Research Article
A New Approach to Compressing ECG Signals with Trained Overcomplete Dictionary
@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
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.
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