  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.
