A soft decision algorithm for obstructive sleep apnea patient classification based on fast estimation of wavelet entropy of RRI data

Research output: Contribution to journalArticle

13 Citations (Scopus)

Abstract

A soft decision algorithm for Obstructive Sleep Apnea (OSA) patient classification using R-R interval (RRI) data is investigated. This algorithm is based on fast and approximate estimation of the entropy of the wavelet-decomposed bands of the RRI data. The classification is done on the whole record as OSA patient or non-patient (normal). The ratio of the estimated entropy of the low-frequency (LF) band to that of the very-low frequency (VLF) band is used as a classification factor. RRI data used in this work are drawn from MIT database. The MIT trial records are used to set the threshold value of the classification factor using the Receiver Operating Characteristics (ROC). This threshold value is used then to classify the MIT challenge (test) records to obtain the efficiency of classification. The new algorithm classifies correctly 30/30 of MIT-test data using different wavelet filters. Comparison of the results of different wavelet filters is done in terms of complexity and distance parameters. The method is also compared with other two techniques using wavelets in their analysis. The consistency of the results is examined using the leave-one-out technique.

Original languageEnglish
Pages (from-to)151-165
Number of pages15
JournalTechnology and Health Care
Volume13
Issue number3
Publication statusPublished - 2005

Fingerprint

Entropy
Obstructive Sleep Apnea
Frequency bands
ROC Curve
Sleep
Databases

Keywords

  • Obstructive sleep apnea
  • Patient classification
  • Soft decision
  • Wavelet transform

ASJC Scopus subject areas

  • Biophysics

Cite this

@article{4f20b06eb45e460ca55f87e51338aad9,
title = "A soft decision algorithm for obstructive sleep apnea patient classification based on fast estimation of wavelet entropy of RRI data",
abstract = "A soft decision algorithm for Obstructive Sleep Apnea (OSA) patient classification using R-R interval (RRI) data is investigated. This algorithm is based on fast and approximate estimation of the entropy of the wavelet-decomposed bands of the RRI data. The classification is done on the whole record as OSA patient or non-patient (normal). The ratio of the estimated entropy of the low-frequency (LF) band to that of the very-low frequency (VLF) band is used as a classification factor. RRI data used in this work are drawn from MIT database. The MIT trial records are used to set the threshold value of the classification factor using the Receiver Operating Characteristics (ROC). This threshold value is used then to classify the MIT challenge (test) records to obtain the efficiency of classification. The new algorithm classifies correctly 30/30 of MIT-test data using different wavelet filters. Comparison of the results of different wavelet filters is done in terms of complexity and distance parameters. The method is also compared with other two techniques using wavelets in their analysis. The consistency of the results is examined using the leave-one-out technique.",
keywords = "Obstructive sleep apnea, Patient classification, Soft decision, Wavelet transform",
author = "Abdulnasir Hossen",
year = "2005",
language = "English",
volume = "13",
pages = "151--165",
journal = "Technology and Health Care",
issn = "0928-7329",
publisher = "IOS Press",
number = "3",

}

TY - JOUR

T1 - A soft decision algorithm for obstructive sleep apnea patient classification based on fast estimation of wavelet entropy of RRI data

AU - Hossen, Abdulnasir

PY - 2005

Y1 - 2005

N2 - A soft decision algorithm for Obstructive Sleep Apnea (OSA) patient classification using R-R interval (RRI) data is investigated. This algorithm is based on fast and approximate estimation of the entropy of the wavelet-decomposed bands of the RRI data. The classification is done on the whole record as OSA patient or non-patient (normal). The ratio of the estimated entropy of the low-frequency (LF) band to that of the very-low frequency (VLF) band is used as a classification factor. RRI data used in this work are drawn from MIT database. The MIT trial records are used to set the threshold value of the classification factor using the Receiver Operating Characteristics (ROC). This threshold value is used then to classify the MIT challenge (test) records to obtain the efficiency of classification. The new algorithm classifies correctly 30/30 of MIT-test data using different wavelet filters. Comparison of the results of different wavelet filters is done in terms of complexity and distance parameters. The method is also compared with other two techniques using wavelets in their analysis. The consistency of the results is examined using the leave-one-out technique.

AB - A soft decision algorithm for Obstructive Sleep Apnea (OSA) patient classification using R-R interval (RRI) data is investigated. This algorithm is based on fast and approximate estimation of the entropy of the wavelet-decomposed bands of the RRI data. The classification is done on the whole record as OSA patient or non-patient (normal). The ratio of the estimated entropy of the low-frequency (LF) band to that of the very-low frequency (VLF) band is used as a classification factor. RRI data used in this work are drawn from MIT database. The MIT trial records are used to set the threshold value of the classification factor using the Receiver Operating Characteristics (ROC). This threshold value is used then to classify the MIT challenge (test) records to obtain the efficiency of classification. The new algorithm classifies correctly 30/30 of MIT-test data using different wavelet filters. Comparison of the results of different wavelet filters is done in terms of complexity and distance parameters. The method is also compared with other two techniques using wavelets in their analysis. The consistency of the results is examined using the leave-one-out technique.

KW - Obstructive sleep apnea

KW - Patient classification

KW - Soft decision

KW - Wavelet transform

UR - http://www.scopus.com/inward/record.url?scp=23944472400&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=23944472400&partnerID=8YFLogxK

M3 - Article

C2 - 15990418

AN - SCOPUS:23944472400

VL - 13

SP - 151

EP - 165

JO - Technology and Health Care

JF - Technology and Health Care

SN - 0928-7329

IS - 3

ER -