Epileptic Seizure Detection System Research Articles

MCU-Enabled Epileptic Seizure Detection System With Compressed Learning

MCU-Enabled Epileptic Seizure Detection System With Compressed Learning

An improved GBSO-TAENN-based EEG signal classification model for epileptic seizure detection

Detection and classification of epileptic seizures from the EEG signals have gained significant attention in recent decades. Among other signals, EEG signals are extensively used by medical experts for diagnosing purposes. So, most of the existing research works developed automated mechanisms for designing an EEG-based epileptic seizure detection system. Machine learning techniques are highly used for reduced time consumption, high accuracy, and optimal performance. Still, it limits by the issues of high complexity in algorithm design, increased error value, and reduced detection efficacy. Thus, the proposed work intends to develop an automated epileptic seizure detection system with an improved performance rate. Here, the Finite Linear Haar wavelet-based Filtering (FLHF) technique is used to filter the input signals and the relevant set of features are extracted from the normalized output with the help of Fractal Dimension (FD) analysis. Then, the Grasshopper Bio-Inspired Swarm Optimization (GBSO) technique is employed to select the optimal features by computing the best fitness value and the Temporal Activation Expansive Neural Network (TAENN) mechanism is used for classifying the EEG signals to determine whether normal or seizure affected. Numerous intelligence algorithms, such as preprocessing, optimization, and classification, are used in the literature to identify epileptic seizures based on EEG signals. The primary issues facing the majority of optimization approaches are reduced convergence rates and higher computational complexity. Furthermore, the problems with machine learning approaches include a significant method complexity, intricate mathematical calculations, and a decreased training speed. Therefore, the goal of the proposed work is to put into practice efficient algorithms for the recognition and categorization of epileptic seizures based on EEG signals. The combined effect of the proposed FLHF, FD, GBSO, and TAENN models might dramatically improve disease detection accuracy while decreasing complexity of system along with time consumption as compared to the prior techniques. By using the proposed methodology, the overall average epileptic seizure detection performance is increased to 99.6% with f-measure of 99% and G-mean of 98.9% values.

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.

A novel finite spectral entropy: Gated term memory unit recursive network integrated with Ladybug Beetle Optimization algorithm for epileptic seizure detection.

Professional medical experts use a visual electroencephalography (EEG) signal for epileptic seizure detection, although this method is time-consuming and highly subject to bias. The majority of previous epileptic detection techniques have poor efficiency, detection performance and also which are unsuited to handle large datasets. In order to solve the aforementioned issues and to assist medical professionals with an advanced technology, a computerized epileptic seizure detection system is essential. Therefore, the proposed work intends to design an automated detection tool for predicting an epileptic seizure from EEG signals. For this purpose, a novel non-linear feature analysis and deep learning algorithms are deployed in this work. Initially, the signal decomposition, filtering and artifacts removal operations are carried out with the use of finite Haar wavelet transformation technique. After that, the finite spectral entropy (FSE) based feature extraction model has been used to extract the time, frequency, and time-frequency features from the normalized signal. Consequently, the novel gated term memory unit recursive network (GTRN) model is employed to predict the given EEG signal as whether healthy or seizure affected including the class with high accuracy. During this process, the recently developed Ladybug Beetle Optimization (LBO) algorithm is used to compute the logistic sigmoid function based on the solution. The purpose of using this algorithm is to simplify the process of classification with increased seizure prediction accuracy and performance. Moreover, the standard and popular benchmark EEG datasets are used to validate and test the results of the proposed FSE-GTRN-LBO mechanism. By leveraging the finite Haar wavelet transformation and FSE-based feature extraction, we can efficiently process EEG signals. The utilization of the GTRN model enables accurate classification of healthy and seizure-affected EEG data. To optimize the classification process further, we integrate the LBO algorithm, streamlining the computation of the logistic sigmoid function. Through comprehensive validation on standard EEG datasets, our proposed FSE-GTRN-LBO mechanism achieves outstanding seizure prediction accuracy and performance, surpassing existing state-of-the-art techniques.

