1
|
Sadiq MT, Yousaf A, Siuly S, Almogren A. Fast Fractional Fourier Transform-Aided Novel Graphical Approach for EEG Alcoholism Detection. Bioengineering (Basel) 2024; 11:464. [PMID: 38790331 PMCID: PMC11117540 DOI: 10.3390/bioengineering11050464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 04/01/2024] [Accepted: 04/24/2024] [Indexed: 05/26/2024] Open
Abstract
Given its detrimental effect on the brain, alcoholism is a severe disorder that can produce a variety of cognitive, emotional, and behavioral issues. Alcoholism is typically diagnosed using the CAGE assessment approach, which has drawbacks such as being lengthy, prone to mistakes, and biased. To overcome these issues, this paper introduces a novel paradigm for identifying alcoholism by employing electroencephalogram (EEG) signals. The proposed framework is divided into various steps. To begin, interference and artifacts in the EEG data are removed using a multiscale principal component analysis procedure. This cleaning procedure contributes to information quality improvement. Second, an innovative graphical technique based on fast fractional Fourier transform coefficients is devised to visualize the chaotic character and complexities of the EEG signals. This elucidates the properties of regular and alcoholic EEG signals. Third, thirty-four graphical features are extracted to interpret the EEG signals' haphazard behavior and differentiate between regular and alcoholic trends. Fourth, we propose an ensembled feature selection method for obtaining an effective and reliable feature group. Following that, we study many neural network classifiers to choose the optimal classifier for building an efficient framework. The experimental findings show that the suggested method obtains the best classification performance by employing a recurrent neural network (RNN), with 97.5% accuracy, 96.7% sensitivity, and 98.3% specificity for the sixteen selected features. The proposed framework can aid physicians, businesses, and product designers to develop a real-time system.
Collapse
Affiliation(s)
- Muhammad Tariq Sadiq
- School of Computer Science and Electronic Engineering, University of Essex, Colchester Campus, Colchester CO4 3SQ, UK;
| | - Adnan Yousaf
- Department of Electrical Engineering, Superior University, Lahore 54000, Pakistan;
| | - Siuly Siuly
- Institute for Sustainable Industries and Liveable Cities, Victoria University, Melbourne 3011, Australia
| | - Ahmad Almogren
- Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11633, Saudi Arabia;
| |
Collapse
|
2
|
Zemla K, Sedek G, Wróbel K, Postepski F, Wojcik GM. Investigating the Impact of Guided Imagery on Stress, Brain Functions, and Attention: A Randomized Trial. SENSORS (BASEL, SWITZERLAND) 2023; 23:6210. [PMID: 37448060 PMCID: PMC10346678 DOI: 10.3390/s23136210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 06/28/2023] [Accepted: 07/04/2023] [Indexed: 07/15/2023]
Abstract
The aim of this study was to investigate the potential impact of guided imagery (GI) on attentional control and cognitive performance and to explore the relationship between guided imagery, stress reduction, alpha brainwave activity, and attentional control using common cognitive performance tests. Executive function was assessed through the use of attentional control tests, including the anti-saccade, Stroop, and Go/No-go tasks. Participants underwent a guided imagery session while their brainwave activity was measured, followed by attentional control tests. The study's outcomes provide fresh insights into the influence of guided imagery on brain wave activity, particularly in terms of attentional control. The findings suggest that guided imagery has the potential to enhance attentional control by augmenting the alpha power and reducing stress levels. Given the limited existing research on the specific impact of guided imagery on attention control, the study's findings carry notable significance.
Collapse
Affiliation(s)
- Katarzyna Zemla
- Institute of Psychology, SWPS University of Social Sciences and Humanities, 03-815 Warsaw, Poland; (K.Z.)
| | - Grzegorz Sedek
- Institute of Psychology, SWPS University of Social Sciences and Humanities, 03-815 Warsaw, Poland; (K.Z.)
| | - Krzysztof Wróbel
- Department of Neuroinformatics and Biomedical Engineering, Institute of Computer Science, Maria Curie-Sklodowska University, 20-033 Lublin, Poland (F.P.)
| | - Filip Postepski
- Department of Neuroinformatics and Biomedical Engineering, Institute of Computer Science, Maria Curie-Sklodowska University, 20-033 Lublin, Poland (F.P.)
| | - Grzegorz M. Wojcik
- Department of Neuroinformatics and Biomedical Engineering, Institute of Computer Science, Maria Curie-Sklodowska University, 20-033 Lublin, Poland (F.P.)
