The FPGA-based diagnostic system achieved 99.20% accuracy in detecting common arrhythmias and assessing QT-segment lengthening.
A novel FPGA-based diagnostic algorithm for wearable ECG monitoring achieved 99.20% accuracy in detecting arrhythmias and other abnormalities in noisy signals.
Absolute Event Rate: 0% vs 0%
The high mortality rate associated with cardiac abnormalities highlights the need of accurately detecting heart disorders in the early stage so to avoid severe health consequence for patients. Health trackers have become popular in the form of wearable devices. They are aimed to perform cardiac monitoring outside of medical clinics during peoples’ daily lives. Our paper proposes a new diagnostic algorithm and its implementation adopting a FPGA-based design. The conceived system automatically detects the most common arrhythmias and is also able to evaluate QT-segment lengthening and pulmonary embolism risk often caused by myocarditis. Debug and simulations have been carried out firstly in Matlab environment and then in Quartus IDE by Intel. The hardware implementation of the embedded system and the test for the functional accuracy verification have been performed adopting the DE1SoC development board by Terasic, which is equipped with the Cyclone V 5CSEMA5F31C6 FPGA by Intel. Properly modified real ECG signals corrupted by a mixture of muscle noise, electrode movement artifacts, and baseline wander are used as a test bench. A value of 99. 20% accuracy is achieved by taking into account 0. 02 mV for the root mean square value of noise voltage. The implemented low-power circuit is suitable as a wearable decision support device.
Giorgio et al. (Sat,) reported a other. The FPGA-based diagnostic system achieved 99.20% accuracy in detecting common arrhythmias and assessing QT-segment lengthening.
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