Seizure Detection System Research Articles

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

SummaryAutomated seizure detection in long-term video-EEG recordings is far from being integrated into common clinical practice. Here, we leverage classical and state-of-the-art complexity measures to robustly and automatically detect seizures from scalp recordings. Brain activity is scored through eight features, encompassing traditional time domain and novel measures of recurrence. A binary classification algorithm tailored to treat unbalanced dataset is used to determine whether a time window is ictal or non-ictal from its features. The application of the algorithm on a cohort of ten adult patients with focal refractory epilepsy indicates sensitivity, specificity, and accuracy of 90%, along with a true alarm rate of 95% and less than four false alarms per day. The proposed approach emphasizes ictal patterns against noisy background without the need of data preprocessing. Finally, we benchmark our approach against previous studies on two publicly available datasets, demonstrating the good performance of our algorithm.

EpilepsyGAN: Synthetic Epileptic Brain Activities With Privacy Preservation.

Epilepsy is a chronic neurological disorder affecting more than 65 million people worldwide and manifested by recurrent unprovoked seizures. The unpredictability of seizures not only degrades the quality of life of the patients, but it can also be life-threatening. Modern systems monitoring electroencephalography (EEG) signals are being currently developed with the view to detect epileptic seizures in order to alert caregivers and reduce the impact of seizures on patients' quality of life. Such seizure detection systems employ state-of-the-art machine learning algorithms that require a large amount of labeled personal data for training. However, acquiring EEG signals during epileptic seizures is a costly and time-consuming process for medical experts and patients. Furthermore, this data often contains sensitive personal information, presenting privacy concerns. In this work, we generate synthetic seizure-like brain electrical activities, i.e., EEG signals, that can be used to train seizure detection algorithms, alleviating the need for sensitive recorded data. Our experiments show that the synthetic seizure data generated with our GAN model succeeds at preserving the privacy of the patients without producing any degradation in performance during seizure monitoring.

Decision support systems, assistance systems and telemedicine in epileptology

HintergrundDie wissenschaftlichen Erkenntnisse über Epilepsien und deren klinische Implikationen nehmen rasant zu. Für Nichtexperten stellt sich die zunehmende Herausforderung, den Überblick hierüber zu bewahren. Hier setzen Clinical-decision-support-Systeme (CDSS) an, indem sie standard- und expertengetriggertes Wissen zur Diagnostik und Therapie individualisiert und automatisiert liefern. Zudem sind Medizin-Apps und telemedizinische Verfahren zur Diagnostik und Therapie sowie Assistenzsysteme zur Anfallsdetektion bei Epilepsien verfügbar.Ziel der ArbeitEs soll ein Überblick über die aktuellen Entwicklungen und Anwendungsmöglichkeiten verfügbarer tele-epileptologischer Methoden gegeben werden.Material und MethodenAuf der Basis persönlicher Kenntnis und eines Literaturreviews werden epilepsiespezifische CDSS, Medizin-Apps, Assistenzsysteme sowie telemedizinische Anwendungen charakterisiert und deren klinische Einsatzmöglichkeiten dargestellt.Ergebnisse und DiskussionPersonen mit Epilepsie könnten aufgrund des chronischen Verlaufs und der Komplexität der Erkrankung und ihrer Folgen von CDSS profitieren. Es erscheint wünschenswert, dass epilepsiespezifische CDSS sowohl für die Behandelnden als auch für Patienten nutzbar werden. Apps für Menschen mit Epilepsie dienen derzeit meist der Verlaufsdokumentation von Anfallsfrequenz, Medikamentencompliance und Nebenwirkungen. Gegenwärtige Anfallsdetektionssysteme erkennen vor allem generalisiert tonisch-klonische Anfälle (GTKA). Ein klinischer Nutzen ist noch nicht hinreichend belegt, erscheint aber wahrscheinlich, insbesondere da GTKA mit dem Risiko eines plötzlichen Todes von Epilepsiepatienten assoziiert sind und Interventionen als wirksam gelten.

