A new non-exact aho-corasick framework for ECG classification

Authors:
Kuo-Kun Tseng;Fu-Fu Zeng;Huang-Nan Huang;Yiming Liu;Jeng-Shyang Pan;W. H. Ip;C. H. Wu
Affiliations:
Shenzhen Key Laboratory of Internet Information Collaboration and Harbin Institute of Technology Shenzhen Graduate School, China;Shenzhen Key Laboratory of Internet Information Collaboration and Harbin Institute of Technology Shenzhen Graduate School, China;Department of Mathematics, Tunghai University, Taiwan;Shenzhen Key Laboratory of Internet Information Collaboration and Harbin Institute of Technology Shenzhen Graduate School, China;Shenzhen Key Laboratory of Internet Information Collaboration and Harbin Institute of Technology Shenzhen Graduate School, China;Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong;Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong
Venue:
ACM SIGARCH Computer Architecture News
Year:
2013

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Abstract

The Aho-Corasick (AC) algorithm is a popular and useful exact string matching algorithm for text searching and deep packet inspection. However, it has seldom been used for non-exact classification or identification. We propose a novel framework to make use of AC for non-exact matching in the ECG identification. The AC classification (ACC) algorithm converts ECG waveforms into several short patterns for AC, and decides the identification result by AC matched counting value. In our experiments, the results are surprisingly good and superior to previous algorithms. So, we designed an AC algorithm application for non-exact classification with high accuracy. Meanwhile, ACC inherits the advantage from AC of being capable of handling a large pattern set with linear time complexity.