Search Results (91)

Background: Electroencephalography (EEG) signals play a crucial role in diagnosing epilepsy by reflecting distinct patterns associated with normal brain activity, ictal (seizure) states, and interictal (between-seizure) periods. However, the manual classification of these patterns is labor-intensive, time-consuming, and depends heavily on specialized expertise. While deep learning methods have shown promise, many current models suffer from limitations such as excessive complexity, high computational demands, and insufficient generalizability. Developing lightweight and accurate models for real-time epilepsy detection remains a key challenge. Methods: This study proposes a novel dual-channel deep learning model to classify epileptic EEG signals into three categories: normal, ictal, and interictal states. Channel 1 integrates a bidirectional long short-term memory (BiLSTM) network with a Squeeze-and-Excitation (SE) ResNet attention module to dynamically emphasize critical feature channels. Channel 2 employs a dual-branch convolutional neural network (CNN) to extract deeper and distinct features. The model’s performance was evaluated on the publicly available Bonn EEG dataset. Results: The proposed model achieved an outstanding accuracy of 98.57%. The dual-channel structure improved specificity to 99.43%, while the dual-branch CNN boosted sensitivity by 5.12%. Components such as SE-ResNet attention modules contributed 4.29% to the accuracy improvement, and BiLSTM further enhanced specificity by 1.62%. Ablation studies validated the significance of each module. Conclusions: By leveraging a lightweight design and attention-based mechanisms, the dual-channel model offers high diagnostic precision while maintaining computational efficiency. Its applicability to real-time automated diagnosis positions it as a promising tool for clinical deployment across diverse patient populations. Full article

Epileptic seizure detection and classification from EEG recordings faces significant challenges due to extreme class imbalance. Analysis of the Temple University Hospital Seizure (TUSZ) dataset reveals imbalance ratios of 150:1 between common and rare seizure types, with high temporal heterogeneity (seizure durations of 1–1638 s). We propose a cascaded deep learning architecture with two specialized CNNs: a binary detector followed by a multi-class classifier. This approach decomposes the classification problem, reducing the maximum imbalance from 150:1 to manageable levels (9:1 binary, 5:1 type). The architecture implements a high-confidence filtering mechanism (threshold = 0.9), creating a 99.5% pure dataset for type classification, dynamic class-weighted optimization proportional to inverse class frequencies, and information flow refinement through progressive stages. Loss dynamics analysis reveals that our weighting scheme strategically redistributes optimization attention, reducing variance by 90.7% for majority classes while increasing variance for minority classes, ensuring all seizure types receive proportional learning signals regardless of representation. The binary classifier achieves 99.64% specificity and 98.23% sensitivity (ROC-AUC = 0.995). The type classifier demonstrates >99% accuracy across seven seizure categories with perfect (100%) classification for three seizure types despite minimal representation. Cross-dataset validation on the University of Bonn dataset confirms robust generalization (96.0% accuracy) for binary seizure detection. This framework effectively addresses multi-level imbalance in neurophysiological signal classification with hierarchical class structures. Full article

Background/Objectives: Epilepsy is a common neurological disorder with pathological mechanisms closely associated with the spatiotemporal dynamic characteristics of electroencephalogram (EEG) signals. Although significant progress has been made in epileptic seizure detection methods using time–frequency analysis, current research still faces challenges in terms of an insufficient utilization of phase information. Methods: In this study, we propose an effective epileptic seizure detection framework based on continuous wavelet transform (CWT) and a hybrid network consisting of convolutional neural network (CNN) and vision transformer (ViT). First, the raw EEG signals are processed by the CWT. Then, the phase spectrogram and power spectrogram of the EEG are generated, and they are sent into the designed CNN and ViT branches of the network to extract more discriminative EEG features. Finally, the features output from the two branches are fused and fed into the classification network to obtain the detection results. Results: Experimental results on the CHB-MIT public dataset and our SH-SDU clinical dataset show that the proposed framework achieves sensitivities of 98.09% and 89.02%, specificities of 98.21% and 95.46%, and average accuracies of 98.45% and 94.66%, respectively. Furthermore, we compared the spectral characteristics of CWT with other time–frequency transforms within the hybrid architecture, demonstrating the advantages of the CWT-based CNN-ViT architecture. Conclusions: These results highlight the outstanding epileptic seizure detection performance of the proposed framework and its significant clinical feasibility. Full article