EEG seizure detection: concepts, techniques, challenges, and future trends.

A central nervous system disorder is usually referred to as epilepsy. In epilepsy brain activity becomes abnormal, leading to times of abnormal behavior or seizures, and at times loss of awareness. Consequently, epilepsy patients face problems in daily life due to precautions they must take to adapt to this condition, particularly when they use heavy equipment, e.g., vehicle derivation. Epilepsy studies rely primarily on electroencephalography (EEG) signals to evaluate brain activity during seizures. It is troublesome and time-consuming to manually decide the location of seizures in EEG signals. The automatic detection framework is one of the principal tools to help doctors and patients take appropriate precautions. This paper reviews the epilepsy mentality disorder and the types of seizure, preprocessing operations that are performed on EEG data, a generally extracted feature from the signal, and a detailed view on classification procedures used in this problem and provide insights on the difficulties and future research directions in this innovative theme. Therefore, this paper presents a review of work on recent methods for the epileptic seizure process along with providing perspectives and concepts to researchers to present an automated EEG-based epileptic seizure detection system using IoT and machine learning classifiers for remote patient monitoring in the context of smart healthcare systems. Finally, challenges and open research points in EEG seizure detection are investigated.

A Machine Learning Approach to the Smartwatch-based Epileptic Seizure Detection System

Worldwide around 70 million people have epilepsy, and every year, more than 1 out of 1000 cases of epilepsy result in Sudden Unexpected Death in Epilepsy (SUDEP). Video – EEG is the standard clinical method for monitoring epilepsy and seizures. However, wearable systems are required to monitor epileptic activity in daily living due to the complexity of using EEG outside the laboratory. Also, to prevent SUDEP, early prediction of seizure onset is required. In this work, we propose a machine learning model to detect ictal and preictal conditions using an Empatica E4 smartwatch. The Empatica E4 records real-time photoplethysmography, electrodermal activity, accelerometry, and temperature. Clinical data were recorded from 11 patients with epilepsy (PWE) for 19 seizure onsets. Features from all the modalities were extracted by taking segments of the signal during the seizure (ictal), pre-seizure, and inter-ictal (non-seizure) conditions. These features were used on support vector machine (SVM-RBF), decision tree (DTC), and logistic regression (LRC)-based supervised training for ictal vs. non-ictal and pre-ictal vs. inter-ictal conditions. The highest accuracy of 99.40% was recorded for DTC-based seizure detection classifier during 10-fold cross-validation. Also, the highest accuracy of 95.42% was recorded for DTC-based pre-seizure onset detection classifier during 10-fold cross-validation.

Epileptic-Net: An Improved Epileptic Seizure Detection System Using Dense Convolutional Block with Attention Network from EEG.

Epilepsy is a complex neurological condition that affects a large number of people worldwide. Electroencephalography (EEG) measures the electrical activity of the brain and is widely used in epilepsy diagnosis, but it usually requires manual inspection, which can be hours long, by a neurologist. Several automatic systems have been proposed to detect epilepsy but still have some unsolved issues. In this study, we proposed a dynamic method using a deep learning model (Epileptic-Net) to detect an epileptic seizure. The proposed method is largely heterogeneous and comprised of the dense convolutional blocks (DCB), feature attention modules (FAM), residual blocks (RB), and hypercolumn technique (HT). Firstly, DCB is used to get the discriminative features from the EEG samples. Then, FAM extracts the essential features from the samples. After that, RB learns more vital parts as it entirely uses information in the convolutional layer. Finally, HT retains the efficient local features extracted from the layers situated at the different levels of the model. Its performance has been evaluated on the University of Bonn EEG dataset, divided into five distinct classes. The proposed Epileptic-Net achieves the average accuracy of 99.95% in the two-class classification, 99.98% in the three-class classification, 99.96% in the four-class classification, and 99.96% in classifying the complicated five-class problem. Thus the proposed approach shows more competitive results than the existing model to detect epileptic seizures. We also hope that this method can support experts in achieving objective and reliable results, lowering the misdiagnosis rate, and assisting in decision-making.

