Rhythm‐based identification of alcohol EEG signals

Taran, Sachin; Bajaj, Varun

doi:10.1049/iet-smt.2017.0232

Cited by 40 publications

(23 citation statements)

References 32 publications

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“… (i) Simple square integral (SSI) – mathematically SSI is described as the summation of the square of the input signal's amplitude. This indicates the energy of the signal [16 ]. (ii) Mean absolute value (MAV) – as the name suggests, it is the mean of the absolute value of the input signal's amplitude [16 ]. (iii) Coefficient of variation (COV) – the ratio of the standard deviation of the input signal and mean value of the signal defines the COV [17 ]. (iv) Hjorth mobility (HM) – HM is described as the square root of the rationed variance of instantaneous rate change of the input signal with respect to the variance of the input signal [18 ]. (v) Hjorth complexity (HC) – this Hjorth parameter is described as a ratio between mobility of the instantaneous rate of change of signal and the mobility of the input signal [18 ]. …”

Section: Feature Extractionmentioning

confidence: 99%

Automatic drowsiness detection using electroencephalogram signal

2020

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Drowsiness refers to the state of being sleepy or the state of minimal concentration. It is characterised by a decrease in a person's memory capacity and brain information processing speed. These conditions cause hazards in the real‐time working environment such as driving, monitoring power generation and patient health etc. These hazards can be sidestepped by introducing the automatic drowsiness detection system. This Letter suggested the electroencephalogram (EEG)‐based automatic drowsiness detection method. The clustering variational mode decomposition (CVMD) explores the non‐stationary behaviour of EEG for drowsiness detection. In CVMD optimum allocation sampling analyses non‐homogenous EEG signals and converts those into homogeneous EEG clusters. These clusters are then decomposed into band‐limited modes. The oscillatory mode characteristics are extracted in terms of several features. These features are fed as input into the least‐squares support vector machine classifier. The proposed method provides better drowsiness detection performance in comparison with the different methods using the same data set.

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Section: Feature Extractionmentioning

confidence: 99%

Automatic drowsiness detection using electroencephalogram signal

2020

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“…The ratio of standard deviation to mean value of the EEG signal is a coefficient of variation. It can be expressed as [19]. Kurtosis: It is a one of the statistical moment; it gives the time series data peaked nature.…”

Section: Coefficient Of Variation (Cov)mentioning

confidence: 99%

EEG Signal Research for Identification of Epilepsy using Machine Learning Classification Accession

2019

IJITEE

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Epilepsy is censorious neurological disorder in which nerve cell activity in the brain is disturbed causing recurrent seizures which are sudden, uncontrolled electrical discharges in the brain cell. In clinical treatment of epileptic patients seizure reorganization has much prominence. Hence in detecting the phenomenon of epilepsy Electroencephalogram (EEG) signal is widely used as it includes important carnal data of the brain. Though it is critical to analyze the EEG signal and identify the seizures. So feature extraction of EEG signal plays a vital role for epilepsy detection. This paper describes an worthwhile feature extraction based on variational mode decomposition (VMD) to identify epilepsy. The extracted features fed to ANN, KNN and SVM in order to classify epilepsy. The performance of the SVM classifier shows the better classification compared to existing methods.

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“…Depending on the maximal weight matching concept, the functional connectivity in alcoholic EEG signals was done and evaluated by Zhu et al [ 19 ]. The alcoholic EEG signals were identified by their respective rhythms in [ 20 ]. The alcoholism disorder was explored by an EEG-based Machine Learning technique that utilizes resting state EEG features to classify the alcoholic patients by Mumtaz et al [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

Alcoholic EEG signal classification with Correlation Dimension based distance metrics approach and Modified Adaboost classification

Prabhakar

Rajaguru²

2020

Heliyon

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The basic function of the brain is severely affected by alcoholism. For the easy depiction and assessment of the mental condition of a human brain, Electroencephalography (EEG) signals are highly useful as it can record and measure the electrical activities of the brain much to the satisfaction of doctors and researchers. Utilizing the standard conventional techniques is quite hectic to derive the useful information as these signals are highly non-linear and non-stationary in nature. While recording the EEG signals, the activities of the neurons are recorded from various scalp regions which has varied characteristics and has a very low magnitude. Therefore, human interpretation of such signals is very difficult and consumes a lot of time. Hence, with the advent of Computer Aided Diagnosis (CAD) Techniques, identifying the normal versus alcoholic EEG signals has been of great utility in the medical field. In this work, we perform the initial clustering of the alcoholic EEG signals by means of using Correlation Dimension (CD) for easy feature extraction and then the suitable features are selected in it by means of employing various distance metrics like correlation distance, city block distance, cosine distance and chebyshev distance. Proceeding in such a methodology aids and assures that a good discrimination could be achieved between normal and alcoholic EEG signals using non-linear features. Finally, classification is then carried out with the suitable classifiers chosen such as Adaboost.RT classifier, the proposed Modified Adaboost.RT classifier by means of introducing Ridge and Lasso based soft thresholding technique, Random Forest with bootstrap resampling technique, Artificial Neural Networks (ANN) such as Radial Basis Functions (RBF) and Multi-Layer Perceptron (MLP), Support Vector Machine (SVM) with Linear, Polynomial and RBF Kernel, Naïve Bayesian Classifier (NBC), K-means classifier, and K Nearest Neighbor (KNN) Classifier and the results are analyzed. Results report a comparatively high classification accuracy of about 98.99% when correlation distance metrics are utilized with CD and the proposed Modified Adaboost.RT classifier using Ridge based soft thresholding technique.

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Rhythm‐based identification of alcohol EEG signals

Cited by 40 publications

References 32 publications

Automatic drowsiness detection using electroencephalogram signal

Automatic drowsiness detection using electroencephalogram signal

EEG Signal Research for Identification of Epilepsy using Machine Learning Classification Accession

Alcoholic EEG signal classification with Correlation Dimension based distance metrics approach and Modified Adaboost classification

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