Efficient ECG Compression and QRS Detection for E-Health Applications

• Published in: Scientific reports Vol. 7; no. 1; p. 459
• Main Authors: Elgendi, Mohamed, Mohamed, Amr, Ward, Rabab
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 28-03-2017; Nature Portfolio
• Subjects: 639/166/985; 692/53/2421; 9/10; Humanities and Social Sciences; multidisciplinary; Science; Science (multidisciplinary)
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-017-00540-x

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.