Automatic epileptic seizure detection approach based on multi-stage Quantized Kernel Least Mean Square filters

Epilepsy is one of the most common neurological disorders of the brain all over the world. For its detection, Electroencephalogram (EEG) is an important noninvasive diagnostic approach. This paper proposes a new automated real-time approach for EEG epileptic seizure detection based on serial concatenation of Quantized Kernel Least Mean Square (QKLMS) adaptive filters. The QKLMS filter is employed in the proposed system to achieve accurate real-time diagnosis with low hardware complexity. In the proposed serial concatenation filter design, the energy of prediction signal is investigated to detect the seizure interval on the EEG records and to classify healthy, epileptic inter-ictal, and epileptic ictal cases. The Grey Wolf Optimizer (GWO) algorithm is employed to find the optimum values of the energy threshold and the parameters of the QKLMS algorithm. The performance of the proposed QKLMS technique is compared with other adaptive filters, and the results reveal the superior performance of the QKLMS algorithm. The proposed system is examined with real EEG records taken from the Bonn University database, and the experimental results show that the proposed epileptic seizure detection system outperforms other state-of-the-art systems with an accuracy of 97.88 %, sensitivity of 98.80 %, specificity of 97.65 %, and computational time of 0.58 sec. This demonstrates that the proposed approach can be used to assist neurologists in enhancing the accuracy of epileptic seizure detection and speeding-up the diagnosis process without the need for visual inspection or analysis of a large volume of EEG data.

Time domain implementation of pediatric epileptic seizure detection system for enhancing the performance of detection and easy monitoring of pediatric patients

ObjectiveThe clinical phenomenon of epilepsy varies greatly among patients and this in turn, has its effect on the quality of life they lead. Studies reveal a requisite for efficient epileptic seizure detection techniques. In this work, an extensive study has been carried out for the detection of pediatric epileptic seizures. The major challenges of pediatric epileptic seizure detection lie in the extraction of appropriate features, adaptability of the method with respect to seizure signals, applicability to all types of seizure conditions and dependency on signal channels. MethodsElectroencephalogram (EEG) signals have been used as input which is processed with discrete wavelet transform (DWT) using multi-resolution analysis. Four different wavelets such as Daubechies, Symlet, Bi-orthogonal and Coiflet have been used for feature extraction. The classifier used for this work is artificial neural network (ANN). Microcontroller based prototype model has been used for substantiation of the designed architecture. ResultsThe work has been verified using the CHB-MIT EEG database for pediatric patients. Records of 10 patients have been selected and the measurement criteria showed the highest accuracy, sensitivity and specificity of 95.3%, 97.2% and 93.5% respectively for sym4 wavelet. ConclusionA prototype microcontroller-based model has been designed using MATLAB/SIMULINK and ARDUINO. The advantage of the work is that it is not patient-specific or channels specific, hence it can be used to detect partial as well as generalized seizure. SignificanceThe significance of the proposed microcontroller-based architecture lies in its low power consumption, accurate seizure detection in a minimum amount of time and having the compatibility of working with computers and sensors. This proposed prototype can be used in the future for designing a hardware-based detection system that would be portable and non-invasive in nature.

A Low-Cost Electroencephalography (EEG) Instrumentation for Epileptic Seizure Detection

Noninvasive detection of epileptic seizures can help the patients to save themselves from sudden fall and assist the doctors to study the disease and treat the patient. Generally, an EEG based epileptic seizure detection system consists of EEG sensors, electronic instrumentation and a signal processing algorithm. In this paper, an EEG acquisition system (EAS) has been developed with a multi-stage amplifier and filter blocks. The instrumentation is studied, tested and evaluated with periodic signals generated with a function generator as well as the EEG-like signals simulated by virtual instrumentation (VI) developed with NI MyDAQ controlled by a Lab VIEW. The noise response of the instrumentation developed has been studied with SNR analysis. Results, obtained from the circuit simulation and experimentation conducted with EEG-like waveforms, show that the developed EAS is suitable to process the EEG signals and suitable for epileptic seizures detection systems.

