VLSI architecture of lossless ECG compression design based on fuzzy decision and optimisation method for wearable devices

Abstract

A hardware-oriented lossless electrocardiogram compression algorithm is presented for very large-scale integration (VLSI) circuit design. To achieve high performance and low complexity, a novel prediction method based on the fuzzy decision and particle swarm optimiser (PSO) was developed. The accuracy of prediction was advanced efficiently by using the PSO algorithm to find the optimal parameters, which provided 64 situations for the fuzzy decision. Moreover, a novel low-complexity and high-performance entropy-coding algorithm based on Huffman coding was developed, which used one limited Huffman coding to encode a main region and five-region codes to encode the extending regions. The average compression rate of the whole MIT-BIH Arrhythmia database was up to 2.84 by combing the proposed fuzzy-based PSO prediction and Huffman region entropy-coding techniques. The VLSI architecture contained only a 1.9 K gate count and its core area was 5965 μm2 synthesised using a 90 nm CMOS process. It consumed 201 μW when operating at a 200 MHz processing rate. Compared with previous low-complexity designs, the average compression rate is not only improved by more than 6.4% but also reduced the gate count by at least 8.2%.