ECG Signal Compression Based on Optimization of Wavelet Parameters and Threshold Levels Using Evolutionary Techniques

Abstract

The ECG (electrocardiogram) signals are an indicator of the electrical activity of the heart. Given its noninvasive nature ECG are an extremely popular medium for heart checkups. With the advent of modern technology, the world is moving toward a connected environment, and with the availability of wearable devices, there is an exponential increase in the transmission and storage of ECG and other physiological signals. It becomes necessary to compress the ECG signals for storage and transmission. Therefore, this paper presents an ECG compression algorithm based on discrete wavelet transform (DWT) and several nature-inspired optimization techniques. The ECG compression method uses optimization techniques to find the optimal values of wavelet design parameters and optimal threshold levels. In the proposed work, DWT is used to decompose the signal into sub-bands, and coefficients are obtained. Then, threshold values for each sub-band are selected using the optimization algorithms. After thresholding, the coefficients are further compressed using the modified run-length encoding (MRLE). The proposed work shows promising results and the original signal features are well preserved after reconstruction. The performance of this algorithm is tested by calculating different parameters such as percentage root-mean-square difference (PRD), quality score (QS), signal-to-noise ratio (SNR), and compression ratio (CR). This method is capable of providing a higher compression ratio with minimum distortion in ECG signal.