A hybrid method based on time–frequency images for classification of alcohol and control EEG signals

Bajaj, Varun; Guo, Yanhui; Şengür, Abdulkadir; Siuly, Siuly; Alçın, Ömer Faruk

doi:10.1007/s00521-016-2276-x

Cited by 41 publications

(24 citation statements)

References 25 publications

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“…Bajaj et al [90] introduced a robust method that can automatically identify alcoholic EEG signals based on time-frequency (T-F) image information considering texture image for feature extraction and nonnegative least squares classifier (NNLS) for classification. Kannathal et al [91] developed a feature extraction methodology based on correlation dimension (CD), largest lyapunov exponent (LLE), entropies and Hurst exponent (H) to extract characteristic features from the EEGs of alcoholics.…”

Section: Alcoholism Related-disorders Diagnosismentioning

confidence: 99%

Medical Big Data: Neurological Diseases Diagnosis Through Medical Data Analysis

2016

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Section: Alcoholism Related-disorders Diagnosismentioning

confidence: 99%

Medical Big Data: Neurological Diseases Diagnosis Through Medical Data Analysis

2016

Self Cite

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“…Bernice Projesz et al [24] explain that despite the good results with the thetaband, the beta frequency has become a strong indicator of alcoholism among scientists and medical professionals. However, the results of Wajid Mumtaz et al [10] see a good classifier of alcoholics and non-alcoholics in the thetaband and the Hi-Gamma bands (30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40). Eveline A. de Bruin et al [26] analyze the EEG data of heavy drinking students compared to light drinking students and also comes to the conclusion that the EEG data of heavy drinking students, especially in theta and Gamma band, differ enormously from the EEG data of the control group.…”

Section: Discussionmentioning

confidence: 99%

High-performance detection of alcoholism by unfolding the amalgamated EEG spectra using the Random Forests method

Rieg¹,

Frick²,

Hitzler³

et al. 2019

Proceedings of the Annual Hawaii International Conference on System Sciences

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We show that by unfolding the outdated EEG standard bandwidths in a fine-grade equidistant 99-point spectrum we can precisely detect alcoholism. Using this novel pre-processing step prior to entering a random forests classifier, our method substantially outperforms all previous results with a balanced accuracy of 97.4 percent. Our machine learning work contributes to healthcare and information systems. Due to its drastic and protracted consequences, alcohol consumption is always a critical issue in our society. Consequences of alcoholism in the brain can be recorded using electroencephalography (EEG). Our work can be used to automatically detect alcoholism in EEG mass data within milliseconds. In addition, our results challenge the medically outdated EEG standard bandwidths.

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“…Time frequency (TF) images were also used by Bajaj and Pachori (2013) to classify sleep stages. Bajaj et al (2017) also identified alcoholic EEGs based on T-F images. Based on our previous study (Al-Salman et al, 2018) we found that time frequency images coupled with FD yielded promising results in analyzing and detecting sleep spindles in sleep EEG signals.…”

Section: Related Workmentioning

confidence: 99%

Detection of EEG K-Complexes Using Fractal Dimension of Time Frequency Images Technique Coupled With Undirected Graph Features

Al-Salman

Wen

2019

Front. Neuroinform.

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K-complexes identification is a challenging task in sleep research. The detection of k-complexes in electroencephalogram (EEG) signals based on visual inspection is time consuming, prone to errors, and requires well-trained knowledge. Many existing methods for k-complexes detection rely mainly on analyzing EEG signals in time and frequency domains. In this study, an efficient method is proposed to detect k-complexes from EEG signals based on fractal dimension (FD) of time frequency (T-F) images coupled with undirected graph features. Firstly, an EEG signal is partitioned into smaller segments using a sliding window technique. Each EEG segment is passed through a spectrogram of short time Fourier transform (STFT) to obtain the T-F images. Secondly, the box counting method is applied to each T-F image to discover the FDs in EEG signals. A vector of FD features are extracted from each T-F image and then mapped into an undirected graph. The structural properties of the graphs are used as the representative features of the original EEG signals for the input of a least square support vector machine (LS-SVM) classifier. Key graphic features are extracted from the undirected graphs. The extracted graph features are forwarded to the LS-SVM for classification. To investigate the classification ability of the proposed feature extraction combined with the LS-SVM classifier, the extracted features are also forwarded to a k-means classifier for comparison. The proposed method is compared with several existing k-complexes detection methods in which the same datasets were used. The findings of this study shows that the proposed method yields better classification results than other existing methods in the literature. An average accuracy of 97% for the detection of the k-complexes is obtained using the proposed method. The proposed method could lead to an efficient tool for the scoring of automatic sleep stages which could be useful for doctors and neurologists in the diagnosis and treatment of sleep disorders and for sleep research.

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A hybrid method based on time–frequency images for classification of alcohol and control EEG signals

Cited by 41 publications

References 25 publications

Medical Big Data: Neurological Diseases Diagnosis Through Medical Data Analysis

Medical Big Data: Neurological Diseases Diagnosis Through Medical Data Analysis

High-performance detection of alcoholism by unfolding the amalgamated EEG spectra using the Random Forests method

Detection of EEG K-Complexes Using Fractal Dimension of Time Frequency Images Technique Coupled With Undirected Graph Features

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