Alcoholism is one of the most common diseases in the world. This type of substance abuse leads to mental and physical dependence on ethanol-containing drinks. Alcoholism is accompanied by progressive degradation of the personality and damage to the internal organs. Today still not exists a quick diagnosis method to detect this disease. This article presents the method for the quick and anonymous alcoholism diagnosis by neural networks. For this method, don't need any private information about the subject. For the implementation, we considered various algorithms of machine learning and deep neural networks. In detail analyzed the correlation of the signals from electrodes by neural networks. The wavelet transforms and the fast Fourier transform was considered. The manuscript demonstrates that the deep neural network which operates only with a dataset of EEG correlation signals can anonymously classify the alcoholic and control groups with high accuracy.

On the one hand, this method will allow subjects to be tested for alcoholism without any personal data, which will not cause inconvenience or shame in the subject, and on the other hand, the subject will not be able to deceive specialists who diagnose the subject for the presence of the disease.

Python (Deep Learning and Machine Learning) for EEG Signal Processing on the Example of Recognizing the Disease of Alcoholism

Alcoholism is one of the most common diseases in the world. This type of substance abuse leads to mental and physical dependence on ethanol-containing drinks. Alcoholism is accompanied by progressive degradation of the personality and damage to the internal organs. Today still not exists a quick diagnosis method to detect this disease. This article presents the method for the quick and anonymous alcoholism diagnosis by neural networks. For this method, don’t need any private information about the subject. For the implementation, we considered various algorithms of machine learning and deep neural networks. In detail analyzed the correlation of the signals from electrodes by neural networks. The wavelet transforms and the fast Fourier transform was considered. The manuscript demonstrates that the deep neural network which operates only with a dataset of EEG correlation signals can anonymously classify the alcoholic and control groups with high accuracy. On the one hand, this method will allow subjects to be tested for alcoholism without any personal data, which will not cause inconvenience or shame in the subject, and on the other hand, the subject will not be able to deceive specialists who diagnose the subject for the presence of the disease.

/pdf/deep-learning-and-machine-learning-to-recognizing-the-2wq7r6qeyw.pdf

Deep learning and machine learning to recognizing the disease of alcoholism by EEG signal processing

Alcoholism is one of the most common diseases in the world. This type of substance abuse leads to mental and physical dependence on ethanol-containing drinks. Alcoholism is accompanied by progressive degradation of the personality and damage to the internal organs. Today still not exists a quick diagnosis method to detect this disease. This article presents the method for the quick and anonymous alcoholism diagnosis by neural networks. For this method, don't need any private information about the subject. For the implementation, we considered various algorithms of machine learning and deep neural networks. In detail analyzed the correlation of the signals from electrodes by neural networks. The wavelet transforms and the fast Fourier transform was considered. The manuscript demonstrates that the deep neural network which operates only with a dataset of EEG correlation signals can anonymously classify the alcoholic and control groups with high accuracy. On the one hand, this method will allow subjects to be tested for alcoholism without any personal data, which will not cause inconvenience or shame in the subject, and on the other hand, the subject will not be able to deceive specialists who diagnose the subject for the presence of the disease.

Python (deep learning and machine learning) for EEG signal processing on the example of recognizing the disease of alcoholism

Alcohol consumption impairs driving skills and responsible for a high proportion of traffic accidents. This research examined the effects of blood alcohol consumption (BAC) level on driver’s physiological behavior and driving performance under simulated driving environment. The participants consisted of eight healthy young male drivers with an average of eight years driving experience. The scenario considered was 5 min driving on a highway with different speed limits, crossings, and unexpected event. Results showed that statistically significantly decreased alpha and increased theta power frequency was observed with increased BAC level. Heart rate was statistically significantly elevated with increased BAC level. The accident rate and the over speed rate were significantly higher with higher BAC level. Alcohol intoxication affected drivers’ decision-making ability, vision and integrating visual information ability.

/pdf/study-of-effects-of-blood-alcohol-consumption-bac-level-on-2k8ov2kbdh.pdf

Study of effects of blood alcohol consumption (BAC) level on drivers physiological behavior and driving performance under simulated environment

As it is regardless to say, majority of accidents occur due to drunken driving. Driving while intoxicated (DWI) (drunken driving, which means operating under the influence of alcohol, drinking and driving, impaired driving) is the act of driving a motor vehicle with blood levels of alcohol in excess of a legal limit. Though drunken driving is considered to be a criminal offense in most countries, it still remains to be a serious, unavoidable problem. Therefore, a highly efficient system that provides early prevention of drunken drive to protect the public from drunken drive male facts is the current need to society. In this paper, we intend to propose the designed mechanical system that prevents drunken drive and its subsequent catastrophes by monitoring the EEG of the driver. The power of the EEG signal in frontal region (alpha waves) decreases with the increase in the amount of alcohol intake, and the power of the EEG signal in central, occipital region (delta, beta) increases. Therefore, in this paper, we make use of threshold values of alpha, beta and theta waves to differentiate EEG of alcoholic from non-alcoholic. The continuous EEG monitoring of the driver makes our system highly reliable to prevent drunken drive accidents. Further, in our proposed system, we make use of special indicators called prevention indicator to avoid inconvenience to other drivers and prevent the accidents due to collision of vehicles. Once any evidence of drunk driving is present, SMS which contains the current location of the driver by means of GPS is sent via a GSM module to the police control room. Thus our exemplary system emerges to be a highly efficient and cost effective solution to prevent drunken drive accidents.

Ila Vennila

Papers

A proficient system for preventing and acknowledging about the drunken drive by analysing the neuronal - activitiy of the brain