Epilepsy is a neurologic condition characterized by recurring seizures resulting from aberrant brain activity. It is crucial to promptly and precisely detect epileptic seizures to ensure efficient treatment. The gold standard electroencephalography (EEG) accurately records the brain’s electrical activity in real time. The intent of this study is to precisely detect epileptic episodes by leveraging machine learning and deep learning algorithms on EEG inputs. The proposed approach aims to evaluate the feasibility of developing a novel technique that utilizes the Hurst exponent to identify EEG signal properties that could be crucial for classification. The idea posits that the prolonged duration of EEG in epileptic patients and those who are not experiencing seizures can differentiate between the two groups. To achieve this, we analyzed the long-term memory characteristics of EEG by employing time-dependent Hurst analysis. Together, the Hurst exponent and the Daubechies 4 discrete wavelet transformation constitute the basis of this unique feature extraction. We utilize the ANOVA test and random forest regression as feature selection techniques. Our approach creates and evaluates support vector machine, random forest classifier, and long short-term memory network machine learning models to classify seizures using EEG inputs. The highlight of our research approach is that it examines the efficacy of the aforementioned models in classifying seizures utilizing single-channel EEG with minimally handcrafted features. The random forest classifier outperforms other options, with an accuracy of 97% and a sensitivity of 97.20%. Additionally, the proposed model’s capacity to generalize unobserved data is evaluated on the CHB-MIT scalp EEG database, showing remarkable outcomes. Since this framework is computationally efficient, it can be implemented on edge hardware. This strategy can redefine epilepsy diagnoses and hence provide individualized regimens and improve patient outcomes. Full article

Automated EEG classification algorithms for seizures can facilitate the clinical diagnosis of epilepsy, enabling more expedient and precise classification. However, existing EEG signal preprocessing methods oriented towards artifact removal and signal enhancement have demonstrated suboptimal accuracy and robustness. In response to this challenge, we propose an Adaptive Dual-Modality Learning Model (ADML) for epileptic seizure prediction by combining time series imaging with Transformer-based architecture. Our approach effectively captures both temporal dependencies and spatial relationships in EEG signals through a specialized attention mechanism. Evaluated on the CHB-MIT and Bonn datasets, our method achieves 98.7% and 99.2% accuracy, respectively, significantly outperforming existing approaches. The model demonstrates strong generalization capability across datasets while maintaining computational efficiency. Cross-dataset validation confirms the robustness of our approach, with consistent performance above 96% accuracy. These results suggest that our dual-modality approach provides a reliable and practical solution for clinical epileptic seizure prediction. Full article

Objectives: This study investigates the potential of artificial intelligence (AI), specifically large language models (LLMs) like ChatGPT, to enhance decision support in diagnosing epilepsy. AI tools can improve diagnostic accuracy, efficiency, and decision-making speed. The aim of this study was to compare the level of agreement in epilepsy diagnosis between human experts (epileptologists) and AI (ChatGPT), using the 2014 International League Against Epilepsy (ILAE) criteria, and to identify potential predictors of diagnostic errors made by ChatGPT. Methods: A retrospective analysis was conducted on data from 597 patients who visited the emergency department for either a first epileptic seizure or a recurrence. Diagnoses made by experienced epileptologists were compared with those made by ChatGPT 4.0, which was trained on the 2014 ILAE epilepsy definition. The agreement between human and AI diagnoses was assessed using Cohen’s kappa statistic. Sensitivity and specificity were compared using 2 × 2 contingency tables, and multivariate analyses were performed to identify variables associated with diagnostic errors. Results: Neurologists diagnosed epilepsy in 216 patients (36.2%), while ChatGPT diagnosed it in 109 patients (18.2%). The agreement between neurologists and ChatGPT was very low, with a Cohen’s kappa value of −0.01 (95% confidence intervals, CI: −0.08 to 0.06). ChatGPT’s sensitivity was 17.6% (95% CI: 14.5–20.6), specificity was 81.4% (95% CI: 78.2–84.5), positive predictive value was 34.8% (95% CI: 31.0–38.6), and negative predictive value was 63.5% (95% CI: 59.6–67.4). ChatGPT made diagnostic errors in 41.7% of the cases, with errors more frequent in older patients and those with specific medical conditions. The correct classification was associated with acute symptomatic seizures of unknown etiology. Conclusions: ChatGPT 4.0 does not reach human clinicians’ performance in diagnosing epilepsy, showing poor performance in identifying epilepsy but better at recognizing non-epileptic cases. The overall concordance between human clinicians and AI is extremely low. Further research is needed to improve the diagnostic accuracy of ChatGPT and other LLMs. Full article

