Automatic epileptic seizure detection in EEGs based on line length feature and artificial neural networks

The biomedical applications of artificial intelligence: an overview of decades of research

A significant area of computer science called artificial intelligence (AI) is successfully applied to the analysis of intricate biological data and the extraction of substantial associations from datasets for a variety of biomedical uses. AI has attracted significant interest in biomedical research due to its features: (i) better patient care through early diagnosis and detection; (ii) enhanced workflow; (iii) lowering medical errors; (v) lowering medical costs; (vi) reducing morbidity and mortality; (vii) enhancing performance; (viii) enhancing precision; and (ix) time efficiency. Quantitative metrics are crucial for evaluating AI implementations, providing insights, enabling informed decisions, and measuring the impact of AI-driven initiatives, thereby enhancing transparency, accountability, and overall impact. The implementation of AI in biomedical fields faces challenges such as ethical and privacy concerns, lack of awareness, technology unreliability, and professional liability. A brief discussion is given of the AI techniques, which include Virtual screening (VS), DL, ML, Hidden Markov models (HMMs), Neural networks (NNs), Generative models (GMs), Molecular dynamics (MD), and Structure-activity relationship (SAR) models. The study explores the application of AI in biomedical fields, highlighting its enhanced predictive accuracy, treatment efficacy, diagnostic efficiency, faster decision-making, personalised treatment strategies, and precise medical interventions.

A Secure and Interpretable AI for Smart Healthcare System: A Case Study on Epilepsy Diagnosis Using EEG Signals

The efficient patient-independent and interpretable framework for electroencephalogram (EEG) epileptic seizure detection (ESD) has informative challenges due to the complex pattern of EEG nature. Automated detection of ES is crucial, while Explainable Artificial Intelligence (XAI) is urgently needed to justify the model detection of epileptic seizures in clinical applications. Therefore, this study implements an XAI-based computer-aided ES detection system (XAI-CAESDs), comprising three major modules, including of feature engineering module, a seizure detection module, and an explainable decision-making process module in a smart healthcare system. To ensure the privacy and security of biomedical EEG data, the blockchain is employed. Initially, the Butterworth filter eliminates various artifacts, and the Dual-Tree Complex Wavelet Transform (DTCWT) decomposes EEG signals, extracting real and imaginary eigenvalue features using frequency domain (FD), time domain (TD) linear feature, and Fractal Dimension (FD) of non-linear features. The best features are selected by using Correlation Coefficients (CC) and Distance Correlation (DC). The selected features are fed into the Stacking Ensemble Classifiers (SEC) for EEG ES detection. Further, the Shapley Additive Explanations (SHAP) method of XAI is implemented to facilitate the interpretation of predictions made by the proposed approach, enabling medical experts to make accurate and understandable decisions. The proposed Stacking Ensemble Classifiers (SEC) in XAI-CAESDs have demonstrated 2% best average accuracy, recall, specificity, and F1-score using the University of California, Irvine, Bonn University, and Boston Children's Hospital-MIT EEG data sets. The proposed framework enhances decision-making and the diagnosis process using biomedical EEG signals and ensures data security in smart healthcare systems.

Highly local activation of inhibition at the seizure wavefront in vivo

The propagation of a seizure wavefront in the cortex divides an intensely firing seizure core from a low-firing seizure penumbra. Seizure propagation is currently thought to generate strong activation of inhibition in the seizure penumbra that leads to its decreased neuronal firing. However, the direct measurement of neuronal excitability during seizures has been difficult to perform in vivo. We used simultaneous optogenetics and calcium imaging (all-optical interrogation) to characterize real-time neuronal excitability in an acute mouse model of seizure propagation. We find that single-neuron excitability is decreased in close proximity to the seizure wavefront but becomes increased distal to the seizure wavefront. This suggests that inhibitory neurons of the seizure wavefront create a proximal circumference of hypoexcitability but do not influence neuronal excitability in the penumbra.

Lightweight Seizure Detection Based on Multi-Scale Channel Attention

Epilepsy is one kind of neurological disease characterized by recurring seizures. Recurrent seizures can cause ongoing negative mental and cognitive damage to the patient. Therefore, timely diagnosis and treatment of epilepsy are crucial for patients. Manual electroencephalography (EEG) signals analysis is time and energy consuming, making automatic detection using EEG signals particularly important. Many deep learning algorithms have thus been proposed to detect seizures. These methods rely on expensive and bulky hardware, which makes them unsuitable for deployment on devices with limited resources due to their high demands on computer resources. In this paper, we propose a novel lightweight neural network for seizure detection using pure convolutions, which is composed of inverted residual structure and multi-scale channel attention mechanism. Compared with other methods, our approach significantly reduces the computational complexity, making it possible to deploy on low-cost portable devices for seizures detection. We conduct experiments on the CHB-MIT dataset and achieves 98.7% accuracy, 98.3% sensitivity and 99.1% specificity with 2.68[Formula: see text]M multiply-accumulate operations (MACs) and only 88[Formula: see text]K parameters.

A library of quantitative markers of seizure severity

OBJECTIVE

Understanding fluctuations in seizure severity within individuals is important for determining treatment outcomes and responses to therapy, as well as assessing novel treatments for epilepsy. Current methods for grading seizure severity rely on qualitative interpretations from patients and clinicians. Quantitative measures of seizure severity would complement existing approaches to electroencephalographic (EEG) monitoring, outcome monitoring, and seizure prediction. Therefore, we developed a library of quantitative EEG markers that assess the spread and intensity of abnormal electrical activity during and after seizures.

METHODS

We analyzed intracranial EEG (iEEG) recordings of 1009 seizures from 63 patients. For each seizure, we computed 16 markers of seizure severity that capture the signal magnitude, spread, duration, and postictal suppression of seizures.

