Wavelet compression of multichannel ECG data by enhanced set partitioning in hierarchical trees algorithm

Multichannel ECG data compression based on multiscale principal component analysis

In this paper, multiscale principal component analysis (MSPCA) is proposed for multichannel electrocardiogram (MECG) data compression. In wavelet domain, principal components analysis (PCA) of multiscale multivariate matrices of multichannel signals helps reduce dimension and remove redundant information present in signals. The selection of principal components (PCs) is based on average fractional energy contribution of eigenvalue in a data matrix. Multichannel compression is implemented using uniform quantizer and entropy coding of PCA coefficients. The compressed signal quality is evaluated quantitatively using percentage root mean square difference (PRD), and wavelet energy-based diagnostic distortion (WEDD) measures. Using dataset from CSE multilead measurement library, multichannel compression ratio of 5.98:1 is found with PRD value 2.09% and the lowest WEDD value of 4.19%. Based on, gold standard subjective quality measure, the lowest mean opinion score error value of 5.56% is found.

A method for the determination and hardware validation of biosignal processing parameters

This paper proposes an iterative procedure for the determination of parameters and working conditions of biomedical signal processing algorithms. A systematization of the process required to determine the parameters is proposed, avoiding "trial and error" tests, and considering all the specific requirements of a Telemedicine system, such as reliability and guaranteed signal reconstruction quality. An application of the method is included, also, focusing on ECG processing. Hardware platform Biolet(E) is designed for validation in an electronic implementation.