In this article, an efficient algorithm for EEG signals compressing and transmission based on RLE and DWT was introduced. The compression ratio (CR) provided by this algorithm is relatively high with low percent root-mean-square difference (PRDN) values. 50 records of EEG patients were monitored from the life database. Each record of EEG signals is commonly reported at the sampling rates in clinical and research settings between 250 and 2000 Hz. On the other hand, the new EEG data collection systems are able to record at sampling rates more than 20,000 Hz. The signals can be analyzed in both the time domain (TD) and frequency domain (FD) under using DWT where it preserves the necessary and main features of the EEG signals. Next step to implement this proposed algorithm is using the thresholding and the quantization over EEG signals coefficients and then encoded the signals by using RLE that enhancement significantly the compression ratio (CR). This article presents a robust method of EEG signal compression and transmission consists of DWT (discrete wavelet transform) and RLE (run length encoding) in order to improve and enhanced the compression. The suggested model presents an average values of CR (compression ratio), PRD (percentage root mean square difference), PRDN (normalized percentage root mean square difference), QS (quality score ), and SNR (signal to noise ratio) of 44.0, 0.36, 5.87, 143, 3.53 and 59.52 alternately over 50 records of EEG data.
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