RESULTS

Quantitative EEG markers of seizure severity distinguished focal versus subclinical seizures across patients. In individual patients, 53% had a moderate to large difference (rank sumr > .3 ,p < .05 ) between focal and subclinical seizures in three or more markers. Circadian and longer term changes in severity were found for the majority of patients.

SIGNIFICANCE

We demonstrate the feasibility of using quantitative iEEG markers to measure seizure severity. Our quantitative markers distinguish between seizure types and are therefore sensitive to established qualitative differences in seizure severity. Our results also suggest that seizure severity is modulated over different timescales. We envisage that our proposed seizure severity library will be expanded and updated in collaboration with the epilepsy research community to include more measures and modalities.

Epileptic Patient Activity Recognition System Using Extreme Learning Machine Method

The Human Activity Recognition (HAR) system is the hottest research area in clinical research. The HAR plays a vital role in learning about a patient’s abnormal activities; based upon this information, the patient’s psychological state can be estimated. An epileptic seizure is a neurological disorder of the human brain and affects millions of people worldwide. If epilepsy is diagnosed correctly and in an early stage, then up to 70% of people can be seizure-free. There is a need for intelligent automatic HAR systems that help clinicians diagnose neurological disorders accurately. In this research, we proposed a Deep Learning (DL) model that enables the detection of epileptic seizures in an automated way, addressing a need in clinical research. To recognize epileptic seizures from brain activities, EEG is a raw but good source of information. In previous studies, many techniques used raw data from EEG to help recognize epileptic patient activities; however, the applied method of extracting features required much intensive expertise from clinical aspects such as radiology and clinical methods. The image data are also used to diagnose epileptic seizures, but applying Machine Learning (ML) methods could address the overfitting problem. In this research, we mainly focused on classifying epilepsy through physical epileptic activities instead of feature engineering and performed the detection of epileptic seizures in three steps. In the first step, we used the open-source numerical dataset of epilepsy of Bonn university from the UCI Machine Learning repository. In the second step, data were fed to the proposed ELM model for training in different training and testing ratios with a little bit of rescaling because the dataset was already pre-processed, normalized, and restructured. In the third step, epileptic and non-epileptic activity was recognized, and in this step, EEG signal feature extraction was automatically performed by a DL model named ELM; features were selected by a Feature Selection (FS) algorithm based on ELM and the final classification was performed using the ELM classifier. In our presented research, seven different ML algorithms were applied for the binary classification of epileptic activities, including K-Nearest Neighbor (KNN), Naïve Bayes (NB), Logistic Regression (LR), Stochastic Gradient Boosting Classifier (SGDC), Gradient Boosting Classifier (GB), Decision Trees (DT), and three deep learning models named Extreme Learning Machine (ELM), Long Short-Term Memory (LSTM), and Artificial Neural Network (ANN). After deep analysis, it is observed that the best results were obtained by our proposed DL model, Extreme Learning Machine (ELM), with an accuracy of 100% accuracy and a 0.99 AUC. Such high performance has not attained in previous research. The proposed model’s performance was checked with other models in terms of performance parameters, namely confusion matrix, accuracy, precision, recall, F1-score, specificity, sensitivity, and the ROC curve.

Epileptic Seizure Detection Using a Hybrid 1D CNN-Machine Learning Approach from EEG Data

Electroencephalography (EEG) is a widely used technique for the detection of epileptic seizures. It can be recorded in a noninvasive manner to present the electrical activity of the brain. The visual inspection of nonlinear and highly complex EEG signals is both costly and time-consuming. Therefore, an effective automatic detection system is needed to assist in the long-term evaluation and treatment of patients. Traditional approaches based on machine learning require feature extraction, while deep learning approaches are time-consuming and require more layers for effective feature learning and processing of complex EEG waveforms. Deep learning-based approaches also have weak generalization ability. This paper proposes a solution based on the combination of convolution neural networks (CNN) and machine learning classifiers. It preprocesses the EEG signal using the Butterworth filter and performs feature extraction using CNN. From the extracted set of features, the approach selects only the relevant features using mutual information-based estimators to reduce the curse of dimensionality and improve classification accuracy. The selected features are then passed as input to different machine learning classifiers. The suggested solution is evaluated on the University of Bonn dataset and CHB-MIT datasets. Our model effectively predicts 2, 3, 4, and 5 classes with accuracy of 100%, 99%, 94.6%, and 94%, respectively, for the Bonn dataset and 98% for CHB-MIT datasets.

Evaluating the Window Size's Role in Automatic EEG Epilepsy Detection

Electroencephalography is one of the most commonly used methods for extracting information about the brain's condition and can be used for diagnosing epilepsy. The EEG signal's wave shape contains vital information about the brain's state, which can be challenging to analyse and interpret by a human observer. Moreover, the characteristic waveforms of epilepsy (sharp waves, spikes) can occur randomly through time. Considering all the above reasons, automatic EEG signal extraction and analysis using computers can significantly impact the successful diagnosis of epilepsy. This research explores the impact of different window sizes on EEG signals' classification accuracy using four machine learning classifiers. The machine learning methods included a neural network with ten hidden nodes trained using three different training algorithms and the k-nearest neighbours classifier. The neural network training methods included the Broyden-Fletcher-Goldfarb-Shanno algorithm, the multistart method for global optimization problems, and a genetic algorithm. The current research utilized the University of Bonn dataset containing EEG data, divided into epochs having 50% overlap and window lengths ranging from 1 to 24 s. Then, statistical and spectral features were extracted and used to train the above four classifiers. The outcome from the above experiments showed that large window sizes with a length of about 21 s could positively impact the classification accuracy between the compared methods.

