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Krothapalli M, Buddendorff L, Yadav H, Schilaty ND, Jain S. From Gut Microbiota to Brain Waves: The Potential of the Microbiome and EEG as Biomarkers for Cognitive Impairment. Int J Mol Sci 2024; 25:6678. [PMID: 38928383 PMCID: PMC11203453 DOI: 10.3390/ijms25126678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Revised: 06/09/2024] [Accepted: 06/12/2024] [Indexed: 06/28/2024] Open
Abstract
Alzheimer's disease (AD) is a prevalent neurodegenerative disorder and a leading cause of dementia. Aging is a significant risk factor for AD, emphasizing the importance of early detection since symptoms cannot be reversed once the advanced stage is reached. Currently, there is no established method for early AD diagnosis. However, emerging evidence suggests that the microbiome has an impact on cognitive function. The gut microbiome and the brain communicate bidirectionally through the gut-brain axis, with systemic inflammation identified as a key connection that may contribute to AD. Gut dysbiosis is more prevalent in individuals with AD compared to their cognitively healthy counterparts, leading to increased gut permeability and subsequent systemic inflammation, potentially causing neuroinflammation. Detecting brain activity traditionally involves invasive and expensive methods, but electroencephalography (EEG) poses as a non-invasive alternative. EEG measures brain activity and multiple studies indicate distinct patterns in individuals with AD. Furthermore, EEG patterns in individuals with mild cognitive impairment differ from those in the advanced stage of AD, suggesting its potential as a method for early indication of AD. This review aims to consolidate existing knowledge on the microbiome and EEG as potential biomarkers for early-stage AD, highlighting the current state of research and suggesting avenues for further investigation.
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Affiliation(s)
- Mahathi Krothapalli
- USF Center for Microbiome Research, Microbiomes Institute, University of South Florida, Tampa, FL 33612, USA; (M.K.); (L.B.); (H.Y.)
- Department of Neurosurgery and Brain Repair, University of South Florida, Tampa, FL 33612, USA;
| | - Lauren Buddendorff
- USF Center for Microbiome Research, Microbiomes Institute, University of South Florida, Tampa, FL 33612, USA; (M.K.); (L.B.); (H.Y.)
- Department of Neurosurgery and Brain Repair, University of South Florida, Tampa, FL 33612, USA;
| | - Hariom Yadav
- USF Center for Microbiome Research, Microbiomes Institute, University of South Florida, Tampa, FL 33612, USA; (M.K.); (L.B.); (H.Y.)
- Department of Neurosurgery and Brain Repair, University of South Florida, Tampa, FL 33612, USA;
| | - Nathan D. Schilaty
- Department of Neurosurgery and Brain Repair, University of South Florida, Tampa, FL 33612, USA;
- Center for Neuromusculoskeletal Research, University of South Florida, Tampa, FL 33612, USA
| | - Shalini Jain
- USF Center for Microbiome Research, Microbiomes Institute, University of South Florida, Tampa, FL 33612, USA; (M.K.); (L.B.); (H.Y.)
- Department of Neurosurgery and Brain Repair, University of South Florida, Tampa, FL 33612, USA;
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Yu K, Feng L, Chen Y, Wu M, Zhang Y, Zhu P, Chen W, Wu Q, Hao J. Accurate wavelet thresholding method for ECG signals. Comput Biol Med 2024; 169:107835. [PMID: 38096762 DOI: 10.1016/j.compbiomed.2023.107835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 11/25/2023] [Accepted: 12/05/2023] [Indexed: 02/08/2024]
Abstract
Current wavelet thresholding methods for cardiogram signals captured by flexible wearable sensors face a challenge in achieving both accurate thresholding and real-time signal denoising. This paper proposes a real-time accurate thresholding method based on signal estimation, specifically the normalized ACF, as an alternative to traditional noise estimation without the need for parameter fine-tuning and extensive data training. This method is experimentally validated using a variety of electrocardiogram (ECG) signals from different databases, each containing specific types of noise such as additive white Gaussian (AWG) noise, baseline wander noise, electrode motion noise, and muscle artifact noise. Although this method only slightly outperforms other methods in removing AWG noise in ECG signals, it far outperforms conventional methods in removing other real noise. This is attributed to the method's ability to accurately distinguish not only AWG noise that is significantly different spectrum of the ECG signal, but also real noise with similar spectra. In contrast, the conventional methods are effective only for AWG noise. In additional, this method improves the denoising visualization of the measured ECG signals and can be used to optimize other parameters of other wavelet methods to enhancing the denoised periodic signals, thereby improving diagnostic accuracy.
