Low-Cost Implementation of Independent Component Analysis for Biomedical Signal Separation Using Very-Large-Scale Integration

Abstract

This brief proposes a low-cost implementation of the extended InfoMax independent component analysis (ICA) algorithm for biomedical signal separation by using very-large-scale integration (VLSI). On the basis of this algorithm, the implemented VLSI ICA core can be used to separate a mixed signal from a super-Gaussian signal source. By using mathematical operations of merging and simplification, the ICA core uses the systolic array and lookup table to implement the extended InfoMax ICA algorithm, thus achieving low cost and high speed. The measured results indicated that the ICA core consumed 9.5 mW when operated at 100 MHz using TSMC 90-nm complementary metal–oxide–semiconductor technology, with a gate count of only 36.8K. This represents a 59% reduction in area and a 35% saving in power compared with the previous design. Furthermore, the proposed ICA core was applied to mixed electrocardiogram signals, and the separated signals exhibited excellent quality.