Characterizing Functional Connectivity Network Based on Multi-Domain Analysis for Epilepsy Classification

Epileptic is a neural disease exemplified through untypical concurrent signal discharge from the neurons present in the brain region. This abnormal brain functionality could be captured through electroencephalography (EEG) system. Generally the observed EEG signals are examined by the experienced neurologist, which may be time consuming when observing hours of EEG signal. Therefore, this proposed work provides a fully automatic epileptic seizure detection system by means of the multi-domain features along with various machine learning algorithms. Initially, the obtained EEG signals are processed to clear noise and artefacts. Subsequently, the pre-processed signals are segregated as 5 seconds epochs and for each epoch various features are extracted from frequency domain, time domain. Additionally entropy, correlation and graph theory approaches has been used for analysis the connectivity of the brain network. Subsequently, distinguishable features are chosen carefully in this regard from the immense feature set by virtue of multi-objective evolutionary method and convincingly, classification has been performed using support vector machine(SVM).A Bayesian optimization (BaO) algorithm was utilized to optimize the SVM's hyper-plane parameters. In addition, Quadratic Discriminant Analysis (QDA), Linear Discriminant Analysis (LDA),Random Forest Ensemble (RFE) and k-Nearest Neighbor Ensemble (k- NNE) was also used for comparing the proposed results. These obtained results validates by considering the performance of this work is competing along with state-of the-arts approaches. The proposed work is implemented on a CHB-MIT database .The obtained performance measure of the classifiers are 99.09%, 81.49%,80.90%,76.85% and 84.14 % in SVM , LDA, QDA, k- NNE and RFE respectively. Finally SVM with Bayesian Optimization (BaO) algorithm outperforms than other classifiers with accuracy, AUC, sensitivity and specificity, as 99.09%, 99.67%, 98.06% and 98.12%, respectively.

Automatic Detection of Epileptic Seizures in EEG Using Sparse CSP and Fisher Linear Discrimination Analysis Algorithm.

In order to realize the automatic epileptic seizure detection, feature extraction and classification of electroencephalogram (EEG) signals are performed on the interictal, the pre-ictal, and the ictal status of epilepsy patients. There is no effective strategy for selecting the number of channels and spatial filters in feature extraction of multichannel EEG data. Therefore, this paper combined sparse idea and greedy search algorithm to improve the feature extraction of common space pattern. The feature extraction can effectively overcome the repeating selection problem of feature pattern in the eigenvector space by the traditional method. Then we used the Fisher linear discriminant analysis to realize the classification. The results show that the proposed method can get high classification accuracy using fewer data. For 10 subjects, the averaged accuracy of epilepsy detection is more than 99%. So, the detection of an epileptic seizure based on sparse features using Fisher linear discriminant analysis classifiers is more suitable for a reliable, automatic epileptic seizure detection system to enhance the patient's care and the quality of life.

IoT and cloud computing based automatic epileptic seizure detection using HOS features based random forest classification

Epilepsy, a fatal neurological disorder, has been emerged as a worldwide problem and is one of the major risks to human lives. There exists an urgent need for an efficient technique for early detection of epileptic seizures at its initial stage in order to save the lives of thousands of epileptic patients annually. Now a days, internet of things in combination with machine learning techniques and cloud computing services has emerged as a powerful technology to resolve many problems in healthcare sector. This paper also presents an automatic epileptic seizure detection system and its layered architecture for early detection of epileptic seizures using existing communication technologies in collaboration with machine learning and cloud computing. This model transmits sensed EEG signals from patient’s scalp to cloud through 4G cellular network or Wi-Fi network. At cloud, EEG signals are processed using Fast Walsh Hadamard transform and higher order spectra (HOS) based feature extraction for extracting higher order statistics and entropy-based features. The correlation-based feature selection algorithm has been employed for reducing the dimensionality of EEG datasets so as to tackle the problem of large volume of data and to reduce delays in service offered to the end user. Random Forest algorithm has been employed for classification of EEG signals into three different seizure stages, viz., normal, preictal and ictal. For performance analysis, other well-known machine learning algorithms like Bayes Net, Naive Bayes, Multilayer Perceptron, Radial Basis function neural network and C4.5 Decision Tree are also considered. The simulation and testing results show that Random Forest classifier provides maximum values of classification accuracy of 99.40%, sensitivity of 99.40% and specificity of 99.66%, minimum mean square error of 0.0871 along with optimum training time of 20 ms, which makes this model more real time compatible, thereby making HOS features based Random Forest algorithm’s cloud model an efficient technique for early and automatic detection of epileptic seizures in real time.

