Epileptic Seizure Detection Research Articles

Persistent Homology-Based Classification of Chaotic Multi-variate Time Series: Application to Electroencephalograms

In this work, we present a combination of dimension reduction techniques and persistent homology for detection of epileptic events in electroencephalograms for a special kind of epilepsy called petit-mal epilepsy. Persistent homology, one of the main methods in topological data analysis, extracts information about the structures appearing in a given data set. Since during an epileptic seizure of the above type the electrical brain activity is more synchronized, we take the resulting structure in the EEG signal as classification feature that is analyzed topologically by means of persistent homology. As preprocessing step, the dimension of the data is reduced by two alternative techniques, principal component analysis and dynamical component analysis, and their performance is compared. Our results show that in comparison to principal component analysis, dynamical component analysis captures the dynamics of the system when projected onto a low-dimensional subspace. Furthermore, the results prove that persistent homology is well-suited for the detection of petit-mal epileptic seizures by means of their inherent structure.

Using Explainable Artificial Intelligence to Obtain Efficient Seizure-Detection Models Based on Electroencephalography Signals.

Epilepsy is a condition that affects 50 million individuals globally, significantly impacting their quality of life. Epileptic seizures, a transient occurrence, are characterized by a spectrum of manifestations, including alterations in motor function and consciousness. These events impose restrictions on the daily lives of those affected, frequently resulting in social isolation and psychological distress. In response, numerous efforts have been directed towards the detection and prevention of epileptic seizures through EEG signal analysis, employing machine learning and deep learning methodologies. This study presents a methodology that reduces the number of features and channels required by simpler classifiers, leveraging Explainable Artificial Intelligence (XAI) for the detection of epileptic seizures. The proposed approach achieves performance metrics exceeding 95% in accuracy, precision, recall, and F1-score by utilizing merely six features and five channels in a temporal domain analysis, with a time window of 1 s. The model demonstrates robust generalization across the patient cohort included in the database, suggesting that feature reduction in simpler models-without resorting to deep learning-is adequate for seizure detection. The research underscores the potential for substantial reductions in the number of attributes and channels, advocating for the training of models with strategically selected electrodes, and thereby supporting the development of effective mobile applications for epileptic seizure detection.

Hybrid approach for the detection of epileptic seizure using electroencephalography input

Hybrid approach for the detection of epileptic seizure using electroencephalography input

Automatic epileptic seizure detection based on EEG using a moth-flame optimization of one-dimensional convolutional neural networks.

Frequent epileptic seizures can cause irreversible damage to the brains of patients. A potential therapeutic approach is to detect epileptic seizures early and provide artificial intervention to the patient. Currently, extracting electroencephalogram (EEG) features to detect epileptic seizures often requires tedious methods or the repeated adjustment of neural network hyperparameters, which can be time- consuming and demanding for researchers. This study proposes an automatic detection model for an EEG based on moth-flame optimization (MFO) optimized one-dimensional convolutional neural networks (1D-CNN). First, according to the characteristics and need for early epileptic seizure detection, a data augmentation method for dividing an EEG into small samples is proposed. Second, the hyperparameters are tuned based on MFO and trained for an EEG. Finally, the softmax classifier is used to output EEG classification from a small-sample and single channel. The proposed model is evaluated with the Bonn EEG dataset, which verifies the feasibility of EEG classification problems that involve up to five classes, including healthy, preictal, and ictal EEG from various brain regions and individuals. Compared with existing advanced optimization algorithms, such as particle swarm optimization, genetic algorithm, and grey wolf optimizer, the superiority of the proposed model is further verified. The proposed model can be implemented into an automatic epileptic seizure detection system to detect seizures in clinical applications.

Automatic epileptic seizure detection based on persistent homology.

