Transmission of biomedical signals through communication channels is being used increasingly in clinical practice. This technique requires dealing with large volumes of information, and the electroencephalographic (EEG) signal is an example of this situation. In the EEG, various channels are recorded during several hours, resulting in a great demand of storage capacity or channel bandwidth. This situation demands the use of efficient data compression systems. The objective of this work was to develop an efficient algorithm for EEG lossy compression. In this algorithm, the EEG signal is segmented and then decomposed through Wavelet Packets (WP). The WP decomposition coefficients are thresholded and those having absolute values below the threshold are deleted. The remaining coefficients are appropriately quantized and coded using a run-length coding scheme. The compressed EEG signal can be recovered by an inverse process. Extensive experimental tests were made by applying the algorithm to EEG records and measuring the compression rate (CR) and the distortion in signal segments. The WP transform showed a high robustness, allowing a reasonably low distortion after a compression-decompression process, for CR typically in the range 5-8. The algorithm has a relatively low computational cost, making it appropriate for practical applications.

A direct waveform mean-shape vector quantization (MSVQ) is proposed here as an alternative for electrocardiographic (ECG) signal compression. In this method, the mean values for short ECG signal segments are quantized as scalars and compression of the single-lead ECG by average beat substraction and residual differencing their waveshapes coded through a vector quantizer. An entropy encoder is applied to both, mean and vector codes, to further increase compression without degrading the quality of the reconstructed signals. In this paper, the fundamentals of MSVQ are discussed, along with various parameters specifications such as duration of signal segments, the wordlength of the mean-value quantization and the size of the vector codebook. The method is assessed through percent-residual-difference measures on reconstructed signals, whereas its computational complexity is analyzed considering its real-time implementation. As a result, MSVQ has been found to be an efficient compression method, leading to high compression ratios (CR's) while maintaining a low level of waveform distortion and, consequently, preserving the main clinically interesting features of the ECG signals. CR's in excess of 39 have been achieved, yielding low data rates of about 140 bps. This compression factor makes this technique especially attractive in the area of ambulatory monitoring.

No abstract

No abstract

No abstract