@inproceedings{c5ac08bed0bb4f6eb3a27b45fb6991b5,
title = "Analysis of the respiratory flow signal for the diagnosis of patients with chronic heart failure using artificial intelligence techniques",
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.",
keywords = "Chronic heart failure, Forward selection, Non-periodic breathing, Periodic breathing, Support vector machine",
author = "Rodr{\'i}guez, {J. C.} and Arizmendi, {Carlos J.} 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",
year = "2017",
doi = "10.1007/978-981-10-4086-3_121",
language = "Ingl{\'e}s",
isbn = "9789811040856",
series = "IFMBE Proceedings",
publisher = "Springer Verlag",
pages = "46--49",
editor = "John Bustamante and Sierra, {Daniel A.} and Isnardo Torres",
booktitle = "7th Latin American Congress on Biomedical Engineering, CLAIB 2016",
}