Multichannel biomedical time series clustering via hierarchical probabilistic latent semantic analysis

作者：Wang Jin^*; Sun Xiangping; Nahavandi Saeid; Kouzani Abbas; Wu Yuchuan; She Mary

来源：Computer Methods and Programs in Biomedicine, 2014, 117(2): 238-246.

DOI：10.1016/j.cmpb.2014.06.014

• Bag-of-words
• PLSA
• Topic model
• Unsupervised learning
• ECG

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.

• 出版日期2014-11
• 单位迪肯大学; 武汉纺织大学