Multichannel biomedical time series clustering via hierarchical probabilistic latent semantic analysis

Wang,J, Sun,X, Nahavandi,S, Kouzani,A, Wu,Y and She,M 2014, Multichannel biomedical time series clustering via hierarchical probabilistic latent semantic analysis, Computer methods and programs in biomedicine, vol. 117, no. 2, pp. 238-246, doi: 10.1016/j.cmpb.2014.06.014.

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Title Multichannel biomedical time series clustering via hierarchical probabilistic latent semantic analysis
Author(s) Wang,J
Sun,X
Nahavandi,SORCID iD for Nahavandi,S orcid.org/0000-0002-0360-5270
Kouzani,AORCID iD for Kouzani,A orcid.org/0000-0002-6292-1214
Wu,Y
She,MORCID iD for She,M orcid.org/0000-0001-8191-0820
Journal name Computer methods and programs in biomedicine
Volume number 117
Issue number 2
Start page 238
End page 246
Publisher Elsevier Ireland
Place of publication Shannon, Ireland
Publication date 2014-11
ISSN 0169-2607
1872-7565
Keyword(s) Bag-of-words
ECG
PLSA
Topic model
Unsupervised learning
Science & Technology
Technology
Life Sciences & Biomedicine
Computer Science, Interdisciplinary Applications
Computer Science, Theory & Methods
Engineering, Biomedical
Medical Informatics
Computer Science
Engineering
WAVELET TRANSFORM
CLASSIFICATION
HEARTBEAT
SIGNAL
Summary Biomedical time series clustering that automatically groups a collection of time series according to their internal similarity is of importance for medical record management and inspection such as bio-signals archiving and retrieval. In this paper, a novel framework that automatically groups a set of unlabelled multichannel biomedical time series according to their internal structural similarity is proposed. Specifically, we treat a multichannel biomedical time series as a document and extract local segments from the time series as words. We extend a topic model, i.e., the Hierarchical probabilistic Latent Semantic Analysis (H-pLSA), which was originally developed for visual motion analysis to cluster a set of unlabelled multichannel time series. The H-pLSA models each channel of the multichannel time series using a local pLSA in the first layer. The topics learned in the local pLSA are then fed to a global pLSA in the second layer to discover the categories of multichannel time series. Experiments on a dataset extracted from multichannel Electrocardiography (ECG) signals demonstrate that the proposed method performs better than previous state-of-the-art approaches and is relatively robust to the variations of parameters including length of local segments and dictionary size. Although the experimental evaluation used the multichannel ECG signals in a biometric scenario, the proposed algorithm is a universal framework for multichannel biomedical time series clustering according to their structural similarity, which has many applications in biomedical time series management.
Language eng
DOI 10.1016/j.cmpb.2014.06.014
Field of Research 080204 Mathematical Software
Socio Economic Objective 890201 Application Software Packages (excl. Computer Games)
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2014, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30070373

Document type: Journal Article
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