通过基于注意力的CNN-LSTM混合模型利用ERP信号的非线性特征诊断阿尔茨海默病

Elias Mazrooei Rad , Sayyed Majid Mazinani , Seyyed Ali Zendehbad
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引用次数: 0

摘要

生物信号具有动态和非线性的性质,因此非线性分析对于理解信号非常重要。本研究提出了一种卷积神经网络(CNN)和长短期记忆(LSTM)混合模型,用于从脑电图(EEG)数据中获得的事件相关电位(ERP)信号诊断阿尔茨海默病(AD)。ERP信号的P300分量来源于声刺激,是AD的关键指标,其振幅和潜伏期具有特征。该模型利用相图、相关维数、熵和李亚普诺夫指数等非线性特征对AD阶段进行分类。CNN-LSTM混合架构,通过注意机制增强,捕获ERP信号的空间和时间依赖性,达到很高的准确性:健康人95%,轻度AD患者92.5%,重度AD患者97.5%。在回忆模式下,该模型对健康个体的准确率为75%,对轻度AD的准确率为72.5%,对重度AD的准确率为87.5%。结果表明,该模型优于传统方法,提供了一个鲁棒性和准确性较高的AD诊断框架。该方法的结果表明,将非线性脑电图分析与先进的深度学习方法相结合可以提供早期和精确的AD检测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Diagnosis of Alzheimer's disease using non-linear features of ERP signals through a hybrid attention-based CNN-LSTM model
Biological signals have a dynamic and non-linear nature, and hence nonlinear analysis is important for understanding the signals. In this study, a hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model is proposed for the diagnosis of Alzheimer’s disease (AD) from the Event-Related Potential (ERP) signals obtained from the Electroencephalogram (EEG) data. The P300 component of the ERP signal, derived from acoustic stimulation, is a key indicator of AD, and its amplitude and latency are characterized. By using nonlinear features such as phase diagrams, correlation dimension, entropy, and Lyapunov exponents, the proposed model classifies AD stages. The hybrid CNN-LSTM architecture, enhanced by an attention mechanism, captures both spatial and temporal dependencies in the ERP signals, achieving high accuracy: For healthy people, 95 %, for mild AD patients, 92.5 %, and for severe AD patients, 97.5 %. The model achieves 75 % accuracy in recall mode for healthy individuals, 72.5 % for mild AD, and 87.5 % for severe AD. Results show that the proposed model outperforms traditional methods and provides a robust and accurate diagnostic framework for AD. The result of this approach is to show that the combination of non-linear EEG analysis with advanced deep learning methods could provide early and precise AD detection.
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来源期刊
CiteScore
5.90
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