对比神经网络与科尔莫戈罗夫网络在阿尔茨海默病脑电图诊断中的表现
A Comprehensive Comparison Between ANNs and KANs For Classifying EEG Alzheimer's Data
- 用不同训练参数比较ANN与KAN在脑电信号分类中的表现
- 在多种学习率、节点数和时序段下，ANN准确率更高
- 适合关注脑疾病早期诊断模型选择的研究者
阿尔茨海默病是一种全球范围内影响数百万人的不可治愈认知障碍。尽管已有部分诊断方法，但多数难以在早期阶段识别该病。近年来，研究人员探索使用脑电图（EEG）技术进行诊断，因为其可非侵入性记录大脑电活动，且患者与健康人群的脑电信号存在显著差异。过去，人工神经网络（ANN）已被用于从EEG数据中预测阿尔茨海默病，但存在误诊问题。本研究在多种时间窗、学习率和神经元数量条件下，系统比较了人工神经网络（ANNs）与科尔莫戈罗夫-阿诺德网络（KANs）的损失表现。结果表明，在所有测试参数组合下，ANNs在预测阿尔茨海默病方面均表现出更高的准确性。
原文摘要 · Abstract (English)
Alzheimer's Disease is an incurable cognitive condition that affects thousands of people globally. While some diagnostic methods exist for Alzheimer's Disease, many of these methods cannot detect Alzheimer's in its earlier stages. Recently, researchers have explored the use of Electroencephalogram (EEG) technology for diagnosing Alzheimer's. EEG is a noninvasive method of recording the brain's electrical signals, and EEG data has shown distinct differences between patients with and without Alzheimer's. In the past, Artificial Neural Networks (ANNs) have been used to predict Alzheimer's from EEG data, but these models sometimes produce false positive diagnoses. This study aims to compare losses between ANNs and Kolmogorov-Arnold Networks (KANs) across multiple types of epochs, learning rates, and nodes. The results show that across these different parameters, ANNs are more accurate in predicting Alzheimer's Disease from EEG signals.
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