Conference Proceedings

Support Vector Regression Model for Assessing Respiratory Effort during Central Apnea Events Using ECG Signals

AH Khandoker, M Palaniswami, A Murray (ed.)

CINC: 2009 36TH ANNUAL COMPUTERS IN CARDIOLOGY CONFERENCE | IEEE | Published : 2009

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 wave..

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Grants

Funding Acknowledgements

This work was partially supported by an early career grant awarded to AH Khandoker by University of Melbourne.