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Affiliation(s)
- Kaimin Yu
- School of Marine Equipment and Mechanical Engineering, Jimei University, Xiamen, 361021, Fujian, China
| | - Lei Feng
- School of Ocean Information Engineering, Jimei University, Xiamen, 361021, Fujian, China
| | - Yunfei Chen
- School of Ocean Information Engineering, Jimei University, Xiamen, 361021, Fujian, China
| | - Minfeng Wu
- School of Electrical Engineering and Artificial Intelligence, Xiamen University Malaysia, Sepang, 43900, Malaysia
| | - Yuanfang Zhang
- School of Ocean Information Engineering, Jimei University, Xiamen, 361021, Fujian, China
| | - Peibin Zhu
- School of Ocean Information Engineering, Jimei University, Xiamen, 361021, Fujian, China
| | - Wen Chen
- School of Ocean Information Engineering, Jimei University, Xiamen, 361021, Fujian, China.
| | - Qihui Wu
- School of Marine Equipment and Mechanical Engineering, Jimei University, Xiamen, 361021, Fujian, China
| | - Jianzhong Hao
- Institute for Infocomm Research (I(2)R), Agency for Science, Technology and Research (A⋆STAR), 138632, Singapore
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Kawala-Sterniuk A, Wójcik GM, Bauer W. Editorial: Biomedical Data in Human-Machine Interaction. SENSORS (BASEL, SWITZERLAND) 2023; 23:7983. [PMID: 37766038 PMCID: PMC10537884 DOI: 10.3390/s23187983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 09/07/2023] [Indexed: 09/29/2023]
Abstract
Analysis of biomedical data can provide useful information regarding human condition and as a result-analysis of these signals has become one of the most popular diagnostic methods [...].
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Affiliation(s)
- Aleksandra Kawala-Sterniuk
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland
| | - Grzegorz Marcin Wójcik
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland
- Department of Neuroinformatics and Biomedical Engineering, Maria Curie-Sklodowska University, 20-400 Lublin, Poland
| | - Waldemar Bauer
- Department of Automatic Control and Robotics, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Kraków, Poland
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Antony MJ, Sankaralingam BP, Khan S, Almjally A, Almujally NA, Mahendran RK. Brain-Computer Interface: The HOL-SSA Decomposition and Two-Phase Classification on the HGD EEG Data. Diagnostics (Basel) 2023; 13:2852. [PMID: 37685390 PMCID: PMC10486696 DOI: 10.3390/diagnostics13172852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 08/25/2023] [Accepted: 08/28/2023] [Indexed: 09/10/2023] Open
Abstract
An efficient processing approach is essential for increasing identification accuracy since the electroencephalogram (EEG) signals produced by the Brain-Computer Interface (BCI) apparatus are nonlinear, nonstationary, and time-varying. The interpretation of scalp EEG recordings can be hampered by nonbrain contributions to electroencephalographic (EEG) signals, referred to as artifacts. Common disturbances in the capture of EEG signals include electrooculogram (EOG), electrocardiogram (ECG), electromyogram (EMG) and other artifacts, which have a significant impact on the extraction of meaningful information. This study suggests integrating the Singular Spectrum Analysis (SSA) and Independent Component Analysis (ICA) methods to preprocess the EEG data. The key objective of our research was to employ Higher-Order Linear-Moment-based SSA (HOL-SSA) to decompose EEG signals into multivariate components, followed by extracting source signals using Online Recursive ICA (ORICA). This approach effectively improves artifact rejection. Experimental results using the motor imagery High-Gamma Dataset validate our method's ability to identify and remove artifacts such as EOG, ECG, and EMG from EEG data, while preserving essential brain activity.
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Affiliation(s)
- Mary Judith Antony
- Department of Computer Science & Engineering, Panimalar College of Engineering, Chennai 600123, India
| | - Baghavathi Priya Sankaralingam
- Department of Computer Science & Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Chennai 601103, India
| | - Shakir Khan
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia; (S.K.); (A.A.)