Epilepsy is a prevalent brain disease, which is quite difficult-to-treat or cure. This study developed a novel automatic seizure detection method based on the persistent homology method. In this study, a Vietoris-Rips (VR) complex filtration model was constructed based on the EEG data. And the persistent homology method was applied to calculate the VR complex filtration barcodes to describe the topological changes of EEG recordings. Afterward, the barcodes as the topological characteristics of EEG signals were fed into the GoogLeNet for classification. The persistent homology is applicable for multi-channel EEG data analysis, where the global topological information is calculated and the features are extracted by considering the multi-channel EEG data as a whole, without the multiple calculations or the post-stitching. Three databases were used to evaluate the proposed approach and the results showed that the approach had high performances in the epilepsy detection. The results obtained from the CHB-MIT Database recordings revealed that the proposed approach can achieve a segment-based averaged accuracy, sensitivity and specificity values of 97.05%, 96.71% and 97.38%, and achieve an event-based averaged sensitivity value of 100% with 1.22s average detection latency. In addition, on the Siena Scalp Database, the proposed method yields averaged accuracy, sensitivity and specificity values of 96.42%, 95.23% and 97.6%. Multiple tasks of the Bonn Database also showed achieved accuracy of 99.55%, 98.63%, 98.28% and 97.68%, respectively. The experimental results on these three EEG databases illustrate the efficiency and robustness of our approach for automatic detection of epileptic seizure.

Effective Detection of Epileptic Seizures through EEG Signals Using Deep Learning Approaches

Epileptic seizures are a prevalent neurological condition that impacts a considerable portion of the global population. Timely and precise identification can result in as many as 70% of individuals achieving freedom from seizures. To achieve this, there is a pressing need for smart, automated systems to assist medical professionals in identifying neurological disorders correctly. Previous efforts have utilized raw electroencephalography (EEG) data and machine learning techniques to classify behaviors in patients with epilepsy. However, these studies required expertise in clinical domains like radiology and clinical procedures for feature extraction. Traditional machine learning for classification relied on manual feature engineering, limiting performance. Deep learning excels at automated feature learning directly from raw data sans human effort. For example, deep neural networks now show promise in analyzing raw EEG data to detect seizures, eliminating intensive clinical or engineering needs. Though still emerging, initial studies demonstrate practical applications across medical domains. In this work, we introduce a novel deep residual model called ResNet-BiGRU-ECA, analyzing brain activity through EEG data to accurately identify epileptic seizures. To evaluate our proposed deep learning model’s efficacy, we used a publicly available benchmark dataset on epilepsy. The results of our experiments demonstrated that our suggested model surpassed both the basic model and cutting-edge deep learning models, achieving an outstanding accuracy rate of 0.998 and the top F1-score of 0.998.

Improving the generalization of patient non-specific model for epileptic seizure detection

Epilepsy is the second most common neurological disorder characterized by recurrent and unpredictable seizures. Accurate seizure detection is important for diagnosis and treatment of epilepsy. Many researches achieved good performance on patient-specific seizure detection. However, they were tailored to each specific individual which are less applicable clinically than the patient non-specific detection, which lacked good performance. Despite several decades of research on automatic seizure detection, seizure detection is currently still based on visual inspection of video-EEG (Electroencephalogram) in clinical setting. It is time consuming and prone to human error and subjectivity. This study aims to improve patient non-specific seizure detection to assist neurologist with efficient and objective evaluation of epileptic EEG. The clinical data used was from the open access Siena Scalp EEG Database which consists of 14 patients. First the data were pre-processed to remove artifacts and noises. Second the features from time domain, frequency domain and entropy were extracted from each channel and then concatenated into a feature vector. Finally, a machine learning approach based on random forest was employed for seizure detection with leave-one-patient-out cross-validation scheme. Automatic seizure detection was carried out with the trained model. The study achieved a specificity of 99.38%, sensitivity of 81.43% and 3.61 FP/h (False Positives per hour), which outperformed some other patient non-specific detectors found in literature. The findings from the study shows the possibility of clinical application of automatic seizure detection and indicate that further work should focus on dealing with reducing false positives.

Robust Epileptic Seizure Detection Using Long Short-Term Memory and Feature Fusion of Compressed Time–Frequency EEG Images

