Epilepsy Seizure Detection Research Articles

Inception networks, data augmentation and transfer learning in EEG-based photosensitivity diagnosis

Abstract Photosensitivity refers to a neurophysiological condition in which the brain generates epileptic discharges known as Photoparoxysmal Responses (PPR) in response to light flashes. In severe cases, these PPR can lead to epileptic seizures. The standardized diagnostic procedure for this condition is called Intermittent Photic Stimulation. During this procedure, the patient is exposed to a flashing light, aiming to trigger these epileptic reactions while preventing their full development. Meanwhile, brain activity is monitored using Electroencephalography, which is visually analyzed by clinical staff to identify these responses. Hence, the automatic detection of PPR becomes a highly unbalanced problem that has been barely studied in the literature due to photosensitivity’s low prevalence. 
This research tackles this problem and proposes using Inception-based Deep Learning (DL) neural networks that, together with transfer learning, are trained in epilepsy seizure detection and tuned in the PPR automatic detection task. A Data Augmentation (DA) technique is also applied to balance the available data set, evaluating its effects on the DL models. The proposal outperformed state-of-the-art solutions in the literature, achieving higher ratios on standard performance metrics, and with DA significantly improving the Sensitivity without affecting Accuracy and Specificity. This project is currently being developed with patients from Burgos University Hospital, Spain.

A multi-domain feature fusion epilepsy seizure detection method based on spike matching and PLV functional networks.

The identification of spikes, as a typical characteristic wave of epilepsy, is crucial for diagnosing and locating the epileptogenic region. The traditional seizure detection methods lack spike features and have low sample richness. This paper proposes a seizure detection method with spike-based phase locking value (PLV) functional brain networks and multi-domain fused features. 

Approach. In the spiking detection part, brain functional networks based on PLV are constructed to explore the changes in brain functional states during spiking discharge, from the perspective of microscopic neuronal activity to macroscopic brain region interactions. Then, in the epilepsy seizure detection task, multi-domain fused feature sequences are constructed using time-domain, frequency-domain, inter-channel correlation, and the spike detection features. Finally, Bi-LSTM and Transformer encoders and their optimized models are used to verify the effectiveness of the proposed method. 

Main results. Experimental results achieve the best seizure detection metrics on Bi-LSTM Attention, with accuracy, sensitivity, and specificity reaching 98.40%, 98.94%, and 97.86%, respectively. 

Significance. The method is significant as it innovatively applies multi channel spike network features to seizure detection. It can potentially improve the diagnosis and location of the epileptogenic region by accurately detecting seizures through the identification of spikes, which is a crucial characteristic wave of epilepsy.

Application of deconvolutional networks for feature interpretability in epilepsy detection

IntroductionScalp electroencephalography (EEG) is commonly used to assist in epilepsy detection. Even automated detection algorithms are already available to assist clinicians in reviewing EEG data, many algorithms used for seizure detection in epilepsy fail to account for the contributions of different channels. The Fully Convolutional Network (FCN) can provide the model’s interpretability but has not been applied in seizure detection.MethodsTo address these challenges, a novel convolutional neural network (CNN) model, combining SE (Squeeze-and-Excitation) modules, was proposed on top of the FCN. The epilepsy detection performance for patient-independent was evaluated on the CHB-MIT dataset. Then, the SE module was removed from the model and integrated the model with Inception, ResNet, and CBAM modules separately.ResultsThe method showed superior advancement, stability, and reliability compared to the other three methods. The method demonstrated a G-Mean of 82.7% for sensitivity (SEN) and specificity (SPE) on the CHB-MIT dataset. In addition, The contributions of each channel to the seizure detection task have also been quantified, which led us to find that the FZ, CZ, PZ, FT9, FT10, and T8 brain regions have a more pronounced impact on epileptic seizures.DiscussionThis article presents a novel algorithm for epilepsy detection that accurately identifies seizures in different patients and enhances the model’s interpretability.

