A hybrid metaheuristic framework for epileptic seizure detection in healthcare decision support systems

Epilepsy is a chronic neurological brain disorder that affects 50 million people globally. There are several challenges associated with the care of epileptic patients, including: 1) the timely and accurate diagnosis of the condition; 2) the long-term non-intrusive monitoring and detection of epileptic seizures in real time for suitable interventions; 3) alleviating the mental health issues associated with epilepsy, such as anxiety and depression; and 4) the lack of availability of large scale datasets related to epileptic patients with different profiles, needed to advance research in epilepsy. In this work, we propose EpiSense – a smart healthcare solution for epileptic patients’ care. EpiSense leverages sensory, mobile, and web technologies, as well as machine learning techniques for the real-time detection of epileptic seizures. As part of the system, a patient’s mobile app. is provided to allow the detection of seizures’ occurrence in real time and the sending of alarm notifications to care takers, for appropriate actions. Moreover, a web portal enables doctors to view the progress of their patients and get notified about seizures’ occurrence and statistics. The EpiSense system was designed and implemented, and three machine learning models were tested for real-time epileptic seizure detection. This work gives interesting insights about the possibility of using sensory technologies and data analytics for the improvement of epileptic patients’ care, and offers the possibility of personalized healthcare management.

Investigating neural dynamics through EEG signals offers valuable insights into brain activity, especially for automated seizure detection. The identification of epileptogenic zones is crucial for effective epilepsy treatment, particularly in surgical planning. This work introduces a novel method for seizure detection using EEG signals, designed to benefit clinicians by integrating spectral features with Long Short-Term Memory (LSTM) networks, enhanced by brain region-specific analysis. This research work captures critical frequency domain characteristics by extracting pivotal spectral features from EEG data, thereby improving the signal representation for LSTM networks. Additionally, this proposed work has employed the Synthetic Minority Over-sampling Technique (SMOTE) to handle the class imbalance problem. Furthermore, a comprehensive spatial analysis of EEG signals is performed to evaluate performance variations across distinct brain regions, enabling targeted region-wise analysis. This strategy effectively reduces the number of channels required, minimizing the need to process all 22 channels specified in the CHB-MIT dataset, thus significantly decreasing computational complexity while preserving high seizuredetection performance. This work has obtained a mean value of accuracy of 95.43%, precision of 95.46%, sensitivity of 95.59%, F1-score of 95.48%, and specificity of 95.25% for the brain region providing the best performance for seizure discrimination. The results demonstrate that integrating spectral features and LSTM, augmented by spatial insights, enhances seizure detection performance and hence assists in identifying epileptogenic regions. This tool enhances clinical applications by improving diagnostic precision, personalized treatment strategies, and supporting precise surgical planning for epilepsy, ensuring safer resection and better outcomes.

Internet of things (IoT) has a collection of multiple network-enabled devices like sensors, gateways, smartphones, and communication links (short and long ranges). Tremendous capacity of IoT system has made possible to monitoring and detection of epileptical seizures in real time. For this purpose, various smart devices and applications, helps to transmit information securely. Amalgamation of IoT with healthcare system provides opportunity to deal issues like security, detection of seizures and real time monitoring. The proposed model of cloud-enabled Health IoT system has been presented in this paper, gives the idea about monitoring of epileptical patients. For secured transmission of Electroencephalogram (EEG) data, digital watermarking technique has been used over two dimensional EEG data obtained through one dimensional EEG data by applying Short Time Fourier Transform (STFT). In this paper, watermarking of two dimensional EEG data has been done using discrete wavelet transform - discrete cosine transform (DWT-DCT) based Bacterial Foraging Optimization (BFO) technique and its performance has been figure out. Here, satisfactory watermarking performance in terms of Peak Signal to Noise Ratio (PSNR) 49.50 for class Z and 49.61 for class S EEG data along with Normalized Cross Correlation (NCC) 0.0039 for both classes of EEG data have been achieved.