- University Centre for Research and Development, Department of Computer Science and Engineering, Chandigarh University, Mohali 140413, India
| | - Abrar Almjally
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia; (S.K.); (A.A.)
| | - Nouf Abdullah Almujally
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia
| | - Rakesh Kumar Mahendran
- Department of Computer Science and Engineering, Rajalakshmi Engineering College, Chennai 602105, India;
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Barnova K, Mikolasova M, Kahankova RV, Jaros R, Kawala-Sterniuk A, Snasel V, Mirjalili S, Pelc M, Martinek R. Implementation of artificial intelligence and machine learning-based methods in brain-computer interaction. Comput Biol Med 2023; 163:107135. [PMID: 37329623 DOI: 10.1016/j.compbiomed.2023.107135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 05/13/2023] [Accepted: 06/04/2023] [Indexed: 06/19/2023]
Abstract
Brain-computer interfaces are used for direct two-way communication between the human brain and the computer. Brain signals contain valuable information about the mental state and brain activity of the examined subject. However, due to their non-stationarity and susceptibility to various types of interference, their processing, analysis and interpretation are challenging. For these reasons, the research in the field of brain-computer interfaces is focused on the implementation of artificial intelligence, especially in five main areas: calibration, noise suppression, communication, mental condition estimation, and motor imagery. The use of algorithms based on artificial intelligence and machine learning has proven to be very promising in these application domains, especially due to their ability to predict and learn from previous experience. Therefore, their implementation within medical technologies can contribute to more accurate information about the mental state of subjects, alleviate the consequences of serious diseases or improve the quality of life of disabled patients.
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Affiliation(s)
- Katerina Barnova
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Czechia.
| | - Martina Mikolasova
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Czechia.
| | - Radana Vilimkova Kahankova
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Czechia
| | - Rene Jaros
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Czechia.
| | - Aleksandra Kawala-Sterniuk
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Poland.
| | - Vaclav Snasel
- Department of Computer Science, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Czechia.
| | - Seyedali Mirjalili
- Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Australia.
| | - Mariusz Pelc
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Poland; School of Computing and Mathematical Sciences, University of Greenwich, London, UK.
| | - Radek Martinek
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Czechia; Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Poland.
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Karwowski W, Soekadar SR, Kawala-Sterniuk A. Editorial: Brain imaging relations through simultaneous recordings. Front Neurosci 2023; 17:1139336. [PMID: 36824216 PMCID: PMC9941736 DOI: 10.3389/fnins.2023.1139336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Accepted: 01/16/2023] [Indexed: 02/10/2023] Open
Affiliation(s)
- Waldemar Karwowski
- Department of Industrial and Systems Engineering, University of Central Florida, Orlando, FL, United States,*Correspondence: Waldemar Karwowski ✉
| | - Surjo R. Soekadar
- Clinical Neurotechnology Laboratory, Department of Psychiatry and Neurosciences, Charité Campus Mitte (CCM), Charité–Universitätsmedizin Berlin, Berlin, Germany,Surjo R. Soekadar ✉
| | - Aleksandra Kawala-Sterniuk
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Opole, Poland,Aleksandra Kawala-Sterniuk ✉
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Bauer W, Dylag KA, Lysiak A, Wieczorek-Stawinska W, Pelc M, Szmajda M, Martinek R, Zygarlicki J, Bańdo B, Stomal-Slowinska M, Kawala-Sterniuk A. Initial study on quantitative electroencephalographic analysis of bioelectrical activity of the brain of children with fetal alcohol spectrum disorders (FASD) without epilepsy. Sci Rep 2023; 13:109. [PMID: 36596841 PMCID: PMC9810692 DOI: 10.1038/s41598-022-26590-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 12/16/2022] [Indexed: 01/04/2023] Open
Abstract
Fetal alcohol spectrum disorders (FASD) are spectrum of neurodevelopmental conditions associated with prenatal alcohol exposure. The FASD manifests mostly with facial dysmorphism, prenatal and postnatal growth retardation, and selected birth defects (including central nervous system defects). Unrecognized and untreated FASD leads to severe disability in adulthood. The diagnosis of FASD is based on clinical criteria and neither biomarkers nor imaging tests can be used in order to confirm the diagnosis. The quantitative electroencephalography (QEEG) is a type of EEG analysis, which involves the use of mathematical algorithms, and which has brought new possibilities of EEG signal evaluation, among the other things-the analysis of a specific frequency band. The main objective of this study was to identify characteristic patterns in QEEG among individuals affected with FASD. This study was of a pilot prospective study character with experimental group consisting of patients with newly diagnosed FASD and of the control group consisting of children with gastroenterological issues. The EEG recordings of both groups were obtained, than analyzed using a commercial QEEG module. As a results we were able to establish the dominance of the alpha rhythm over the beta rhythm in FASD-participants compared to those from the control group, mostly in frontal and temporal regions. Second important finding is an increased theta/beta ratio among patients with FASD. These findings are consistent with the current knowledge on the pathological processes resulting from the prenatal alcohol exposure. The obtained results and conclusions were promising, however, further research is necessary (and planned) in order to validate the use of QEEG tools in FASD diagnostics.
