An Adaptive Feature Extraction Algorithm For Classification Of Seismocardiographic Signals

Keywords

adaptive feature extraction; cardiorespiratory; classification; Seismocardiography (SCG); support vector machine (SVM)

Abstract

This paper proposes a novel adaptive feature extraction algorithm for seismocardiographic (SCG) signals. The proposed algorithm divides the SCG signal into a number of bins., where the length of each bin is determined based on the signal change within that bin. For example., when the signal variation is steeper., the bins are shorter and vice versa. The proposed algorithm was used to extract features of the SCG signals recorded from 7 healthy individuals (Age: 29.4±4.5 years) during different lung volume phases. The output of the feature extraction algorithm was fed into a support vector machines classifier to classify SCG events into two classes of high and low lung volume (HLV and LLV). The classification results were compared with currently available non-adaptive feature extraction methods for different number of bins. Results showed that the proposed algorithm led to a classification accuracy of 90%. The proposed algorithm outperformed the non-adaptive algorithm., especially as the number of bins was reduced. For example, for 16 bins, F1 score for the adaptive and non-adaptive methods were 0.91±0.05 and 0.63±0.08., respectively.

Publication Date

10-1-2018

Publication Title

Conference Proceedings - IEEE SOUTHEASTCON

Volume

2018-April

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/SECON.2018.8478958

Socpus ID

85056116933 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85056116933

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