Application of Machine Learning in Epileptic Seizure Detection

Epileptic seizure is a neurological condition caused by short and unexpectedly occurring electrical disruptions in the brain. It is estimated that roughly 60 million individuals worldwide have had an epileptic seizure. Experiencing an epileptic seizure can have serious consequences for the patient. Automatic seizure detection on electroencephalogram (EEG) recordings is essential due to the irregular and unpredictable nature of seizures. By thoroughly analyzing EEG records, neurophysiologists can discover important information and patterns, and proper and timely treatments can be provided for the patients. This research presents a novel machine learning-based approach for detecting epileptic seizures in EEG signals. A public EEG dataset from the University of Bonn was used to validate the approach. Meaningful statistical features were extracted from the original data using discrete wavelet transform analysis, then the relevant features were selected using feature selection based on the binary particle swarm optimizer. This facilitated the reduction of 75% data dimensionality and 47% computational time, which eventually sped up the classification process. After having been selected, relevant features were used to train different machine learning models, then hyperparameter optimization was utilized to further enhance the models' performance. The results achieved up to 98.4% accuracy and showed that the proposed method was very effective and practical in detecting seizure presence in EEG signals. In clinical applications, this method could help relieve the suffering of epilepsy patients and alleviate the workload of neurologists.

Epileptic Seizure Detection Based on Variational Mode Decomposition and Deep Forest Using EEG Signals

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.

Spatio-Spectral Feature Representation for Motor Imagery Classification Using Convolutional Neural Networks

Convolutional neural networks (CNNs) have recently been applied to electroencephalogram (EEG)-based brain-computer interfaces (BCIs). EEG is a noninvasive neuroimaging technique, which can be used to decode user intentions. Because the feature space of EEG data is highly dimensional and signal patterns are specific to the subject, appropriate methods for feature representation are required to enhance the decoding accuracy of the CNN model. Furthermore, neural changes exhibit high variability between sessions, subjects within a single session, and trials within a single subject, resulting in major issues during the modeling stage. In addition, there are many subject-dependent factors, such as frequency ranges, time intervals, and spatial locations at which the signal occurs, which prevent the derivation of a robust model that can achieve the parameterization of these factors for a wide range of subjects. However, previous studies did not attempt to preserve the multivariate structure and dependencies of the feature space. In this study, we propose a method to generate a spatiospectral feature representation that can preserve the multivariate information of EEG data. Specifically, 3-D feature maps were constructed by combining subject-optimized and subject-independent spectral filters and by stacking the filtered data into tensors. In addition, a layer-wise decomposition model was implemented using our 3-D-CNN framework to secure reliable classification results on a single-trial basis. The average accuracies of the proposed model were 87.15% (±7.31), 75.85% (±12.80), and 70.37% (±17.09) for the BCI competition data sets IV_2a, IV_2b, and OpenBMI data, respectively. These results are better than those obtained by state-of-the-art techniques, and the decomposition model obtained the relevance scores for neurophysiologically plausible electrode channels and frequency domains, confirming the validity of the proposed approach.

EEG-Based Epileptic Seizure Detection via Machine/Deep Learning Approaches: A Systematic Review

Epileptic seizure is one of the most chronic neurological diseases that instantaneously disrupts the lifestyle of affected individuals. Toward developing novel and efficient technology for epileptic seizure management, recent diagnostic approaches have focused on developing machine/deep learning model (ML/DL)-based electroencephalogram (EEG) methods. Importantly, EEG's noninvasiveness and ability to offer repeated patterns of epileptic-related electrophysiological information have motivated the development of varied ML/DL algorithms for epileptic seizure diagnosis in the recent years. However, EEG's low amplitude and nonstationary characteristics make it difficult for existing ML/DL models to achieve a consistent and satisfactory diagnosis outcome, especially in clinical settings, where environmental factors could hardly be avoided. Though several recent works have explored the use of EEG-based ML/DL methods and statistical feature for seizure diagnosis, it is unclear what the advantages and limitations of these works are, which might preclude the advancement of research and development in the field of epileptic seizure diagnosis and appropriate criteria for selecting ML/DL models and statistical feature extraction methods for EEG-based epileptic seizure diagnosis. Therefore, this paper attempts to bridge this research gap by conducting an extensive systematic review on the recent developments of EEG-based ML/DL technologies for epileptic seizure diagnosis. In the review, current development in seizure diagnosis, various statistical feature extraction methods, ML/DL models, their performances, limitations, and core challenges as applied in EEG-based epileptic seizure diagnosis were meticulously reviewed and compared. In addition, proper criteria for selecting appropriate and efficient feature extraction techniques and ML/DL models for epileptic seizure diagnosis were also discussed. Findings from this study will aid researchers in deciding the most efficient ML/DL models with optimal feature extraction methods to improve the performance of EEG-based epileptic seizure detection.

Deep Convolutional Gated Recurrent Unit Combined with Attention Mechanism to Classify Pre-Ictal from Interictal EEG with Minimized Number of Channels

The early prediction of epileptic seizures is important to provide appropriate treatment because it can notify clinicians in advance. Various EEG-based machine learning techniques have been used for automatic seizure classification based on subject-specific paradigms. However, because subject-specific models tend to perform poorly on new patient data, a generalized model with a cross-patient paradigm is necessary for building a robust seizure diagnosis system. In this study, we proposed a generalized model that combines one-dimensional convolutional layers (1D CNN), gated recurrent unit (GRU) layers, and attention mechanisms to classify preictal and interictal phases. When we trained this model with ten minutes of preictal data, the average accuracy over eight patients was 82.86%, with 80% sensitivity and 85.5% precision, outperforming other state-of-the-art models. In addition, we proposed a novel application of attention mechanisms for channel selection. The personalized model using three channels with the highest attention score from the generalized model performed better than when using the smallest attention score. Based on these results, we proposed a model for generalized seizure predictors and a seizure-monitoring system with a minimized number of EEG channels.