Integrating old and new complexity measures toward automated seizure detection from long-term video EEG recordings

EEG-Based Seizure detection using linear graph convolution network with focal loss

Background: Epilepsy, a neurological disorder characterized by recurring seizures, affects millions globally and presents significant medical challenges. The unpredictable nature of seizures necessitates advancements in their detection and prediction. This study introduces a novel approach for classifying and identifying epileptic seizures through the analysis of EEG (Electroencephalogram) data using Convolutional Neural Networks (CNNs). Methods: We employed CNNs to analyze EEG signals, identifying recognizable patterns in temporal and spatial information, thereby enhancing the accuracy of seizure detection. Our proposed CNN framework incorporates Batch Normalization (BN), dropout layers, and dense layers specifically designed for EEG signal analysis. This novel approach improves the model’s capacity for extracting and detecting complex spatial-temporal patterns in EEG data, supporting effective seizure prediction and detection. The implementation of this Deep Learning (DL) methodology allows for continuous epilepsy monitoring, significantly advancing seizure prediction accuracy. Results: Extensive validation of the framework on a publicly accessible dataset demonstrated its superiority over traditional Machine Learning (ML) techniques, achieving an accuracy rate of 98.52%. This CNN-based approach successfully distinguished between abnormal brain activity due to seizures and normal brain function. Conclusion: The developed DL framework represents a significant advancement in epileptic seizure detection and prediction. By leveraging CNNs for EEG signal analysis, this study provides a robust and accurate tool for continuous epilepsy monitoring, offering improved patient outcomes and contributing to the broader field of neurological disorder management.

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.

Automated seizure detection is a valuable asset to health professionals, which makes adequate treatment possible in order to minimize brain damage. Most research focuses on two separate aspects of automated seizure detection: EEG feature computation and classification methods. Little research has been published regarding optimal training dataset composition for patient-independent seizure detection. This paper evaluates the performance of classifiers trained on different datasets in order to determine the optimal dataset for use in classifier training for automated, age-independent, seizure detection. Three datasets are used to train a support vector machine (SVM) classifier: (1) EEG from neonatal patients, (2) EEG from adult patients and (3) EEG from both neonates and adults. To correct for baseline EEG feature differences among patients feature, normalization is essential. Usually dedicated detection systems are developed for either neonatal or adult patients. Normalization might allow for the development of a single seizure detection system for patients irrespective of their age. Two classifier versions are trained on all three datasets: one with feature normalization and one without. This gives us six different classifiers to evaluate using both the neonatal and adults test sets. As a performance measure, the area under the receiver operating characteristics curve (AUC) is used. With application of FBC, it resulted in performance values of 0.90 and 0.93 for neonatal and adult seizure detection, respectively. For neonatal seizure detection, the classifier trained on EEG from adult patients performed significantly worse compared to both the classifier trained on EEG data from neonatal patients and the classier trained on both neonatal and adult EEG data. For adult seizure detection, optimal performance was achieved by either the classifier trained on adult EEG data or the classifier trained on both neonatal and adult EEG data. Our results show that age-independent seizure detection is possible by training one classifier on EEG data from both neonatal and adult patients. Furthermore, our results indicate that for accurate age-independent seizure detection, it is important that EEG data from each age category are used for classifier training. This is particularly important for neonatal seizure detection. Our results underline the under-appreciated importance of training dataset composition with respect to accurate age-independent seizure detection.Electronic supplementary materialThe online version of this article (doi:10.1007/s11517-016-1468-y) contains supplementary material, which is available to authorized users.

Diagnosing and managing seizures presents substantial challenges for clinicians caring for patients with epilepsy. Although machine learning (ML) has been proposed for automated seizure detection using EEG data, there is little evidence of these technologies being broadly adopted in clinical practice. Moreover, there is a noticeable lack of surveys investigating this topic from the perspective of medical practitioners, which limits the understanding of the obstacles for the development of effective automated seizure detection. Besides the issue of generalisability and replicability seen in a small amount of studies, obstacles to the adoption of automated seizure detection remain largely unknown. To understand the obstacles preventing the application of seizure detection tools in clinical practice, we conducted a survey targeting medical professionals involved in the management of epilepsy. Our study aimed to gather insights on various factors such as the clinical utility, professional sentiment, benchmark requirements, and perceived barriers associated with the use of automated seizure detection tools.Our key findings are: I) The minimum acceptable sensitivity reported by most of our respondents (80%) seems achievable based on studies reported from most currently available ML-based EEG seizure detection algorithms, but replication studies often fail to meet this minimum. II) Respondents are receptive to the adoption of ML seizure detection tools and willing to spend time in training. III) The top three barriers for usage of such tools in clinical practice are related to availability, lack of training, and the blackbox nature of ML algorithms. Based on our findings, we developed a guide that can serve as a basis for developing ML-based seizure detection tools that meet the requirements of medical professionals, and foster the integration of these tools into clinical practice.

