Biomedical Engineering Department, Faculty of Engineering, Jordan University of Science and Technology, Irbid 22110, Jordan.
Biomed Eng Online. 2011 May 24;10:38. doi: 10.1186/1475-925X-10-38.
Classification method capable of recognizing abnormal activities of the brain functionality are either brain imaging or brain signal analysis. The abnormal activity of interest in this study is characterized by a disturbance caused by changes in neuronal electrochemical activity that results in abnormal synchronous discharges. The method aims at helping physicians discriminate between healthy and seizure electroencephalographic (EEG) signals.
Discrimination in this work is achieved by analyzing EEG signals obtained from freely accessible databases. MATLAB has been used to implement and test the proposed classification algorithm. The analysis in question presents a classification of normal and ictal activities using a feature relied on Hilbert-Huang Transform. Through this method, information related to the intrinsic functions contained in the EEG signal has been extracted to track the local amplitude and the frequency of the signal. Based on this local information, weighted frequencies are calculated and a comparison between ictal and seizure-free determinant intrinsic functions is then performed. Methods of comparison used are the t-test and the Euclidean clustering.
The t-test results in a P-value < 0.02 and the clustering leads to accurate (94%) and specific (96%) results. The proposed method is also contrasted against the Multivariate Empirical Mode Decomposition that reaches 80% accuracy. Comparison results strengthen the contribution of this paper not only from the accuracy point of view but also with respect to its fast response and ease to use.
An original tool for EEG signal processing giving physicians the possibility to diagnose brain functionality abnormalities is presented in this paper. The proposed system bears the potential of providing several credible benefits such as fast diagnosis, high accuracy, good sensitivity and specificity, time saving and user friendly. Furthermore, the classification of mode mixing can be achieved using the extracted instantaneous information of every IMF, but it would be most likely a hard task if only the average value is used. Extra benefits of this proposed system include low cost, and ease of interface. All of that indicate the usefulness of the tool and its use as an efficient diagnostic tool.
能够识别大脑功能异常活动的分类方法要么是脑成像，要么是脑信号分析。本研究中感兴趣的异常活动的特征是由神经元电化学活动变化引起的干扰，导致异常同步放电。该方法旨在帮助医生区分健康和癫痫脑电图（EEG）信号。
这项工作的区分是通过分析从免费获取的数据库中获得的 EEG 信号来实现的。MATLAB 已被用于实现和测试所提出的分类算法。所提出的方法使用基于希尔伯特-黄变换的特征对正常和发作活动进行分类。通过这种方法，提取了与 EEG 信号中包含的固有函数相关的信息，以跟踪信号的局部幅度和频率。基于此局部信息，计算加权频率，然后对发作和无发作固有函数进行比较。所使用的比较方法是 t 检验和欧几里得聚类。
t 检验的 P 值<0.02，聚类导致准确（94%）和特异（96%）的结果。所提出的方法也与多元经验模式分解进行了对比，达到了 80%的准确率。比较结果不仅从准确性的角度，而且从快速响应和易于使用的角度，加强了本文的贡献。
本文提出了一种用于 EEG 信号处理的原始工具，为医生提供了诊断大脑功能异常的可能性。所提出的系统具有提供快速诊断、高准确性、良好的灵敏度和特异性、节省时间和用户友好等多种可信优势。此外，还可以使用提取的每个 IMF 的瞬时信息对模式混合进行分类，但如果只使用平均值，这将是一项艰巨的任务。该系统的额外好处包括低成本和易于接口。所有这些都表明了该工具的有用性及其作为有效诊断工具的用途。