Significant Low-dimensional Spectral-temporal Features for Seizure Detection

Absence seizure as a generalized onset seizure, simultaneously spreading seizure to both sides of the brain, involves around ten-second sudden lapses of consciousness. It common occurs in children than adults, which affects living quality even threats lives. Absence seizure can be confused with inattentive attention-deficit hyperactivity disorder since both have similar symptoms, such as inattention and daze. Therefore, it is necessary to detect absence seizure onset. However, seizure onset detection in electroencephalography (EEG) signals is a challenging task due to the non-stereotyped seizure activities as well as their stochastic and non-stationary characteristics in nature. Joint spectral-temporal features are believed to contain sufficient and powerful feature information for absence seizure detection. However, the resulting high-dimensional features involve redundant information and require heavy computational load. Here, we discover significant low-dimensional spectral-temporal features in terms of mean-standard deviation of wavelet transform coefficient (MS-WTC), based on which a novel absence seizure detection framework is developed. The EEG signals are transformed into the spectral-temporal domain, with their low-dimensional features fed into a convolutional neural network. Superior detection performance is achieved on the widely-used benchmark dataset as well as a clinical dataset from the Chinese 301 Hospital. For the former, seven classification tasks were evaluated with the accuracy from 99.8% to 100.0%, while for the latter, the method achieved a mean accuracy of 94.7%, overwhelming other methods with low-dimensional temporal and spectral features. Experimental results on two seizure datasets demonstrate reliability, efficiency and stability of our proposed MS-WTC method, validating the significance of the extracted low-dimensional spectral-temporal features.

Covid19-Mexican-Patients' Dataset (Covid19MPD) Classification and Prediction Using Feature Importance

Coronavirus disease, Covid19, pandemic has a great effect on human heath worldwide since it was first detected in late 2019. A clear understanding of the structure of the available Covid19 datasets might give the healthcare provider a better understanding of identifying some of the cases at an early stage. In this article, we will be looking into a Covid19 Mexican Patients' Dataset (Covid109MPD), and we will apply number of machine learning algorithms on the dataset to select the best possible classification algorithm for the death and survived cases in Mexico, then we will study the performance of the enhancement of the specified classifiers in term of their features selection in order to be able to predict sever, and or death, cases from the available dataset. Results show that J48 classifier gives the best classification accuracy with 94.41% and RMSE = 0.2028 and ROC = 0.919, compared to other classifiers, and when using feature selection method, J48 classifier can predict a surviving Covid19MPD case within 94.88% accuracy, and by using only 10 out of the total 19 features.

Review on Epileptic Seizure Prediction: Machine Learning and Deep Learning Approaches

Epileptic seizures occur due to brain abnormalities that can indirectly affect patient's health. It occurs abruptly without any symptoms and thus increases the mortality rate of humans. Almost 1% of world's population suffers from epileptic seizures. Prediction of seizures before the beginning of onset is beneficial for preventing seizures by medication. Nowadays, modern computational tools, machine learning, and deep learning methods have been used to predict seizures using EEG. However, EEG signals may get corrupted with background noise, and artifacts such as eye blinks and physical movements of muscles may lead to "pops" in the signal, resulting in electrical interference, which is cumbersome to detect through visual inspection for longer duration recordings. These limitations in automatic detection of interictal spikes and epileptic seizures are preferred, which is an essential tool for examining and scrutinizing the EEG recording more precisely. These restrictions bring our attention to present a review of automated schemes that will help neurologists categorize epileptic and nonepileptic signals. While preparing this review paper, it is observed that feature selection and classification are the main challenges in epilepsy prediction algorithms. This paper presents various techniques depending on various features and classifiers over the last few years. The methods presented will give a detailed understanding and ideas about seizure prediction and future research directions.

The Effects of Optogenetic Activation of Astrocytes on Spike-and-Wave Discharges in Genetic Absence Epileptic Rats

Background

Absence seizures (petit mal seizures) are characterized by a brief loss of consciousness without loss of postural tone. The disease is diagnosed by an electroencephalogram (EEG) showing spike-wave discharges (SWD) caused by hypersynchronous thalamocortical (TC) oscillations. There has been an explosion of research highlighting the role of astrocytes in supporting and modulating neuronal activity. Despite established in vitro evidence, astrocytes' influence on the TC network remains to be elucidated in vivo in the absence epilepsy (AE).

Purpose

In this study, we investigated the role of astrocytes in the generation and modulation of SWDs. We hypothesize that disturbances in astrocytes' function may affect the pathomechanism of AE.

Methods

To direct the expression of channelrhodopsin-2 (ChR2) rAAV8-GFAP-ChR2(H134R)-EYFP or to control the effect of surgical intervention, AAV-CaMKIIa-EYFP was injected into the ventrobasal nucleus (VB) of the thalamus of 18 animals. After four weeks following the injection, rats were stimulated using blue light (~473 nm) and, simultaneously, the electrophysiological activity of the frontal cortical neurons was recorded for three consecutive days. The animals were then perfused, and the brain tissue was analyzed by confocal microscopy.

Results

A significant increase in the duration of SWD without affecting the number of SWD in genetic absence epileptic rats from Strasbourg (GAERS) compared to control injections was observed. The duration of the SWD was increased from 12.50 ± 4.41 s to 17.44 ± 6.07 following optogenetic stimulation in GAERS. The excitation of the astrocytes in Wistar Albino Glaxo Rijswijk (WAG-Rij) did not change the duration of SWD; however, stimulation resulted in a significant increase in the number of SWD from 18.52 ± 11.46 bursts/30 min to 30.17 ± 18.43 bursts/30 min. Whereas in control injection, the duration and the number of SWDs were similar at pre- and poststimulus. Both the background and poststimulus average firing rates of the SWD in WAG-Rij were significantly higher than the firing recorded in GAERS.

