# Review of medical data analysis based on spiking neural networks

arXiv: https://arxiv.org/abs/2212.02234 · HTML: https://mlsanity.com/paper/2212.02234/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 71 references that are in the map.
- First posted on arXiv 13 Nov 2022.

## Builds on (most cited first, 6 of 6)

- [MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis](https://mlsanity.com/paper/2010.14925/) (2020)
- [Machine Learning for Predicting Epileptic Seizures Using EEG Signals: A Review](https://mlsanity.com/paper/2002.01925/) (2020)
- [Ultra Low-Power and Real-time ECG Classification Based on STDP and R-STDP Neural Networks for Wearable Devices](https://mlsanity.com/paper/1905.02954/) (2019)
- [Artificial Neural Network Based Breast Cancer Screening: A Comprehensive Review](https://mlsanity.com/paper/2006.01767/) (2020)
- [Unsupervised Heart-rate Estimation in Wearables With Liquid States and A Probabilistic Readout](https://mlsanity.com/paper/1708.05356/) (2017)
- [Energy-Efficient Respiratory Anomaly Detection in Premature Newborn Infants](https://mlsanity.com/paper/2202.10570/) (2022)

## Built on by (most cited first, 1 of 1)

- [Membership Privacy Evaluation in Deep Spiking Neural Networks](https://mlsanity.com/paper/2409.19413/) (2024)

## Abstract (arXiv)

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.