Abe Jair Minoro, Lopes Helder Frederico da Silva, Anghinah Renato
Institute For Advanced Studies - University of São Paulo, Brazil.
Graduate student of M edical School of University of São Paulo - Brazil.
Dement Neuropsychol. 2007 Jul-Sep;1(3):241-247. doi: 10.1590/S1980-57642008DN10300004.
EEG visual analysis has proved useful in aiding AD diagnosis, being indicated in some clinical protocols. However, such analysis is subject to the inherent imprecision of equipment, patient movements, electric registers, and individual variability of physician visual analysis.
To employ the Paraconsistent Artificial Neural Network to ascertain how to determine the degree of certainty of probable dementia diagnosis.
Ten EEG records from patients with probable Alzheimer disease and ten controls were obtained during the awake state at rest. An EEG background between 8 Hz and 12 Hz was considered the normal pattern for patients, allowing a variance of 0.5 Hz.
The PANN was capable of accurately recognizing waves belonging to Alpha band with favorable evidence of 0.30 and contrary evidence of 0.19, while for waves not belonging to the Alpha pattern, an average favorable evidence of 0.19 and contrary evidence of 0.32 was obtained, indicating that PANN was efficient in recognizing Alpha waves in 80% of the cases evaluated in this study. Artificial Neural Networks - ANN - are well suited to tackle problems such as prediction and pattern recognition. The aim of this work was to recognize predetermined EEG patterns by using a new class of ANN, namely the Paraconsistent Artificial Neural Network - PANN, which is capable of handling uncertain, inconsistent and paracomplete information. An architecture is presented to serve as an auxiliary method in diagnosing Alzheimer disease.
We believe the results show PANN to be a promising tool to handle EEG analysis, bearing in mind two considerations: the growing interest of experts in visual analysis of EEG, and the ability of PANN to deal directly with imprecise, inconsistent, and paracomplete data, thereby providing a valuable quantitative analysis.
脑电图视觉分析已被证明有助于阿尔茨海默病的诊断，在一些临床方案中有所应用。然而，这种分析受设备固有不精确性、患者运动、电记录以及医生视觉分析的个体差异影响。
运用弗协调人工神经网络来确定如何判定可能的痴呆诊断的确定性程度。
获取了10例可能患有阿尔茨海默病患者和10例对照者在静息清醒状态下的脑电图记录。8赫兹至12赫兹的脑电图背景被视为患者的正常模式，允许有0.5赫兹的方差。
弗协调人工神经网络能够以0.30的有利证据和0.19的反证据准确识别属于阿尔法波段的波，而对于不属于阿尔法模式的波，平均有利证据为0.19，反证据为0.32，这表明在本研究评估的80%的病例中，弗协调人工神经网络能有效识别阿尔法波。人工神经网络非常适合解决预测和模式识别等问题。这项工作的目的是使用一种新型的人工神经网络，即弗协调人工神经网络来识别预先确定的脑电图模式，该网络能够处理不确定、不一致和准完备的信息。提出了一种架构作为诊断阿尔茨海默病的辅助方法。
我们认为结果表明弗协调人工神经网络是处理脑电图分析的一种有前景的工具，需考虑两点：专家对脑电图视觉分析的兴趣日益浓厚，以及弗协调人工神经网络直接处理不精确、不一致和准完备数据的能力，从而提供有价值的定量分析。
