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探索神经网络在生物医学应用中的潜力:优势,局限性和未来前景

Eunsu Kim¹, Youngmin Kim¹

¹School of Electronic and Electrical engineering, Hongik University, Seoul, 04066 Korea.

Biomedical engineering letters

|September 2, 2024

概括

尖端神经网络 (SNN) 为生物医学应用提供了独特的优势,例如分析脑电图 (EEG) 和心电图 (ECG) 信号,尽管存在一些限制.

科学领域:

生物医学工程生物医学工程
计算神经科学是一种神经科学.
人工智能的人工智能

背景情况:

尖端神经网络 (SNN) 越来越多地被探索其在处理时间数据方面的效率.
像LSTM这样的传统模型在特定的生物医学信号处理任务中存在局限性.
在神经形态计算,脑计算机接口 (BCI) 和疾病诊断方面,SNN是有前途的.

研究的目的:

综合探索SNNs在生物医学领域的利用.
将SNN的实验验证优势与替代模型进行比较.
在医疗保健中批判性地检查SNN分类器和算法的局限性.

主要方法:

对SNN在生物医学中的应用现有文献的审查.
在处理生物医学信号 (EEG,EMG,ECG) 中分析SNN性能.
在特定的解剖区域 (眼睛,鼻子) 探索SNNs,以及诸如手势分类等应用.

主要成果:

在处理和分析EEG,EMG和ECG等生物医学信号方面,SNN具有显著的优势.
通过实验验证了SNNs对LSTM等模型的优势.
突出了在特定解剖区域的手势分类,运动解码和信号分析中的应用.

结论:

在生物医学应用方面,SNN是一个强大的工具,具有明显的优势.
了解SNN的局限性和挑战对于其在医疗保健中的有效实施至关重要.
对SNN模型的进一步研究可以释放整个生物医学领域的变革潜力.

关键词:

生物医学生物医学这是一个ECGECGECGECGECG.这是一个EEGEEGEEGEEGEEGEEGEEG.在EMGEMGEMGEMGEMGEMGEMGEMGEM 低功率低功率低功率在这里,SNN SNN

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