quantification of PD severity, it is important to determine the best locations of these devices for capturing the subtleties of the symptoms. Multiple methods have been used to investigate the severity of PD using accelerometers [12,15,36] and have been able to differentiate between PD and non-PD patients [37,38]. To the best of our knowledge, these investigations have not compared different regions within the hand, including the wrist and finger joints, to find maximal tremor locations.…”

Section: Discussionmentioning

confidence: 99%

Non-Contact Hand Movement Analysis for Optimal Configuration of Smart Sensors to Capture Parkinson’s Disease Hand Tremor

Khwaounjoo

Singh

Grenfell

et al. 2022

Sensors

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Parkinson’s disease affects millions worldwide with a large rise in expected burden over the coming decades. More easily accessible tools and techniques to diagnose and monitor Parkinson’s disease can improve the quality of life of patients. With the advent of new wearable technologies such as smart rings and watches, this is within reach. However, it is unclear what method for these new technologies may provide the best opportunity to capture the patient-specific severity. This study investigates which locations on the hand can be used to capture and monitor maximal movement/tremor severity. Using a Leap Motion device and custom-made software the volume, velocity, acceleration, and frequency of Parkinson’s (n = 55, all right-handed, majority right-sided onset) patients’ hand locations (25 joints inclusive of all fingers/thumb and the wrist) were captured simultaneously. Distal locations of the right hand, i.e., the ends of fingers and the wrist showed significant trends (p < 0.05) towards having the largest movement velocities and accelerations. The right hand, compared with the left hand, showed significantly greater volumes, velocities, and accelerations (p < 0.01). Supplementary analysis showed that the volumes, acceleration, and velocities had significant correlations (p < 0.001) with clinical MDS-UPDRS scores, indicating the potential suitability of using these metrics for monitoring disease progression. Maximal movements at the distal hand and wrist area indicate that these locations are best suited to capture hand tremor movements and monitor Parkinson’s disease.

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“…The Spearman’s correlation coefficient between variables was estimated to determine whether the proposed model is consistent. The correlation coefficient was classified into four categories: very weak (0.00–0.19), weak (0.20–0.39), moderate (0.40–0.59), strong (0.60–0.79), and very strong (0.80–1.00) [ 22 ].…”

Section: Methodsmentioning

confidence: 99%

Multidimensional Assessment of Individuals with Parkinson’s Disease: Development and Structure Validation of a Self-Assessment Questionnaire

et al. 2022

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(1) Background: Several instruments are used to assess individuals with Parkinson’s disease (PD). However, most instruments necessitate the physical presence of a clinician for evaluation, were not designed for PD, nor validated for remote application. (2) Objectives: To develop and validate a self-assessment questionnaire that can be used remotely, and to assess the respondents’ health condition. (3) Methods: A questionnaire, so-called Multidimensional Assessment Questionnaire for Individuals with PD (MAQPD), was developed, administered remotely, and completed by 302 people with PD. MAQPD was validated using factor analysis (FA). The participants’ level of impairment was estimated using factor loadings. The scale’s accuracy was assessed estimating floor and ceiling effects and Cronbach’s alpha. (4) Results: FA suggested classifying the questions into daily activities, cognition, and pain. The respondents did not have extremely severe impairment (most scores ranged from 100 to 180 points), and the factors with the lowest scores were cognition and pain. The instrument had no significant floor or ceiling effects (rates less than 15%), and the Cronbach’s alpha value was larger than 0.90. (5) Conclusion: MAQPD is the only remote self-administered tool found in the literature capable of providing a detailed assessment of the general health status of individuals with PD.

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Task-Specific Tremor Quantification in a Clinical Setting for Parkinson’s Disease

Cited by 13 publications

References 43 publications

Identification and Classification of Parkinsonian and Essential Tremors for Diagnosis Using Machine Learning Algorithms

Identification and Classification of Parkinsonian and Essential Tremors for Diagnosis Using Machine Learning Algorithms

Non-Contact Hand Movement Analysis for Optimal Configuration of Smart Sensors to Capture Parkinson’s Disease Hand Tremor

Multidimensional Assessment of Individuals with Parkinson’s Disease: Development and Structure Validation of a Self-Assessment Questionnaire

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