title: Multichannel biomedical time series clustering via hierarchical probabilistic latent semantic analysis.
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.
authors.affiliation: School of Computer Science & Software Engineering, The University of Western Australia, Australia; Center for Intelligent Systems Research, Deakin University, Australia. Electronic address: jay.wangjin@gmail.com.
authors.forename: Jin
authors.initials: J
authors.lastname: Wang
authors.affiliation: Center for Intelligent Systems Research, Deakin University, Australia; School of Engineering, Deakin University, Australia.
authors.forename: Xiangping
authors.initials: X
authors.lastname: Sun
authors.affiliation: Center for Intelligent Systems Research, Deakin University, Australia.
authors.forename: Saeid
authors.initials: S
authors.lastname: Nahavandi
authors.affiliation: School of Engineering, Deakin University, Australia.
authors.forename: Abbas
authors.initials: A
authors.lastname: Kouzani
authors.affiliation: School of Mechanical Engineering and Automation, Wuhan Textile University, Wuhan, PR China.
authors.forename: Yuchuan
authors.initials: Y
authors.lastname: Wu
authors.affiliation: Center for Intelligent Systems Research, Deakin University, Australia; School of Engineering, Deakin University, Australia.
authors.forename: Mary
authors.initials: M
authors.lastname: She
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004562:Electrocardiography / Q000379:methods*; D005260:Female; D006339:Heart Rate / Q000502:physiology*; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D015203:Reproducibility of Results; D012660:Semantics; D012680:Sensitivity and Specificity; D055815:Young Adult
keywords: Bag-of-words; ECG; PLSA; Topic model; Unsupervised learning