Review of medical data analysis based on spiking neural networks
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Medical data mainly includes various biomedical signals and medical images, and doctors can make judgments on the physical condition of patients through medical data. However, the interpretation of medical data requires a lot of labor costs and may be misjudged, so many scholars use neural networks and deep learning to classify and study medical data, thereby improving doctors' work efficiency and accuracy, achieving early detection of diseases and early diagnosis, so it has a wide range of application prospects. However, traditional neural networks have disadvantages such as high energy consumption and high latency (slow calculation speed). This paper introduces the research on signal classification and disease diagnosis based on the third-generation neural network - pulse neural network in recent years, using medical data, such as electroencephalogram (EEG), electrocardiogram (ECG), electromyography (EMG), magnetic resonance imaging (MRI), etc., summarizes the advantages and disadvantages of pulse neural networks compared with traditional networks, and looks forward to the future development direction.
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