Conclusion

These findings suggest that VB astrocytes play a role in modulating the SWD generation in both rat models with distinct mechanisms and can present an essential target for the possible therapeutic approach for AE.

A Deep Learning-Based Classification Method for Different Frequency EEG Data

In recent years, the research on electroencephalography (EEG) has focused on the feature extraction of EEG signals. The development of convenient and simple EEG acquisition devices has produced a variety of EEG signal sources and the diversity of the EEG data. Thus, the adaptability of EEG classification methods has become significant. This study proposed a deep network model for autonomous learning and classification of EEG signals, which could self-adaptively classify EEG signals with different sampling frequencies and lengths. The artificial design feature extraction methods could not obtain stable classification results when analyzing EEG data with different sampling frequencies. However, the proposed depth network model showed considerably better universality and classification accuracy, particularly for EEG signals with short length, which was validated by two datasets.

Distributed Subnetworks of Depression Defined by Direct Intracranial Neurophysiology

Major depressive disorder is a common and disabling disorder with high rates of treatment resistance. Evidence suggests it is characterized by distributed network dysfunction that may be variable across patients, challenging the identification of quantitative biological substrates. We carried out this study to determine whether application of a novel computational approach to a large sample of high spatiotemporal resolution direct neural recordings in humans could unlock the functional organization and coordinated activity patterns of depression networks. This group level analysis of depression networks from heterogenous intracranial recordings was possible due to application of a correlational model-based method for inferring whole-brain neural activity. We then applied a network framework to discover brain dynamics across this model that could classify depression. We found a highly distributed pattern of neural activity and connectivity across cortical and subcortical structures that was present in the majority of depressed subjects. Furthermore, we found that this depression signature consisted of two subnetworks across individuals. The first was characterized by left temporal lobe hypoconnectivity and pathological beta activity. The second was characterized by a hypoactive, but hyperconnected left frontal cortex. These findings have applications toward personalization of therapy.

Applying nonlinear measures to the brain rhythms: an effective method for epilepsy diagnosis

Background

Epilepsy is a neurological disorder from which almost 50 million people have been suffering. These statistics indicate the importance of epilepsy diagnosis. Electroencephalogram (EEG) signals analysis is one of the most common methods for epilepsy characterization; hence, various strategies were applied to classify epileptic EEGs.

Methods

In this paper, four different nonlinear features such as Fractal dimensions including Higuchi method (HFD) and Katz method (KFD), Hurst exponent, and L-Z complexity measure were extracted from EEGs and their frequency sub-bands. The features were ranked later by implementing Relieff algorithm. The ranked features were applied sequentially to three different classifiers (MLPNN, Linear SVM, and RBF SVM).

Results

According to the dataset used for this study, there are five classification problems named ABCD/E, AB/CD/E, A/D/E, A/E, and D/E. In all cases, MLPNN was the most accurate classifier. Its performances for mentioned classification problems were 99.91%, 98.19%, 98.5%, 100% and 99.84%, respectively.

Conclusion

The results demonstrate that KFD is the highest-ranking feature; In addition, beta and theta sub-bands are the most important frequency bands because, for all cases, the top features were KFDs extracted from beta and theta sub-bands. Moreover, high levels of accuracy have been obtained just by using these two features which reduce the complexity of the classification.

Advanced Bioelectrical Signal Processing Methods: Past, Present and Future Approach-Part II: Brain Signals

As it was mentioned in the previous part of this work (Part I)-the advanced signal processing methods are one of the quickest and the most dynamically developing scientific areas of biomedical engineering with their increasing usage in current clinical practice. In this paper, which is a Part II work-various innovative methods for the analysis of brain bioelectrical signals were presented and compared. It also describes both classical and advanced approaches for noise contamination removal such as among the others digital adaptive and non-adaptive filtering, signal decomposition methods based on blind source separation, and wavelet transform.

A Hierarchical Discriminative Sparse Representation Classifier for EEG Signal Detection

Classification of electroencephalogram (EEG) signal data plays a vital role in epilepsy detection. Recently sparse representation-based classification (SRC) methods have achieved the good performance in EEG signal automatic detection, by which the EEG signals are sparsely represented using a few active coefficients in the dictionary and classified according to the reconstruction criteria. However, most of SRC learn a linear dictionary for encoding, and cannot extract enough information and nonlinear relationship of data for classification. To solve this problem, a hierarchical discriminative sparse representation classification model (called HD-SRC) for EEG signal detection is proposed. Based on the framework of neural network, HD-SRC learns the hierarchical nonlinear transformation and maps the signal data into the nonlinear transformed space. Through incorporating this idea into label consistent K singular value decomposition (LC-KSVD) at the top layer of neural network, HD-SRC seeks discriminative representation together with dictionary, while minimizing errors of classification, reconstruction and discriminative sparse-code for pattern classification. By learning the hierarchical feature mapping and discriminative dictionary simultaneously, more discriminative information of data can be exploited. In the experiment the proposed model is evaluated on the Bonn EEG database, and the results show it obtains satisfactory classification performance in multiple EEG signal detection tasks.