Objective. The objective of the work described in this paper is the development of a computationally efficient methodology for patient-specific automatic seizure detection in long-term multi-channel EEG recordings. Approach. A novel patient-specific seizure detection approach based on a signal-derived empirical mode decomposition (EMD)-based dictionary approach is proposed. For this purpose, we use an empirical framework for EMD-based dictionary creation and learning, inspired by traditional dictionary learning methods, in which the EMD-based dictionary is learned from the multi-channel EEG data being analyzed for automatic seizure detection. We present the algorithm for dictionary creation and learning, whose purpose is to learn dictionaries with a small number of atoms. Using training signals belonging to seizure and non-seizure classes, an initial dictionary, termed as the raw dictionary, is formed. The atoms of the raw dictionary are composed of intrinsic mode functions obtained after decomposition of the training signals using the empirical mode decomposition algorithm. The raw dictionary is then trained using a learning algorithm, resulting in a substantial decrease in the number of atoms in the trained dictionary. The trained dictionary is then used for automatic seizure detection, such that coefficients of orthogonal projections of test signals against the trained dictionary form the features used for classification of test signals into seizure and non-seizure classes. Thus no hand-engineered features have to be extracted from the data as in traditional seizure detection approaches. Main results. The performance of the proposed approach is validated using the CHB–MIT benchmark database, and averaged accuracy, sensitivity and specificity values of 92.9%, 94.3% and 91.5%, respectively, are obtained using support vector machine classifier and five-fold cross-validation method. These results are compared with other approaches using the same database, and the suitability of the approach for seizure detection in long-term multi-channel EEG recordings is discussed. Significance. The proposed approach describes a computationally efficient method for automatic seizure detection in long-term multi-channel EEG recordings. The method does not rely on hand-engineered features, as are required in traditional approaches. Furthermore, the approach is suitable for scenarios where the dictionary once formed and trained can be used for automatic seizure detection of newly recorded data, making the approach suitable for long-term multi-channel EEG recordings.

The P2P lending industry faces significant challenges in predicting loan defaults due to the high dimensionality of the data, which affects lending decision quality. This study aims to propose the Dynamic ACO + GWO algorithm, designed to enhance the efficiency of feature selection and the accuracy of default predictions. The algorithm combines a dynamic Ant Colony Optimization (ACO) based on Monte Carlo, which effectively regulates the evaporation rate, with the Grey Wolf Optimizer (GWO) to improve population initialization and accelerate convergence. The results show that the Dynamic ACO + GWO algorithm outperforms both ACO + GWO and GWO in several aspects. In terms of execution time, Dynamic ACO + GWO requires only 10.61 s for SA = 50, significantly faster than ACO + GWO at 208.37 s and GWO at 61.68 s. For fitness values, Dynamic ACO + GWO achieves 0.094, which is better than ACO + GWO (0.113) and GWO (0.115). Additionally, the algorithm records a default prediction accuracy of 91.23%, higher than ACO + GWO at 90.58% and GWO at 86.13%. Therefore, the Dynamic ACO + GWO algorithm not only improves execution efficiency but also provides more accurate default predictions, making it a superior solution for feature selection in P2P lending.

Automated seizure detection based on scalp electroencephalography (EEG) can significantly accelerate the epilepsy diagnosis process. However, most existing deep learning-based epilepsy detection methods are deficient in mining the local features and global time series dependence of EEG signals, limiting the performance enhancement of the models in seizure detection. Our study proposes an epilepsy detection model, CMFViT, based on a Multi-Stream Feature Fusion (MSFF) strategy that fuses a Convolutional Neural Network (CNN) with a Vision Transformer (ViT). The model converts EEG signals into time-frequency domain images using the Tunable Q-factor Wavelet Transform (TQWT), and then utilizes the CNN module and the ViT module to capture local features and global time-series correlations, respectively. It fuses different feature representations through the MSFF strategy to enhance its discriminative ability, and finally completes the classification task through the average pooling layer and the fully connected layer. The effectiveness of the model was validated by experimental evaluations on the publicly available CHB-MIT dataset and the Kaggle 121 people epilepsy dataset. The model achieved 98.85% classification accuracy and other excellent metrics in single-subject experiments on the CHB-MIT dataset, and also demonstrated strong performance in cross-subject experiments on the Kaggle dataset. Ablation experiments demonstrate the complementary roles of the CNN and ViT modules, and their integration significantly improves detection accuracy and generalization. Comparisons with other methods highlight the advantages of the CMFViT model. The CMFViT model provides an efficient, accurate, and innovative solution for complex EEG signal analysis and seizure detection tasks for single and cross-subjects while laying the foundation for developing real-time, accurate seizure detection systems.

