Key result
Combined MDWPT and DCT algorithm achieves a ~93% average compression factor.
A novel s-EMG signal compression method combining MDWPT and DCT demonstrates superior performance compared to recent algorithms in simulation testing.
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High-fidelity cardiac signal compression may aid data handling; leaves open prospective clinical validation before adoption.
A new Modified Discrete Wavelets Packets Transform (MDWPT) based method for the compression of Surface EMG signal (s-EMG) data is presented. A Modified Discrete Wavelets Packets Transform (MDWPT) is applied to the digitized s-EMG signal. A Discrete Cosine Transforms (DCT) is applied to the MDWPT coefficients (only on detail coefficients). The MDWPT+ DCT coefficients are quantized with a Uniform Scalar Dead-Zone Quantizer (USDZQ). An arithmetic coder is employed for the entropy coding of symbol streams. The proposed approach was tested on more than 35 actuals S-EMG signals divided into three categories. The proposed approach was evaluated by the following parameters: Compression Factor (CF), Signal to Noise Ratio (SNR), Percent Root mean square Difference (PRD), Mean Frequency Distortion (MFD) and the Mean Square Error (MSE). Simulation results show that the proposed coding algorithm outperforms some recently developed s-EMG compression algorithms.
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Welba et al. (2020) studied Surface Electromyography (s-EMG) signal compression (n=28). Modified Discrete Wavelet Packet Transform (MDWPT) combined with Discrete Cosine Transform (DCT) was evaluated on Compression Factor (CF), Percent Root mean square Difference (PRD), and Signal to Noise Ratio (SNR). The proposed MDWPT and DCT combination algorithm achieved an optimal average compression factor of 93.42% with a Percent Root mean square Difference of 1.06% and a Signal to Noise Ratio of 40.63 dB.
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