SABiNN: FPGA Implementation of Shift Accumulate Binary Neural Network Model for Real-Time Automatic Detection of Sleep Apnea
Author:
Affiliation:
1. University of Missouri,Department of Electrical Engineering and Computer Science,Columbia,MO,USA,65211
2. University of Missouri,Department of Health Management and Informatics,Columbia,MO,USA,65211
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9806445/9806446/09806534.pdf?arnumber=9806534
Reference29 articles.
1. Detection Performance Regarding Sleep Apnea-Hypopnea Episodes with Fuzzy Logic Fusion on Single-Channel Airflow Indexes
2. Optimization of sleep apnea detection using SpO2 and ANN
3. Recurrent Neural Network Based Classification of ECG Signal Features for Obstruction of Sleep Apnea Detection
4. Obstructive sleep apnoea detection using convolutional neural network based deep learning framework
5. SpO2 based sleep apnea detection using deep learning
Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Design of a Power-Efficient Digital Classifier for Neural Network-Based Sleep Apnea Detection System;2024 IEEE International Symposium on Medical Measurements and Applications (MeMeA);2024-06-26
2. Energy-Efficient FPGA Based Sleep Apnea Detection Using EEG Signals;IEEE Access;2024
3. An Optimized Hardware Inference of SABiNN: Shift-Accumulate Binarized Neural Network for Sleep Apnea Detection;IEEE Transactions on Instrumentation and Measurement;2023
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