UM
A time series pre-processing methodology with statistical and spectral analysis for classifying non-stationary stochastic biosignals
Simon Fong1; Kyungeun Cho2; Osama Mohammed3; Jinan Fiaidhi3; Sabah Mohammed3
2016-10-01
Source PublicationJournal of Supercomputing
ISSN0920-8542
Volume72Issue:10Pages:3887-3908
Abstract

Biosignal classification is an important non-invasive diagnosis tool in biomedical application, e.g. electrocardiogram (ECG), electroencephalogram (EEG), and electromyogram (EMG) that helps medical experts to automatically classify whether a sample of biosignal under test/monitor belongs to the normal type or otherwise. Most biosignals are stochastic and non-stationary in nature, that means their values are time dependent and their statistics vary over different points of time. However, most classification algorithms in data mining are designed to work with data that possess multiple attributes to capture the non-linear relationships between the values of the attributes to the predicted target class. Therefore, it has been a crucial research topic for transforming univariate time series to multivariate dataset to fit into classification algorithms. For this, we propose a pre-processing methodology called statistical feature extraction (SFX). Using the SFX we can faithfully remodel statistical characteristics of the time series via a sequence of piecewise transform functions. The new methodology is tested through simulation experiments over three representative types of biosignals, namely EEG, ECG and EMG. The experiments yield encouraging results supporting the fact that SFX indeed produces better performance in biosignal classification than traditional analysis techniques like Wavelets and LPC-CC.

KeywordBiosignal Classification Data Mining Medical Informatics Time Series Pre-processing
DOIhttps://doi.org/10.1007/s11227-016-1635-9
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000385417400012
PublisherSPRINGER, VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
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Citation statistics
Cited Times [WOS]:1   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Corresponding AuthorSimon Fong; Kyungeun Cho; Osama Mohammed; Jinan Fiaidhi; Sabah Mohammed
Affiliation1.Department of Computer and Information Science, University of Macau, Macau SAR, China
2.Department of Multimedia Engineering Dongguk University, Seoul, South Korea
3.Department of Computer Science, Lakehead University, Thunder Bay, Canada
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Simon Fong,Kyungeun Cho,Osama Mohammed,et al. A time series pre-processing methodology with statistical and spectral analysis for classifying non-stationary stochastic biosignals[J]. Journal of Supercomputing,2016,72(10):3887-3908.
APA Simon Fong,Kyungeun Cho,Osama Mohammed,Jinan Fiaidhi,&Sabah Mohammed.(2016).A time series pre-processing methodology with statistical and spectral analysis for classifying non-stationary stochastic biosignals.Journal of Supercomputing,72(10),3887-3908.
MLA Simon Fong,et al."A time series pre-processing methodology with statistical and spectral analysis for classifying non-stationary stochastic biosignals".Journal of Supercomputing 72.10(2016):3887-3908.
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