Classification Of Seismocardiographic Cycles Into Lung Volume Phases

Abstract

In this study, a machine learning algorithm was developed to classify seismocardiographic (SCG) signals occurring during low and high lung volumes. The results demonstrated that morphological differences can be observed in SCG waveforms during respiration. SCG events were classified using a Radial Basis Function (RBF) support vector machine (SVM) algorithm into the two classes of low and high lung volume. Classification accuracy was found to be about 75%.

Publication Date

7-1-2017

Publication Title

2017 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2017 - Proceedings

Volume

2018-January

Number of Pages

1-2

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/SPMB.2017.8257033

Socpus ID

85050545748 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85050545748

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