With the increasing research of deep learning in the EEG field, it becomes more and more important to fully extract the characteristics of EEG signals. Traditional EEG signal classification prediction neither considers the topological structure between the electrodes of the signal collection device nor the data structure of the Euclidean space to accurately reflect the interaction between signals. Graph neural networks can effectively extract features of non-Euclidean spatial data. Therefore, this paper proposes a feature selection method for epilepsy EEG classification based on graph convolutional neural networks (GCNs) and long short-term memory (LSTM) cells. While enriching the input of LSTM, it also makes full use of the information hidden in the EEG signals. In the automatic detection of epileptic seizures based on neural networks, due to the strong non-stationarity and large background noise of the EEG signal, the analysis and processing of the EEG signal has always been a challenging research. Therefore, experiments were conducted using the preprocessed Boston Children’s Hospital epilepsy EEG dataset, and input it into the GCN-LSTM model for deep feature extraction. The GCN network built by the graph convolution layer learns spatial features, then LSTM extracts sequence information, and the final prediction is performed by fully connected and softmax layers. The introduced method has been experimentally proven to be effective in improving the accuracy of epileptic EEG seizure detection. Experimental results show that the average accuracy of binary classification on the CHB-MIT dataset is 99.39%, and the average accuracy of ternary classification is 98.69%. Full article

Increased electrical activity in the brain causes epilepsy, which causes seizures, resulting in various medical complications that can sometimes be fatal. Doctors use electroencephalography (EEG) for the profiling and diagnosis of epilepsy. According to the World Health Organization (WHO), approximately 50 million people worldwide have epilepsy, making it one of the most common neurological disorders globally. This number represents about 0.7% of the global population. The conventional method of EEG analysis employed by medical professionals is a visual investigation that is time-consuming and requires expertise because of the variability in EEG signals. This paper describes a method for detecting epileptic seizures in EEG signals by combining signal processing and machine learning techniques. SVM and other machine learning techniques detect anomalies in the input EEG signal. To extract features, DWT is used for decomposition to sub-bands. The proposed method aims to improve the accuracy of the machine learning model while using as few features as possible. The classification results show an accuracy of 100% with just one feature, mean absolute value, from datasets A and E. With additional features, the overall accuracy remains high at 99%, with specificity and sensitivity values of 97.2% and 99.1%, respectively. These results outperform previous research on the same dataset, demonstrating the effectiveness of our approach. This research contributes to developing more accurate and efficient epilepsy diagnosis systems, potentially improving patient outcomes. Full article

Objectives: Naturally, there are several challenges, such as muscular artifacts, ocular movements and electrical interferences that depend on precise diagnosis and classification, which hamper exact epileptic seizure detection. This study has been conducted to improve seizure detection accuracy in epilepsy patients using an advanced preprocessing technique that could remove such noxious artifacts. Methods: In the frame of this paper, the core tool in the area of epilepsy, EEG, will be applied to record and analyze the electrical patterns of the brain. The dataset includes recordings of seven epilepsy patients taken by the Unit of Neurology and Neurophysiology, University of Siena. The preprocessing techniques employed include advanced artifact removal and signal enhancement methods. We introduced Peak-to-Peak Amplitude Fluctuation (PPAF) to assess amplitude variability within Event-Related Potential (ERP) waveforms. This approach was applied to data from patients experiencing 3–5 seizures, categorized into three distinct groups. Results: The results indicated that the frontal and parietal regions, particularly the electrode areas Cz, Pz and Fp2, are the main contributors to epileptic seizures. Additionally, the implementation of the PPAF metric enhanced the effectiveness of seizure detection and classification algorithms, achieving accuracy rates of 99%, 98% and 95% for datasets with three, four and five seizures, respectively. Conclusions: The present research extends the epilepsy diagnosis with clues on brain activity during seizures and further demonstrates the effectiveness of advanced preprocessing techniques. The introduction of PPAF as a metric could have promising potential in improving both the accuracy and reliability of epilepsy seizure detection algorithms. These observations provide important implications for control and treatment both in focal and in generalized epilepsy. Full article

