Detection of transient ST segment episodes during ambulatory ECG monitoring
Computers and Biomedical Research
An FPGA Based Arrhythmia Recognition System for Wearable Applications
ISDA '09 Proceedings of the 2009 Ninth International Conference on Intelligent Systems Design and Applications
Classification of Cardiac Arrhythmias Using Interval Type-2 TSK Fuzzy System
KSE '09 Proceedings of the 2009 International Conference on Knowledge and Systems Engineering
A Preferable Method on Digital Filter in ECG Signal's Processing Based on FPGA
IITSI '10 Proceedings of the 2010 Third International Symposium on Intelligent Information Technology and Security Informatics
Supervised fuzzy clustering for rule extraction
IEEE Transactions on Fuzzy Systems
Computer Methods and Programs in Biomedicine
Computer Methods and Programs in Biomedicine
Computer Methods and Programs in Biomedicine
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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^(R)-3A FPGA. The field programmable gate array (FPGA) implemented a Xilinx Microblaze^(R) Soft-Core Processor running at a 50MHz clock rate.