Multichannel biomedical time series clustering via hierarchical probabilistic latent semantic analysis
Data(s) |
01/11/2014
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Resumo |
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. |
Identificador | |
Idioma(s) |
eng |
Publicador |
Elsevier Ireland |
Relação |
http://dro.deakin.edu.au/eserv/DU:30070373/wang-multichannelbiomedical-2014.pdf http://www.dx.doi.org/10.1016/j.cmpb.2014.06.014 http://www.ncbi.nlm.nih.gov/pubmed/25023531 |
Direitos |
2014, Elsevier |
Palavras-Chave | #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 |
Tipo |
Journal Article |