Wavelet analysis of EEG for the identification of alcoholics using probabilistic classifiers and neural networks

Malar, E.; Gauthaam, M.

doi:10.1504/ijisc.2020.104822

Cited by 15 publications

(5 citation statements)

References 12 publications

(13 reference statements)

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“…We used autoSSA and f astICA functions on MATLAB 2020b to implement sliding SSA-ICA algorithm. [8] Correlation dimension (CD) based discriminant analysis (DAC) 88.00 Rieg et al [17] Random Forest 96.67 Sharma et al [18] LS-SVM 97.08 Malar et al [19] ELM 87.60 P. Dewi Purnamasari et al [20] BPNN 90.00 Faust et al [28] HOS based fuzzy sugeno classifier 92.00 Kannathal et al [29] CD, Lyapunov exponent, entropy with DAC 90.00 Acharya et al [9] Approximate entropy, SampleEn, Lyapunov exponent, Higher Order Spectra (HOS) with SVM 91.70 L. Farsi et al [30] PCA • The dataset was annotated and then it was shuffled so that each data point contributes independently and there is no biasing. • The feature selection was done in steps of 5.…”

Section: Resultsmentioning

confidence: 99%

“…They have proved that due to this division there is a significant information content increase. In [19] Malar et al have stated that electroencephalograph can be used to differentiate between the alcoholic and non-alcoholic signals, also it has been claimed that alcohol intake influences EEG through power spectral density analysis. For statistical feature extraction they have used wavelet transformation.…”

Section: Discussionmentioning

confidence: 99%

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Classification of Alcoholic and Non-Alcoholic EEG Signals Based on Sliding-SSA and Independent Component Analysis

Agarwal

Zubair

2021

IEEE Sensors J.

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Alcoholism is a widely affected disorder that leads to critical brain deficiencies such as emotional and behavioural impairments. One of the prominent sources to detect alcoholism is by analysing Electroencephalogram (EEG) signals. Previously, most of the works have focused on detecting alcoholism using various machine and deep learning algorithms. This paper has used a novel algorithm named Sliding Singular Spectrum Analysis (S-SSA) to decompose and de-noise the EEG signals. We have considered independent component analysis (ICA) to select the prominent alcoholic and non-alcoholic components from the preprocessed EEG data. Later, these components were used to train and test various machine learning models like SVM, KNN, ANN, GBoost, AdaBoost and XGBoost to classify alcoholic and nonalcoholic EEG signals. The sliding SSA-ICA algorithm helps in reducing the computational time and complexity of the machine learning models. To validate the performance of the ICA algorithm, we have compared the computational time and accuracy of ICA with its counterpart, like principal component analysis (PCA). The proposed algorithm is tested on a publicly available UCI alcoholic EEG dataset. To verify the performance of machine learning models, we have calculated various metrics like accuracy, precision, recall and F1 score. Our work reported the highest accuracy of 98.97% with the XGBoost classifier. The validation of the proposed method is done by comparing the classification metrics with the latest state-of-the-art works.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Classification of Alcoholic and Non-Alcoholic EEG Signals Based on Sliding-SSA and Independent Component Analysis

Agarwal

Zubair

2021

IEEE Sensors J.

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“…We used autoSSA and f astICA functions on MATLAB 2020b to implement sliding SSA-ICA algorithm. [8] Correlation dimension (CD) based discriminant analysis (DAC) 88.00 Rieg et al [17] Random Forest 96.67 Sharma et al [18] LS-SVM 97.08 Malar et al [19] ELM 87.60 P. Dewi Purnamasari et al [20] BPNN 90.00 Faust et al [28] HOS based fuzzy sugeno classifier 92.00 Kannathal et al [29] CD, Lyapunov exponent, entropy with DAC 90.00 Acharya et al [9] Approximate entropy, SampleEn, Lyapunov exponent, Higher Order Spectra (HOS) with SVM 91.70 L. Farsi et al [30] PCA We tested different machine learning models and a deep learning model to classify the EEG signals as alcoholic or nonalcoholic. The models used in for classification are AdaBoost, SVM, KNN, Gradient Boosting, XGBoost and ANN.…”

Section: Resultsmentioning

confidence: 99%

Classification of Alcoholic and Non-Alcoholic EEG Signals Based on Sliding-SSA and Independent Component Analysis

Zubair¹

2021

Preprint

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<pre>Alcoholism is a widely affected disorder that leads to critical brain deficiencies such as emotional and behavioural impairments. One of the prominent sources to detect alcoholism is by analysing Electroencephalogram (EEG) signals. Previously, most of the works have focused on detecting alcoholism using various machine and deep learning algorithms. This paper has used a novel algorithm named Sliding Singular Spectrum Analysis (S-SSA) to decompose and de-noise the EEG signals. We have considered independent component analysis (ICA) to select the prominent alcoholic and non-alcoholic components from the preprocessed EEG data. Later, these components were used to train and test various machine learning models like SVM, KNN, ANN, GBoost, AdaBoost and XGBoost to classify alcoholic and non-alcoholic EEG signals. The sliding SSA-ICA algorithm helps in reducing the computational time and complexity of the machine learning models. To validate the performance of the ICA algorithm, we have compared the computational time and accuracy of ICA with its counterpart, like principal component analysis (PCA). The proposed algorithm is tested on a publicly available UCI alcoholic EEG dataset. To verify the performance of machine learning models, we have calculated various metrics like accuracy, precision, recall and F1 score. Our work reported the highest accuracy of 98.97% with the XGBoost classifier. The validation of the proposed method is done by comparing the classification metrics with the latest state-of-the-art works.</pre>

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“…Energy storage system-based control systems can respond much faster to the minor disturbances in power system, in view of their ability to pump active power with state-of-the-art inverters. This advantage makes ESS based damping controller systems more effective than PSS or FACTS that operate by modulation of reactive power only with voltage inputs [8,[13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

“…Fig.10PSO controlled E-STAT-COM versus MVMO controlled E-STATCOM node-14 for a zone-3 line outage(15)(16) …”

mentioning

confidence: 99%

Improvement of Oscillatory Stability in Wide Area Controlled Systems Using Power Injection

Prakash

Kumar

2020

Wireless Pers Commun

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This paper presents a novel method for determination of location and tuning of parameters of electronic static synchronous compensator (E-STATCOM) for oscillation control in Wide area monitored power systems. The effect of active and reactive power injection by E-STATCOM, on the damping performance of a two-area interconnected system has been studied and analyzed. Dynamic non-linear simulations of the test system with appropriate modeling were carried out for evaluation of performance of the proposed controller design, and to take forward the previous works done on wide area control of power systems. Novel attempt has been made for optimal selection of nodes for positioning E-STATCOMs in multi-machine power system, by considering the participation factor of angular variation of voltage vector in modal analysis. Many operating conditions have been considered for the proposed study under both normal states and contingencies. Results of the simulation indicate that the proposed method is highly effective in suppressing the low frequency oscillations of the targeted modes and performs better than the flexible alternating current transmission system devices without the support of energy storage. This method is also used for cloud application.

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Wavelet analysis of EEG for the identification of alcoholics using probabilistic classifiers and neural networks

Cited by 15 publications

References 12 publications

Classification of Alcoholic and Non-Alcoholic EEG Signals Based on Sliding-SSA and Independent Component Analysis

Classification of Alcoholic and Non-Alcoholic EEG Signals Based on Sliding-SSA and Independent Component Analysis

Classification of Alcoholic and Non-Alcoholic EEG Signals Based on Sliding-SSA and Independent Component Analysis

Improvement of Oscillatory Stability in Wide Area Controlled Systems Using Power Injection

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