4th International Conference on Wireless Mobile Communication and Healthcare - "Transforming healthcare through innovations in mobile and wireless technologies"

Research Article

A New Approach to Compressing ECG Signals with Trained Overcomplete Dictionary

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  • @INPROCEEDINGS{10.4108/icst.mobihealth.2014.257383,
        author={SEUNGJAE LEE and Jun Luan and Pai Chou},
        title={A New Approach to Compressing ECG Signals with Trained Overcomplete Dictionary},
        proceedings={4th International Conference on Wireless Mobile Communication and Healthcare - "Transforming healthcare through innovations in mobile and wireless technologies"},
        publisher={IEEE},
        proceedings_a={MOBIHEALTH},
        year={2014},
        month={12},
        keywords={k-svd ecg compression overcomplete dictionary},
        doi={10.4108/icst.mobihealth.2014.257383}
    }
    
  • SEUNGJAE LEE
    Jun Luan
    Pai Chou
    Year: 2014
    A New Approach to Compressing ECG Signals with Trained Overcomplete Dictionary
    MOBIHEALTH
    IEEE
    DOI: 10.4108/icst.mobihealth.2014.257383
SEUNGJAE LEE,*, Jun Luan1, Pai Chou1
  • 1: University of California Irvine
*Contact email: leesj3@uci.edu

Abstract

We propose a new ECG data compression algorithm based on a learned overcomplete dictionary to exploit the correlation between signals in adjacent heart beats. The learned overcomplete dictionary is constructed by K-SVD dictionary learning algorithm, after preprocessing and normalization of length and magnitude. Using the overcomplete dictionary, the proposed algorithm can find sparse estimation, which can repre- sent the ECG signal effectively. Experimental results on MIT-BIH arrhythmia database confirms that our proposed algorithm has high compression ratio while minimizing data distortion.