A Review of Algorithm & Hardware Design for AI-Based Biomedical Applications

Ying Wei, Jun Zhou, Yin Wang, Yinggang Liu, Qingsong Liu, Jiansheng Luo, Chao Wang, Fengbo Ren, Li Huang

Research output: Contribution to journalReview articlepeer-review

1 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number9000730
Pages (from-to)145-163
Number of pages19
JournalIEEE transactions on biomedical circuits and systems
Volume14
Issue number2
DOIs
StatePublished - Apr 2020

Keywords

  • AI
  • algorithm
  • biomedical application
  • proce-ssor

ASJC Scopus subject areas

  • Biomedical Engineering
  • Electrical and Electronic Engineering

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