基于模糊逻辑初始化的径向基神经网络对中晚期阿尔茨海默病患者的分类

IF 5.6 4区 医学 Q1 ENGINEERING, BIOMEDICAL
Irbm Pub Date : 2023-10-01 DOI:10.1016/j.irbm.2023.100795
Carlos Roncero Parra , Alfonso Parreño Torres , Jorge Mateo Sotos , Alejandro L. Borja
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引用次数: 0

摘要

背景阿尔茨海默病可以通过多种临床方法进行诊断。其中,脑电图已被证明是一种强大、无创、负担得起且无痛的诊断工具。目的本研究采用SVM、BLDA、DT、GNB、KNN、RF等八种机器学习方法,以及RNN和RBF等深度学习方法,将阿尔茨海默病分为两个阶段:中度阿尔茨海默病(ADM)和晚期阿尔茨海默病(ADA),十年来从五家不同医院收集的脑电图数据已经被使用。提出了一种基于神经网络的新方法,以提高精度并获得快速的分类时间。结果基于模糊均值初始化的径向基函数的深度神经元网络实现了最佳的平衡精度,ADA分类的准确率为96.66%,ADM分类的准确度为93.31%。结论除了提高准确性外,值得注意的是,该算法以前从未用于阿尔茨海默病患者的分类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Classification of Moderate and Advanced Alzheimer's Patients Using Radial Basis Function Based Neural Networks Initialized with Fuzzy Logic

Classification of Moderate and Advanced Alzheimer's Patients Using Radial Basis Function Based Neural Networks Initialized with Fuzzy Logic

Background

Alzheimer's disease can be diagnosed through various clinical methods. Among them, electroencephalography has proven to be a powerful, non-invasive, affordable, and painless tool for its diagnosis.

Objectives

In this study, eight machine learning (ML) approaches, including SVM, BLDA, DT, GNB, KNN, RF, and deep learning (DL) methods such as RNN and RBF, were employed to classify Alzheimer's disease into two stages: moderate Alzheimer's disease (ADM) and advanced Alzheimer's disease (ADA).

Material and methods

To this aim, electroencephalography data collected from five different hospitals over a decade has been used. A novel method based on neural networks has been proposed to increase accuracy and obtain fast classification times.

Results

Results show that deep neuronal networks based on radial basis functions initialized with fuzzy means achieved the best balanced accuracy with 96.66% accuracy in ADA classification and 93.31% accuracy in ADM classification.

Conclusion

Apart from improving accuracy, it is noteworthy that this algorithm had never been used before to classify patients with Alzheimer's disease.

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来源期刊
Irbm
Irbm ENGINEERING, BIOMEDICAL-
CiteScore
10.30
自引率
4.20%
发文量
81
审稿时长
57 days
期刊介绍: IRBM is the journal of the AGBM (Alliance for engineering in Biology an Medicine / Alliance pour le génie biologique et médical) and the SFGBM (BioMedical Engineering French Society / Société française de génie biologique médical) and the AFIB (French Association of Biomedical Engineers / Association française des ingénieurs biomédicaux). As a vehicle of information and knowledge in the field of biomedical technologies, IRBM is devoted to fundamental as well as clinical research. Biomedical engineering and use of new technologies are the cornerstones of IRBM, providing authors and users with the latest information. Its six issues per year propose reviews (state-of-the-art and current knowledge), original articles directed at fundamental research and articles focusing on biomedical engineering. All articles are submitted to peer reviewers acting as guarantors for IRBM''s scientific and medical content. The field covered by IRBM includes all the discipline of Biomedical engineering. Thereby, the type of papers published include those that cover the technological and methodological development in: -Physiological and Biological Signal processing (EEG, MEG, ECG…)- Medical Image processing- Biomechanics- Biomaterials- Medical Physics- Biophysics- Physiological and Biological Sensors- Information technologies in healthcare- Disability research- Computational physiology- …
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