High-accuracy automatic classification of Parkinsonian tremor severity using machine learning method

Jeon, Hee Jung; Lee, Woongwoo; Park, Hye-Young; Lee, Hong Ji; Kim, Sang Kyong; Kim, Han Byul; Park, Kwang Suk

doi:10.1088/1361-6579/aa8e1f

Cited by 38 publications

(61 citation statements)

References 36 publications

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“…The double integrals of the acceleration signal provides the displacement [48,49]. There are two main methods to convert the acceleration signal into displacement: time domain integration and frequency domain integration.…”

Section: Gait Event Determinationmentioning

confidence: 99%

A Determination Method for Gait Event Based on Acceleration Sensors

Mei

Gao

Liu

2019

Sensors

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A gait event is a crucial step towards the effective assessment and rehabilitation of motor dysfunctions. However, for the data acquisition of a three-dimensional motion capture (3D Mo-Cap) system, the high cost of setups, such as the high standard laboratory environment, limits widespread clinical application. Inertial sensors are increasingly being used to recognize and classify physical activities in a variety of applications. Inertial sensors are now sufficiently small in size and light in weight to be part of a body sensor network for the collection of human gait data. The acceleration signal has found important applications in human gait recognition. In this paper, using the experimental data from the heel and toe, first the wavelet method was used to remove noise from the acceleration signal, then, based on the threshold of comprehensive change rate of the acceleration signal, the signal was primarily segmented. Subsequently, the vertical acceleration signals, from heel and toe, were integrated twice, to compute their respective vertical displacement. Four gait events were determined in the segmented signal, based on the characteristics of the vertical displacement of heel and toe. The results indicated that the gait events were consistent with the synchronous record of the motion capture system. The method has achieved gait event subdivision, while it has also ensured the accuracy of the defined gait events. The work acts as a valuable reference, to further study gait recognition.

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Section: Gait Event Determinationmentioning

confidence: 99%

A Determination Method for Gait Event Based on Acceleration Sensors

Mei

Gao

Liu

2019

Sensors

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“…The k was used between 5 and 10 to facilitate the generalization of the model during test phase. Previous investigations – such as Jeon et al (2017b) – have also found high accuracies using k NN algorithms. They assessed 85 PD patients to predict UPDRS results by using a wrist-watch-type wearable device for measuring tremors and found an accuracy level close to 84% for k NN and RF algorithms.…”

Section: Discussionmentioning

confidence: 74%

“…SVC has been widely used to detect tremor in PD patients. The accuracy level of its classifiers has ranged between 80 and 90% to quantify PD tremor (Alam et al, 2016;Jeon et al, 2017b). We used a radial compared to the best SVC used by Jeon et al (2017b) finding similar results.…”

Section: Discussionmentioning

confidence: 97%

“…Literature has proposed alternative ways to quantify PD symptoms in order to assist its diagnosis and progression ( Jilbab et al, 2017 ). Inertial measures of the hand resting tremor associated to machine learning algorithms have been extensively investigated to distinct data from healthy people and patients with PD ( Jeon et al, 2017a , b ), to quantify the progression of the disease ( Pedrosa et al, 2018 ), and to evaluate the effect of therapeutics on hands’ tremor ( LeMoyne et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

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Hand Resting Tremor Assessment of Healthy and Patients With Parkinson’s Disease: An Exploratory Machine Learning Study

Araújo

Santos

Sá

et al. 2020

Front. Bioeng. Biotechnol.

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“…In developing an optimal AM approach, development of tremor prediction machine‐learning models is important . At the moment, most of these models are very accurate in scaling tremor severity rather than predicting reoccurrence . Future studies should analyze different evaluation frequencies, corresponding computational costs, and tremor reduction to compare the feasibility of different AM approaches.…”

Section: Stimulation Parameter Modulation Demands Per Phenotypementioning

confidence: 99%

An update on adaptive deep brain stimulation in Parkinson's disease

et al. 2018

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Advancing conventional open‐loop DBS as a therapy for PD is crucial for overcoming important issues such as the delicate balance between beneficial and adverse effects and limited battery longevity that are currently associated with treatment. Closed‐loop or adaptive DBS aims to overcome these limitations by real‐time adjustment of stimulation parameters based on continuous feedback input signals that are representative of the patient's clinical state. The focus of this update is to discuss the most recent developments regarding potential input signals and possible stimulation parameter modulation for adaptive DBS in PD. Potential input signals for adaptive DBS include basal ganglia local field potentials, cortical recordings (electrocorticography), wearable sensors, and eHealth and mHealth devices. Furthermore, adaptive DBS can be applied with different approaches of stimulation parameter modulation, the feasibility of which can be adapted depending on specific PD phenotypes. Implementation of technological developments like machine learning show potential in the design of such approaches; however, energy consumption deserves further attention. Furthermore, we discuss future considerations regarding the clinical implementation of adaptive DBS in PD. © 2018 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

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High-accuracy automatic classification of Parkinsonian tremor severity using machine learning method

Abstract: These results indicate the feasibility of the proposed system as a clinical decision tool for Parkinsonian tremor-severity automatic scoring.

Cited by 38 publications

References 36 publications

A Determination Method for Gait Event Based on Acceleration Sensors

A Determination Method for Gait Event Based on Acceleration Sensors

Hand Resting Tremor Assessment of Healthy and Patients With Parkinson’s Disease: An Exploratory Machine Learning Study

An update on adaptive deep brain stimulation in Parkinson's disease

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