Ubiquitous evolvable hardware system for heart disease diagnosis applications

Authors:
Yong-Min Lee;Chang-Seok Choi;Seung-Gon Hwang;Hyun Dong Kim;Chul Hong Min;Jae-Hyun Park;Hanho Lee;Tae Seon Kim;Chong-Ho Lee
Affiliations:
School of Information and Communication Engineering, Inha University, Incheon, Korea;School of Information and Communication Engineering, Inha University, Incheon, Korea;School of Information and Communication Engineering, Inha University, Incheon, Korea;School of Information, Communication and Electronics Engineering, Catholic University of Korea, Bucheon, Korea;School of Information, Communication and Electronics Engineering, Catholic University of Korea, Bucheon, Korea;School of Information and Communication Engineering, Inha University, Incheon, Korea;School of Information and Communication Engineering, Inha University, Incheon, Korea;School of Information, Communication and Electronics Engineering, Catholic University of Korea, Bucheon, Korea;School of Information and Communication Engineering, Inha University, Incheon, Korea
Venue:
ARC'07 Proceedings of the 3rd international conference on Reconfigurable computing: architectures, tools and applications
Year:
2007

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Abstract

This paper presents a stand-alone ubiquitous evolvable hard-ware (u-EHW) system that is effective for automated heart disease diagnosis applications. The proposed u-EHW system consists of a novel reconfigurable evolvable hardware (rEHW) chip, an evolvable embedded processor, and a hand-held terminal. Through adaptable reconfiguration of the filter components, the proposed u-EHW system can effectively remove various types of noise from ECG signals. Filtered signals are sent to a PDA for automated heart disease diagnosis, and diagnosis results with filtered signals are sent to the medical doctor's computer for final decision. The rEHW chip features FIR filter evolution capability, which is realized using a genetic algorithm. A parallel genetic algorithm evolves FIR filters to find the optimal filter combination configuration, associated parameters, and the structure of the feature space adaptively to noisy environments for adaptive signal processing. The embedded processor implements feature extraction and a classifier for each group of signal types.