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首页> 外文期刊>Computer Methods and Programs in Biomedicine: An International Journal Devoted to the Development, Implementation and Exchange of Computing Methodology and Software Systems in Biomedical Research and Medical Practice >Biomedical time series clustering based on non-negative sparse coding and probabilistic topic model
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Biomedical time series clustering based on non-negative sparse coding and probabilistic topic model

机译：基于非负稀疏编码和概率主题模型的生物医学时间序列聚类

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摘要

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.
机译：根据其基本相似性将一组未标记的时间信号分组的生物医学时间序列聚类，对于生物医学记录管理和分析（例如生物信号存档和诊断）非常有用。本文提出了一种新的长期生物医学时间序列聚类的框架，例如心电图（ECG）和脑电图（EEG）信号。具体而言，通过非负稀疏编码将从时间序列中提取的局部片段投影为经过训练的词典中少量基本元素的组合。然后，通过对时间序列中局部片段的所有稀疏系数求和来构造单词袋（BoW）表示。基于BoW表示，扩展了最初为文本文档分析开发的概率主题模型，以发现时间序列集合的潜在相似性。由于概率主题模型的统计性质，生物医学时间序列的基本相似性得到了很好的体现。对使用公开的EEG和ECG信号构建的三个数据集进行的实验表明，该方法比现有的最新方法具有更高的准确性，并且对模型参数（例如局部片段的长度和字典大小）不敏感。

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