title: Biomedical time series clustering based on non-negative sparse coding and probabilistic topic model.
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.
authors.affiliation: Center for Intelligent Systems Research, Deakin University, Waurn Ponds 3217, Australia. jay.wangjin@gmail.com
authors.forename: Jin
authors.initials: J
authors.lastname: Wang
authors.forename: Ping
authors.initials: P
authors.lastname: Liu
authors.forename: Mary
authors.initials: M
authors.lastname: F H She
authors.forename: Saeid
authors.initials: S
authors.lastname: Nahavandi
authors.forename: Abbas
authors.initials: A
authors.lastname: Kouzani
mesh_terms: D016000:Cluster Analysis; D004562:Electrocardiography; D004569:Electroencephalography; D006801:Humans; D015233:Models, Statistical; D011336:Probability; D059629:Signal-To-Noise Ratio
keywords: Bag-of-Words; Probabilistic topic model; Sparse coding; Unsupervised learning