Abstract
In this paper, a novel Region of Interest (ROI) based 2-Dimensional (2D) compression of ECG signals using JPEG2000 compression standard is proposed. Because of its high efficiency JPEG2000 is the global benchmark for compression of stationary images. This work is to illustrate that the JPEG2000 compression technique is not only restricted to compress images but also it can be applied to compress ECG signals. First the one dimensional ECG signal is transformed to 2D representation or image to explore the correlation among the samples and among the beats. This necessitates few steps that includes QRS detection and arrangement of QRS complex at relative position, Period sorting, mean extension, period normalization, amplitude normalization and locating ROI. Then this resultant 2D ECG data array is compressed using JPEG2000. The ROI region is extracted by applying the Otsu thresholding and the vertical projection profile on the resultant 2D ECG data. The core idea in this work is to compress the ROI region at low compression rate and non-ROI region at high compression rate. The proposed method is evaluated on the selected data from MITs Beth Israel hospital and it was conceded that this method surpasses some of the prevailing methods in the literature by attaining a higher Compression Ratio (CR) and moderate Percentage root- mean square difference (PRD).
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Shreekanth, T., Shashidhar, R. (2018). An Application of Image Processing Technique for Compression of ECG Signals Based on Region of Interest Strategy. In: Hemanth, D., Smys, S. (eds) Computational Vision and Bio Inspired Computing . Lecture Notes in Computational Vision and Biomechanics, vol 28. Springer, Cham. https://doi.org/10.1007/978-3-319-71767-8_85
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DOI: https://doi.org/10.1007/978-3-319-71767-8_85
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