Analysis of the respiratory flow signal for the diagnosis of patients with chronic heart failure using artificial intelligence techniques

J. C. Rodríguez, Carlos J. Arizmendi, C. A. Forero, S. K. Lopez, B. F. Giraldo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Patients with Chronic Heart Failure (CHF) often develop oscillatory breathing patterns. This work proposes the characterization of respiratory pattern by Wavelet Transform (WT) technique to identify Periodic Breathing pattern (PB) and Non-Periodic Breathing pattern (nPB) through the respiratory flow signal. A total of 62 subjects were analyzed: 27 CHF patients and 35 healthy subjects. Respiratory time series were extracted, and statistical methods were applied to obtain the most relevant information to classify patients. Support Vector Machine (SVM) were applied using forward selection technique to discriminate patients, considering four kernel functions. Differences between these parameters are assessed by investigating the following four classification issues: healthy subjects versus CHF patients, PB versus nPB patients, PB patients versus healthy subjects, and nPB patients versus healthy subjects. The results are presented in terms of average accuracy for each kernel function, and comparison groups.

Original languageEnglish
Title of host publication7th Latin American Congress on Biomedical Engineering, CLAIB 2016
EditorsJohn Bustamante, Daniel A. Sierra, Isnardo Torres
PublisherSpringer Verlag
Pages46-49
Number of pages4
ISBN (Print)9789811040856
DOIs
StatePublished - 2017
Event7th Latin American Congress on Biomedical Engineering, CLAIB 2016 - Bucaramanga, Santander, Colombia
Duration: 26 Oct 201628 Oct 2016

Publication series

NameIFMBE Proceedings
Volume60
ISSN (Print)1680-0737

Conference

Conference7th Latin American Congress on Biomedical Engineering, CLAIB 2016
Country/TerritoryColombia
CityBucaramanga, Santander
Period26/10/1628/10/16

Keywords

  • Chronic heart failure
  • Forward selection
  • Non-periodic breathing
  • Periodic breathing
  • Support vector machine

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