| |
Collapse
|
3
|
Wojcik GM, Shriki O, Kwasniewicz L, Kawiak A, Ben-Horin Y, Furman S, Wróbel K, Bartosik B, Panas E. Investigating brain cortical activity in patients with post-COVID-19 brain fog. Front Neurosci 2023; 17:1019778. [PMID: 36845422 PMCID: PMC9947499 DOI: 10.3389/fnins.2023.1019778] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 01/12/2023] [Indexed: 02/11/2023] Open
Abstract
Brain fog is a kind of mental problem, similar to chronic fatigue syndrome, and appears about 3 months after the infection with COVID-19 and lasts up to 9 months. The maximum magnitude of the third wave of COVID-19 in Poland was in April 2021. The research referred here aimed at carrying out the investigation comprising the electrophysiological analysis of the patients who suffered from COVID-19 and had symptoms of brain fog (sub-cohort A), suffered from COVID-19 and did not have symptoms of brain fog (sub-cohort B), and the control group that had no COVID-19 and no symptoms (sub-cohort C). The aim of this article was to examine whether there are differences in the brain cortical activity of these three sub-cohorts and, if possible differentiate and classify them using the machine-learning tools. he dense array electroencephalographic amplifier with 256 electrodes was used for recordings. The event-related potentials were chosen as we expected to find the differences in the patients' responses to three different mental tasks arranged in the experiments commonly known in experimental psychology: face recognition, digit span, and task switching. These potentials were plotted for all three patients' sub-cohorts and all three experiments. The cross-correlation method was used to find differences, and, in fact, such differences manifested themselves in the shape of event-related potentials on the cognitive electrodes. The discussion of such differences will be presented; however, an explanation of such differences would require the recruitment of a much larger cohort. In the classification problem, the avalanche analysis for feature extractions from the resting state signal and linear discriminant analysis for classification were used. The differences between sub-cohorts in such signals were expected to be found. Machine-learning tools were used, as finding the differences with eyes seemed impossible. Indeed, the A&B vs. C, B&C vs. A, A vs. B, A vs. C, and B vs. C classification tasks were performed, and the efficiency of around 60-70% was achieved. In future, probably there will be pandemics again due to the imbalance in the natural environment, resulting in the decreasing number of species, temperature increase, and climate change-generated migrations. The research can help to predict brain fog after the COVID-19 recovery and prepare the patients for better convalescence. Shortening the time of brain fog recovery will be beneficial not only for the patients but also for social conditions.
Collapse
Affiliation(s)
- Grzegorz M. Wojcik
- Department of Neuroinformatics and Biomedical Engineering, Institute of Computer Science, Maria Curie-Sklodowska University in Lublin, Lublin, Poland,*Correspondence: Grzegorz M. Wojcik ✉
| | - Oren Shriki
- Department of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel,Department of Computer Science, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Lukasz Kwasniewicz
- Department of Neuroinformatics and Biomedical Engineering, Institute of Computer Science, Maria Curie-Sklodowska University in Lublin, Lublin, Poland
| | - Andrzej Kawiak
- Department of Neuroinformatics and Biomedical Engineering, Institute of Computer Science, Maria Curie-Sklodowska University in Lublin, Lublin, Poland
| | - Yarden Ben-Horin
- Department of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Sagi Furman
- Department of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Krzysztof Wróbel
- Department of Neuroinformatics and Biomedical Engineering, Institute of Computer Science, Maria Curie-Sklodowska University in Lublin, Lublin, Poland
| | - Bernadetta Bartosik
- Department of Neuroinformatics and Biomedical Engineering, Institute of Computer Science, Maria Curie-Sklodowska University in Lublin, Lublin, Poland
| | - Ewelina Panas
- Department of International Relations, Faculty of Political Science and Journalism, Maria Curie-Sklodowska University in Lublin, Lublin, Poland
| |
Collapse
|
4
|
Li H, Wu L. EEG Classification of Normal and Alcoholic by Deep Learning. Brain Sci 2022; 12:778. [PMID: 35741663 PMCID: PMC9220822 DOI: 10.3390/brainsci12060778] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 06/06/2022] [Accepted: 06/11/2022] [Indexed: 12/21/2022] Open
Abstract
Alcohol dependence is a common mental disease worldwide. Excessive alcohol consumption may lead to alcoholism and many complications. In severe cases, it will lead to inhibition and paralysis of the centers of the respiratory and circulatory systems and even death. In addition, there is a lack of effective standard test procedures to detect alcoholism. EEG signals are data obtained by measuring brain changes in the cerebral cortex and can be used for the diagnosis of alcoholism. Existing diagnostic methods mainly employ machine learning techniques, which rely on human intervention to learn. In contrast, deep learning, as an end-to-end learning method, can automatically extract EEG signal features, which is more convenient. Nonetheless, there are few studies on the classification of alcohol's EEG signals using deep learning models. Therefore, in this paper, a new deep learning method is proposed to automatically extract and classify EEG's features. The method first adopts a multilayer discrete wavelet transform to denoise the input data. Then, the denoised data are used as input, and a convolutional neural network and bidirectional long short-term memory network are used for feature extraction. Finally, alcohol EEG signal classification is performed. The experimental results show that the method proposed in this study can be utilized to effectively diagnose patients with alcoholism, achieving a diagnostic accuracy of 99.32%, which is better than most current algorithms.
Collapse
Affiliation(s)
- Houchi Li
- School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411100, China;
| | - Lei Wu
- Hunan Engineering Research Center for Intelligent Decision Making and Big Data on Industrial Development, Hunan University of Science and Technology, Xiangtan 411100, China
| |
Collapse
|