This paper reviews the state of the arts and trends of the AI-based biomedical processing algorithms and hardwares. The algorithms and hardwares 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.

中文翻译：

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基于AI的生物医学应用的算法和硬件设计综述。

本文回顾了基于AI的生物医学处理算法和硬件的技术现状和趋势。已经审查和讨论了用于不同生物医学应用（例如ECG，EEG和助听器）的算法和硬件。对于算法设计，已经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。在应用场景的背景下，对分类算法的优缺点进行了分析和比较。还讨论了基于AI的生物医学处理算法及其应用的研究趋势。对于硬件设计，审查和讨论了各种基于AI的生物医学处理器，包括ECG分类处理器，EEG分类处理器，EMG分类处理器和助听器处理器。已经分析和比较了有关架构和电路级的各种技术。还讨论了基于AI的生物医学处理器的研究趋势。