Retraction Note: Brain epilepsy seizure detection using bio-inspired krill herd and artificial alga optimized neural network approaches

Retraction Note: Brain epilepsy seizure detection using bio-inspired krill herd and artificial alga optimized neural network approaches

A novel method for optimizing epilepsy detection features through multi-domain feature fusion and selection.

The methods used to detect epileptic seizures using electroencephalogram (EEG) signals suffer from poor accuracy in feature selection and high redundancy. This problem is addressed through the use of a novel multi-domain feature fusion and selection method (PMPSO). Discrete Wavelet Transforms (DWT) and Welch are used initially to extract features from different domains, including frequency domain, time-frequency domain, and non-linear domain. The first step in the detection process is to extract important features from different domains, such as frequency domain, time-frequency domain, and non-linear domain, using methods such as Discrete Wavelet Transform (DWT) and Welch. To extract features strongly correlated with epileptic classification detection, an improved particle swarm optimization (PSO) algorithm and Pearson correlation analysis are combined. Finally, Support Vector Machines (SVM), Artificial Neural Networks (ANN), Random Forest (RF) and XGBoost classifiers are used to construct epileptic seizure detection models based on the optimized detection features. According to experimental results, the proposed method achieves 99.32% accuracy, 99.64% specificity, 99.29% sensitivity, and 99.32% score, respectively. The detection performance of the three classifiers is compared using 10-fold cross-validation. Surpassing other methods in detection accuracy. Consequently, this optimized method for epilepsy seizure detection enhances the diagnostic accuracy of epilepsy seizures.

Enhancing Epilepsy Seizure Detection Through Advanced EEG Preprocessing Techniques and Peak-to-Peak Amplitude Fluctuation Analysis.

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.

Employing convolutional neural networks and explainable artificial intelligence for the detection of seizures from electroencephalogram signal

A seizure is a rapid, uncontrolled burst of electrical activity in the brain. Epilepsy is a neurological disorder caused by repeated seizures. Effective management requires early detection. This research aims to create a convolutional neural network (CNN) and explainable artificial intelligence (XAI) integrated system for epileptic seizure detection, using techniques such as Shapley additive explanation (SHAP) for better interpretability. The relevant data is acquired from a variety of electroencephalogram (EEG) dataset to facilitate the development of the model. To identify the EEG data, the proposed system combines deep learning models with feature extraction technique. Model visualization and XAI approaches like feature importance analysis from SHAP values offer clear insights into the model's decision-making process. Evaluation criteria like specificity and accuracy are used to assess the models' performance. This framework's objective is to create simple seizure detection systems that assist early epilepsy patient identification and individualized treatment plans. This research aims in opening the door to improved patient care and treatment by developing the field of epilepsy seizure detection.

Phase spectrogram of EEG from S-transform Enhances epileptic seizure detection

Automatic epilepsy seizure detection has high clinical value since it can alleviate the burden of manual monitoring. Nevertheless, it remains a technically challenging task to achieve a reliable system. In this study, we investigated the significance of the phase information in EEG signals in seizure detection using machine learning. We used the Stockwell transform (S-transform) to extract both phase and power spectra of the EEG signal in epilepsy patients. A dual-stream convolution neural network (CNN) model was adopted as the classifier, which takes both spectra as inputs. We demonstrated that the phase input allows the CNN model to capture the heightened phase synchronization among EEG channels in seizure and add network attention to both the low- and high-frequency features of the inputs in the CHB-MIT and Bonn databases. We improved the detection AUC-ROC by 6.68% on the CHB-MIT database when adding phase inputs to the power inputs. By incorporating a channel fusion post-processing to the outputs of this CNN model, it achieves a sensitivity and specificity of 79.59% and 92.23%, respectively, surpassing some of the state-of-the-art methods. Our results show that the phase inputs are useful features in seizure detection. This discovery has significant implications for improving the effectiveness of automatic seizure detection systems.

