Study on EEG signal analysis of depressed patients based on wireless EEG system

Размещено на http://www.allbest.ru/

Размещено на http://www.allbest.ru/

Study on EEG signal analysis of depressed patients based on wireless EEG system

Wang Ju - PhD, Assistant Professor

Zou Ji - PhD, Associate Professor, Head of The Department “Automation”

Zhang Chao - PhD, Associate Professor, Deputy Director for Research,

(Changchun University, Changchun, China)

Electroencephalogram (EEG) signal is produced by tens of thousands of neurons in the cerebral cortex with high degree of combination, which contains a large amount of biological information. In this paper, through the use of wireless EEG system for depression in patients with brain electrical signal acquisition, application of wavelet transform into brain electrical signal frequency spectrum, finally through the depression patients with brain electrical signal spectrum analysis under different wave band, depression can be drawn from the EEG signal and normal EEG signal in low frequency area is obvious different, summarized the difference between normal and depression of EEG signals. The results of specificity analysis are relatively reliable.

Keywords: EEG signal, wavelet transform, feature extraction.

Анализ сигналов электроэнцефалограммы с использованием беспроводной системы передачи

Ван Джу - Чанчуньский университет, Чанчунь, КНР

Цзоу Цзи - Чанчуньский университет, Чанчунь, КНР

Чжан Чао- Чанчуньский университет, Чанчунь, КНР

Аннотация

Сигнал электроэнцефалограммы (ЭЭГ) вырабатывается десятками тысяч нейронов в коре головного мозга с высокой степенью комбинации и несет большое количество информации о состоянии организма. В этой статье рассматривается вопрос беспроводной передачи сигналов ЭЭГ и последующего применения вейвлет-преобразования для частотно-временного анализа электрического сигнала головного мозга.

Ключевые слова: сигнал электроэнцефалограммы, вейвлет-преобразование, выделение признаков.

Introduction

At present, there are more than 100 million patients with mental illness in China, and among these mental diseases, there are about 16 million adults with depression, and the trend is increasing year by year. The pace of social life is too fast, most people are in anxiety and anxiety, therefore, more and more people suffer from depression. Electroencephalogram (EEG) signals control various physiological activities of the human body and are crucial to people. It is highly integrated by multiple neurons to enable the brain to send out bioelectrical signals and control the external performance characteristics of the human body, such as happiness, anger, sorrow, and happiness. EEG is generally considered to be the sum of post- synaptic potentials during the electrophysiological activities of the cerebral cortex nerve cells. EEG signals are collected by EEG signal collector in clinical medicine, and the manifestations of EEG signals in various pathological situations are different, so EEG provides a basis for disease diagnosis.

The overall design of the system

This system is based on the wireless EEG system to collect and analyze EEG signals of patients with depression, and then through the comparison of spectrum map, the EEG signal fluctuations of patients with depression in different environments can be summarized and summarized. The overall structure block diagram of the system is shown in Figure 1.

Fig. 1. System structure block diagram

Fig. 2. Installation completion

Experimental procedure

signal electroencephalogram wireless

First, 20 volunteers were recruited from depressed patients who had only a history of depression and were already in a stable state after treatment. Collect 20 volunteers, male is 10 and female is 10. Lay the sensor belt flat on the table, use the sponge sensor to mark the 9-channel electrode, and evenly inject the synaptic gel into the sponge sensor to ensure that the sensor can establish effective contact with the brain to collect EEG signals. B-Alert X10 was then attached to the experimenter's head with the sensor band via a fixation band as Figure 2. After being ready, the simulation experiment was started, and the EEG of a volunteer was obtained as shown in Figure 3.

Fig. 3. Electroencephalogram

EEG signal processing

Image signal processing by computer is inevitably affected by noise. EEG signal belongs to nonlinear signal, so the analysis of EEG signal increases the difficulty of image analysis, so it is necessary to carry out denoising before reanalysis. After denoising by wavelet transform threshold, the nature is recombined to get the denoising waveform and the original EEG signal, as shown in Figure 4.

Fig. 4. Comparison of original EEG signals with EEG signals after denoising

Feature extraction of EEG signals

The average value of EEG wavelet transform in the right brain of normal and depressed patients with different frequency bands is shown in Figure. 6, the average value of EEG wavelet transform in the right brain of normal and depressed patients with different frequency bands is shown in Figure. 5.

Fig. 5. Wavelet transform mean values of left EEG signals in patients with normal and depressed brain under different frequency bands

Fig. 6. Mean value of wavelet transform of EEG signals in the right brain of normal and depressed patients under different frequency bands

Davidson's study highlights the corresponding intensity of the right anterior EEG signal activity as an indicator of the evolution of depression and other mental disorders. This introduction was supported by numerous experiments showing that asymmetrical patterns of EEG signals in the anterior and posterior lobes are a possible sign of future depression.

Summary

In this paper, EEG signals from normal people and depressed people were collected, and wavelet transform was used to perform signal denoising and fe a- ture extraction. The results of multiple collection and decomposition showed that the energy contribution of normal people and depressed people was diffe rent under different frequency bands. As you can see, EEG signals in depressed patients are very clearly limited to the low frequency range of 0 to 4HZ. Therefore, this method is very effective in distinguishing normal EEG signals from those of depressed patients in low frequency bands, which plays a certain role in early prevention and intervention of depressed patients.

References

1. Decrease of complexity in EEG as a symptom of depression / Nandrino J.L., Pezard L., Martinerie J., Massioui F., Renault B., Jouvent R., Allilaire J.F., Widlocher D. // Neuroreport. 1994. № 5. Р. 528-530.

2. Frequency analysis of thesleep EEG in depression / Mendelson, Wallace B., David A. Sack, Steven P. James, Joseph V. Martin, RichardWagner, Debra Garnett, John Milton and Thomas A. Wehr. // Psychiatry Res. 1987. № 21. Р. 89-94.

3. More normal EEGs of depression patients duringmental arithmetic than rest / Li Y., Li Y., Tong S., Tang Y., Zhu Y. // 6th Int Symp on Noninvasive Functional Source Imaging of the Brain and Heart and the Int Conf Func Biomed Imaging. 2007. Р. 165168.

4. Hamilton M. A rating scale for depression. // J Neurol Neurosurg Psychiatry. 1960. № 3. Р. 56-62.

5. The topographical features o EEGs in patients with a®ective disorders / Kano K., Nakamura M., Matsuoka T., Iida H., Nakajima T. // Electroencephalogr Clin Neurophysio. 1992. № l83. Р. 124-129.

6. EEG Power, frequency, asymmetry andcoherence in male depression / Knott V., Mahoney C., Kennedy S., Evans K. // Psychiatry Res. 2001. № 106. Р. 123-140.

Размещено на Allbest.ru