Classification of the Tremor signal from Accelerometers and Gyroscopes in Multiple Sclerosis

Kód výsledku v IS VaVaI

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Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Classification of the Tremor signal from Accelerometers and Gyroscopes in Multiple Sclerosis

Popis výsledku v původním jazyce

Tremor, an involuntary rhythmic oscillatory movement of a body part is a common problem for patients suffering from multiple sclerosis (MS). This paper aims to use accelerometric and gyroscopic measurements of postural tremor from the upper limbs to determine whether a patient suffers from MS. The used data includes signals from a group of 16 MS patients (3 males and 13 females) and a group of 18 healthy control subjects (9 males and 9 females). Methods involving neural networks were used for signal classification from the power spectral density (PSD) of the given signals. Different fully-connected neural network (FNN), convolutional neural network (CNN) and recurrent neural network (RNN) architectures were explored. The best reached results were a recall of 100% and a precision of 89%, achieved by one of the proposed CNN models.

Název v anglickém jazyce

Classification of the Tremor signal from Accelerometers and Gyroscopes in Multiple Sclerosis

Popis výsledku anglicky

Tremor, an involuntary rhythmic oscillatory movement of a body part is a common problem for patients suffering from multiple sclerosis (MS). This paper aims to use accelerometric and gyroscopic measurements of postural tremor from the upper limbs to determine whether a patient suffers from MS. The used data includes signals from a group of 16 MS patients (3 males and 13 females) and a group of 18 healthy control subjects (9 males and 9 females). Methods involving neural networks were used for signal classification from the power spectral density (PSD) of the given signals. Different fully-connected neural network (FNN), convolutional neural network (CNN) and recurrent neural network (RNN) architectures were explored. The best reached results were a recall of 100% and a precision of 89%, achieved by one of the proposed CNN models.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2023 International Conference on Applied Electronics

ISBN

979-8-3503-3553-8

ISSN

1803-7232

e-ISSN

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Počet stran výsledku

4

Strana od-do

67-70

Název nakladatele

Západočeská univerzita v Plzni

Místo vydání

Plzeň

Místo konání akce

Plzeň

Datum konání akce

6. 9. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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