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Despite the growing interest in the transmission and storage of electromyographic signals for long periods of time, only a few studies dealt with the compression of these signals. In this article we propose a novel algorithm for EMG signal compression using the wavelet transform. For EMG signals acquired during isometric contractions, the proposed algorithm provided compression factors ranging from 50 to 90%, with an average PRD ranging from 1.4 to 7.5%. The proposed method uses a new scheme for normalizing the wavelet coefficients. The wavelet coefficients are quantized using dynamic bit allocation, which is carried out by a Kohonen Neural Network. After the quantization, these coefficients are encoded using an arithmetic encoder. The compression results using the proposed algorithm were compared to other algorithms based on the wavelet transform. The proposed algorithm had a better performance in compression ratio and fidelity of the reconstructed signal.