Cardiovascular Disease Classification using ECG Signal
How to Cite?
Salam Devayani Devi, Dr. Loitongbam Surajkumar Singh, "Cardiovascular Disease Classification using ECG Signal," International Journal of Engineering Trends and Technology, vol. 69, no. 11, pp. 134-139, 2021. Crossref, https://doi.org/10.14445/22315381/IJETT-V69I11P217
Abstract
This paper presents an ECG signal monitoring system to classify normal and abnormal ECG signals. The system consists of pre-processing step followed by classification. In the pre-processing step, reduction of the noise contained in the ECG signal is performed. Then the filtered signal is given as the input to the classification block. A long short-term neural network is used for the classification of arrhythmia from the normal ECG signal beats. The benefit of this work is the reduction in the size and power consumption compared to other approaches. This monitoring system is implemented on Spartan 7 FPGA using Xilinx vivado software, achieving an accuracy of 99.92% with a power consumption of 0.009W. The ECG dataset used in this work is the MIT-BIH arrhythmia database.
Keywords
ECG, FPGA, LSTM, cardiovascular disease classification, abnormal ECG signal.
Reference
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