Introduction: This article provides an overview of several experimental models, including in vivo, genetics, chemical, knock-in, knock-out, electrical, in vitro, and optogenetics models, that have been employed to investigate epileptogenesis. The present review introduces a novel categorization of these models, taking into account the fact that the most recent classification that gained widespread acceptance was established by Fisher in 1989. A significant number of such models have become virtually outdated. Objective: This paper specifically examines the models that have contributed to the investigation of partial seizures, generalized seizures, and status epilepticus. Discussion: A description is provided of the primary features associated with the processes that produce and regulate the symptoms of various epileptogenesis models. Numerous experimental epilepsy models in animals have made substantial contributions to the investigation of particular brain regions that are capable of inducing seizures. Experimental models of epilepsy have also enabled the investigation of the therapeutic mechanisms of anti-epileptic medications. Typically, animals are selected for the development and study of experimental animal models of epilepsy based on the specific form of epilepsy being investigated. Conclusions: Currently, it is established that specific animal species can undergo epileptic seizures that resemble those described in humans. Nevertheless, it is crucial to acknowledge that a comprehensive assessment of all forms of human epilepsy has not been feasible. However, these experimental models, both those derived from channelopathies and others, have provided a limited comprehension of the fundamental mechanisms of this disease. Full article

Epilepsy affects millions worldwide, making timely seizure detection crucial for effective treatment and enhanced well-being. Electroencephalogram (EEG) analysis offers a non-intrusive solution, but its visual interpretation is prone to errors and requires a lot of time. Many existing works focus solely on achieving competitive levels of accuracy without considering processing speed or the computational complexity of their models. This study aimed to develop an automated technique for identifying epileptic seizures in EEG data through analysis methods. The efforts have been primarily focused on achieving high accuracy results by operating exclusively within a narrow frequency band of the signal, while also aiming to minimize computational complexity. In this article, a new automated approach is presented for seizure detection by combining signal processing and machine learning techniques. The proposed method comprises four stages: (1) Preprocessing: Savitzky–Golay filter to remove the background noise. (2) Decomposition: discrete wavelet transform (DWT) to extract spontaneous alpha and beta frequency bands. (3) Feature extraction: six features (mean, standard deviation, skewness, kurtosis, energy, and entropy) are computed for each frequency band. (4) Classification: a support vector machine (SVM) method classifies signals as normal or containing a seizure. The method was assessed using two publicly available EEG datasets. For the alpha band, the highest achieved accuracy was 92.82%, and for the beta band it was 90.55%, which demonstrates adequate capability in both bands for accurate seizure detection. Furthermore, the obtained low computational cost suggests a potentially valuable application in real-time assessment scenarios. The obtained results indicate its capacity as a valuable instrument for diagnosing epilepsy and monitoring patients. Further research is necessary for clinical validation and potential real-time deployment. Full article

Neonatal epilepsy is an early postnatal brain disorder, and automatic seizure detection is crucial for timely diagnosis and treatment to reduce potential brain damage. This work proposes a novel Lightweight Multi-Attention Network, LMA-EEGNet, for diagnosing neonatal epileptic seizures from multi-channel EEG signals employing dilated depthwise separable convolution (DDS Conv) for feature extraction and using pointwise convolution followed by global average pooling for classification. The proposed approach substantially reduces the model size, number of parameters, and computational complexity, which are crucial for real-time detection and clinical diagnosis of neonatal epileptic seizures. LMA-EEGNet integrates temporal and spectral features through distinct temporal and spectral branches. The temporal branch uses DDS Conv to extract temporal features, enhanced by a channel attention mechanism. The spectral branch utilizes similar convolutions alongside a spatial attention mechanism to highlight key frequency components. Outputs from both branches are merged and processed through a pointwise convolution layer and a global average pooling layer for efficient neonatal seizure detection. Experimental results show that our model, with only 2471 parameters and a size of 23 KB, achieves an accuracy of 95.71% and an AUC of 0.9862, demonstrating its potential for practical deployment. This study provides an effective deep learning solution for the early detection of neonatal epileptic seizures, improving diagnostic accuracy and timeliness. Full article