Semi-Supervised EEG Signals Classification System for Epileptic Seizure Detection

In the past few decades, measuring and recording the brain electrical activities using Electroencephalogram (EEG) has become a standout amongst the tools utilized for neurological disorders’ diagnosis, especially seizure detection. In this letter, a novel epileptic seizure detection system based on classifying raw EEG signals’ recordings, eliminating the overhead of engineered feature extraction, is proposed. The system employs a mixing of unsupervised and supervised deep learning utilizing a one-dimensional convolutional variational autoencoder. To ascertain the robustness of the system against classifying unseen data, the evaluation of the proposed system is done using k-fold cross-validation. The classification results between normal and ictal cases have achieved a 100% accuracy while the classification results between the normal, inter-ictal and ictal cases accomplished a 99% overall accuracy which makes our system one of the most efficient among other state-of-the-art systems.

A low-power, low-noise, high-accurate epileptic-seizure detection system for wearable applications

This paper presents a low-power epileptic-seizure-detection-system (ESDS), including an instrumentational-amplifier (IA), a level-detection-ADC (LD-ADC) and frequency-detection-units (FDUs). In the proposed ESDS, by utilizing low-power techniques, and a new mixed-mode detection-system, the power is reduced to 2 μW. Also, by using large input capacitors and a high-pass-filter (HPF), the electrode-offset-voltage (EOV) is rejected. Hence, the supply voltage is reduced without saturating the input transistors. In the IA, by using the choppers, the input-referred-noise is obtained 0.96 μVrms over 0.5–100 Hz. Also, to avoid signal attenuation, an impedance-boosting-loop (IBL) is used to increase the input impedance up to 18.2 GΩ at 10 Hz. After amplifying the EEG signals, the LD-ADC and FDUs are utilized to detect the amplitudes and activities of the EEG signals. In the proposed ESDS, by using a PGA with variable-gain, a LD-ADC with variable sampling-frequency and FDUs with variable reset-time, the sensitivity is increased up to 100% with the latency of 8.8 s. Besides, a compatible ESDS with different low-voltage fast-activity epileptic-seizures is achieved.

Epileptic Seizure Detection System Based on Multi-Domain Feature and Spike Feature of EEG

Epilepsy is caused by sudden abnormal discharges of neurons in the brain. This paper constructs an automatic seizure detection system, which combines the predicting result of multi-domain feature with the predicting result of spike rate feature to detect the occurrence of epileptic seizures. After segmenting EEG data into 5[Formula: see text]s with 80% overlap epochs, the paper extracts time domain features, frequency domain features and hurst exponents (HE) from each epoch and these features are reduced by linear discriminant analysis (LDA) to be input parameters of the random forest (RF) classifier, which provides classification of the EEG epochs concerning the existence of seizures. In parallel, the paper extracts spikes from EEG data with morphological filter and calculates the spike rate to determine whether there is seizure. Then the results obtained by these two methods are merged as the final detection result. The paper shows that the accuracy (AC), sensitivity (SE), specificity (SP) and the false positive ratio based on event (FPRE) obtained by hybrid method are 98.94%, 76.60%, 98.99% and 2.43 times/h, respectively. Finally, the paper applies the seizure detection method to do seizure warning and recording to help the family member to take care of the patients and the doctor to adjust the antiepileptic drugs (AEDs).

