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Kantipudi MVVP, Kumar NSP, Aluvalu R, Selvarajan S, Kotecha K. An improved GBSO-TAENN-based EEG signal classification model for epileptic seizure detection. Sci Rep 2024; 14:843. [PMID: 38191643 PMCID: PMC10774431 DOI: 10.1038/s41598-024-51337-8] [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: 09/08/2023] [Accepted: 01/03/2024] [Indexed: 01/10/2024] Open
Abstract
Detection and classification of epileptic seizures from the EEG signals have gained significant attention in recent decades. Among other signals, EEG signals are extensively used by medical experts for diagnosing purposes. So, most of the existing research works developed automated mechanisms for designing an EEG-based epileptic seizure detection system. Machine learning techniques are highly used for reduced time consumption, high accuracy, and optimal performance. Still, it limits by the issues of high complexity in algorithm design, increased error value, and reduced detection efficacy. Thus, the proposed work intends to develop an automated epileptic seizure detection system with an improved performance rate. Here, the Finite Linear Haar wavelet-based Filtering (FLHF) technique is used to filter the input signals and the relevant set of features are extracted from the normalized output with the help of Fractal Dimension (FD) analysis. Then, the Grasshopper Bio-Inspired Swarm Optimization (GBSO) technique is employed to select the optimal features by computing the best fitness value and the Temporal Activation Expansive Neural Network (TAENN) mechanism is used for classifying the EEG signals to determine whether normal or seizure affected. Numerous intelligence algorithms, such as preprocessing, optimization, and classification, are used in the literature to identify epileptic seizures based on EEG signals. The primary issues facing the majority of optimization approaches are reduced convergence rates and higher computational complexity. Furthermore, the problems with machine learning approaches include a significant method complexity, intricate mathematical calculations, and a decreased training speed. Therefore, the goal of the proposed work is to put into practice efficient algorithms for the recognition and categorization of epileptic seizures based on EEG signals. The combined effect of the proposed FLHF, FD, GBSO, and TAENN models might dramatically improve disease detection accuracy while decreasing complexity of system along with time consumption as compared to the prior techniques. By using the proposed methodology, the overall average epileptic seizure detection performance is increased to 99.6% with f-measure of 99% and G-mean of 98.9% values.
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Affiliation(s)
- M V V Prasad Kantipudi
- Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune, 412115, India
| | - N S Pradeep Kumar
- S.E.A College of Engineering and Technology, Bengaluru, 560049, India
| | - Rajanikanth Aluvalu
- Department of Information Technology, Chaitanya Bharathi Institute of Technology, Hyderabad, 500075, India
| | - Shitharth Selvarajan
- School of Built Environment, Engineering and Computing, Leeds Beckett University, Leeds, LS1 3HE, UK.
- Department of Computer Science, Kebri Dehar University, Somali, Ethiopia.
| | - K Kotecha
- Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune, 412115, India
- Symbiosis Centre for Applied Artificial Intelligence (SCAAI), Symbiosis International (Deemed) University, Pune, 412115, India
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Díaz Beltrán L, Madan CR, Finke C, Krohn S, Di Ieva A, Esteban FJ. Fractal Dimension Analysis in Neurological Disorders: An Overview. ADVANCES IN NEUROBIOLOGY 2024; 36:313-328. [PMID: 38468040 DOI: 10.1007/978-3-031-47606-8_16] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
Fractal analysis has emerged as a powerful tool for characterizing irregular and complex patterns found in the nervous system. This characterization is typically applied by estimating the fractal dimension (FD), a scalar index that describes the topological complexity of the irregular components of the nervous system, both at the macroscopic and microscopic levels, that may be viewed as geometric fractals. Moreover, temporal properties of neurophysiological signals can also be interpreted as dynamic fractals. Given its sensitivity for detecting changes in brain morphology, FD has been explored as a clinically relevant marker of brain damage in several neuropsychiatric conditions as well as in normal and pathological cerebral aging. In this sense, evidence is accumulating for decreases in FD in Alzheimer's disease, frontotemporal dementia, Parkinson's disease, multiple sclerosis, and many other neurological disorders. In addition, it is becoming increasingly clear that fractal analysis in the field of clinical neurology opens the possibility of detecting structural alterations in the early stages of the disease, which highlights FD as a potential diagnostic and prognostic tool in clinical practice.
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Affiliation(s)
- Leticia Díaz Beltrán
- Department of Medical Oncology, Clinical Research Unit, University Hospital of Jaén, Jaén, Spain
| | | | - Carsten Finke
- Department of Neurology, Charité-Universitätsmedizin, Berlin, Germany
| | - Stephan Krohn
- Department of Neurology, Charité-Universitätsmedizin, Berlin, Germany
| | - Antonio Di Ieva
- Computational NeuroSurgery (CNS) Lab, Faculty of Medicine, Health and Human Sciences, Macquarie Medical School, Macquarie University, Sydney, NSW, Australia
| | - Francisco J Esteban
- Systems Biology Unit, Department of Experimental Biology, University of Jaén, Jaén, Spain.