A Robust Automatic Epilepsy Seizure Detection Algorithm Based on Interpretable Features and Machine Learning

Epilepsy, as a serious neurological disorder, can be detected by analyzing the brain signals produced by neurons. Electroencephalogram (EEG) signals are the most important data source for monitoring these brain signals. However, these complex, noisy, nonlinear and nonstationary signals make detecting seizures become a challenging task. Feature-based seizure detection algorithms have become a dominant approach for automatic seizure detection. This study presents an algorithm for automatic seizure detection based on novel features with clinical and statistical significance. Our algorithms achieved the best results on two benchmark datasets, outperforming traditional feature-based methods and state-of-the-art deep learning algorithms. Accuracy exceeded 99.99% on both benchmark public datasets, with the 100% correct detection of all seizures on the second one. Due to the interpretability and robustness of our algorithm, combined with its minimal computational resource requirements and time consumption, it exhibited substantial potential value in the realm of clinical application. The coefficients of variation of datasets proposed by us makes the algorithm data-specific and can give theoretical guidance on the selection of appropriate random spectral features for different datasets. This will broaden the applicability scenario of our feature-based approach.

Software advancements in automatic epilepsy diagnosis and seizure detection: 10-year review

Epilepsy is a chronic neurological disorder that may be diagnosed and monitored using routine diagnostic tests like Electroencephalography (EEG). However, manual introspection and analysis of EEG signals is presently difficult and repetitive task even for experienced neuro-technologists with high false-positive rates and inter- and intra-rater reliability. Software advancements using Artificial Intelligence (AI) algorithms have the potential to early detect and predict abnormal patterns observed in EEG signals. The present review focuses on systematically reporting software advancements and their implementation using hardware systems in automatic epilepsy diagnosis and seizure detection for the past 10 years. Traditional, hybrid, and end-to-end AI-based pipelines and associated EEG datasets have been discussed. The review summarizes and compares reported articles, datasets, and patents through various subjective and objective parameters in this field. Latest advancements demonstrate that AI-based pipelines can reduce the introspection time by at least 50% without compromising the diagnostic accuracy or abnormal event detection. A significant rise in hardware implementation of software-based pipelines, end-to-end deep learning architectures for real-time analysis, and granted patents has been noticed since 2011. More than twenty-eight datasets have been developed to automatically diagnose epileptic EEG signals from 2001 to 2023. Extensive analysis using explainability tools, cross-dataset generalizations, reproducibility analysis, and ablation experiments can further improve the existing AI-based pipelines in this field. There is a need for the development of standardized protocols for data collection and its AI pipeline for a robust, inter- and intra-rater reliability-free, and real-time automatic epilepsy diagnosis.

A real-time epilepsy seizure detection approach based on EEG using short-time Fourier transform and Google-Net convolutional neural network

Epilepsy is one of the most common brain disorders, and seizures of epilepsy have severe adverse effects on patients. Real-time epilepsy seizure detection using electroencephalography (EEG) signals is an important research area aimed at improving the diagnosis and treatment of epilepsy. This paper proposed a real-time approach based on EEG signal for detecting epilepsy seizures using the STFT and Google-net convolutional neural network (CNN). The CHB-MIT database was used to evaluate the performance, and received the results of 97.74 % in accuracy, 98.90 % in sensitivity, 1.94 % in false positive rate. Additionally, the proposed method was implemented in a real-time manner using the sliding window technique. The processing time of the proposed method just 0.02 s for every 2-s EEG episode and achieved average 9.85- second delay in each seizure onset.

Literature Survey Paper on Epilepsy and Autism Spectrum Disorder Detection and Analysis Using Machine Learning

The detection and cure of epilepsy and autism spectrum disorder (ASD) are significantly complicated by their co-occurrence. This survey research investigates an integrated method for identifying ASD using behavioural characteristic questionnaires and epilepsy using EEG corpus inside a single system. We provide an overview of all the relevant research, emphasizing the difficulties in diagnosing each of these disorders separately and in combination. Our suggested approach combines behavioural questionnaire assessments for ASD with EEG-based analysis for epilepsy detection in an effort to improve diagnostic accuracy and expedite the evaluation process. This study examines the approaches, difficulties, and developments in both domains, providing perspectives on possible overlaps and prospects for further investigation. So, an attempt has been made to review on the pattern detection methods for epilepsy seizure detection from EEG signals. More than 150 research papers have been discussed to determine the techniques for detecting epileptic seizures. Further, the literature review confirms that the pattern recognition techniques required to detect epileptic seizures varies across the electroencephalogram (EEG) datasets of different conditions. This is mostly owing to the fact that EEG detected under different conditions have different characteristics.

