The proposed lossy ECG compression method achieved a compression ratio of 4.5 and a percentage root-mean-square difference of 0.53, with an overall QRS detection sensitivity of 99.78% and positive predictivity of 99.92%.
Does the proposed lossy ECG compression method (Method III) improve compression ratio and maintain high QRS detection accuracy compared to existing methods in ECG databases?
The proposed lossy ECG compression method achieves high compression ratios while maintaining excellent QRS detection accuracy, making it suitable for low-power wearable e-health applications.
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.
Elgendi et al. (2017) studied Cardiovascular diseases (ECG monitoring). Lossy ECG compression method (Method III) vs. Lossless and lossy ECG compression methods (Method I and Method II) was evaluated on Compression ratio, percentage root-mean-square difference, and QRS detection accuracy. The proposed lossy ECG compression method achieved a compression ratio of 4.5 and a percentage root-mean-square difference of 0.53, with an overall QRS detection sensitivity of 99.78% and positive predictivity of 99.92%.