Efficient Neural Network Classification of Parkinson's Disease and Schizophrenia Using Resting-State EEG Data.

IF 2.3 3区 医学 Q3 CLINICAL NEUROLOGY
Wenjing Xiong, Lin Ma, Haifeng Li
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引用次数: 0

Abstract

Timely identification of Parkinson's disease and schizophrenia is crucial for the effective management and enhancement of patients' quality of life. The utilization of electroencephalogram (EEG) monitoring applications has proven instrumental in diagnosing various brain disorders. Prior research has predominantly relied on predefined knowledge of physiological alterations associated with different diseases, employing feature extraction to discern brain conditions. This study introduces SwiftBrainNet, a neural network designed for the classification of Parkinson's disease and schizophrenia using short resting-state EEG segments. SwiftBrainNet aims to minimize reliance on manual feature extraction, relying solely on short EEG segments. Functioning as a single-input, dual-output neural network, SwiftBrainNet incorporates a deep supervisory mechanism facilitated by an auxiliary decoder, which enhances its classification performance by guiding the network in extracting shallow features. Our study conducts a clinical application-oriented experiment that uses continuous multi-segment EEG voting classification. This experiment demonstrates a noticeable improvement in accuracy compared to leave-one-out cross-validation (LOOCV), especially when combined with our data augmentation techniques. These findings underscore the method's practical value in clinical settings, where continuous data frames and enhanced generalization across subjects can significantly improve diagnostic accuracy. Additionally, the high accuracy observed in subject-dependent classification with very short data segments suggests that SwiftBrainNet might capture subject-specific EEG patterns, which could be further explored to enhance disease-related feature learning. This paper provides new evidence supporting the use of short-term EEG data for neurodiagnostic applications, making SwiftBrainNet a promising tool for the early detection of neurological disorders.

基于静息状态脑电图数据的帕金森病和精神分裂症的高效神经网络分类
及时识别帕金森病和精神分裂症对于有效管理和提高患者的生活质量至关重要。利用脑电图(EEG)监测应用已被证明是诊断各种脑部疾病的工具。先前的研究主要依赖于与不同疾病相关的生理变化的预定义知识,采用特征提取来识别大脑状况。本研究引入SwiftBrainNet,一种利用静息状态短脑电图片段对帕金森病和精神分裂症进行分类的神经网络。SwiftBrainNet旨在最大限度地减少对人工特征提取的依赖,仅依赖于短的EEG片段。SwiftBrainNet作为一个单输入双输出的神经网络,采用了一种由辅助解码器促进的深度监督机制,通过指导网络提取浅层特征来提高其分类性能。本研究进行了面向临床应用的连续多段脑电投票分类实验。与留一交叉验证(LOOCV)相比,这个实验证明了准确性的显著提高,特别是当与我们的数据增强技术结合使用时。这些发现强调了该方法在临床环境中的实用价值,在临床环境中,连续的数据框架和跨受试者的增强泛化可以显着提高诊断准确性。此外,在非常短的数据片段中观察到的受试者依赖分类的高精度表明,SwiftBrainNet可能捕获受试者特定的EEG模式,可以进一步探索以增强疾病相关特征学习。本文提供了支持短期脑电图数据用于神经诊断应用的新证据,使SwiftBrainNet成为早期发现神经系统疾病的有前途的工具。
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来源期刊
Brain Topography
Brain Topography 医学-临床神经学
CiteScore
4.70
自引率
7.40%
发文量
41
审稿时长
3 months
期刊介绍: Brain Topography publishes clinical and basic research on cognitive neuroscience and functional neurophysiology using the full range of imaging techniques including EEG, MEG, fMRI, TMS, diffusion imaging, spectroscopy, intracranial recordings, lesion studies, and related methods. Submissions combining multiple techniques are particularly encouraged, as well as reports of new and innovative methodologies.
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