Classification of temporal data using dynamic time warping and compressed learning

Shih Feng Huang, Hong Ping Lu

研究成果: 雜誌貢獻期刊論文同行評審

14 引文 斯高帕斯(Scopus)

摘要

This study proposes an algorithm combining the dynamic time warping (DTW) and compressed learning (CL) techniques for temporal data classification. The DTW is used to address nonsynchronous effects in temporal data for determining an adequate reference trajectory. The CL is employed to represent the temporal data effectively and classify the data efficiently by cooperating with the reference trajectory. By applying the proposed algorithm and four other classification methods to several data sets, the proposed algorithm is shown to have satisfactory classification accuracies within a reasonable time. According to this advantage, the proposed algorithm is extended to establish an online monitoring system to detect abnormal types of cardiac arrhythmia for users with wearable healthcare devices. The numerical results indicate that the proposed classifier has satisfactory recognition results for detecting personal abnormal heartbeats in real time.

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文章編號101781
期刊Biomedical Signal Processing and Control
57
DOIs
出版狀態已出版 - 3月 2020

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