IEEE Trans Biomed Circuits Syst. 2020 Apr;14(2):145-163. doi: 10.1109/TBCAS.2020.2974154. Epub 2020 Feb 17.
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.
本文综述了基于人工智能的生物医学处理算法和硬件的现状和趋势。综述和讨论了用于不同生物医学应用的算法和硬件，如心电图 (ECG)、脑电图 (EEG) 和助听器。对于算法设计，讨论了各种广泛使用的生物医学信号分类算法，包括支持向量机 (SVM)、反向传播神经网络 (BPNN)、卷积神经网络 (CNN)、概率神经网络 (PNN)、递归神经网络 (RNN)、短期记忆网络 (LSTM)、模糊神经网络等。分析和比较了分类算法在应用场景下的优缺点。还讨论了基于人工智能的生物医学处理算法和应用的研究趋势。对于硬件设计，综述和讨论了各种基于人工智能的生物医学处理器，包括心电图分类处理器、脑电图分类处理器、肌电图分类处理器和助听器处理器。分析和比较了架构和电路级别的各种技术。还讨论了基于人工智能的生物医学处理器的研究趋势。