Automated FBSE-EWT based learning framework for detection of epileptic seizures using time-segmented EEG signals

Epilepsy is a neurological disorder that has severely affected many people's lives across the world. Electroencephalogram (EEG) signals are used to characterize the brain's state and detect various disorders. The EEG signals are non-stationary and non-linear in nature. Therefore, it is challenging to accurately process and learn from the recorded EEG signals in order to detect disorders like epilepsy. This paper proposed an automated learning framework using the Fourier-Bessel series expansion-based empirical wavelet transform (FBSE-EWT) method for detecting epileptic seizures from EEG signals. The scale-space boundary detection method was adopted to segment the Fourier-Bessel series expansion (FBSE) spectrum of multiple frame-size time-segmented EEG signals. Multiple frame-size time-segmented EEG signal's analysis was done using four different frame sizes: full, half, quarter, and half-quarter length of recorded EEG signals. Two different time-segmentation approaches were investigated on EEG signals: 1) segmenting signals based on multiple frame-size and 2) segmenting signals based on multiple frame-size with zero-padding the remaining signal. The FBSE-EWT method was applied to decompose the EEG signals into narrow sub-band signals. Features such as line-length (LL), log-energy-entropy (LEnt), and norm-entropy (NEnt) were computed from various frequency range sub-band signals. The relief-F feature ranking method was employed to select the most significant features; this reduces the computational burden of the models. The top-ranked accumulated features were used for classification using least square-support machine learning (LS-SVM), support vector machine (SVM), k-nearest neighbor (k-NN), and ensemble bagged tree classifiers. The proposed framework for epileptic seizure detection was evaluated on two publicly available benchmark EEG datasets: the Bonn EEG dataset and Children's Hospital Boston (CHB) and the Massachusetts Institute of Technology (MIT), well known as the CHB-MIT scalp EEG dataset. Training and testing of the models were performed using the 10-fold cross-validation technique. The FBSE-EWT based learning framework was compared with other state-of-the-art methods using both datasets. Experimental results showed that the proposed framework achieved 100 % classification accuracy on the Bonn EEG dataset, whereas 99.84 % classification accuracy on the CHB-MIT scalp EEG dataset.

A Recent Investigation on Detection and Classification of Epileptic Seizure Techniques Using EEG Signal

The benefits of early detection and classification of epileptic seizures in analysis, monitoring and diagnosis for the realization and actualization of computer-aided devices and recent internet of medical things (IoMT) devices can never be overemphasized. The success of these applications largely depends on the accuracy of the detection and classification techniques employed. Several methods have been investigated, proposed and developed over the years. This paper investigates various seizure detection algorithms and classifications in the last decade, including conventional techniques and recent deep learning algorithms. It also discusses epileptiform detection as one of the steps towards advanced diagnoses of disorders of consciousness (DOCs) and their understanding. A performance comparison was carried out on the different algorithms investigated, and their advantages and disadvantages were explored. From our survey, much attention has recently been paid to exploring the efficacy of deep learning algorithms in seizure detection and classification, which are employed in other areas such as image processing and classification. Hybrid deep learning has also been explored, with CNN-RNN being the most popular.

Nonlinear System Identification of Neural Systems from Neurophysiological Signals

The human nervous system is one of the most complicated systems in nature. Complex nonlinear behaviours have been shown from the single neuron level to the system level. For decades, linear connectivity analysis methods, such as correlation, coherence and Granger causality, have been extensively used to assess the neural connectivities and input-output interconnections in neural systems. Recent studies indicate that these linear methods can only capture a certain amount of neural activities and functional relationships, and therefore cannot describe neural behaviours in a precise or complete way. In this review, we highlight recent advances in nonlinear system identification of neural systems, corresponding time and frequency domain analysis, and novel neural connectivity measures based on nonlinear system identification techniques. We argue that nonlinear modelling and analysis are necessary to study neuronal processing and signal transfer in neural systems quantitatively. These approaches can hopefully provide new insights to advance our understanding of neurophysiological mechanisms underlying neural functions. These nonlinear approaches also have the potential to produce sensitive biomarkers to facilitate the development of precision diagnostic tools for evaluating neurological disorders and the effects of targeted intervention.

MNL-Network: A Multi-Scale Non-local Network for Epilepsy Detection From EEG Signals

Epilepsy is a prevalent neurological disorder that threatens human health in the world. The most commonly used method to detect epilepsy is using the electroencephalogram (EEG). However, epilepsy detection from the EEG is time-consuming and error-prone work because of the varying levels of experience we find in physicians. To tackle this challenge, in this paper, we propose a multi-scale non-local (MNL) network to achieve automatic EEG signal detection. Our MNL-Network is based on 1D convolution neural network involving two specific layers to improve the classification performance. One layer is named the signal pooling layer which incorporates three different sizes of 1D max-pooling layers to learn the multi-scale features from the EEG signal. The other one is called a multi-scale non-local layer, which calculates the correlation of different multi-scale extracted features and outputs the correlative encoded features to further enhance the classification performance. To evaluate the effectiveness of our model, we conduct experiments on the Bonn dataset. The experimental results demonstrate that our MNL-Network could achieve competitive results in the EEG classification task.

Association of cortical spreading depression and seizures in patients with medically intractable epilepsy

OBJECTIVE

Monitoring of the ultra-low frequency potentials, particularly cortical spreading depression (CSD), is excluded in epilepsy monitoring due to technical barriers imposed by the scalp ultra-low frequency electroencephalogram (EEG). As a result, clinical studies of CSD have been limited to invasive EEG. Therefore, the occurrence of CSD and its interaction with epileptiform field potentials (EFP) require investigation in epilepsy monitoring.

METHODS

Using a novel AC/DC-EEG approach, the occurrence of DC potentials in patients with intractable epilepsy presenting different symptoms of aura was investigated during long-term video-EEG monitoring.

RESULTS

Various forms of slow potentials, including simultaneous negative direct current (DC) potentials and prolonged EFP, propagated negative DC potentials, and non-propagated single negative DC potentials were recorded from the scalp of the epileptic patients. The propagated and single negative DC potentials preceded the prolonged EFP with a time lag and seizure appeared at the final shoulder of some instances of the propagated negative DC potentials. The slow potential deflections had a high amplitude and prolonged duration and propagated slowly through the brain. The high-frequency EEG was suppressed in the vicinity of the negative DC potential propagations.

