基于mlp的抑郁症qEEG识别模型

Sushmita Mitra, Suptendra Nath Sarbadhikari, Sankar K. Pal
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引用次数: 11

摘要

手工区分脑电图(EEG)纸质记录对抑郁症的帮助不大。为此,采用多层感知器(MLP)对清醒状态下的脑电功率密度谱(qEEG)与对照组、运动组和抑郁组进行区分。qEEG范围从1到30 Hz,以1 Hz的增量(30个输入特征)以及慢、中、快活动(由输入的三个频率范围表示)被使用。在仅使用抑郁组和对照组qEEG进行训练后,发现MLP在80%以上的情况下成功地区分了正常和抑郁大鼠,在此过程中,大多数运动组的EEG都是正常的。将输入特征的维数从30个单独的频率减少到3个频带也产生了类似的结果。为作出这种区分而产生的规则已被发现与临床观点相似。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An MLP-based model for identifying qEEG in depression

Manual differentiation of electroencephalography (EEG) paper recordings in cases of depression is not very helpful. So, a Multilayer Perceptron (MLP) has been used to differentiate the EEG power density spectra (qEEG) in the wakeful state from animals (control, exercised and depressed). The qEEG ranging from 1 to 30 Hz, at 1 Hz increments (30 input features) and also as slow, medium and fast activity (represented by three ranges of frequencies at the input) were used. After training with depressed and control qEEG only, the MLP has been found to distinguish successfully between the normal and the depressed rats in more than 80% of the cases, identifying, in the process, most of the exercised groups' EEG as normal. The reduction in the dimension of input features from 30 individual frequencies to 3 frequency bands has produced similar results. The rules generated for making such distinctions have been found to be similar to the clinical views.

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