A Smartphone-Based Clinical Decision Support System for Tremor Assessment

Guillaume Zamora; Caro Fuchs; Aurélie Degeneffe; Pieter Kubben; Uzay Kaymak

doi:10.1007/978-3-030-63061-4_1

A Smartphone-Based Clinical Decision Support System for Tremor Assessment

Guillaume Zamora, Caro Fuchs^*, Aurélie Degeneffe, Pieter Kubben, Uzay Kaymak

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceeding › Academic › peer-review

Abstract

Tremor severity assessment is an important element for the diagnosis and treatment decision-making process of patients suffering from Essential Tremor (ET). Classically, questionnaires like the ETRS and QUEST surveys have been used to assess tremor severity. Recently, attention around computerized tremor analysis has grown. In this study, we use regression trees to map the relationship between tremor data that is collected using the TREMOR12 smartphone application with ETRS and QUEST scores. We aim to develop a model that is able to automatically assess tremor severity of patients suffering from Essential Tremor without the use of more subjective questionnaires. This study shows that tremor data gathered using the TREMOR12 application is useful for constructing machine learning models that can be used to support the diagnosis and monitoring of patients who suffer from Essential Tremor.

Original language	English
Title of host publication	Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2019
Editors	P. Cazzaniga, D. Besozzi, I. Merelli, L. Manzoni
Publisher	Springer, Cham
Pages	3-12
Number of pages	10
ISBN (Electronic)	978-3-030-63061-4
ISBN (Print)	978-3-030-63060-7
DOIs	https://doi.org/10.1007/978-3-030-63061-4_1
Publication status	Published - 10 Dec 2020

Publication series

Series	Lecture Notes in Computer Science
Volume	12313
ISSN	0302-9743

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10.1007/978-3-030-63061-4_1

Cite this

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title = "A Smartphone-Based Clinical Decision Support System for Tremor Assessment",

abstract = "Tremor severity assessment is an important element for the diagnosis and treatment decision-making process of patients suffering from Essential Tremor (ET). Classically, questionnaires like the ETRS and QUEST surveys have been used to assess tremor severity. Recently, attention around computerized tremor analysis has grown. In this study, we use regression trees to map the relationship between tremor data that is collected using the TREMOR12 smartphone application with ETRS and QUEST scores. We aim to develop a model that is able to automatically assess tremor severity of patients suffering from Essential Tremor without the use of more subjective questionnaires. This study shows that tremor data gathered using the TREMOR12 application is useful for constructing machine learning models that can be used to support the diagnosis and monitoring of patients who suffer from Essential Tremor.",

author = "Guillaume Zamora and Caro Fuchs and Aur{\'e}lie Degeneffe and Pieter Kubben and Uzay Kaymak",

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Zamora, G, Fuchs, C, Degeneffe, A, Kubben, P & Kaymak, U 2020, A Smartphone-Based Clinical Decision Support System for Tremor Assessment. in P Cazzaniga, D Besozzi, I Merelli & L Manzoni (eds), Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2019. Springer, Cham, Lecture Notes in Computer Science, vol. 12313, pp. 3-12. https://doi.org/10.1007/978-3-030-63061-4_1

A Smartphone-Based Clinical Decision Support System for Tremor Assessment. / Zamora, Guillaume; Fuchs, Caro; Degeneffe, Aurélie et al.
Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2019. ed. / P. Cazzaniga; D. Besozzi; I. Merelli; L. Manzoni. Springer, Cham, 2020. p. 3-12 (Lecture Notes in Computer Science, Vol. 12313).

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceeding › Academic › peer-review

TY - GEN

T1 - A Smartphone-Based Clinical Decision Support System for Tremor Assessment

AU - Zamora, Guillaume

AU - Fuchs, Caro

AU - Degeneffe, Aurélie

AU - Kubben, Pieter

AU - Kaymak, Uzay

PY - 2020/12/10

Y1 - 2020/12/10

N2 - Tremor severity assessment is an important element for the diagnosis and treatment decision-making process of patients suffering from Essential Tremor (ET). Classically, questionnaires like the ETRS and QUEST surveys have been used to assess tremor severity. Recently, attention around computerized tremor analysis has grown. In this study, we use regression trees to map the relationship between tremor data that is collected using the TREMOR12 smartphone application with ETRS and QUEST scores. We aim to develop a model that is able to automatically assess tremor severity of patients suffering from Essential Tremor without the use of more subjective questionnaires. This study shows that tremor data gathered using the TREMOR12 application is useful for constructing machine learning models that can be used to support the diagnosis and monitoring of patients who suffer from Essential Tremor.

AB - Tremor severity assessment is an important element for the diagnosis and treatment decision-making process of patients suffering from Essential Tremor (ET). Classically, questionnaires like the ETRS and QUEST surveys have been used to assess tremor severity. Recently, attention around computerized tremor analysis has grown. In this study, we use regression trees to map the relationship between tremor data that is collected using the TREMOR12 smartphone application with ETRS and QUEST scores. We aim to develop a model that is able to automatically assess tremor severity of patients suffering from Essential Tremor without the use of more subjective questionnaires. This study shows that tremor data gathered using the TREMOR12 application is useful for constructing machine learning models that can be used to support the diagnosis and monitoring of patients who suffer from Essential Tremor.

U2 - 10.1007/978-3-030-63061-4_1

DO - 10.1007/978-3-030-63061-4_1

M3 - Conference article in proceeding

SN - 978-3-030-63060-7

T3 - Lecture Notes in Computer Science

SP - 3

EP - 12

BT - Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2019

A2 - Cazzaniga, P.

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PB - Springer, Cham

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Zamora G, Fuchs C, Degeneffe A, Kubben P, Kaymak U. A Smartphone-Based Clinical Decision Support System for Tremor Assessment. In Cazzaniga P, Besozzi D, Merelli I, Manzoni L, editors, Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2019. Springer, Cham. 2020. p. 3-12. (Lecture Notes in Computer Science, Vol. 12313). doi: 10.1007/978-3-030-63061-4_1

A Smartphone-Based Clinical Decision Support System for Tremor Assessment

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