Review on SVM Based Epileptic Seizure Detection System

Review on SVM Based Epileptic Seizure Detection System

An epileptic seizure detection system based on cepstral analysis and generalized regression neural network

This study introduces a new and effective epileptic seizure detection system based on cepstral analysis utilizing generalized regression neural network for classifying electroencephalogram (EEG) recordings. The EEG recordings are obtained from an open database which has been widely studied with many different combinations of feature extraction and classification techniques. Cepstral analysis technique is mainly used for speech recognition, seismological problems, mechanical part tests, etc. Utility of cepstral analysis based features in EEG signal classification is explored in the paper. In the proposed study, mel frequency cepstral coefficients (MFCCs) are computed in the feature extraction stage and used in neural network based classification stage. MFCCs are calculated based on a frequency analysis depending on filter bank of approximately critical bandwidths. The experimental results have shown that the proposed method is superior to most of the previous studies using the same dataset in classification accuracy, sensitivity and specificity. This achieved success is the result of applying cepstral analysis technique to extract features. The system is promising to be used in real time seizure detection systems as the neural network adopted in the proposed method is inherently of non-iterative nature.

Electroencephalography‐based feature extraction using complex network for automated epileptic seizure detection

AbstractElectroencephalography signals are typically used for analyzing epileptic seizures. These signals are highly nonlinear and nonstationary, and some specific patterns exist for certain disease types that are hard to develop an automatic epileptic seizure detection system. This paper discussed statistical mechanics of complex networks, which inherit the characteristic properties of electroencephalography signals, for feature extraction via a horizontal visibility algorithm in order to reduce processing time and complexity. The algorithm transforms a time series signal into a complex network, which some features are abbreviated. The statistical mechanics are calculated to capture distinctions pertaining to certain diseases to form a feature vector. The feature vector is classified by multiclass classification via a k‐nearest neighbor classifier, a multilayer perceptron neural network, and a support vector machine with a 10‐fold cross‐validation criterion. In performance evaluation of proposed method with healthy, seizure‐free interval, and seizure signals, firstly, input data length is regarded among some practical signal samples by optimizing between accuracy‐processing time, and the proposed method yields outstanding performance on the average classification accuracy for 3‐class problems mainly for detection of seizure‐free interval and seizure signals and acceptable results for 2‐class and 5‐class problems comparing with conventional methods. The proposed method is another tool that can be used for classifying signal patterns, as an alternative to time/frequency analyses.

Statistical Wavelet Features, PCA, MLPNN, SVM and K-NN Based Approach for the Classification of EEG Physiological Signal

Brain is the most complex organ amongst all the systems in human body. The study of the electrical signals produced by neural activities of human brain is called Electroencephalogram. Electroencephalogram (EEG) is a technique which is used to identify the neurological disorder of brain. Epilepsy is one of the most common neurological disorders of brain. Epilepsy needs to be detected efficiently using required EEG feature extraction such as: mean, standard deviation, median, entropy, kurtosis and skewness etc. The framework of proposed technique is an efficient EEG signal classification approach. The proposed approach is used to classify the EEG signal into two classes: epileptic seizure or not. Extraction of the features by applying Discrete Wavelet Transform (DWT) in order to decompose the EEG signals into sub-bands. These features, extracted from details and approximation coefficients of DWT sub-bands, are used as input to Principal Component Analysis (PCA). The classification is based on reducing the feature dimension using PCA and deriving the Support Vector Machine (SVM), neural network analysis (NNA) and k-nearest neighbour (K-NN). In classification of normal and epileptic, results obtained exhibited an accuracy of 100% by applying NNA and k-NN. It has been found that the computation time of NNA classifier is lesser than SVM and k-NN to provide 100% accuracy. So, the detection of an epileptic seizure based on DWT statistical features using NNA classifiers is more suitable in real time for a reliable, automatic epileptic seizure detection system to enhance the patient's care and the quality of life.