A novel ECG data compression method based on adaptive Fourier decomposition

Chunyu Tan; Liming Zhang

doi:10.1117/12.2299967

19 December 2017 A novel ECG data compression method based on adaptive Fourier decomposition

Chunyu Tan, Liming Zhang

Proceedings Volume 10613, 2017 International Conference on Robotics and Machine Vision; 106130F (2017) https://doi.org/10.1117/12.2299967
Event: Second International Conference on Robotics and Machine Vision, 2017, Kitakyushu, Japan

Abstract

This paper presents a novel electrocardiogram (ECG) compression method based on adaptive Fourier decomposition (AFD). AFD is a newly developed signal decomposition approach, which can decompose a signal with fast convergence, and hence reconstruct ECG signals with high fidelity. Unlike most of the high performance algorithms, our method does not make use of any preprocessing operation before compression. Huffman coding is employed for further compression. Validated with 48 ECG recordings of MIT-BIH arrhythmia database, the proposed method achieves the compression ratio (CR) of 35.53 and the percentage root mean square difference (PRD) of 1.47% on average with N = 8 decomposition times and a robust PRD-CR relationship. The results demonstrate that the proposed method has a good performance compared with the state-of-the-art ECG compressors.

Citation Download Citation

Chunyu Tan and Liming Zhang "A novel ECG data compression method based on adaptive Fourier decomposition", Proc. SPIE 10613, 2017 International Conference on Robotics and Machine Vision, 106130F (19 December 2017); https://doi.org/10.1117/12.2299967

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