Sarbadhikari S N
School of Biomedical Engineering, Banaras Hindu University, India.
Med Eng Phys. 1995 Dec;17(8):579-82. doi: 10.1016/1350-4533(95)00011-b.
The use of an artificial neural network (ANN) system to differentiate the EEG power density spectra in depressed from normal rats was tried. The beneficial effects of chronic physical exercise in reducing the effects of stress and therefore depression was also to be tested in animals by the same method. In this study, rats were divided into 4 groups, subjected to (i) chronic stress (D group); (ii) chronic exercise by treadmill running (EO group); (iii) exercise with stress (ES group) and (iv) handling (C group). The prefrontal cortical EEG, EMG and EOG were recorded simultaneously on paper and the digitized EEG signals were also stored in the hard-disk of a PC-AT through an ADC. After filtering the digitized signals, the EEG power spectra were calculated by an FFT routine. Three successive 4 s artefact-free epochs were averaged. The REM and NREM sleep periods as well as the awake period signals were analyzed separately. The FFT values from each of the 3 states, in the 4 groups of animals were tested by an ANN with 30 first layer neurons and a 2nd layer of a majority-vote-taker. The ANN could distinguish the depressed from the normal rats' EEG very well in REM (99%) sleep, NREM (95%) sleep and awake (81%) states. In most of the cases it identified the exercised rats' EEG as normal.
尝试使用人工神经网络（ANN）系统来区分抑郁大鼠和正常大鼠的脑电图功率密度谱。还将通过相同方法在动物身上测试长期体育锻炼在减轻压力从而减轻抑郁方面的有益效果。在本研究中，大鼠被分为4组，分别接受（i）慢性应激（D组）；（ii）通过跑步机跑步进行慢性锻炼（EO组）；（iii）应激状态下的锻炼（ES组）和（iv）处理（C组）。同时在纸上记录前额叶皮质脑电图、肌电图和眼电图，数字化的脑电图信号也通过模数转换器存储在PC-AT的硬盘中。对数字化信号进行滤波后，通过快速傅里叶变换（FFT）程序计算脑电图功率谱。对三个连续的4秒无伪迹时段进行平均。分别分析快速眼动（REM）和非快速眼动（NREM）睡眠期以及清醒期的信号。对4组动物中每种状态下的3个FFT值，通过一个具有30个第一层神经元和第二层多数投票器的人工神经网络进行测试。在快速眼动睡眠（99%）、非快速眼动睡眠（95%）和清醒（81%）状态下，人工神经网络能够很好地区分抑郁大鼠和正常大鼠的脑电图。在大多数情况下，它将运动大鼠的脑电图识别为正常。
