Review of medical data analysis based on spiking neural networks

TL;DR

This paper reviews the application of spiking neural networks in medical data analysis, highlighting their potential to improve diagnosis efficiency and accuracy over traditional neural networks.

Contribution

It summarizes recent research on spiking neural networks for medical data classification and diagnosis, and discusses their advantages, disadvantages, and future development directions.

Findings

Spiking neural networks can classify EEG, ECG, EMG signals, and MRI images effectively.

Compared to traditional neural networks, they offer lower energy consumption and latency.

The paper identifies current limitations and future prospects of pulsed neural networks in medicine.

Abstract

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.