Efficient EPE based thresholding and adaptive coding for wavelet based ECG compression

Electrocardiogram (ECG) is an efficient and non-invasive application to monitor the condition of the heart. This has become the need of the hour owing to an exponential increase in the number of cases of heart-related diseases. An efficient compression algorithm is beneficial to store, process and transmit large amounts of ECG data. The wavelet transform is a powerful tool to achieve signal compression, especially in biomedical signals because of its energy compaction property. This research work proposes a wavelet-based ECG compression method, using Energy Packing Efficiency (EPE) based thresholds. EPE based thresholding scheme for compression exploits the energy compaction property of the wavelet transform. The coefficients obtained after thresholding are further efficiently encoded by an improved adaptive encoding technique, which enhances the compression performance. This work has been validated on MIT-BIH databases and it was found that the percentage root mean square difference (PRD) and compression ratio (CR) achieved were better than existing schemes.