Monitoring the Burden of Seizures and Highly Epileptiform Patterns in Critical Care with a Novel Machine Learning Method

IntroductionCurrent electroencephalography (EEG) practice relies on interpretation by expert neurologists, which introduces diagnostic and therapeutic delays that can impact patients’ clinical outcomes. As EEG practice expands, these experts are becoming increasingly limited resources. A highly sensitive and specific automated seizure detection system would streamline practice and expedite appropriate management for patients with possible nonconvulsive seizures. We aimed to test the performance of a recently FDA-cleared machine learning method (Claritγ, Ceribell Inc.) that measures the burden of seizure activity in real time and generates bedside alerts for possible status epilepticus (SE).MethodsWe retrospectively identified adult patients (n = 353) who underwent evaluation of possible seizures with Rapid Response EEG system (Rapid-EEG, Ceribell Inc.). Automated detection of seizure activity and seizure burden throughout a recording (calculated as the percentage of ten-second epochs with seizure activity in any 5-min EEG segment) was performed with Claritγ, and various thresholds of seizure burden were tested (≥ 10% indicating ≥ 30 s of seizure activity in the last 5 min, ≥ 50% indicating ≥ 2.5 min of seizure activity, and ≥ 90% indicating ≥ 4.5 min of seizure activity and triggering a SE alert). The sensitivity and specificity of Claritγ’s real-time seizure burden measurements and SE alerts were compared to the majority consensus of at least two expert neurologists.ResultsMajority consensus of neurologists labeled the 353 EEGs as normal or slow activity (n = 249), highly epileptiform patterns (HEP, n = 87), or seizures [n = 17, nine longer than 5 min (e.g., SE), and eight shorter than 5 min]. The algorithm generated a SE alert (≥ 90% seizure burden) with 100% sensitivity and 93% specificity. The sensitivity and specificity of various thresholds for seizure burden during EEG recordings for detecting patients with seizures were 100% and 82% for ≥ 50% seizure burden and 88% and 60% for ≥ 10% seizure burden. Of the 179 EEG recordings in which the algorithm detected no seizures, seizures were identified by the expert reviewers in only two cases, indicating a negative predictive value of 99%.DiscussionClaritγ detected SE events with high sensitivity and specificity, and it demonstrated a high negative predictive value for distinguishing nonepileptiform activity from seizure and highly epileptiform activity.ConclusionsRuling out seizures accurately in a large proportion of cases can help prevent unnecessary or aggressive over-treatment in critical care settings, where empiric treatment with antiseizure medications is currently prevalent. Claritγ’s high sensitivity for SE and high negative predictive value for cases without epileptiform activity make it a useful tool for triaging treatment and the need for urgent neurological consultation.

Performance analysis of EEG seizure detection features

Automatic detection of epileptic seizures can serve as a valuable clinical tool which involves a more objective and computationally efficient method for the analysis of EEG data in order to generate increasingly accurate and reliable results. Automatic seizure detection is also an important component of closed-loop responsive cortical stimulation systems. The goal of this study is to evaluate EEG-based features recently proposed for seizure detection to discover the optimum ones for a reliable seizure detection system. We extracted seizure detection features from intracranial EEG signals that were recorded during invasive pre-surgical epilepsy monitoring of people with drug resistant focal epilepsy at the Epilepsy Center of the University Hospital of Freiburg. Features from time, frequency and phase space domains as well as similarity/dissimilarity features were considered. The performance of each feature was investigated using the statistical test ANOVA. Performance analysis was conducted separately on the recordings from the channels within the seizure-onset zone (SOZ-in) and the recordings from the channels outside the seizure-onset zone (SOZ-out). Similarity/dissimilarity features that measure dynamic properties of the EEG signal and the evolving phenomena of the seizures could significantly separate ictal (during seizure) states from pre-ictal (before seizure) states (p < 0.01). Among them, our proposed feature, Bhattacharyya-based dissimilarity index (BBDI), successfully passed Tukey's post-hoc test as well suggesting that it can distinguish both pre-ictal and post-ictal (after seizure) periods from ictal period. BBDI was further applied to detect epileptic seizures and achieved area under the curve of the receiver-operator characteristic (ROC) equal to 0.96 and 0.94 for SOZ-in and SOZ-out channels, respectively. No significant difference (p = 0.59) was observed in the performance of features between SOZ-in recordings and SOZ-out recordings. The discriminative value of EEG seizure detection features was determined by statistical tests. As a result, the best features to be selected for a reliable seizure detection system designed for people with drug-resistant focal epilepsy were suggested, which include similarity/dissimilarity indices.