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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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Lih OS, Jahmunah V, Palmer EE, Barua PD, Dogan S, Tuncer T, García S, Molinari F, Acharya UR. EpilepsyNet: Novel automated detection of epilepsy using transformer model with EEG signals from 121 patient population. Comput Biol Med 2023; 164:107312. [PMID: 37597408 DOI: 10.1016/j.compbiomed.2023.107312] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 08/02/2023] [Accepted: 08/04/2023] [Indexed: 08/21/2023]
Abstract
BACKGROUND Epilepsy is one of the most common neurological conditions globally, and the fourth most common in the United States. Recurrent non-provoked seizures characterize it and have huge impacts on the quality of life and financial impacts for affected individuals. A rapid and accurate diagnosis is essential in order to instigate and monitor optimal treatments. There is also a compelling need for the accurate interpretation of epilepsy due to the current scarcity in neurologist diagnosticians and a global inequity in access and outcomes. Furthermore, the existing clinical and traditional machine learning diagnostic methods exhibit limitations, warranting the need to create an automated system using deep learning model for epilepsy detection and monitoring using a huge database. METHOD The EEG signals from 35 channels were used to train the deep learning-based transformer model named (EpilepsyNet). For each training iteration, 1-min-long data were randomly sampled from each participant. Thereafter, each 5-s epoch was mapped to a matrix using the Pearson Correlation Coefficient (PCC), such that the bottom part of the triangle was discarded and only the upper triangle of the matrix was vectorized as input data. PCC is a reliable method used to measure the statistical relationship between two variables. Based on the 5 s of data, single embedding was performed thereafter to generate a 1-dimensional array of signals. In the final stage, a positional encoding with learnable parameters was added to each correlation coefficient's embedding before being fed to the developed EpilepsyNet as input data to epilepsy EEG signals. The ten-fold cross-validation technique was used to generate the model. RESULTS Our transformer-based model (EpilepsyNet) yielded high classification accuracy, sensitivity, specificity and positive predictive values of 85%, 82%, 87%, and 82%, respectively. CONCLUSION The proposed method is both accurate and robust since ten-fold cross-validation was employed to evaluate the performance of the model. Compared to the deep models used in existing studies for epilepsy diagnosis, our proposed method is simple and less computationally intensive. This is the earliest study to have uniquely employed the positional encoding with learnable parameters to each correlation coefficient's embedding together with the deep transformer model, using a huge database of 121 participants for epilepsy detection. With the training and validation of the model using a larger dataset, the same study approach can be extended for the detection of other neurological conditions, with a transformative impact on neurological diagnostics worldwide.
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Affiliation(s)
- Oh Shu Lih
- Cogninet Australia, Sydney, NSW, 2010, Australia
| | - V Jahmunah
- School of Engineering, Nanyang Polytechnic, Singapore
| | - Elizabeth Emma Palmer
- Centre of Clinical Genetics, Sydney Children's Hospitals Network, Randwick, 2031, Australia; School of Women's and Children's Health, University of New South Wales, Randwick, 2031, Australia
| | - Prabal D Barua
- School of Business (Information System), University of Southern Queensland, Australia
| | - Sengul Dogan
- Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, Turkey
| | - Turker Tuncer
- Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, Turkey
| | - Salvador García
- Andalusian Institute of Data Science and Computational Intelligence, Department of Computer Science and Artificial Intelligence, University of Granada, Spain
| | - Filippo Molinari
- Biolab, PolitoBIOMedLab, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | - U Rajendra Acharya
- School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, Australia.
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EEG Channel Selection Techniques in Motor Imagery Applications: A Review and New Perspectives. BIOENGINEERING (BASEL, SWITZERLAND) 2022; 9:bioengineering9120726. [PMID: 36550932 PMCID: PMC9774545 DOI: 10.3390/bioengineering9120726] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 10/28/2022] [Accepted: 10/30/2022] [Indexed: 11/25/2022]
Abstract
Communication, neuro-prosthetics, and environmental control are just a few applications for disabled persons who use robots and manipulators that use brain-computer interface (BCI) systems. The brain's motor imagery (MI) signal is an essential input for a brain-related task in BCI applications. Due to their noninvasive, portability, and cost-effectiveness, electroencephalography (EEG) signals are the most widely used input in BCI systems. The EEG data are often collected from more than 100 different locations in the brain; channel selection techniques are critical for selecting the optimum channels for a given application. However, when analyzing EEG data, the principal purpose of channel selection is to reduce computational complexity, improve classification accuracy by avoiding overfitting, and reduce setup time. Several channel selection assessment algorithms, both with and without classification-based methods, extracted appropriate channel subsets using defined criteria. Therefore, based on the exhaustive analysis of the EEG channel selection, this manuscript analyses several existing studies to reduce the number of noisy channels and improve system performance. We review several existing works to find the most promising MI-based EEG channel selection algorithms and associated classification methodologies on various datasets. Moreover, we focus on channel selection methods that choose fewer channels with great precision. Finally, our main finding is that a smaller channel set, typically 10-30% of total channels, provided excellent performance compared to other existing studies.
