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Biomedical time series clustering based on non-negative sparse coding and probabilistic topic model
Wang, Jin Liu, Ping She, Mary F. H. Nahavandi, Saeid Kouzani, Abbas
Wang, Jin
Liu, Ping
She, Mary F. H.
Nahavandi, Saeid
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.
Keywords
unsupervised learning, bag-of-words, probabilistic topic model, sparse coding
Date
2013
Type
Journal article
Journal
Computer Methods and Programs in Biomedicine
Book
Volume
111
Issue
3
Page Range
629-641
Article Number
ACU Department
Non-faculty
Collections
Relation URI
Source URL
Event URL
Open Access Status
License
File Access
Controlled