Implementation of High Performance EEG Based Seizure Detection And Analysis On Multicore Platform

About 50 million people worldwide suffer from epilepsy, the neurological disorder characterized by seizures. The primary tool for diagnosis of an epileptic seizure is an electroencephalography (EEG) which records the brain’s spontaneous electrical activity. This requires the placement of a minimum of 16 electrodes on the scalp with each electrode being interpreted as a channel. The classification of seizure detection and analysis techniques mainly work in two stages, where features are extracted from raw EEG data in the first stage and then the obtained features are used as input for the classification process in the second stage. Traditionally the Seizure detection algorithms were implemented using DSP Processor or FPGAs. But these single core platforms are constrained with respect to speed of operation and power consumption. There is a greater need to reduce the power consumption as well to increase the speed of EEG seizure detection system. This problem can be addressed using the Multicore Processors, which process data simultaneously. This project presents a high performance multicore platform for EEG based seizure detection and analysis. This platform performs continuous multichannel detection and analysis of seizures for epilepsy patients. The detection unit will detect the seizures based on feature extraction process once the seizure detection is done enables the analysis circuit that process the data based Uridva Triyabhakyam based 128 point FFT and transmits energy and frequency contents of EEG data. All proposed blocks are simulated and synthesized using Xilinx ISE and coding is done in Verilog.

Detection of Epileptic Seizure EEG Signal Using Multiscale Entropies and Complete Ensemble Empirical Mode Decomposition

Epilepsy is a severe neurological disease which is diagnosed by analyzing Electroencephalogram. The epileptic seizure detection technique based on multiscale entropies and complete ensemble empirical mode decomposition (CEEMD) is proposed in this paper. CEEMD is used for the estimation of sub-bands and two multiscale entropies; multiscale dispersion entropy (MDE) and refined composite MDE are extracted from the sub-bands. The feature selection method, configured by hybridizing the filter based and wrapper based method, is used to select relevant multiscale entropies. The hybrid method has not only reduced features but also improved classification performance. An artificial neural network is trained with relevant features and performance is measured using classification accuracy, sensitivity and specificity. Five clinically relevant classification problems are used to assess the proposed technique. The performance is also compared with the state of the art techniques. The proposed technique has shown an improvement in detection of seizures and can be used to build the clinical system for epileptic seizure detection.

DWT-Net: Seizure Detection System with Structured EEG Montage and Multiple Feature Extractor in Convolution Neural Network

Automated seizure detection system based on electroencephalograms (EEG) is an interdisciplinary research problem between computer science and neuroscience. Epileptic seizure affects 1% of the worldwide population and can lead to severe long-term harm to safety and life quality. The automation of seizure detection can greatly improve the treatment of patients. In this work, we propose a neural network model to extract features from EEG signals with a method of arranging the dimension of feature extraction inspired by the traditional method of neurologists. A postprocessor is used to improve the output of the classifier. The result of our seizure detection system on the TUSZ dataset reaches a false alarm rate of 12 per 24 hours with a sensitivity of 59%, which approaches the performance of average human detector based on qEEG tools.

Photoplethysmographic evaluation of generalized tonic-clonic seizures.

