Signal Folding for Efficient Classification of Near-Cyclostationary Biological Signals

Tianxiang Zheng and Pavel Loskot
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Tianxiang Zheng: ZJU-UIUC Institute (Zhejiang University-University of Illinois at Urbana-Champaign Institute), Haining 314400, China
Pavel Loskot: ZJU-UIUC Institute (Zhejiang University-University of Illinois at Urbana-Champaign Institute), Haining 314400, China

Mathematics, 2022, vol. 10, issue 2, 1-20

Abstract: The classification of biological signals is important in detecting abnormal conditions in observed biological subjects. The classifiers are trained on feature vectors, which often constitute the parameters of the observed time series data models. Since the feature extraction is usually the most time-consuming step in training a classifier, in this paper, signal folding and the associated folding operator are introduced to reduce the variability in near-cyclostationary biological signals so that these signals can be represented by models that have a lower order. This leads to a substantial reduction in computational complexity, so the classifier can be learned an order of magnitude faster and still maintain its decision accuracy. The performance of different classifiers involving signal folding as a pre-processing step is studied for sleep apnea detection in one-lead ECG signals assuming ARIMA modeling of the time series data. It is shown that the R-peak-based folding of ECG segments has superior performance to other more general, similarity based signal folding methods. The folding order can be optimized for the best classification accuracy. However, signal folding requires precise scaling and alignment of the created signal fragments.

Keywords: ARIMA; classification; cyclostationary; ECG; feature extraction; signal folding; sleep apnea (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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