Decentralized EEG-based detection of major depressive disorder via transformer architectures and split learning.

IF 2.1 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Frontiers in Computational Neuroscience Pub Date : 2025-04-16 eCollection Date: 2025-01-01 DOI:10.3389/fncom.2025.1569828
Muhammad Umair, Jawad Ahmad, Nada Alasbali, Oumaima Saidani, Muhammad Hanif, Aizaz Ahmad Khattak, Muhammad Shahbaz Khan
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引用次数: 0

Abstract

Introduction: Major Depressive Disorder (MDD) remains a critical mental health concern, necessitating accurate detection. Traditional approaches to diagnosing MDD often rely on manual Electroencephalography (EEG) analysis to identify potential disorders. However, the inherent complexity of EEG signals along with the human error in interpreting these readings requires the need for more reliable, automated methods of detection.

Methods: This study utilizes EEG signals to classify MDD and healthy individuals through a combination of machine learning, deep learning, and split learning approaches. State of the art machine learning models i.e., Random Forest, Support Vector Machine, and Gradient Boosting are utilized, while deep learning models such as Transformers and Autoencoders are selected for their robust feature-extraction capabilities. Traditional methods for training machine learning and deep learning models raises data privacy concerns and require significant computational resources. To address these issues, the study applies a split learning framework. In this framework, an ensemble learning technique has been utilized that combines the best performing machine and deep learning models.

Results: Results demonstrate a commendable classification performance with certain ensemble methods, and a Transformer-Random Forest combination achieved 99% accuracy. In addition, to address data-sharing constraints, a split learning framework is implemented across three clients, yielding high accuracy (over 95%) while preserving privacy. The best client recorded 96.23% accuracy, underscoring the robustness of combining Transformers with Random Forest under resource-constrained conditions.

Discussion: These findings demonstrate that distributed deep learning pipelines can deliver precise MDD detection from EEG data without compromising data security. Proposed framework keeps data on local nodes and only exchanges intermediate representations. This approach meets institutional privacy requirements while providing robust classification outcomes.

基于变压器架构和分裂学习的去中心化脑电图重度抑郁症检测。
重度抑郁症(MDD)仍然是一个重要的心理健康问题,需要准确的检测。诊断重度抑郁症的传统方法通常依靠人工脑电图(EEG)分析来识别潜在的疾病。然而,脑电图信号固有的复杂性以及在解释这些读数时的人为错误需要更可靠,自动化的检测方法。方法:采用机器学习、深度学习和分裂学习相结合的方法,利用脑电信号对重度抑郁症和健康个体进行分类。使用最先进的机器学习模型,即随机森林,支持向量机和梯度增强,而深度学习模型,如变压器和自动编码器,因其强大的特征提取能力而被选择。训练机器学习和深度学习模型的传统方法会引起数据隐私问题,并且需要大量的计算资源。为了解决这些问题,本研究采用了分裂学习框架。在这个框架中,使用了集成学习技术,结合了性能最好的机器和深度学习模型。结果:在一定的集成方法下,变压器-随机森林组合的分类准确率达到了99%。此外,为了解决数据共享的限制,在三个客户端上实现了一个分裂学习框架,在保护隐私的同时产生了高准确率(超过95%)。最佳客户端准确率为96.23%,强调了在资源受限条件下变形金刚与随机森林相结合的鲁棒性。讨论:这些发现表明,分布式深度学习管道可以在不影响数据安全性的情况下,从EEG数据中提供精确的MDD检测。该框架将数据保存在本地节点上,只交换中间表示。这种方法在提供可靠的分类结果的同时满足了机构的隐私要求。
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来源期刊
Frontiers in Computational Neuroscience
Frontiers in Computational Neuroscience MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES
CiteScore
5.30
自引率
3.10%
发文量
166
审稿时长
6-12 weeks
期刊介绍: Frontiers in Computational Neuroscience is a first-tier electronic journal devoted to promoting theoretical modeling of brain function and fostering interdisciplinary interactions between theoretical and experimental neuroscience. Progress in understanding the amazing capabilities of the brain is still limited, and we believe that it will only come with deep theoretical thinking and mutually stimulating cooperation between different disciplines and approaches. We therefore invite original contributions on a wide range of topics that present the fruits of such cooperation, or provide stimuli for future alliances. We aim to provide an interactive forum for cutting-edge theoretical studies of the nervous system, and for promulgating the best theoretical research to the broader neuroscience community. Models of all styles and at all levels are welcome, from biophysically motivated realistic simulations of neurons and synapses to high-level abstract models of inference and decision making. While the journal is primarily focused on theoretically based and driven research, we welcome experimental studies that validate and test theoretical conclusions. Also: comp neuro
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