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Epileptic Seizure Detection Based on Variational Mode Decomposition and Deep Forest Using EEG Signals. Brain Sci 2022; 12:brainsci12101275. [PMID: 36291210 PMCID: PMC9599930 DOI: 10.3390/brainsci12101275] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Revised: 09/16/2022] [Accepted: 09/20/2022] [Indexed: 11/16/2022] Open
Abstract
Electroencephalography (EEG) records the electrical activity of the brain, which is an important tool for the automatic detection of epileptic seizures. It is certainly a very heavy burden to only recognize EEG epilepsy manually, so the method of computer-assisted treatment is of great importance. This paper presents a seizure detection algorithm based on variational modal decomposition (VMD) and a deep forest (DF) model. Variational modal decomposition is performed on EEG recordings, and the first three variational modal functions (VMFs) are selected to construct the time–frequency distribution of the EEG signals. Then, the log−Euclidean covariance matrix (LECM) is computed to represent the EEG properties and form EEG features. The deep forest model is applied to complete the EEG signal classification, which is a non-neural network deep model with a cascade structure that performs feature learning through the forest. In addition, to improve the classification accuracy, postprocessing techniques are performed to generate the discriminant results by moving average filtering and adaptive collar expansion. The algorithm was evaluated on the Bonn EEG dataset and the Freiburg long−term EEG dataset, and the former achieved a sensitivity and specificity of 99.32% and 99.31%, respectively. The mean sensitivity and specificity of this method for the 21 patients in the Freiburg dataset were 95.2% and 98.56%, respectively, with a false detection rate of 0.36/h. These results demonstrate the superior performance advantage of our algorithm and indicate its great research potential in epilepsy detection.
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Classification of EEG Signals for Prediction of Epileptic Seizures. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12147251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Epilepsy is a common brain disorder that causes patients to face multiple seizures in a single day. Around 65 million people are affected by epilepsy worldwide. Patients with focal epilepsy can be treated with surgery, whereas generalized epileptic seizures can be managed with medications. It has been noted that in more than 30% of cases, these medications fail to control epileptic seizures, resulting in accidents and limiting the patient’s life. Predicting epileptic seizures in such patients prior to the commencement of an oncoming seizure is critical so that the seizure can be treated with preventive medicines before it occurs. Electroencephalogram (EEG) signals of patients recorded to observe brain electrical activity during a seizure can be quite helpful in predicting seizures. Researchers have proposed methods that use machine and/or deep learning techniques to predict epileptic seizures using scalp EEG signals; however, prediction of seizures with increased accuracy is still a challenge. Therefore, we propose a three-step approach. It includes preprocessing of scalp EEG signals with PREP pipeline, which is a more sophisticated alternative to basic notch filtering. This method uses a regression-based technique to further enhance the SNR, with a combination of handcrafted, i.e., statistical features such as temporal mean, variance, and skewness, and automated features using CNN, followed by classification of interictal state and preictal state segments using LSTM to predict seizures. We train and validate our proposed technique on the CHB-MIT scalp EEG dataset and achieve accuracy of 94%, sensitivity of 93.8%, and 91.2% specificity. The proposed technique achieves better sensitivity and specificity than existing methods.
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Engineering nonlinear epileptic biomarkers using deep learning and Benford's law. Sci Rep 2022; 12:5397. [PMID: 35354911 PMCID: PMC8967852 DOI: 10.1038/s41598-022-09429-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 03/22/2022] [Indexed: 12/15/2022] Open
Abstract
In this study, we designed two deep neural networks to encode 16 features for early seizure detection in intracranial EEG and compared them and their frequency responses to 16 widely used engineered metrics to interpret their properties: epileptogenicity index (EI), phase locked high gamma (PLHG), time and frequency domain Cho Gaines distance (TDCG, FDCG), relative band powers, and log absolute band powers (from alpha, beta, theta, delta, low gamma, and high gamma bands). The deep learning models were pretrained for seizure identification on the time and frequency domains of 1 s, single-channel clips of 127 seizures (from 25 different subjects) using "leave-one-out" (LOO) cross validation. Each neural network extracted unique feature spaces that were interpreted using spectral power modulations before being used to train a Random Forest Classifier (RFC) for seizure identification. The Gini Importance of each feature was calculated from the pretrained RFC, enabling the most significant features (MSFs) for each task to be identified. The MSFs were extracted to train another RFC for UPenn and Mayo Clinic's Seizure Detection Kaggle Challenge. They obtained an AUC score of 0.93, demonstrating a transferable method to identify and interpret biomarkers for seizure detection.
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