CONCLUSIONS

The study is the first to report the recording of the propagated and single negative DC potentials with EFP at the scalp of patients with intractable epilepsy. The negative DC potentials preceded the prolonged EFP and may trigger seizures. The propagated and single negative DC potentials may be considered as CSD.

SIGNIFICANCE

Recordings of CSD may serve as diagnostic and prognostic monitoring tools in epilepsy.

The Neural Engine: A Reprogrammable Low Power Platform for Closed-Loop Optogenetics

Brain-machine Interfaces (BMI) hold great potential for treating neurological disorders such as epilepsy. Technological progress is allowing for a shift from open-loop, pacemaker-class, intervention towards fully closed-loop neural control systems. Low power programmable processing systems are therefore required which can operate within the thermal window of 2° C for medical implants and maintain long battery life. In this work, we have developed a low power neural engine with an optimized set of algorithms which can operate under a power cycling domain. We have integrated our system with a custom-designed brain implant chip and demonstrated the operational applicability to the closed-loop modulating neural activities in in-vitro and in-vivo brain tissues: the local field potentials can be modulated at required central frequency ranges. Also, both a freely-moving non-human primate (24-hour) and a rodent (1-hour) in-vivo experiments were performed to show system reliable recording performance. The overall system consumes only 2.93 mA during operation with a biological recording frequency 50 Hz sampling rate (the lifespan is approximately 56 hours). A library of algorithms has been implemented in terms of detection, suppression and optical intervention to allow for exploratory applications in different neurological disorders. Thermal experiments demonstrated that operation creates minimal heating as well as battery performance exceeding 24 hours on a freely moving rodent. Therefore, this technology shows great capabilities for both neuroscience in-vitro/in-vivo applications and medical implantable processing units.

Representation based on ordinal patterns for seizure detection in EEG signals

EEG signals carry rich information about brain activity and play an important role in the diagnosis and recognition of epilepsy. Numerous algorithms using EEG signals to detect seizures have been developed in recent decades. However, most of them require well-designed features that highly depend on domain-specific knowledge and algorithm expertise. In this study, we introduce the unigram ordinal pattern (UniOP) and bigram ordinal pattern (BiOP) representations to capture the different underlying dynamics of time series, which only assumes that time series derived from different dynamics can be characterized by repeated ordinal patterns. Specifically, we first transform each subsequence in a time series into the corresponding ordinal pattern in terms of the ranking of values and consider the distribution of ordinal patterns of all subsequences as the UniOP representation. Furthermore, we consider the distribution of the cooccurrence of ordinal patterns as the BiOP representation to characterize the contextual information for each ordinal pattern. We then combine the proposed representations with the nearest neighbor algorithm to evaluate its effectiveness on three publicly available seizure datasets. The results on the Bonn EEG dataset demonstrate that this method provides more than 90% accuracy, sensitivity, and specificity in most cases and outperforms several state-of-the-art methods, which proves its ability to capture the key information of the underlying dynamics of EEG time series at healthy, seizure-free, and seizure states. The results on the second dataset are comparable with the state-of-the-art method, showing the good generalization ability of the proposed method. All performance metrics on the third dataset are approximately 89%, which demonstrates that the proposed representations are suitable for large-scale datasets.

Localization of the epileptogenic zone based on ictal stereo-electroencephalogram: Brain network and single-channel signal feature analysis

Accurate localization of the epileptogenic zone (EZ) is crucial for refractory focal epilepsy patients to achieve freedom from seizures following epilepsy surgery. In this study, ictal stereo-electroencephalography data from 35 patients with refractory focal epilepsy were analyzed. Effective networks based on partial directed coherence were analyzed, and a gray level co-occurrence matrix was applied to extract the time-varying features of the in-degree. These features, combined with the single-channel signal time-frequency features, including approximate entropy and line length, were used to localize the EZ based on a cluster algorithm. For all seizure-free patients (n = 23), the proposed method was effective in identifying the clinical-EZ-contacts and clinical-EZ-blocks, with an F1-score of 62.47 % and 72.18 %, respectively. The sensitivity was 96.00 % for the clinical-EZ-block identification, which provided the information for the decision-making of clinicians, prompting clinicians to focus on the identified EZ-blocks and their nearby contacts. The agreement between the EZ identified by the proposed method and the clinical-EZ was worse for non-seizure-free patients (n = 12) than for seizure-free patients. Furthermore, our method provided better results than using only brain network or single-channel signal features. This suggests that combining these complementary features can facilitate more accurate localization of the EZ.

Graph Eigen Decomposition-Based Feature-Selection Method for Epileptic Seizure Detection Using Electroencephalography

Epileptic seizure is a sudden alteration of behavior owing to a temporary change in the electrical functioning of the brain. There is an urgent demand for an automatic epilepsy detection system using electroencephalography (EEG) for clinical application. In this paper, the EEG signal is divided into short time frames. Discrete wavelet transform is used to decompose each frame into a number of subbands. Different entropies as well as a group of features with which to characterize the spike events are extracted from each subband signal of an EEG frame. The features extracted from individual subbands are concatenated, yielding a high-dimensional feature vector. A discriminative subset of features is selected from the feature vector using a graph eigen decomposition (GED)-based approach. Thus, the reduced number of features obtained is effective for differentiating the underlying characteristics of EEG signals that indicate seizure events and those that indicate nonseizure events. The GED method ranks the features according to their contribution to correct classification. The selected features are used to classify seizure and nonseizure EEG signals using a feedforward neural network (FfNN). The performance of the proposed method is evaluated by conducting various experiments with a standard dataset obtained from the University of Bonn. The experimental results show that the proposed seizure-detection scheme achieves a classification accuracy of 99.55%, which is higher than that of state-of-the-art methods. The efficiency of FfNN is compared with linear discriminant analysis and support vector machine classifiers, which have classification accuracies of 98.72% and 99.39%, respectively. Hence, the proposed method is confirmed as a potential marker for EEG-based seizure detection.