Efficient and generalizable cross-patient epileptic seizure detection through a spiking neural network.

Epilepsy is a global chronic disease that brings pain and inconvenience to patients, and an electroencephalogram (EEG) is the main analytical tool. For clinical aid that can be applied to any patient, an automatic cross-patient epilepsy seizure detection algorithm is of great significance. Spiking neural networks (SNNs) are modeled on biological neurons and are energy-efficient on neuromorphic hardware, which can be expected to better handle brain signals and benefit real-world, low-power applications. However, automatic epilepsy seizure detection rarely considers SNNs. In this article, we have explored SNNs for cross-patient seizure detection and discovered that SNNs can achieve comparable state-of-the-art performance or a performance that is even better than artificial neural networks (ANNs). We propose an EEG-based spiking neural network (EESNN) with a recurrent spiking convolution structure, which may better take advantage of temporal and biological characteristics in EEG signals. We extensively evaluate the performance of different SNN structures, training methods, and time settings, which builds a solid basis for understanding and evaluation of SNNs in seizure detection. Moreover, we show that our EESNN model can achieve energy reduction by several orders of magnitude compared with ANNs according to the theoretical estimation. These results show the potential for building high-performance, low-power neuromorphic systems for seizure detection and also broaden real-world application scenarios of SNNs.

Epilepsy seizure detection based on EEG QuPWM features and Logistic Regression

Epilepsy is one of the most common neurological diseases that affects around 50 million people worldwide of all ages according to the World Health Organization. A standard test widely used to detect abnormalities in the electrical activity of the brain, such as seizures is the electroencephalogram (EEG). EEG is commonly analyzed and examined manually by expert reviewers to detect epileptic activity. A method that is time-consuming and tedious, may lead to errors and inconsistencies, especially for long EEG recordings. Therefore, automatic seizure detection is meaningful for the accurate diagnosis and monitoring of epilepsy, to initiate the proper treatment, and subsequently reduce the risk of future seizures. This study proposes a computer-aided diagnosis for automatic epileptic seizure detection from EEG data. In this study, an adaptation of the quantization-based position weight matrix (QuPWM) is proposed to extract features from the frequency domain of the framed EEG signal, these features are fed to a Logistic Regression(LR) to detect epileptic seizures. The performance has been evaluated on the University of Bonn EEG dataset and compared to the state-of-the-art methods. The proposed model resulted in satisfactory classification accuracy, sensitivity, and specificity rates.

Low-Power and Low-Cost AI Processor with Distributed-Aggregated Classification Architecture for Wearable Epilepsy Seizure Detection.

Wearable devices with continuous monitoring capabilities are critical for the daily detection of epileptic seizures, as they provide users with accurate and comprehensible analytical results. However, current AI classifiers rely on a two-stage recognition process for continuous monitoring, which only reduces operation time but remains challenged by the high cost of additional hardware. To address this problem, this article proposes a novel fusion architecture for AI processors, which enables event-triggered cross-paradigm integration and computation. Our method introduces a distributed-aggregated classification architecture (D-ACA) that facilitates the reuse of hardware resources across two-stage recognition, thereby obviating the need for standby hardware and enhancing energy efficiency. Integrating a non-encoding biomedical circuit method based on spiking neural networks (SNNs), the architecture eliminates encoded neurons at the hardware level, significantly optimizing energy consumption and hardware resource utilization. Additionally, we develop a configurable and highly flexible control method that supports various neuron modules, enabling continuous detection of epileptic seizures and activating high-precision recognition upon event detection. Finally, we implement the design on the Xilinx ZCU 102 FPGA board, where the AI processor achieves a high classification accuracy of 98.1% while consuming extremely low classification energy (3.73 μJ per classification).

