Using Machine Learning and Accelerometry Data for Differential Diagnosis of Parkinson’s Disease and Essential Tremor

Duque, Julián D. Loaiza; González-Vargas, Andrés M.; Egea, Antonio J. Sánchez; Rojas, Hermán A. González

doi:10.1007/978-3-030-31019-6_32

Cited by 15 publications

(10 citation statements)

References 19 publications

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“…The results obtained in this work supported the above, the most significant feature for the differentiation of patients with PD and ET seems to be the novel HIR feature, as it was implemented in 12 of the 18 best models depicted in Figure 4. Also, as already observed in previous works [4], [19], RE and RPC features provide essential information. The RPC feature also contains relevant information for the differentiation of TP and HS in both analyzed frequency ranges.…”

Section: Discussionsupporting

confidence: 75%

“…The results obtained in this work show that the characterization and differentiation between tremor in PD and ET are possible with a mobile phone's built-in gyroscope. The accuracy of the tremor differentiation using this sensor is comparable to the performance obtained using a mobile phone's built-in accelerometer [4], [19]. Although there is a clear difference between the number of TP (39 in total) and HS (12 in total), the accuracy of the models differentiating the two conditions is high.…”

Section: Discussionmentioning

confidence: 62%

“…They also used ML methods to perform the differential classification between PD and other movement disorders. Besides, in previous works, we proposed different methods for the differential diagnosis of the two diseases using the mobile phone's built-in triaxial accelerometer [4], [18], [19]. The developed methods allow to characterize and recognize the discriminative features of hand tremor in PD and ET patients and to use ML algorithms to improve the differentiation between them.…”

Section: Introductionmentioning

confidence: 99%

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Angular Velocity Analysis Boosted by Machine Learning for Helping in the Differential Diagnosis of Parkinson’s Disease and Essential Tremor

et al. 2020

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Recent research has shown that smartphones/smartwatches have a high potential to help physicians to identify and differentiate between different movement disorders. This work aims to develop Machine Learning models to improve the differential diagnosis between patients with Parkinson's Disease and Essential Tremor. For this purpose, we use a mobile phone's built-in gyroscope to record the angular velocity signals of two different arm positions during the patient's follow-up, more precisely, in rest and posture positions. To develop and to find the best classification models, diverse factors were considered, such as the frequency range, the training and testing divisions, the kinematic features, and the classification method. We performed a two-stage kinematic analysis, first to differentiate between healthy and trembling subjects and then between patients with Parkinson's Disease and Essential Tremor. The models developed reached an average accuracy of 97.2 ± 3.7% (98.5% Sensitivity, 93.3% Specificity) to differentiate between Healthy and Trembling subjects and an average accuracy of 77.8 ± 9.9% (75.7% Sensitivity, 80.0% Specificity) to discriminate between Parkinson's Disease and Essential Tremor patients. Therefore, we conclude, that the angular velocity signal can be used to develop Machine Learning models for the differential diagnosis of Parkinson's disease and Essential Tremor.

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Section: Discussionsupporting

confidence: 75%

Section: Discussionmentioning

confidence: 62%

Section: Introductionmentioning

confidence: 99%

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Angular Velocity Analysis Boosted by Machine Learning for Helping in the Differential Diagnosis of Parkinson’s Disease and Essential Tremor

et al. 2020

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“…Of the patients with undecided diagnoses, all PD cases (two) and two of four ET cases were correctly classified. Duque et al, also performed machine learning classification using the linear acceleration of tremor recorded by the smartphone’s built-in accelerometer, and showed performances ranging from 90.0% to 100.0% sensitivity, and 80% to 100% specificity [ 60 ]. Thus, the smartphone, a familiar device, is expected to be utilized.…”

Section: Resultsmentioning

confidence: 99%

Diagnosis and Treatment of Tremor in Parkinson’s Disease Using Mechanical Devices

Fujikawa

Morigaki

Yamamoto

et al. 2022

Life

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Background: Parkinsonian tremors are sometimes confused with essential tremors or other conditions. Recently, researchers conducted several studies on tremor evaluation using wearable sensors and devices, which may support accurate diagnosis. Mechanical devices are also commonly used to treat tremors and have been actively researched and developed. Here, we aimed to review recent progress and the efficacy of the devices related to Parkinsonian tremors. Methods: The PubMed and Scopus databases were searched for articles. We searched for “Parkinson disease” and “tremor” and “device”. Results: Eighty-six articles were selected by our systematic approach. Many studies demonstrated that the diagnosis and evaluation of tremors in patients with PD can be done accurately by machine learning algorithms. Mechanical devices for tremor suppression include deep brain stimulation (DBS), electrical muscle stimulation, and orthosis. In recent years, adaptive DBS and optimization of stimulation parameters have been studied to further improve treatment efficacy. Conclusions: Due to developments using state-of-the-art techniques, effectiveness in diagnosing and evaluating tremor and suppressing it using these devices is satisfactorily high in many studies. However, other than DBS, no devices are in practical use. To acquire high-level evidence, large-scale studies and randomized controlled trials are needed for these devices.

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“…In addition, a study used a NN to successfully discern PD from ET using surface electromyography data [26]. Other machine learning algorithms such as support vector machine (SVM) and k-nearest neighbor (kNN) were used to differentiate between PD and ET based on IMU sensors, but they mainly investigated upper body tremors [27][28][29][30]. To our knowledge, no study has utilized machine learning techniques to differentiate between PD and ET based on data collected from gait and balance characteristics from wearable IMU sensors.…”

Section: Introductionmentioning

confidence: 99%

Classification of Parkinson’s disease and essential tremor based on gait and balance characteristics from wearable motion sensors: A data-driven approach

Moon

Song

Sharma

et al. 2020

Preprint

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Parkinson's disease (PD) and essential tremor (ET) are movement disorders that can have similar clinical characteristics including tremor and gait difficulty. These disorders can be misdiagnosed leading to delay in appropriate treatment. The aim of the study was to determine whether gait and balance variables obtained with wearable sensors can be utilized to differentiate between PD and ET using machine learning techniques. Additionally, we compared classification performances of several machine learning models. A balance and gait data set collected from 567 people with PD or ET was investigated. Performance of several machine learning techniques including neural networks (NN), support vector machine (SVM), k-nearest neighbor (kNN), decision tree (DT), random forest (RF), and gradient boosting (GB), were compared using F1-scores. Machine learning models classified PD and ET based on balance and gait characteristics better than chance or logistic regression. The highest F1-score was 0.61 of NN, followed by 0.59 of GB, 0.56 of RF, 0.55 of SVM, 0.53 of DT, and 0.49 of kNN. The results demonstrated the utility of machine learning models to classify different movement disorders. Further study will provide a more accurate clinical tool to help clinical decision-making.

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Using Machine Learning and Accelerometry Data for Differential Diagnosis of Parkinson’s Disease and Essential Tremor

Cited by 15 publications

References 19 publications

Angular Velocity Analysis Boosted by Machine Learning for Helping in the Differential Diagnosis of Parkinson’s Disease and Essential Tremor

Angular Velocity Analysis Boosted by Machine Learning for Helping in the Differential Diagnosis of Parkinson’s Disease and Essential Tremor

Diagnosis and Treatment of Tremor in Parkinson’s Disease Using Mechanical Devices

Classification of Parkinson’s disease and essential tremor based on gait and balance characteristics from wearable motion sensors: A data-driven approach

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