Study On The Characteristics Of Awake EEG In Lead Poisoned Rats

Lead is an environmental pollutant with neurotoxicity.It is absorbed through the digestive tract,the majority is absorbed via the lungs and skin,and is distributed to the body through bloodstream.Finally,it is stored in various systems,tissues and organs of the human body for a long time.Lead poisoning exerts toxic effects on many human systems and organs,especially the nervous system.In the central nervous system,the cerebral cortex,hippocampal gyrus and cerebellum are the main target tissues of lead.Additionally,lead exposure disrupts receptors,enzymes and neurotransmitter release.Lead also interferes with reducing synapse formation and channels states,which induces glial cell activation and neuronal apoptosis.EEG is an electrophysiological signal that records the activity of brain neurons.Clinically,it can quickly reflect the functional state of the central nervous system.Lead poisoning causes changes in EEGs recordings which can determine the degree of brain damage have not been reported in detail.In order to detect the central nerve system abnormality caused by lead poisoning,this study was carried out on brain electrophysiology recordings in lead poisoning to provide a certain theoretical basis for the clinical diagnosis of lead poisoning diseases and the determination of the degree of brain damage.The main research contents and conclusions are as follows:Firstly,the lead poisoned rat model was established by feeding lead acetate solution,the electrode implantation process was standardized,and the EEG signal acquisition method of conscious cortex was optimized.Secondly,the characteristics of EEG recordings of lead poisoned rats were analyzed and processed.In the time domain,the amplitude of each frequency band was calculated;in the frequency domain,absolute power and the relative power of each frequency band were also calculated;in the nonlinearity,four kinds of entropy were used to analyze the changes of EEG complexity in lead poisoned rats,and the coupling relationship between left and right brain regions of rats was analyzed to explore the effect of lead poisoning on information exchange between left and right brain of rats.The results show that lead poisoning causes the transformation of EEG signals from high frequency to low frequency.Additionally,lead poisoning reduces the complexity of signals of EEG,the diversity and difference of brain activities,and it provides obvious indicators for the EEG diagnosis of lead poisoning.Information transmission in the left and right half brains of low-dose lead poisoned rats was significantly reduced,which provides objective indicators for low-dose lead poisoning.Finally,the degree of brain damage in lead poisoned rats was determined based on machine learning.Based on the feature selection and the feature fusion,the classification model was established.Additionally,two base classifiers were selected the support vector machine and the limit learning machine,and the recognize features were used for the automatic classification of lead poisoning EEG signals.The final results show that this method has high classification accuracy for lead poisoning brain damage,which contributes to provide diagnostic basis for clinical judgment of the damage degree of central nervous system in lead poisoning and fill the gap of artificial intelligence in lead poisoning EEG diagnosis.