Multichannel biomedical time series clustering via hierarchical probabilistic latent semantic analysis

Journal article


Wang, Jin, Sun, Xiangping, Nahavandi, Saeid, Kouzani, Abbas, Wu, Yuchuan and She, Mary. (2014). Multichannel biomedical time series clustering via hierarchical probabilistic latent semantic analysis. Computer Methods and Programs in Biomedicine. 117(2), pp. 238 - 246. https://doi.org/10.1016/j.cmpb.2014.06.014
AuthorsWang, Jin, Sun, Xiangping, Nahavandi, Saeid, Kouzani, Abbas, Wu, Yuchuan and She, Mary
Abstract

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.

Year2014
JournalComputer Methods and Programs in Biomedicine
Journal citation117 (2), pp. 238 - 246
PublisherElsevier Ireland Ltd.
ISSN0169-2607
Digital Object Identifier (DOI)https://doi.org/10.1016/j.cmpb.2014.06.014
Scopus EID2-s2.0-84908046617
Page range238 - 246
Research GroupInstitute for Learning Sciences and Teacher Education (ILSTE)
Publisher's version
File Access Level
Controlled
Place of publicationIreland
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