A heart disease recognition embedded system with fuzzy cluster algorithm

• Published in: Computer methods and programs in biomedicine 2013-06, Vol.110 (3), p.447-454
• Main Authors: de Carvalho, Helton Hugo, Moreno, Robson Luiz, Pimenta, Tales Cleber, Crepaldi, Paulo C, Cintra, Evaldo
• Format: Article
• Language: English
• Subjects: Algorithms; Applied sciences; Arrhythmias, Cardiac - diagnosis; Biological and medical sciences; Cluster Analysis; Computer science; control theory; systems; Data processing. List processing. Character string processing; Diagnosis, Computer-Assisted - instrumentation; Diagnosis, Computer-Assisted - statistics & numerical data; Electrocardiography - statistics & numerical data; Electrocardiography. Vectocardiography; Electrodiagnosis. Electric activity recording; Electronics; Embedded system; Exact sciences and technology; Field programmable gate arrays; Fuzzy clustering; Fuzzy Logic; Heart disease; Heart Diseases - classification; Heart Diseases - diagnosis; Humans; Integrated circuits; Integrated circuits by function (including memories and processors); Internal Medicine; Investigative techniques, diagnostic techniques (general aspects); Medical sciences; Memory organisation. Data processing; Myocardial Infarction - diagnosis; Other; Pattern Recognition, Automated - statistics & numerical data; Real-time identification; Semiconductor electronics. Microelectronics. Optoelectronics. Solid state devices; Signal Processing, Computer-Assisted; Soft-core processors; Software
• ISSN: 0169-2607; 1872-7565
• DOI: 10.1016/j.cmpb.2013.01.005

Abstract This article presents the viability analysis and the development of heart disease identification embedded system. It offers a time reduction on electrocardiogram – ECG signal processing by reducing the amount of data samples, without any significant loss. The goal of the developed system is the analysis of heart signals. The ECG signals are applied into the system that performs an initial filtering, and then uses a Gustafson–Kessel fuzzy clustering algorithm for the signal classification and correlation. The classification indicated common heart diseases such as angina, myocardial infarction and coronary artery diseases. The system uses the European electrocardiogram ST-T Database (EDB) as a reference for tests and evaluation. The results prove the system can perform the heart disease detection on a data set reduced from 213 to just 20 samples, thus providing a reduction to just 9.4% of the original set, while maintaining the same effectiveness. This system is validated in a Xilinx Spartan® -3A FPGA. The field programmable gate array (FPGA) implemented a Xilinx Microblaze® Soft-Core Processor running at a 50 MHz clock rate.