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Comput Methods Programs Biomed. 2013 Jun;110(3):447-54. doi: 10.1016/j.cmpb.2013.01.005. Epub 2013 Feb 5.
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 50MHz clock rate.
本文提出了一种用于心脏病识别的嵌入式系统的生存能力分析和开发。该系统通过减少数据样本量，在不损失任何重要信息的情况下，实现了对心电图（ECG）信号处理时间的缩短。该系统的目标是分析心脏信号。ECG 信号被应用于系统中，系统对信号进行初始滤波，然后使用 Gustafson-Kessel 模糊聚类算法进行信号分类和相关分析。分类结果显示了常见的心脏病，如心绞痛、心肌梗死和冠状动脉疾病。系统使用欧洲心电图 ST-T 数据库（EDB）作为测试和评估的参考。结果表明，该系统可以在一个由 213 个样本减少到 20 个样本的数据集上进行心脏病检测，从而将原始数据集的大小减少了 9.4%，同时保持了相同的效果。该系统在 Xilinx Spartan(®)-3A FPGA 上进行了验证。现场可编程门阵列（FPGA）实现了一个运行在 50MHz 时钟频率的 Xilinx Microblaze(®)软核处理器。
