TY - JOUR
T1 - A Review of Algorithm & Hardware Design for AI-Based Biomedical Applications
AU - Wei, Ying
AU - Zhou, Jun
AU - Wang, Yin
AU - Liu, Yinggang
AU - Liu, Qingsong
AU - Luo, Jiansheng
AU - Wang, Chao
AU - Ren, Fengbo
AU - Huang, Li
N1 - Funding Information:
Manuscript received October 1, 2019; revised December 29, 2019; accepted February 1, 2020. Date of publication February 17, 2020; date of current version March 27, 2020. This work was supported in part by the National Key Research and Development Program of China under Grants 2019YFB2204500 and 2019YFB1311302, in part by National Science Foundation under Grant 61671277, and in part by Shandong Key Research and Development Program under Grants 2018JHZ007 and 2018GGX101051.This article was recommended by Associate Editor M. Sawan. (Corresponding author: Jun Zhou.) Ying Wei and Yinggang Liu are with the Shandong University, Jinan 250100, China (e-mail: eleweiy@sdu.edu.cn; liuyinggang@mail.sdu.edu.cn).
Publisher Copyright:
© 2007-2012 IEEE.
PY - 2020/4
Y1 - 2020/4
N2 - This paper reviews the state of the arts and trends of the AI-Based biomedical processing algorithms and hardware. The algorithms and hardware for different biomedical applications such as ECG, EEG and hearing aid have been reviewed and discussed. For algorithm design, various widely used biomedical signal classification algorithms have been discussed including support vector machine (SVM), back propagation neural network (BPNN), convolutional neural networks (CNN), probabilistic neural networks (PNN), recurrent neural networks (RNN), Short-Term Memory Network (LSTM), fuzzy neural network and etc. The pros and cons of the classification algorithms have been analyzed and compared in the context of application scenarios. The research trends of AI-Based biomedical processing algorithms and applications are also discussed. For hardware design, various AI-Based biomedical processors have been reviewed and discussed, including ECG classification processor, EEG classification processor, EMG classification processor and hearing aid processor. Various techniques on architecture and circuit level have been analyzed and compared. The research trends of the AI-Based biomedical processor have also been discussed.
AB - This paper reviews the state of the arts and trends of the AI-Based biomedical processing algorithms and hardware. The algorithms and hardware for different biomedical applications such as ECG, EEG and hearing aid have been reviewed and discussed. For algorithm design, various widely used biomedical signal classification algorithms have been discussed including support vector machine (SVM), back propagation neural network (BPNN), convolutional neural networks (CNN), probabilistic neural networks (PNN), recurrent neural networks (RNN), Short-Term Memory Network (LSTM), fuzzy neural network and etc. The pros and cons of the classification algorithms have been analyzed and compared in the context of application scenarios. The research trends of AI-Based biomedical processing algorithms and applications are also discussed. For hardware design, various AI-Based biomedical processors have been reviewed and discussed, including ECG classification processor, EEG classification processor, EMG classification processor and hearing aid processor. Various techniques on architecture and circuit level have been analyzed and compared. The research trends of the AI-Based biomedical processor have also been discussed.
KW - AI
KW - algorithm
KW - biomedical application
KW - proce-ssor
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U2 - 10.1109/TBCAS.2020.2974154
DO - 10.1109/TBCAS.2020.2974154
M3 - Review article
C2 - 32078560
AN - SCOPUS:85082634869
SN - 1932-4545
VL - 14
SP - 145
EP - 163
JO - IEEE Transactions on Biomedical Circuits and Systems
JF - IEEE Transactions on Biomedical Circuits and Systems
IS - 2
M1 - 9000730
ER -