Heart beat data compression using temporal beats alignment and 2-D transforms

Adaptive Signal Models

“…In other words, the reconstruction error measure depends on the very nature of the signal and the applications of the representation. One example of this is compression of ambulatory electrocardiogram signals for future off-line analysis; for this purpose it is only important to preserve a few key features of the heartbeat signal, and thus high compression rates can be achieved [128].…”

“…To account for both types of redundancy, the PSR can be processed by a two-dimensional wavelet transform; for separable twodimensional wavelets, this amounts to coupling the PSWT and the DWT. A similar approach has been applied successfully to electrocardiogram data compression [128]. It is an open question, however, if this method can be used for high-quality compression of speech or audio.…”

“…Various methods of ambulatory ECG signal compression have been presented in the literature; these rely on either the redundancy between neighboring samplings of the signal or the redundancy between adjacent periods [108,134]. A method exploiting both forms of redundancy is proposed in [128]; here, the signal is segmented into pulses and arranged into a structure resembling a PSR matrix. Then, this structure is interpreted as an image and compressed using a two-dimensional discrete cosine transform (DCT); the compression is structured such that important features of the pulse shape are represented accurately.…”