Epileptic Seizure Detection Using Feature Importance and ML Classifiers

Epilepsy seizure (ES) monitoring and detection are only two examples of the many problems that maybe addressed by combining the Internet of Medical Things (IoMT) with machine learning (ML)techniques and cloud computing services. Epilepsy, a potentially fatal neurological disorder, is aworldwide problem that poses a significant threat to human health. There is an urgent need for areliable way of identifying epileptic seizures in their early stages to save thousands of epileptic patientsevery year. With the use of IoMT, several medical treatments, such as epileptic monitoring, diagnosis,and other procedures, may be performed remotely, hence lowering healthcare costs and enhancingservice quality. EEG datasets have made use of feature importance-based data reduction to address theproblem of a high number of data points and improve the delivery of service to the end user. In thisarticle, we use the feature importance method by applying two popular machine learning techniquesextra tree classifier (ETC) and the extreme gradient boosting classifier (XGBoost). Finally, theperformance of a number of tests is evaluated using experimental data from Bonn University. Alsoachieved is a comparison of the two approaches used. The collected findings demonstrate the efficacyof the XGBoost technique and its greater accuracy in comparison to the ETC strategy

Epileptic Activity Detection using Mean Value, RMS, Sample Entropy, and Permutation Entropy Methods

In this study, linear and non-linear signal analysis methods are implemented for epilepsy seizure detection using CHB-MIT EEG data taken from Boston children's hospital. In linear signal analysis, EEG signals are considered linear, although they are not linear. In linear signal analysis methods, root mean square (RMS) and mean of the EEG signals are analyzed. It is detected that the RMS value increased and the mean value moved away from zero in the positive and negative directions during the seizure period. Seizure periods in EEG signals are determined with RMS and mean values with 75 % and 58.4 % accuracy, respectively. Since EEG signals are not linear, the linear analysis is assumed insufficient and so entropy is preferred to linear signal analysis methods. Sample entropy (SmpE) and permutation entropy (PE) are preferred among entropy types. While an increase is observed in the sample entropy values at the beginning of the seizure, a decrease is observed in the permutation entropy values at the same time. When the entropy methods are examined separately, the onset of a seizure is determined with an accuracy of 66.6 % for both methods. However, when the entropy methods are examined together with the increase in the sample entropy value or the decrease in the permutation entropy, the accuracy rate increases to 79.2 % The resultant accuracy rates show that when one entropy method fails to catch the onset of a seizure the other can.

EEG Datasets in Machine Learning Applications of Epilepsy Diagnosis and Seizure Detection

EEG Datasets in Machine Learning Applications of Epilepsy Diagnosis and Seizure Detection

Real-time epilepsy seizure detection based on EEG using tunable-Q wavelet transform and convolutional neural network

Epilepsy is a chronic disease caused by sudden abnormal discharge of brain neurons, leading to transient brain dysfunctions. This paper proposed an EEG based real-time approach to detect epilepsy seizures using tunable-Q wavelet transform and convolutional neural network (CNN). Statistical moments and spectral band power were used to reveal the time domain and frequency domain features in EEG, and then were converted into imaged-like data fed into CNN. The proposed approach was evaluated using the database CHB-MIT. The proposed algorithm achieved 97.57% in accuracy, 98.90% in sensitivity, 2.13% in false positive rate and 10.46-second delay. In addition, the proposed method is suitable in real-time implementation. The outcomes indicate that the proposed method can applied to real-time seizure detection in clinical applications.

DSMN-ESS: Dual-Stream Multitask Network for Epilepsy Syndrome Classification and Seizure Detection

DSMN-ESS: Dual-Stream Multitask Network for Epilepsy Syndrome Classification and Seizure Detection