多通道脑电图拓扑特征搜索法:在多动症分类中的应用

IF 4.9 2区 医学 Q1 ENGINEERING, BIOMEDICAL
Tianming Cai , Guoying Zhao , Junbin Zang , Chen Zong , Zhidong Zhang , Chenyang Xue
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引用次数: 0

摘要

近年来,利用脑电图(EEG)对注意力缺陷多动障碍(ADHD)进行初步诊断引起了研究人员的关注。脑电图以其便捷、高效而著称,在多动症的诊断和治疗中发挥着举足轻重的作用。然而,脑电信号的非稳态性和受试者之间的变异性给诊断和分类过程带来了挑战。拓扑数据分析(TDA)与传统的时频域特征不同,为多动症分类提供了一个新的视角。然而,传统的拓扑数据分析模型仅限于单通道时间序列,且易受噪声影响,导致持续图中拓扑特征的丢失。首先,确定多通道脑电图的最佳输入参数。随后,对每个通道的脑电图进行相空间重建(PSR),然后利用 k-PDTM 逼近理想点云。然后,重新嵌入多维时间序列,并应用 TDA 获取拓扑特征信息。在合并持久图中采用基于高斯函数的多变量核密度估计(MKDE),以筛选出所需的拓扑特征映射。最后,采用持久图(PI)方法提取拓扑特征,并讨论了各种加权函数对结果的影响。结果表明,准确率、灵敏度和特异性分别达到了 78.27%、80.62% 和 75.63%。与传统的 TDA 方法相比,我们的方法得到了有效改进,其性能优于典型的非线性描述符。这些发现表明,我们的方法具有更高的精确度和鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Topological feature search method for multichannel EEG: Application in ADHD classification
In recent years, the preliminary diagnosis of Attention Deficit Hyperactivity Disorder (ADHD) using electroencephalography (EEG) has attracted the attention from researchers. EEG, known for its expediency and efficiency, plays a pivotal role in the diagnosis and treatment of ADHD. However, the non-stationarity of EEG signals and inter-subject variability pose challenges to the diagnostic and classification processes. Topological Data Analysis (TDA) offers a novel perspective for ADHD classification, diverging from traditional time–frequency domain features. However, conventional TDA models are restricted to single-channel time series and are susceptible to noise, leading to the loss of topological features in persistence diagrams.This paper presents an enhanced TDA approach applicable to multi-channel EEG in ADHD. Initially, optimal input parameters for multi-channel EEG are determined. Subsequently, each channel’s EEG undergoes phase space reconstruction (PSR) followed by the utilization of k-Power Distance to Measure (k-PDTM) for approximating ideal point clouds. Then, multi-dimensional time series are re-embedded, and TDA is applied to obtain topological feature information. Gaussian function-based Multivariate Kernel Density Estimation (MKDE) is employed in the merger persistence diagram to filter out desired topological feature mappings. Finally, the persistence image (PI) method is employed to extract topological features, and the influence of various weighting functions on the results is discussed.The effectiveness of our method is evaluated using the IEEE ADHD dataset. Results demonstrate that the accuracy, sensitivity, and specificity reach 78.27%, 80.62%, and 75.63%, respectively. Compared to traditional TDA methods, our method was effectively improved and outperforms typical nonlinear descriptors. These findings indicate that our method exhibits higher precision and robustness.
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来源期刊
Biomedical Signal Processing and Control
Biomedical Signal Processing and Control 工程技术-工程:生物医学
CiteScore
9.80
自引率
13.70%
发文量
822
审稿时长
4 months
期刊介绍: Biomedical Signal Processing and Control aims to provide a cross-disciplinary international forum for the interchange of information on research in the measurement and analysis of signals and images in clinical medicine and the biological sciences. Emphasis is placed on contributions dealing with the practical, applications-led research on the use of methods and devices in clinical diagnosis, patient monitoring and management. Biomedical Signal Processing and Control reflects the main areas in which these methods are being used and developed at the interface of both engineering and clinical science. The scope of the journal is defined to include relevant review papers, technical notes, short communications and letters. Tutorial papers and special issues will also be published.
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