Cardiorespiratory interaction using nonlinear data processing techniques in patients undergoing test tube T

J. I. Trapero; Carlos Julio Arizmendi; C. A. Forero; S. K. Lopez; B. F. Giraldo

doi:10.1007/978-981-10-4086-3_117

Cardiorespiratory interaction using nonlinear data processing techniques in patients undergoing test tube T

J. I. Trapero, Carlos Julio Arizmendi, C. A. Forero, S. K. Lopez, B. F. Giraldo

Research output: Articles / Notes › Scientific Article › peer-review

2 Scopus citations

Abstract

The estimate of the optimal time to remove the ventilator from a patient in intensive care remains critical in clinical practice. This study analyzes the breathing pattern from cardiorespiratory signals extubation patients undergoing performing resampling the signal, then the Symbolic Dynamics technique for data processing is implemented, together with the techniques of Support Vector Machines (SVM) and Linear Discriminant Analysis (LDA) for classifying 154 patients conglomerates in the Group Success and Failure Group classification, obtaining the best result obtained from 93.87 ± 0.01 % prediction, for SVM.

Original language	English
Pages (from-to)	465-468
Number of pages	4
Journal	IFMBE Proceedings
DOIs	https://doi.org/10.1007/978-981-10-4086-3_117
State	Published - 2017
Event	7th Latin American Congress on Biomedical Engineering, CLAIB 2016 - Bucaramanga, Santander, Colombia Duration: 26 Oct 2016 → 28 Oct 2016

Keywords

Extubation Process
Support Vector Machines
Symbolic Dynamics

Research Areas UNAB

Automatización y Control

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10.1007/978-981-10-4086-3_117

Cite this

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title = "Cardiorespiratory interaction using nonlinear data processing techniques in patients undergoing test tube T",

abstract = "The estimate of the optimal time to remove the ventilator from a patient in intensive care remains critical in clinical practice. This study analyzes the breathing pattern from cardiorespiratory signals extubation patients undergoing performing resampling the signal, then the Symbolic Dynamics technique for data processing is implemented, together with the techniques of Support Vector Machines (SVM) and Linear Discriminant Analysis (LDA) for classifying 154 patients conglomerates in the Group Success and Failure Group classification, obtaining the best result obtained from 93.87 ± 0.01 % prediction, for SVM.",

keywords = "Extubation Process, Support Vector Machines, Symbolic Dynamics",

author = "Trapero, {J. I.} and Arizmendi, {Carlos Julio} and Forero, {C. A.} and Lopez, {S. K.} and Giraldo, {B. F.}",

note = "Publisher Copyright: {\textcopyright} Springer Nature Singapore Pte Ltd. 2017.; 7th Latin American Congress on Biomedical Engineering, CLAIB 2016 ; Conference date: 26-10-2016 Through 28-10-2016",

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AU - Trapero, J. I.

AU - Arizmendi, Carlos Julio

AU - Forero, C. A.

AU - Lopez, S. K.

AU - Giraldo, B. F.

N1 - Publisher Copyright: © Springer Nature Singapore Pte Ltd. 2017.

PY - 2017

Y1 - 2017

N2 - The estimate of the optimal time to remove the ventilator from a patient in intensive care remains critical in clinical practice. This study analyzes the breathing pattern from cardiorespiratory signals extubation patients undergoing performing resampling the signal, then the Symbolic Dynamics technique for data processing is implemented, together with the techniques of Support Vector Machines (SVM) and Linear Discriminant Analysis (LDA) for classifying 154 patients conglomerates in the Group Success and Failure Group classification, obtaining the best result obtained from 93.87 ± 0.01 % prediction, for SVM.

AB - The estimate of the optimal time to remove the ventilator from a patient in intensive care remains critical in clinical practice. This study analyzes the breathing pattern from cardiorespiratory signals extubation patients undergoing performing resampling the signal, then the Symbolic Dynamics technique for data processing is implemented, together with the techniques of Support Vector Machines (SVM) and Linear Discriminant Analysis (LDA) for classifying 154 patients conglomerates in the Group Success and Failure Group classification, obtaining the best result obtained from 93.87 ± 0.01 % prediction, for SVM.

KW - Extubation Process

KW - Support Vector Machines

KW - Symbolic Dynamics

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AN - SCOPUS:85018426475

SN - 1680-0737

SP - 465

EP - 468

JO - IFMBE Proceedings

JF - IFMBE Proceedings

T2 - 7th Latin American Congress on Biomedical Engineering, CLAIB 2016

Y2 - 26 October 2016 through 28 October 2016

ER -

Cardiorespiratory interaction using nonlinear data processing techniques in patients undergoing test tube T

Abstract

Keywords

Research Areas UNAB

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