吸烟对吸烟者和非吸烟者脑电图的影响及时频域分析

2019 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2) Pub Date : 2019-07-01 DOI:10.1109/IC4ME247184.2019.9036492

Md Mahmudul Hasan, Nafiul Hasan, Azizur Rahman, Md. Mustafizur Rahman

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引用次数: 3

摘要

由于成瘾被认为是一种可以从脑电图中得出的精神健康状况，因此本研究的重点是吸烟引起的脑电图模式的变化。本文提出了一种通过脑电域分析来识别吸烟者和非吸烟者的方法。在本研究中，构建了三个神经网络进行区域分析，以区分吸烟者和非吸烟者。结果表明，同时使用时域和频域特征构建的神经网络比单独使用时域和频域特征构建的神经网络具有更好的质量，其MSE为6.238 \ × 10^{-08}$。另一方面，单纯基于频域的人工神经网络比基于时域特性的神经网络具有更好的性能。结果表明，吸烟者脑电图中PSD和FFT值明显高于非吸烟者。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Effect of Smoking in EEG Pattern and Time-Frequency Domain Analysis for Smoker and Non-Smoker

As addiction is said to be a mental health condition which can be derived from Electroencephalogram, this study focuses on changes in EEG pattern due to smoking. A methodology is proposed here to identify smoker and nonsmoker by EEG domain analysis. In this research, three ANNs were built for domain analysis to differentiate smokers from non-smokers. It was found that the neural network built with the attributes of both time and frequency domain provided best quality with MSE of $6.238 \times 10^{-08}$ than the neural networks using either time or frequency domain features. On the other hand, frequency domain based ANN solely gives better performance than time domain properties based neural network. It is concluded in this paper that values of PSD and FFT is much higher in EEG of smokers than the non-smokers.

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来源期刊

2019 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2)

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