In people with drug resistant epilepsy (DRE), seizures are unpredictable, often occurring with little or no warning. The unpredictability causes anxiety and much of the morbidity and mortality of seizures. In this work, 102 seizures of mesial temporal lobe onset were analyzed from 19 patients with DRE who had simultaneous intracranial EEG (iEEG) and scalp EEG as part of their surgical evaluation. The first aim of this paper was to develop machine learning models for seizure prediction and detection (i) using iEEG only, (ii) scalp EEG only and (iii) jointly analyzing both iEEG and scalp EEG. The second goal was to test if machine learning could detect a seizure on scalp EEG when that seizure was not detectable by the human eye (surface negative) but was seen in iEEG. The final question was to determine if the deep learning algorithm could correctly lateralize the seizure onset. The seizure detection and prediction problems were addressed jointly by training Deep Neural Networks (DNN) on 4 classes: non-seizure, pre-seizure, left mesial temporal onset seizure and right mesial temporal onset seizure. To address these aims, the classification accuracy was tested using two deep neural networks (DNN) against 3 different types of similarity graphs which used different time series of EEG data. The convolutional neural network (CNN) with the Waxman similarity graph yielded the highest accuracy across all EEG data (iEEG, scalp EEG and combined). Specifically, 1 second epochs of EEG were correctly assigned to their seizure, pre-seizure, or non-seizure category over 98% of the time. Importantly, the pre-seizure state was classified correctly in the vast majority of epochs (>97%). Detection from scalp EEG data alone of surface negative seizures and the seizures with the delayed scalp onset (the surface negative portion) was over 97%. In addition, the model accurately lateralized all of the seizures from scalp data, including the surface negative seizures. This work suggests that highly accurate seizure prediction and detection is feasible using either intracranial or scalp EEG data. Furthermore, surface negative seizures can be accurately predicted, detected and lateralized with machine learning even when they are not visible to the human eye.

Automatic seizure detection has significant value in epilepsy diagnosis and treatment. Although a variety of deep learning models have been proposed to automatically learn electroencephalography (EEG) features for seizure detection, the generalization performance and computational burden of such deep models remain the bottleneck of practical application. In this study, a novel lightweight model based on random convolutional kernel transform (ROCKET) is developed for EEG feature learning for seizure detection. Specifically, random convolutional kernels are embedded into the structure of a wavelet scattering network instead of original wavelet transform convolutions. Then the significant EEG features are selected from the scattering coefficients and convolutional outputs by analysis of variance (ANOVA) and minimum redundancy-maximum relevance (MRMR) methods. This model not only preserves the merits of the fast-training process from ROCKET, but also provides insight into seizure detection by retaining only the helpful channels. The extreme gradient boosting (XGboost) classifier was combined with this EEG feature learning model to build a comprehensive seizure detection system that achieved promising epoch-based results, with over 90% of both sensitivity and specificity on the scalp and intracranial EEG databases. The experimental comparisons showed that the proposed method outperformed other state-of-the-art methods for cross-patient and patient-specific seizure detection.

Epilepsy is a chronic brain disease resulted from the central nervous system lesion, which leads to repeated seizure occurs for the patients. Automatic seizure detection with Electroencephalogram (EEG) has witnessed great progress. However, existing methods paid little attention to the topological relationships of different EEG electrodes. Latest neuroscience researches have demonstrated the connectivity between different brain regions. Besides, class-imbalance is a common problem in EEG based seizure detection. The duration of epileptic EEG signals is much shorter than that of normal signals. In order to deal with the above mentioned two challenges, we propose to model the multi-channel EEG data using the Attention-based Graph ResNet (AGRN). In particular, each channel of the EEG signal represents a node of the graph and the inter-channel relations are modeled via the adjacency matrix in the graph. The loss function of the ARGN model is re-designed using focal loss to cope with the class-imbalance problem. The proposed ARGN with focal model could learn discriminative features from the raw EEG data. Experiments are carried out on the CHB-MIT dataset. The proposed model achieves an average accuracy of 98.70%, a sensitivity of 97.94%, a specificity of 98.66% and a precision of 98.62%. The Area Under the ROC Curve (AUC) is 98.69%.