Key result
FPGA-based ECG analysis achieves ~98% classification accuracy while reducing resource utilization.
The proposed FPGA-based ECG analysis system using FIR filtering and neural networks achieves high classification accuracy (97.8%) with efficient hardware resource utilization.
Hypothesis-generating for efficient ECG hardware; prospective clinical validation required before any practice consideration.
This paper presents a proposed design for analyzing electrocardiography (ECG) signals. This methodology employs highpass least-square linear phase Finite Impulse Response (FIR) filtering technique to filter out the baseline wander noise embedded in the input ECG signal to the system. Discrete Wavelet Transform (DWT) was utilized as a feature extraction methodology to extract the reduced feature set from the input ECG signal. The design uses back propagation neural network classifier to classify the input ECG signal. The system is implemented on Xilinx 3AN-XC3S700AN Field Programming Gate Array (FPGA) board. A system simulation has been done. The design is compared with some other designs achieving total accuracy of 97.8%, and achieving reduction in utilizing resources on FPGA implementation.
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Egila et al. (2016) studied this question. The FPGA-based ECG analysis system achieved a total classification accuracy of 97.8% while effectively reducing resource utilization.
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