Multichannel biomedical time series clustering via hierarchical probabilistic latent semantic analysis

Jin Wang, X. Sun, S. Nahavandi, A.Z. Kouzani, Y. Wu, M. She

    Research output: Contribution to journalArticlepeer-review

    8 Citations (Scopus)


    © 2014 Elsevier Ireland Ltd. 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.
    Original languageEnglish
    Pages (from-to)238-246
    JournalComputer Methods and Programs in Biomedicine
    Issue number2
    Publication statusPublished - 2014


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