Epilepsy is a prevalent neurological disorder with considerable risks, including physical impairment and irreversible brain damage from seizures. Given these challenges, the urgency for prompt and accurate seizure detection cannot be overstated. Traditionally, experts have relied on manual EEG signal analyses for seizure detection, which is labor-intensive and prone to human error. Recognizing this limitation, the rise in deep learning methods has been heralded as a promising avenue, offering more refined diagnostic precision. On the other hand, the prevailing challenge in many models is their constrained emphasis on specific domains, potentially diminishing their robustness and precision in complex real-world environments. This paper presents a novel model that seamlessly integrates the salient features from the time–frequency domain along with pivotal statistical attributes derived from EEG signals. This fusion process involves the integration of essential statistics, including the mean, median, and variance, combined with the rich data from compressed time–frequency (CWT) images processed using autoencoders. This multidimensional feature set provides a robust foundation for subsequent analytic steps. A long short-term memory (LSTM) network, meticulously optimized for the renowned Bonn Epilepsy dataset, was used to enhance the capability of the proposed model. Preliminary evaluations underscore the prowess of the proposed model: a remarkable 100% accuracy in most of the binary classifications, exceeding 95% accuracy in three-class and four-class challenges, and a commendable rate, exceeding 93.5% for the five-class classification.

Epileptic Seizure Detection in EEG Signal Using Optimized Convolutional Neural Network with Selected Feature Set

This article implements novel epileptic seizure detection by EEG signal that includes various phases: Feature extraction, Feature selection, and Classification. Originally, the input EEG signal is subjected to the preprocessing phase, and from the preprocessed signal, certain features are extracted in which the features like TDFs, FDFs, and TFDFs are extracted. Here, the Time Domain Features (TDFs) include Non-linear energy (NE), Permutation Entropy (PE), and Weighted Permutation Entropy (WPE); the Frequency Domain Features (FDFs) consists of Intensity Weighted Mean Frequency (IWMF); and the Time-Frequency Domain Features (TFDFs) consists of the “Weighted Multi-scale Rényi Permutation Entropy (WMRPE)”. Moreover, the extracted features are subjected to the feature selection phase. An improved chi-square model is proposed for selecting the appropriate features. Then, the chosen features are subjected to the classification process. Here, the classification is performed using an Optimized Convolutional Neural Network (CNN). To make the detection more precise, the CNN weights are tuned optimally through a Weighting Factor Based Shark Smell Optimization (IWFSSO) model. The MCC of the implemented CNN+IWFSSO method achieved a better value for a learning percentage 90%; nevertheless, the compared existing models accomplish a smaller value for dataset 2.

Hybrid WCA–PSO Optimized Ensemble Extreme Learning Machine and Wavelet Transform for Detection and Classification of Epileptic Seizure from EEG Signals

Hybrid WCA–PSO Optimized Ensemble Extreme Learning Machine and Wavelet Transform for Detection and Classification of Epileptic Seizure from EEG Signals

HEURISTIC-ASSISTED ADAPTIVE HYBRID DEEP LEARNING MODEL WITH FEATURE SELECTION FOR EPILEPSY DETECTION USING EEG SIGNALS

The word epilepsy is related to a neurological disease occurred by abnormalities of brain neurons. Timely detection of epilepsy is helpful for patients to decrease the mortality rate. To detect seizures, the Encephalogram (EEG) signals are analyzed based on monitoring the conditions of patients, and seizures can be detected from the EEG signal at appropriate times. The manual detection from the EEG signal requires more time for detecting the seizures and also it needs domain knowledge. The miss detection is eliminated by improving the classification performance in automatic epilepsy detection. Nowadays, deep learning models have not been greatly harnessed in the detection of epileptic seizures due to inappropriate descriptions of time-domain signals and sub-optimal classifier design. The aforementioned issues are combated by the novel Adaptive Hybrid Deep Learning (AHDL) approaches for epilepsy detection using EEG signals. Initially, the required EEG signal is collected from benchmark datasets. The collected signals are subjected to a signal decomposition phase that is accomplished by five levels of decomposition using Dual-Tree Complex Wavelet Transform (DTCWT), where the parameters are tuned by Improved Probability-based Coyote Optimization Algorithm (IP-COA). Further, the decomposed signal is given for feature extraction, where it divides the signal into two phases. In the first phase, the first feature set is obtained by using One-Dimensional Convolutional Neural Network (1DCNN), whereas in the second phase, the proposed model utilizes Auto Encoder (AE) to provide the second feature set. These resultant features are getting fused and the optimal feature selection process is found, where the features are obtained optimally by the IP-COA. Finally, epilepsy detection is accomplished with the aid of proposed AHDL with both Radial Basis-Recurrent Neural Networks (RB-RNN), where the hyperparameters are optimized using IP-COA. Thus, the experimental results illustrate that the suggested model enhances the detection and classification rate.