Background/Objectives: Recent studies provide the first indications of the impact of climate factors on human health, especially with individuals already grappling with internal and neurological conditions being particularly vulnerable. In the face of escalating climate change, our research delves into the specific influence of a spectrum of climatic factors and seasonal variations on the hospital admissions of patients receiving treatment for epileptic seizures at our clinic in Kaiserslautern. Methods: Our study encompassed data from 9366 epilepsy patients who were admitted to hospital due to epileptic seizures. We considered seven climate parameters that Germany’s National Meteorological Service made available. We employed the Kruskal–Wallis test to examine the correlation between the frequency of admittance to our hospital in the mentioned patient group and seasons. Furthermore, we used conditional Poisson regression and distributed lag linear models (DLMs) to scrutinize the coherence of the frequency of patient admittance and the investigated climate parameters. The mentioned parameters were also analyzed in a subgroup analysis regarding the gender and age of patients and the classification of seizures according to ILAE 2017. Results: Our results demonstrate that climatic factors, such as precipitation and air pressure, can increase the frequency of hospital admissions for seizures in patients with general-onset epilepsy. In contrast, patients with focal seizures are less prone to climatic changes. Consequently, admittance to the hospital for seizures is less affected by climatic factors in the latter patient group. Conclusions: The present study demonstrated that climatic factors are possible trigger factors for the provocation of seizures, particularly in patients with generalized seizures. This was determined indirectly by analyzing the frequency of seizure-related emergency admissions and their relation to prevailing climate factors. Our study is consistent with other studies showing that climate factors, such as cerebral infarcts or cerebral hemorrhages, influence patients’ health. Full article

Epilepsy is characterized by recurring seizures that result from abnormal electrical activity in the brain. These seizures manifest as various symptoms including muscle contractions and loss of consciousness. The challenging task of detecting epileptic seizures involves classifying electroencephalography (EEG) signals into ictal (seizure) and interictal (non-seizure) classes. This classification is crucial because it distinguishes between the states of seizure and seizure-free periods in patients with epilepsy. Our study presents an innovative approach for detecting seizures and neurological diseases using EEG signals by leveraging graph neural networks. This method effectively addresses EEG data processing challenges. We construct a graph representation of EEG signals by extracting features such as frequency-based, statistical-based, and Daubechies wavelet transform features. This graph representation allows for potential differentiation between seizure and non-seizure signals through visual inspection of the extracted features. To enhance seizure detection accuracy, we employ two models: one combining a graph convolutional network (GCN) with long short-term memory (LSTM) and the other combining a GCN with balanced random forest (BRF). Our experimental results reveal that both models significantly improve seizure detection accuracy, surpassing previous methods. Despite simplifying our approach by reducing channels, our research reveals a consistent performance, showing a significant advancement in neurodegenerative disease detection. Our models accurately identify seizures in EEG signals, underscoring the potential of graph neural networks. The streamlined method not only maintains effectiveness with fewer channels but also offers a visually distinguishable approach for discerning seizure classes. This research opens avenues for EEG analysis, emphasizing the impact of graph representations in advancing our understanding of neurodegenerative diseases. Full article

Supervised classification algorithms for processing epileptic EEG signals rely heavily on the label information of the data, and existing supervised methods cannot effectively solve the problem of analyzing unlabeled epileptic EEG signals. In the traditional unsupervised clustering algorithm, the number of clusters and the global parameters must be predetermined, and the algorithm’s analytical results are combined with a huge number of subjective errors, which affects the detection accuracy. For this reason, this paper proposes an unsupervised multivariate feature adaptive clustering analysis algorithm based on epileptic EEG signals. First, CEEMDAN and CWT are introduced into the epileptic EEG signal after preprocessing for joint denoising to further improve the signal quality. Then, the multivariate feature set of the signal is extracted and constructed, which includes nonlinear, time, frequency, and time-frequency characteristics. To reveal the hidden structures and correlations in the high-dimensional feature data, t-SNE dimensionality reduction is introduced. Finally, the DBSCAN clustering algorithm is optimized using the SSA algorithm to achieve adaptive selection of cluster number and global parameters.It not only enhances the clustering performance and reliability of the clustering results, but also avoids subjective errors in the analysis results. It provides a pre-theoretical foundation for the successful development of future seizure prediction devices and has good application prospects in clinical diagnosis and daily monitoring of patients. Full article