Support vector regression model for assessing respiratory effort during central apnea events using ECG signals

Ahsan H. Khandoker, M. Palaniswami

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

Abstract

The aim of the present study is to investigate whether wavelet based features of ECG signals during central sleep apnea (CSA) can act as surrogate of respiratory effort measured by respiratory inductance plethysmography (RIP). Therefore, RIP and ECG signals during 125 pre-scored CSA events and 10 seconds preceding the events were collected from 7 patients. Wavelet decompositions of ECG signals upto 10 levels were used as input to the support vector regression (SVR) model to recognize the drop in RIP signal amplitudes during CSA. Using 25-fold cross validation, an optimal showed that it correctly recognized 115 CSA events (92% detection accuracy) using a subset of selected combination of wavelet decomposition levels (level 9 and 10; 0.12-0.24 Hz) of ECG. Results suggest superior performance of SVR using ECG as the surrogate in recognizing the fall of respiratory effort during CSA.

Original languageBritish English
Title of host publicationComputers in Cardiology 2009, CinC 2009
Pages729-732
Number of pages4
StatePublished - 2009
Event36th Annual Conference of Computers in Cardiology, CinC 2009 - Park City, UT, United States
Duration: 13 Sep 200916 Sep 2009

Publication series

NameComputers in Cardiology
Volume36
ISSN (Print)0276-6574

Conference

Conference36th Annual Conference of Computers in Cardiology, CinC 2009
Country/TerritoryUnited States
CityPark City, UT
Period13/09/0916/09/09

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