Medical data mainly includes various types of biomedical signals and medical images, which can be used by professional doctors to make judgments on patients' health conditions. However, the interpretation of medical data requires a lot of human cost and there may be misjudgments, so many scholars use neural networks and deep learning to classify and study medical data, which can improve the efficiency and accuracy of doctors and detect diseases early for early diagnosis, etc. Therefore, it has a wide range of application prospects. However, traditional neural networks have disadvantages such as high energy consumption and high latency (slow computation speed). This paper presents recent research on signal classification and disease diagnosis based on a third-generation neural network, the spiking neuron network, using medical data including EEG signals, ECG signals, EMG signals and MRI images. The advantages and disadvantages of pulsed neural networks compared with traditional networks are summarized and its development orientation in the future is prospected.

翻译：医学数据主要包括各种生物医学信号和医学影像，专业医生可据此判断患者健康状况。然而，医学数据的解译需要大量人力成本且可能存在误判，因此众多学者利用神经网络与深度学习对医学数据进行分类研究，这不仅能提高医生的诊疗效率与准确性，还能实现疾病的早期检测与早期诊断，具有广阔的应用前景。但传统神经网络存在能耗高、延迟大（计算速度慢）等缺陷。本文综述了基于第三代神经网络——脉冲神经网络（Spiking Neural Network，SNN），利用包括脑电图（EEG）信号、心电图（ECG）信号、肌电图（EMG）信号及磁共振成像（MRI）图像在内的医学数据进行信号分类与疾病诊断的最新研究进展，总结了脉冲神经网络相对于传统网络的优劣，并展望了其未来的发展方向。