Architecture of a model for epileptic seizure detection and classification from EEG recordings

Architecture of a model for epileptic seizure detection and classification from EEG recordings

Retracted: Automatic Detection and Classification of Epileptic Seizures in Patients with Liver Cirrhosis and Overlapping Hev Infection Based on Deep Multimodal Fusion Technology

Retracted: Automatic Detection and Classification of Epileptic Seizures in Patients with Liver Cirrhosis and Overlapping Hev Infection Based on Deep Multimodal Fusion Technology

Epileptic seizure detection using scalogram-based hybrid CNN model on EEG signals

Epileptic seizure detection using scalogram-based hybrid CNN model on EEG signals

Field programmable gate array‐based energy‐efficient and fast epileptic seizure detection using support vector machine and quadratic discriminant analysis classifier

AbstractEpilepsy is a serious neurological disorder that results in seizures. It can be diagnosed by analyzing the brain's electrical activity using an electroencephalogram (EEG). However, the detection of seizures from massive EEG datasets is a challenging task. To address this challenge, researchers have developed several machine‐learning classifiers and feature extraction techniques for detecting seizures. This paper proposes an energy‐efficient and fast field programmable gate array (FPGA) architecture for detecting epileptic seizures using minimal computational resources. The seizure detection system uses the one Hjorth parameter (mobility) and another statistical parameter (nonlinear energy) as features and employs two efficient classifiers, quadratic discriminant analysis and linear support vector machine (LSVM), for classifying signals into seizure and nonseizure categories. The feature extractor block is connected individually to each of these classifiers. Subsequently, the performance of these two proposed models is evaluated in terms of accuracy, sensitivity, power consumption, resource utilization, and other metrics. The results demonstrate that the SVM classifier‐based model achieved the highest accuracy (99.4%) and sensitivity (98.8%) while consuming minimal dynamic power (0.057 mW) and utilizing the minimum FPGA resources. Thus, the proposed hardware system offers a reliable and energy‐efficient solution for detecting seizures in clinical and real‐time applications.

Power and Area Optimized Deep Learning Framework for Accurate Automatic Epileptic Seizure Detection

Power and Area Optimized Deep Learning Framework for Accurate Automatic Epileptic Seizure Detection

Cross-patient automatic epileptic seizure detection using patient-adversarial neural networks with spatio-temporal EEG augmentation

Cross-patient automatic epileptic seizure detection through electroencephalogram (EEG) is significant for clinical application and research. However, most automatic seizure detection methods are patient-specific and have poor generalization ability to unseen patients. In this paper, we consider two aspects for solving the cross-patient generalization problem in deep learning approaches. Firstly, we propose a new data augmentation method to largely improve generalization. We analyze the statistical distribution of seizure EEG signals and propose spatio-temporal EEG augmentation (STEA) to generate synthetic training seizure data with spatio-temporal dependencies in EEG. Secondly, we propose patient-adversarial neural network (PANN) to learn a patient-invariant representation for better generalization. We add a discriminator to distinguish the identity of patients and perform adversarial optimization between the feature extractor and identity discriminator, so that only shared seizure features are maintained. We conduct experiments on both public and clinical EEG datasets with various settings of different window lengths and divisions of training and testing sets. Our model significantly improves the performance of cross-patient detection compared with other methods and achieves state-of-the-art performance. Particularly, our proposed method achieves up to 95% sensitivity on CHB-MIT under 5 s window length segments, and achieves about 85% AUC and sensitivity even on the clinical dataset. Our designed methods can enable detection models extended to unseen patients, with performance achieving the clinical application standards.

CAD system for epileptic seizure detection from EEG through image processing and SURF-BOF technique