A New Neural Mass Model Driven Method and Its Application in Early Epileptic Seizure Detection

OBJECTIVE

Despite numerous neural computational models proposed to explain physiological and pathological mechanisms of brain activity, a large gap remains between theory and application of the models. Building on the successful application of data-driven methods in epileptic seizure detection, we aim to build a bridge between data and models in this paper.

METHODS

We first propose a novel model-driven seizure detection method based on dynamic features in epileptic EEGs, where the rationale for dynamic features in epileptic EEGs can be clarified in theory by characterizing the variation of parameters of the model. Then we apply the proposed D&F-model-driven method to the problem of early epileptic seizure detection, where the evolution of model parameters selected and optimized by the proposed method is measured and used to detect the starting point of the seizure.

RESULTS

Numerical results on two open EEG databases demonstrate that our proposed method does a good job of early epileptic seizure detection. The average detection sensitivity, false positive rate and early detection period attain 100%, 0.1/h, and 7.1 s respectively.

CONCLUSION

This paper provides a strategy to characterize EEG signals using a NMM-related method and the model parameters optimized by real EEG may then serve as features in their own right for early seizure detection.

SIGNIFICANCE

An useful attempt to early detect epileptic seizures by combining the neural mass model with data analysis.

Epileptic Seizure Detection and Experimental Treatment: A Review

One-fourths of the patients have medication-resistant seizures and require seizure detection and treatment continuously to cope with sudden seizures. Seizures can be detected by monitoring the brain and muscle activities, heart rate, oxygen level, artificial sounds, or visual signatures through EEG, EMG, ECG, motion, or audio/video recording on the human head and body. In this article, we first discuss recent advances in seizure sensing, signal processing, time- or frequency-domain analysis, and classification algorithms to detect and classify seizure stages. Then, we show a strong potential of applying recent advancements in non-invasive brain stimulation technology to treat seizures. In particular, we explain the fundamentals of brain stimulation approaches, including (1) transcranial magnetic stimulation (TMS), (2) transcranial direct current stimulation (tDCS), (3) transcranial focused ultrasound stimulation (tFUS), and how to use them to treat seizures. Through this review, we intend to provide a broad view of both recent seizure diagnoses and treatments. Such knowledge would help fresh and experienced researchers to capture the advancements in sensing, detection, classification, and treatment seizures. Last but not least, we provide potential research directions that would attract seizure researchers/engineers in the field.

Epileptic seizure recognition using EEG wavelet decomposition based on nonlinear and statistical features with support vector machine classification

Epileptic seizure (ES) is a neurological brain dysfunction. ES can be detected using the electroencephalogram (EEG) signal. However, visual inspection of ES using long-time EEG recordings is a difficult, time-consuming and a costly procedure. Thus, automatic epilepsy recognition is of primary importance. In this paper, a new method is proposed for automatic ES recognition using short-time EEG recordings. The method is based on first decomposing the EEG signals on sub-signals using discrete wavelet transform. Then, from the obtained sub-signals, different non-linear parameters such as approximate entropy (ApEn), largest Lyapunov exponents (LLE) and statistical parameters are determined. These parameters along with phase entropies, calculated through higher order spectrum analysis, are used as an input vector of a multi-class support vector machine (MSVM) for ES recognition. The proposed method is evaluated using the standard EEG database developed by the Department of Epileptology, University of Bonn, Germany. The evaluation is carried out through a large number of classification experiments. Different statistical metrics namely Sensitivity (Se), Specificity (Sp) and classification accuracy (Ac) are calculated and compared to those obtained in the scientific research literature. The obtained results show that the proposed method achieves high accuracies, which are as good as the best existing state-of-the-art methods studied using the same EEG database.

Asynchronous Prediction of Human Gait Intention in a Pseudo Online Paradigm Using Wavelet Transform

Prediction of human voluntary gait intention is a very significant task to ensure direct cortical control of real-life assistive technologies for locomotion rehabilitation. Neurophysiological studies provide that human voluntary gait intention is represented by slow DC potentials and power shifts in specific frequency ranges of brain wave, which can be detected 1.5- 2 seconds before the actual onset. The goal of this study was to determine whether it is possible to reliably detect the intention of voluntary gait 'starting' and 'stopping' intention before it takes place. A computational algorithm was designed to implement asynchronous prediction of gait intention in an offline and pseudo-online environment using support vector machine. Six healthy subjects participated in the study and performed self- paced voluntary gait cycles. A combination of advanced wavelet transform algorithms resulted in 88.23± 1.59% accuracy, 85.42± 4.03% sensitivity and 90.24± 2.78% specificity for intention of start detection and 87.04± 1.72% accuracy, 82.69± 4.13% sensitivity and 89.59± 3.04% specificity for intention to stop walking in offline testing. Additionally, the wavelet transform methods accompanied with threshold regulation and majority voting algorithm resulted in a True Positive Rate of 85.5± 5.0% and 81.2± 3.3% for 'start' and 'stop' prediction with 6.8± 0.7 and 9.4± 1.0 False Positives per Minute respectively in pseudo online testing. The average detection latencies were -1002 ± 603 ms and -943 ± 603 ms, respectively, for 'start' and 'stop' prediction. The study provides promising outcomes in terms of TPR, FP/min, and detection latency, which suggests that human voluntary gait intention can be predicted before the onset of movement.