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

Duque, Julián D. Loaiza; Egea, Antonio J. Sánchez; Reeb, Theresa; Rojas, Hernan Alberto González; González-Vargas, Andrés M.

doi:10.1109/access.2020.2993647

Cited by 18 publications

(9 citation statements)

References 31 publications

(71 reference statements)

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“…Most PD and ET patients suffer from tremors of the upper limbs ( Zhang et al, 2018 ; Duque et al, 2020 ). Owing to the overlapping tremor features, misdiagnosis between PD and ET is common.…”

Section: Discussionmentioning

confidence: 99%

“…Artificial intelligence technology is widely used to solve problems in the medical field, including differentiating between PD and ET ( Xiao et al, 2019 ; Duque et al, 2020 ). Based on various extracted statistical characteristics of tremor signals and methodologies of machine learning, a series of machine learning algorithms, such as linear models (logistic regression, ridge classification, etc.…”

Section: Introductionmentioning

confidence: 99%

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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: Discussionmentioning

confidence: 99%

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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“…For example, in [65,66], signals from hand-mounted inertial sensors are used to train ML models, such as RFs and SVMs, to detect PD among other neurological disorders and achieve a moderate accuracy of 72-79%. Moreover, in [67], PD patients are efficiently differentiated from essential tremor (ET) patients by an SVM trained on smartphone angular velocity signals, with 77.8% accuracy. A bit higher classification performance (89% accuracy) is achieved by Moon et al [68] when addressing the same problem with NNs trained over fused signals from sternum-, lumbar-, wrist-and foot-worn inertial sensors.…”

Section: Inertial Sensorsmentioning

confidence: 99%

Internet of Things Technologies and Machine Learning Methods for Parkinson’s Disease Diagnosis, Monitoring and Management: A Systematic Review

Giannakopoulou

Roussaki

Demestichas

2022

Sensors

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Parkinson’s disease is a chronic neurodegenerative disease that affects a large portion of the population, especially the elderly. It manifests with motor, cognitive and other types of symptoms, decreasing significantly the patients’ quality of life. The recent advances in the Internet of Things and Artificial Intelligence fields, including the subdomains of machine learning and deep learning, can support Parkinson’s disease patients, their caregivers and clinicians at every stage of the disease, maximizing the treatment effectiveness and minimizing the respective healthcare costs at the same time. In this review, the considered studies propose machine learning models, trained on data acquired via smart devices, wearable or non-wearable sensors and other Internet of Things technologies, to provide predictions or estimations regarding Parkinson’s disease aspects. Seven hundred and seventy studies have been retrieved from three dominant academic literature databases. Finally, one hundred and twelve of them have been selected in a systematic way and have been considered in the state-of-the-art systematic review presented in this paper. These studies propose various methods, applied on various sensory data to address different Parkinson’s disease-related problems. The most widely deployed sensors, the most commonly addressed problems and the best performing algorithms are highlighted. Finally, some challenges are summarized along with some future considerations and opportunities that arise.

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“…Specifically, machine learning has been used for symptom quantification of Parkinson's as well as essential tremor patients via gyroscope and accelerometer sensors [9][10][11][12][13][14][15]. Furthermore, recognition of Parkinson's was recently tested on phonation and speech datasets for identifying dysphonia signs related to the syndrome [16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

A Study on the Essential and Parkinson’s Arm Tremor Classification

Skaramagkas

Ανδρικόπουλος

Kefalopoulou

et al. 2021

Signals

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In this article, the challenge of discriminating between essential and Parkinson’s tremor is addressed. Although a variety of methods have been proposed for diagnosing the severity of these highly occurring tremor types, their rapid and effective identification, especially in their early stages, proves particularly difficult and complicated due to their wide range of causes and similarity of symptoms. To this goal, a clinical analysis was performed, where a number of volunteers including essential and Parkinson’s tremor-diagnosed patients underwent a series of pre-defined motion patterns, during which a wearable sensing setup was used to measure their lower arm tremor characteristics from multiple selected points. Extracted features from the acquired accelerometer signals were used to train classification algorithms, including decision trees, discriminant analysis, support vector machine (SVM), K-nearest neighbor (KNN) and ensemble learning algorithms, for providing a comparative study and evaluating the potential of utilizing machine learning to accurately discriminate among different tremor types. Overall, SVM related classifiers proved to be the most successful in terms of classifying between Parkinson’s, essential and no tremor diagnosed with percentages reaching up to 100% for a single accelerometer measurement at the metacarpal area. In general and in motion while holding an object position, Coarse Gaussian SVM classifier reached 82.62% accuracy.

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

Cited by 18 publications

References 31 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

Internet of Things Technologies and Machine Learning Methods for Parkinson’s Disease Diagnosis, Monitoring and Management: A Systematic Review

A Study on the Essential and Parkinson’s Arm Tremor Classification

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