Roncero-Parra Carlos, Parreño-Torres Alfonso, Sánchez-Reolid Roberto, Mateo-Sotos Jorge, Borja Alejandro L
Departamento de Sistemas Informáticos, Universidad de Castilla-La Mancha, Campus Universitario, Albacete, 02071, Spain.
Departamento de Ingeniería Eléctrica, Electrónica, Automática y Comunicaciones, Universidad de Castilla-La Mancha, Campus Universitario, Albacete, 02071, Spain.
Heliyon. 2024 Feb 15;10(4):e26298. doi: 10.1016/j.heliyon.2024.e26298. eCollection 2024 Feb 29.
Electroencephalography (EEG) has been a fundamental technique in the identification of health conditions since its discovery. This analysis specifically centers on machine learning (ML) and deep learning (DL) methodologies designed for the analysis of electroencephalogram (EEG) data to categorize individuals with Alzheimer's Disease (AD) into two groups: Moderate or Advanced Alzheimer's dementia. Our study is based on a comprehensive database comprising 668 volunteers from 5 different hospitals, collected over a decade. This diverse dataset enables better training and validation of our results. Among the methods evaluated, the CNN (deep learning) approach outperformed others, achieving a remarkable classification accuracy of 97.45% for patients with Moderate Alzheimer's Dementia (ADM) and 97.03% for patients with Advanced Alzheimer's Dementia (ADA). Importantly, all the compared methods were rigorously assessed under identical conditions. The proposed DL model, specifically CNN, effectively extracts time domain features from EEG data in time, resulting in a significant reduction in learnable parameters and data redundancy.
自脑电图（EEG）被发现以来，它一直是识别健康状况的一项基础技术。本分析特别聚焦于为分析脑电图（EEG）数据而设计的机器学习（ML）和深度学习（DL）方法，以将患有阿尔茨海默病（AD）的个体分为两组：中度或重度阿尔茨海默病痴呆。我们的研究基于一个综合数据库，该数据库包含来自5家不同医院的668名志愿者，数据收集历时十年。这个多样的数据集使我们能够更好地训练并验证结果。在所评估的方法中，卷积神经网络（CNN，深度学习）方法表现优于其他方法，对于中度阿尔茨海默病痴呆（ADM）患者，分类准确率达到了97.45%，对于重度阿尔茨海默病痴呆（ADA）患者，分类准确率达到了97.03%。重要的是，所有比较方法均在相同条件下经过了严格评估。所提出的深度学习模型，特别是卷积神经网络（CNN），能够及时有效地从脑电图数据中提取时域特征，从而显著减少可学习参数和数据冗余。