Photoplethysmography (PPG) is an optical technique measuring variations of blood perfusion in peripheral tissues. We evaluated alterations in PPG signals in relationship to the occurrence of generalized tonic-clonic seizures (GTCSs) in patients with epilepsy to evaluate the feasibility of seizure detection. During electroencephalographic (EEG) long-term monitoring, patients wore portable wristband sensor(s) on their wrists or ankles recording PPG signals. We analyzed PPG signals during three time periods, which were defined with respect to seizures detected on EEG: (1) baseline (>30minutes prior to seizure), (2) preseizure period, and (3) postseizure period. Furthermore, we selected five random control segments during seizure-free periods. PPG features, including frequency, amplitude, duration, slope, smoothness, and area under the curve, were automatically calculated. We used a linear mixed-effect model to evaluate changes in PPG features between different time periods in an attempt to identify signal changes that detect seizures. We prospectively enrolled 174 patients from the epilepsy monitoring unit at Boston Children's Hospital. Twenty-five GTCSs were recorded from 13 patients. Data from the first recorded GTCS of each patient were included in the analysis. We observed an increase in PPG frequency during pre- and postseizure periods that was higher than the changes during seizure-free periods (frequency increase: preseizure=0.22Hz, postseizure=0.58Hz vs changes during seizure-free period=0.05Hz). The PPG slope decreased significantly by 56.71nW/s during preseizure periods compared to seizure-free periods. Additionally, the smoothness increased significantly by 0.22nW/s during the postseizure period compared to seizure-free periods. Monitoring of PPG signals may assist in the detection of GTCSs in patients with epilepsy. PPG may serve as a promising biomarker for future seizure detection systems and may contribute to future seizure prediction systems.

Unsupervised Online Learning for Long-Term High Sensitivity Seizure Detection.

Current seizure detection systems rely on machine learning classifiers that are trained offline and subsequently require manual retraining to maintain high detection accuracy over long periods of time. For a true deploy-and-forget implantable seizure detection system, a low power, at-the-edge, online learning algorithm can be employed to dynamically adapt to the neural signal drifts over time. This work proposes SOUL: Stochastic-gradient-descent-based Online Unsupervised Logistic regression classifier, which provides continuous unsupervised online model updates that was initially trained with labels offline. SOUL was tested on two datasets, the CHB-MIT scalp EEG dataset, and a long (>250 hours) human ECoG dataset from the University of Melbourne. SOUL achieves an average cumulative sensitivity of 97.5% and 97.9% for the two datasets respectively, while maintaining <1.2 false alarms per day. When compared with state-of-the-art, a moderate sensitivity improvement of 1-3% is observed on the majority of subjects and a large sensitivity improvement of >12% is observed on three subjects with <1% impact on specificity.

Wearable Detection Systems for Epileptic Seizure: A review

The seizure epilepsy is risky because it happens randomly and leads to death in some cases. The standard epileptic seizures monitoring system involves video/EEG (electro-encephalography), which bothers the patient, as EEG electrodes are attached to the patient’s head.&#x0D; Seriously, helping or alerting the patient before the seizure is one of the issue that attracts the researchers and designers attention. So that there are spectrums of portable seizure detection systems available in markets which are based on non-EEG signal.&#x0D; The aim of this article is to provide a literature survey for the latest articles that cover many issues in the field of designing portable real-time seizure detection that includes the use of multiple body signals, new algorithm methods, and detection devices that are commercially available.&#x0D; As a result, the reviewing process shows that there are many research articles that have covered wearable seizure detection systems that based on body signals. The more effective monitoring and detection seizure system is the system that uses multi-body signals, is highly comfortable and has low power consumption.

Fast automated on‐chip artefact removal of EEG for seizure detection based on ICA‐R algorithm and wavelet denoising

Portable automatic seizure detection systems can greatly improve the quality of life of epileptic patients. To improve the performance of seizure detection, independent component analysis (ICA) is implemented in these systems to extract artefacts of electroencephalogram (EEG), and then wavelet denoising method is used to remove the artefacts. However, classical ICA requires post-identification of the components containing artefacts, which cause inefficiency. In this study, integrated circuit implementation of fast ICA with reference algorithm and wavelet denoising method is carried out to enable on-chip artefact extraction and removal without post-identification. This system consists of extraction and removal module, which are designed highly parallel to speed up computation, and therefore, save time for seizure detection. The presented system is verified on Kintex-7 field-programmable gate array using synthesised signal and real EEG data. Experiment results show that the designed system is fully functional and speeds up the artefact removal process.

