Biomedical time series clustering based on non-negative sparse coding and probabilistic topic model

Journal article


Wang, Jin, Liu, Ping, She, Mary F. H., Nahavandi, Saeid and Kouzani, Abbas. (2013). Biomedical time series clustering based on non-negative sparse coding and probabilistic topic model. Computer Methods and Programs in Biomedicine. 111(3), pp. 629 - 641. https://doi.org/10.1016/j.cmpb.2013.05.022
AuthorsWang, Jin, Liu, Ping, She, Mary F. H., Nahavandi, Saeid and Kouzani, Abbas
Abstract

Biomedical time series clustering that groups a set of unlabelled temporal signals according to their underlying similarity is very useful for biomedical records management and analysis such as biosignals archiving and diagnosis. In this paper, a new framework for clustering of long-term biomedical time series such as electrocardiography (ECG) and electroencephalography (EEG) signals is proposed. Specifically, local segments extracted from the time series are projected as a combination of a small number of basis elements in a trained dictionary by non-negative sparse coding. A Bag-of-Words (BoW) representation is then constructed by summing up all the sparse coefficients of local segments in a time series. Based on the BoW representation, a probabilistic topic model that was originally developed for text document analysis is extended to discover the underlying similarity of a collection of time series. The underlying similarity of biomedical time series is well captured attributing to the statistic nature of the probabilistic topic model. Experiments on three datasets constructed from publicly available EEG and ECG signals demonstrates that the proposed approach achieves better accuracy than existing state-of-the-art methods, and is insensitive to model parameters such as length of local segments and dictionary size.

Keywordsunsupervised learning; bag-of-words; probabilistic topic model; sparse coding
Year2013
JournalComputer Methods and Programs in Biomedicine
Journal citation111 (3), pp. 629 - 641
PublisherElsevier Ireland Ltd.
ISSN0169-2607
Digital Object Identifier (DOI)https://doi.org/10.1016/j.cmpb.2013.05.022
Scopus EID2-s2.0-84880602813
Page range629 - 641
Research GroupInstitute for Learning Sciences and Teacher Education (ILSTE)
Publisher's version
File Access Level
Controlled
Place of publicationIreland
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