Essential Tremor

Benito‐León, Julián; Louis, Elan D.

doi:10.1002/9781444346183.ch6

Cited by 5 publications

(4 citation statements)

References 202 publications

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“…It is also observed that 7-10 million people live with PD. ET incidence increases with age, and more than 4% of people were affected [4,5]. PD is characterized by bradykinesia, rigidity, rest tremor, or asymmetric onset [6].…”

Section: Kinematic Tremormentioning

confidence: 99%

Tremor Classification Using Wearable Iot Based Sensors

Brindha

Sunitha

Wilson

2022

IOP Conf. Ser.: Mater. Sci. Eng.

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Tremors, a significant symptom of movement disorder, affects a part of the body ranging from slight to severe. These Tremors are symptoms of various neurological diseases such as Parkinson’s disease (PD), Essential tremors (ET), Physiological tremors (PT), Cerebellar tremor, Dystonic tremor, Psychogenic tremor, and many more. Tremor features and types differ for various neurological disorders. During the early stages of the disease, clinical examination of tremors plays a significant role in diagnose management. This work aims to develop a wearable assistive system with an Inertial Measurement Unit (IMU) sensor to classify the tremor of three different neurological disorders based on the tremor position and frequency. This research has been carried out in SRM Medical college and Research Centre with 15 patients. The type of neurodegenerative disease of the subject with tremor is evaluated based on the tremor position and tremor frequency level. The data is collected, transmitted, and processed using the IMU sensor with Internet of things (IoT) and Node MCU board. The decision tree algorithm is used for the classification of tremors. ET, PD, and PT tremors are classified based on the tremor frequency and tremor position. A high rate of accuracy is achieved for the developed system when compared with the Neurologist results. The proposed device quantitatively classified the tremor based on the frequency and position among the three different neurological disorders, i.e., ET, PD, and PT tremors.

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Section: Kinematic Tremormentioning

confidence: 99%

Tremor Classification Using Wearable Iot Based Sensors

Brindha

Sunitha

Wilson

2022

IOP Conf. Ser.: Mater. Sci. Eng.

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“…Classically, it has been considered a benign and monosymptomatic disorder characterized primarily by kinetic arm tremor. However, an emerging view that is gaining wider support is that it may be a family of diseases unified by the presence of kinetic tremor, while also displaying etiological, pathologic, and clinical heterogeneity 3 – 5 . Recent evidence suggests that it is a family of disorders rather than a single condition, and that contrary to the traditional perspective, it may be a neurodegenerative disease 6 .…”

Section: Introductionmentioning

confidence: 99%

A data mining approach using cortical thickness for diagnosis and characterization of essential tremor

Serrano

Romero

Castillo

et al. 2017

Sci Rep

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Essential tremor (ET) is one of the most prevalent movement disorders. Being that it is a common disorder, its diagnosis is considered routine. However, misdiagnoses may occur regularly. Over the past decade, several studies have identified brain morphometric changes in ET, but these changes remain poorly understood. Here, we tested the informativeness of measuring cortical thickness for the purposes of ET diagnosis, applying feature selection and machine learning methods to a study sample of 18 patients with ET and 18 age- and sex-matched healthy control subjects. We found that cortical thickness features alone distinguished the two, ET from controls, with 81% diagnostic accuracy. More specifically, roughness (i.e., the standard deviation of cortical thickness) of the right inferior parietal and right fusiform areas was shown to play a key role in ET characterization. Moreover, these features allowed us to identify subgroups of ET patients as well as healthy subjects at risk for ET. Since treatment of tremors is disease specific, accurate and early diagnosis plays an important role in tremor management. Supporting the clinical diagnosis with novel computer approaches based on the objective evaluation of neuroimage data, like the one presented here, may represent a significant step in this direction.

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“…The population prevalence is estimated at around 0.4% [ 4 ], a percentage that increases exponentially for people older than 65. Specifically, it ranges between 4.6% and 14.3% for this population subset [ 5 ]. It has been considered a benign disease caused by the imbalance among different excitatory and inhibiting neurochemicals belonging to the central nervous system, but it can lead to considerable physical and psychosocial disability.…”

Section: Introductionmentioning

confidence: 99%

Support System to Improve Reading Activity in Parkinson’s Disease and Essential Tremor Patients

Bravo

Barrio

Sacristana

et al. 2017

Sensors

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The use of information and communication technologies (ICTs) to improve the quality of life of people with chronic and degenerative diseases is a topic receiving much attention nowadays. We can observe that new technologies have driven numerous scientific projects in e-Health, encompassing Smart and Mobile Health, in order to address all the matters related to data processing and health. Our work focuses on helping to improve the quality of life of people with Parkinson’s Disease (PD) and Essential Tremor (ET) by means of a low-cost platform that enables them to read books in an easy manner. Our system is composed of two robotic arms and a graphical interface developed for Android platforms. After several tests, our proposal has achieved a 96.5% accuracy for A4 80 gr non-glossy paper. Moreover, our system has outperformed the state-of-the-art platforms considering different types of paper and inclined surfaces. The feedback from ET and PD patients was collected at “La Princesa” University Hospital in Madrid and was used to study the user experience. Several features such as ease of use, speed, correct behavior or confidence were measured via patient feedback, and a high level of satisfaction was awarded to most of them. According to the patients, our system is a promising tool for facilitating the activity of reading.

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Essential Tremor

Cited by 5 publications

References 202 publications

Tremor Classification Using Wearable Iot Based Sensors

Tremor Classification Using Wearable Iot Based Sensors

A data mining approach using cortical thickness for diagnosis and characterization of essential tremor

Support System to Improve Reading Activity in Parkinson’s Disease and Essential Tremor Patients

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