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

• Published in: IEEE transactions on biomedical circuits and systems Vol. 14; no. 2; pp. 145 - 163
• Main Authors: Wei, Ying, Zhou, Jun, Wang, Yin, Liu, Yinggang, Liu, Qingsong, Luo, Jiansheng, Wang, Chao, Ren, Fengbo, Huang, Li
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.04.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: algorithm; Algorithms; Artificial Intelligence; Artificial neural networks; biomedical application; Biomedical Engineering; Biomedical materials; Circuits; Classification; Design; EEG; EKG; Electrocardiography; Electrodiagnosis; Electroencephalography; Electromyography; Fuzzy logic; Hardware; Hearing aids; Humans; Microprocessors; Neural networks; proce-ssor; Program processors; Recurrent neural networks; Short term memory; Signal classification; Signal processing algorithms; Signal Processing, Computer-Assisted; Support vector machines; Trends
• ISSN: 1932-4545; 1940-9990; 1940-9990
• DOI: 10.1109/TBCAS.2020.2974154

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.