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Elgendi M, Mohamed A, Ward R. Efficient ECG Compression and QRS Detection for E-Health Applications. Sci Rep. 2017;7(1):459doi: 10.1038/s41598-017-00540-x.
Elgendi, M., Mohamed, A., & Ward, R. (2017). Efficient ECG Compression and QRS Detection for E-Health Applications. Scientific reports, 7(1), 459. https://doi.org/10.1038/s41598-017-00540-x
Elgendi, Mohamed, et al. "Efficient ECG Compression and QRS Detection for E-Health Applications." Scientific reports vol. 7,1 (2017): 459. doi: https://doi.org/10.1038/s41598-017-00540-x
Elgendi M, Mohamed A, Ward R. Efficient ECG Compression and QRS Detection for E-Health Applications. Sci Rep. 2017 Mar 28;7(1):459. doi: 10.1038/s41598-017-00540-x. PMID: 28352071; PMCID: PMC5428727.
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Sci Rep. 2017 Mar 28;7(1):459. doi: 10.1038/s41598-017-00540-x.

Efficient ECG Compression and QRS Detection for E-Health Applications.

Scientific reports

Mohamed Elgendi, Amr Mohamed, Rabab Ward

Affiliations

  1. Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, British Columbia, Canada. [email protected].
  2. Department of Obstetrics and Gynaecology, University of British Columbia, Vancouver, British Columbia, Canada. [email protected].
  3. Department of Computer Science & Engineering, University of Qatar, Doha, Qatar.
  4. Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, British Columbia, Canada.

PMID: 28352071 PMCID: PMC5428727 DOI: 10.1038/s41598-017-00540-x

Abstract

Current medical screening and diagnostic procedures have shifted toward recording longer electrocardiogram (ECG) signals, which have traditionally been processed on personal computers (PCs) with high-speed multi-core processors and efficient memory processing. Battery-driven devices are now more commonly used for the same purpose and thus exploring highly efficient, low-power alternatives for local ECG signal collection and processing is essential for efficient and convenient clinical use. Several ECG compression methods have been reported in the current literature with limited discussion on the performance of the compressed and the reconstructed ECG signals in terms of the QRS complex detection accuracy. This paper proposes and evaluates different compression methods based not only on the compression ratio (CR) and percentage root-mean-square difference (PRD), but also based on the accuracy of QRS detection. In this paper, we have developed a lossy method (Methods III) and compared them to the most current lossless and lossy ECG compression methods (Method I and Method II, respectively). The proposed lossy compression method (Method III) achieves CR of 4.5×, PRD of 0.53, as well as an overall sensitivity of 99.78% and positive predictivity of 99.92% are achieved (when coupled with an existing QRS detection algorithm) on the MIT-BIH Arrhythmia database and an overall sensitivity of 99.90% and positive predictivity of 99.84% on the QT database.

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