학술논문
A Low-Power ABR Characteristic Waveform Automatic Detection Algorithm Design and FPGA Implementation
Document Type
Conference
Author
Source
2024 International Conference on Microelectronics (ICM) Microelectronics (ICM), 2024 International Conference on. :1-5 Dec, 2024
Subject
Language
ISSN
2159-1679
Abstract
Portable, low-power ABR characteristic waveform automatic identification devices have broad application prospects in clinical and scientific research. This paper proposes an FPGA-based ABR characteristic waveform automatic identification device. The device integrates an ABR automatic identification algorithm, which outputs the latencies of waves I, III, and V with the ABR waveform as input into the system. The algorithm consists of two parts: a filtering algorithm and a U-Net neural network algorithm, achieving an average accuracy of 91.96% on a self-built dataset. Compared to using only the U-Net neural network algorithm, this method reduces the energy consumption by 19.64% on the self-built dataset, which is of great practical value in scenarios that require long-term or repeated ABR characteristic waveform automatic detection tasks.