Efficient compression of bio-signals by using Tchebichef moments and Artificial Bee Colony

In this paper, an algorithm is proposed for efficient compression of bio-signals based on discrete Tchebichef moments and Artificial Bee Colony (ABC). The Tchebichef moments are used to extract features of the bio-signals, then, the ABC algorithm is used to select of the optimum features which achieve the best bio-signal quality for a specific compression ratio (CR). The proposed algorithm has been tested by using different datasets of Electrocardiogram (ECG), Electroencephalogram (EEG), and Electromyogram (EMG). The optimum feature selection using ABC significantly improve the quality of the reconstructed bio-signals. Different numerical experiments are performed to compress different records of ECG, EEG and EMG bio-signals by using the proposed algorithm and the most recent existing methods. The performance of the proposed algorithm and the other existing methods are evaluated using different metrics such as CR, PRD, and peak signal to noise ratio (PSNR). The comparison has shown that, at the same CR, the proposed compression algorithm yields the best quality of the reconstructed signals over the other existing methods.