Abstract Epilepsy is one of the most debilitating neurological diseases that abruptly alters a person’s way of life. Manual diagnosis is a laborious and time-consuming task prone to human error. Therefore, automating this task by developing an intelligent system is necessary. Existing deep learning (DL) models require high training time, large datasets, and machines with more memory and processing power. In addition, owing to the black-box nature of DL models, no one can determine the features that the network prefers for classification decisions. To overcome these challenges, this study proposes an accurate, automatic, and fast-intelligent system for epilepsy detection using a computer-aided diagnosis (CAD) -two-dimensional machine learning (ML) framework. Existing ML models struggle to produce reliable and acceptable diagnostic results owing to the low amplitude and nonstationary nature of electroencephalograms (EEGs), particularly in clinical situations where environmental influences are almost impossible to eliminate. The proposed model was built using the Children’s Hospital Boston and the Massachusetts Institute of Technology dataset, and represents the first study that employs the speeded-up robust feature (SURF) bag of features technique for this application, which generates local features from spectrogram images of the respective one-dimensional EEG signal inputs. In addition, DL features were extracted from the spectrogram images for model performance comparison. Both features were used separately to train the ML classifiers. Implementing SURF offers fast computation and makes the model invariant to distortions, noise, scaling, and so on. Therefore, the proposed model is more suitable for real-time applications, and this ML framework provides an enhanced accuracy of 99.78% compared to the support vector machine-RBF classifier, along with 99.56% sensitivity, 100% specificity, and an error rate of 0.22%. The higher detection accuracy demonstrates the effectiveness of the proposed framework for medical disease diagnosis applications.

BIOM-14. INCREASED RATIO OF GLUTAMATE TO TOTAL CREATINE IN GLIOBLASTOMA PATIENTS WITH THE POST-OPERATIVE EPILEPSY

Abstract Epilepsy in glioblastoma patients significantly reduces their quality of life; however, little is known about the association between predicting epilepsy and metabolites in tumors. In this study, we used 3.0-T magnetic resonance imaging (MRI) and 1H-magnetic resonance spectroscopy (MRS) to quantify metabolite concentrations in patients with varying epilepsy histories. MRS data were quantified with linear combination model (LC-Model) software. From January 2013 to April 2020, fifty-one patients with primary glioblastoma underwent pretreatment 3.0-T MRI/1H-MRS scanning. Twenty-one patients (41%) suffered from epilepsy during the postoperative course, although forty-seven patients (97%) took anti-epileptic drug after surgery. MRS data were compared between groups with and without the post-operative epilepsy. The higher ratios of glutamate (Glu) and glutamate + glutamine (Glx) to total creatine (tCr) in the tumor were significantly associated with epilepsy history (Glu/tCr P < 0.001, Glx/tCr P = 0.004, respectively). The receiver operating characteristic curve analysis showed that a Glu/tCr value of 1.81 was 70% sensitive and 90% specific for the prediction of epilepsy during the postoperative course (area under curve: 0.82). These results suggest that the Glu/tCr ratio in tumors can be a predictive maker for early detection of epileptic seizures after surgery in glioblastoma patients.

A Novel Prognostic Model Using Chaotic CNN with Hybridized Spoofing for Enhancing Diagnostic Accuracy in Epileptic Seizure Prediction.

Epileptic seizure detection has undergone progressive advancements since its conception in the 1970s. From proof-of-concept experiments in the latter part of that decade, it has now become a vibrant area of clinical and laboratory research. In an effort to bring this technology closer to practical application in human patients, this study introduces a customized approach to selecting electroencephalogram (EEG) features and electrode positions for seizure prediction. The focus is on identifying precursors that occur within 10 min of the onset of abnormal electrical activity during a seizure. However, there are security concerns related to safeguarding patient EEG recordings against unauthorized access and network-based attacks. Therefore, there is an urgent need for an efficient prediction and classification method for encrypted EEG data. This paper presents an effective system for analyzing and recognizing encrypted EEG information using Arnold transform algorithms, chaotic mapping, and convolutional neural networks (CNNs). In this system, the EEG time series from each channel is converted into a 2D spectrogram image, which is then encrypted using chaotic algorithms. The encrypted data is subsequently processed by CNNs coupled with transfer learning (TL) frameworks. To optimize the fusion parameters of the ensemble learning classifiers, a hybridized spoofing optimization method is developed by combining the characteristics of corvid and gregarious-seeking agents. The evaluation of the model's effectiveness yielded the following results: 98.9 ± 0.3% accuracy, 98.2 ± 0.7% sensitivity, 98.6 ± 0.6% specificity, 98.6 ± 0.6% precision, and an F1 measure of 98.9 ± 0.6%. When compared with other state-of-the-art techniques applied to the same dataset, this novel strategy demonstrated one of the most effective seizure detection systems, as evidenced by these results.