A computationally efficient approach for ECG signal denoising and data compression

In this paper, a new approach to remove noise present in ECG signal is proposed. Baseline wander and high frequency noise is eliminated by using computationally efficient linear phase filter ie. interpolated finite impulse response (IFIR) filter. The IFIR filter is designed by using Kaiser window function to achieve high stop band attenuation. As compared to other methods, the technique presented could achieve a reduction in computational complexity by 80.14 percent. Data compression is also performed in this study using wavelet packet decomposition along with Run-length encoding. Run-length encoding is used to improve the compression performance. For evaluation of the performance of IFIR filter, computational cost reduction (CRC) parameter is used, which directly depended on multipliers and adders. Different fidelity factors are considered to evaluate the performance of the proposed data compression method, viz., compression ratio (CR), signal to noise ratio (SNR), retained energy (RE) and percent root mean square difference (PRD), their magnitude being 25.13, 38.93, 99.10 and 1.75, respectively. MIT-BIH arrhythmia database has been utilized to judge the entire set of computations mentioned above noise removal and ECG signal compression. This work also includes beat detection of original and reconstructed signals. Simulated results show that decompressed signal is a replica of the input signal.