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FPGA Based 1D CNN Accelerator For Real Time Arrhythmia Classification by Debasish Mukherjee is a document available to read on EtoBox.

What is FPGA Based 1D CNN Accelerator For Real Time Arrhythmia Classification about?

This research presents a lightweight 1D convolutional neural network (LW-CNN) designed for real-time arrhythmia classification using ECG signals, achieving 99.59% accuracy with reduced computational complexity. The study implements model compression techniques and a multiplication-free convolutional processing unit on an FPGA, resulting in a classification accuracy of 96.55%, a latency of 63 ms, and low power consumption. The findings highlight the potential of FPGA-based solutions for efficient and effecti

Author

Debasish Mukherjee

Language

EN