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Affiliation(s)
- Waldemar Bauer
- grid.9922.00000 0000 9174 1488Department of Automatic Control and Robotics, AGH University of Science and Technology, 30-059 Kraków, Poland
| | - Katarzyna Anna Dylag
- St. Louis Children Hospital in Krakow, 30-663 Kraków, Poland ,grid.5522.00000 0001 2162 9631Department of Pathophysiology, Jagiellonian University in Krakow – Collegium Medicum, 31-121 Kraków, Poland
| | - Adam Lysiak
- grid.440608.e0000 0000 9187 132XFaculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland
| | | | - Mariusz Pelc
- grid.440608.e0000 0000 9187 132XFaculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland ,grid.36316.310000 0001 0806 5472School of Computing and Mathematical Sciences, University of Greenwich, London, SE10 9LS UK
| | - Miroslaw Szmajda
- grid.440608.e0000 0000 9187 132XFaculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland
| | - Radek Martinek
- grid.440608.e0000 0000 9187 132XFaculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland ,grid.440850.d0000 0000 9643 2828Department of Cybernetics and Biomedical Engineering, VSB—Technical University Ostrava—FEECS, 708 00 Ostrava-Poruba, Czech Republic
| | - Jaroslaw Zygarlicki
- grid.440608.e0000 0000 9187 132XFaculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland
| | - Bożena Bańdo
- St. Louis Children Hospital in Krakow, 30-663 Kraków, Poland
| | | | - Aleksandra Kawala-Sterniuk
- grid.440608.e0000 0000 9187 132XFaculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland
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Dyląg KA, Wieczorek W, Bauer W, Walecki P, Bando B, Martinek R, Kawala-Sterniuk A. Pilot Study on Analysis of Electroencephalography Signals from Children with FASD with the Implementation of Naive Bayesian Classifiers. SENSORS (BASEL, SWITZERLAND) 2021; 22:103. [PMID: 35009650 PMCID: PMC8747358 DOI: 10.3390/s22010103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 12/15/2021] [Accepted: 12/21/2021] [Indexed: 06/14/2023]
Abstract
In this paper Naive Bayesian classifiers were applied for the purpose of differentiation between the EEG signals recorded from children with Fetal Alcohol Syndrome Disorders (FASD) and healthy ones. This work also provides a brief introduction to the FASD itself, explaining the social, economic and genetic reasons for the FASD occurrence. The obtained results were good and promising and indicate that EEG recordings can be a helpful tool for potential diagnostics of FASDs children affected with it, in particular those with invisible physical signs of these spectrum disorders.
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Affiliation(s)
- Katarzyna Anna Dyląg
- St. Louis Children Hospital, 31-503 Krakow, Poland; (K.A.D.); (B.B.)
- Department of Pathophysiology, Jagiellonian University in Krakow—Collegium Medicum, 31-121 Krakow, Poland
| | - Wiktoria Wieczorek
- Department of Bioinformatics and Telemedicine, Jagiellonian University in Krakow—Collegium Medicum, 30-688 Krakow, Poland; (W.W.); (P.W.)
| | - Waldemar Bauer
- Department of Automatic Control and Robotics, AGH University of Science and Technology, 30-059 Krakow, Poland
| | - Piotr Walecki
- Department of Bioinformatics and Telemedicine, Jagiellonian University in Krakow—Collegium Medicum, 30-688 Krakow, Poland; (W.W.); (P.W.)
| | - Bozena Bando
- St. Louis Children Hospital, 31-503 Krakow, Poland; (K.A.D.); (B.B.)
| | - Radek Martinek
- Department of Cybernetics and Biomedical Engineering, VSB—Technical University Ostrava—FEECS, 708 00 Ostrava-Poruba, Czech Republic;
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