Peri-ictal heart rate variability parameters as surrogate markers of seizure severity.

This study aims at defining objective parameters reflecting the severity of peri-ictal autonomic changes and their relation to post-ictal generalized electroencephalography (EEG) suppression (PGES), with the view that such changes could be detected by wearable seizure detection systems and prove useful to assess the risk of sudden unexpected death in epilepsy (SUDEP). To this purpose, we assessed peri-ictal changes in heart rate variability (HRV) and correlated them with seizure duration, intensity of electromyography-based ictal muscle activity, and presence and duration of post-ictal generalized EEG suppression (PGES). We evaluated 75 motor seizures from 40 patients, including 61 generalized tonic-clonic seizures (GTCS) and 14 other major motor seizure types. For all major motor seizures, HRV measurements demonstrated a significantly decreased parasympathetic activity and increased sympathetic activity in the post-ictal period. The post-ictal increased sympathetic activity was significantly higher for GTCS as compared with non-GTCS. The degree of peri-ictal decreased parasympathetic activity and increased sympathetic activity was associated with longer PGES (>20s), longer seizure duration, and greater intensity of ictal muscle activity. Mean post-ictal heart rate (HR) was an independent predictor of PGES duration, seizure duration, and intensity of ictal muscle contraction. Our results indicate that peri-ictal changes in HRV are potential biomarkers of major motor seizure severity.

Convolutional Neural Network for Seizure Detection of Nocturnal Frontal Lobe Epilepsy

The Nocturnal Frontal Lobe Epilepsy (NFLE) is a form of epilepsy in which seizures occur predominantly during sleep. In other forms of epilepsy, the commonly used clinical approach mainly involves manual inspection of encephalography (EEG) signals, a laborious and time-consuming process which often requires the contribution of more than one experienced neurologist. In the last decades, numerous approaches to automate this detection have been proposed and, more recently, machine learning has shown very promising performance. In this paper, an original Convolutional Neural Network (CNN) architecture is proposed to develop patient-specific seizure detection models for three patients affected by NFLE. The performances, in terms of accuracy, sensitivity, and specificity, exceed by several percentage points those in the most recent literature. The capability of the patient-specific models has been also tested to compare the obtained seizure onset times with those provided by the neurologists, with encouraging results. Moreover, the same CNN architecture has been used to develop a cross-patient seizure detection system, resorting to the transfer-learning paradigm. Starting from a patient-specific model, few data from a new patient are enough to customize his model. This contribution aims to alleviate the task of neurologists, who may have a robust indication to corroborate their clinical conclusions.

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.

Implementation of High-Performance EEG Based Seizure Detection And Analysis On Multicore Platform

About 50 million people worldwide su?er from epilepsy, the neurological disorder characterized by seizures. The primary tool for diagnosis of an epileptic seizure is an electroencephalography (EEG) which records the brain’s spontaneous electrical activity. This requires the placement of a minimum of 16 electrodes on the scalp with each electrode being interpreted as a channel. The classification of seizure detection and analysis techniques mainly work in two stages , where features are extracted from raw EEG data in the first stage and then the obtained features are used as input for the classification process in the second stage. Traditionally the Seizure detection algorithms were implemented using DSP Processor or FPGAs. But these single core platforms are constrained with respect to speed of operation and power consumption. There is a greater need to reduce the power consumption as well to increase the speed of EEG seizure detection system. This problem can be addressed using the Multicore Processors, which process data simultaneously. This project presents a high performance multicore platform for EEG based seizure detection and analysis. This platform performs continuous multichannel detection and analysis of seizures for epilepsy patients. The detection unit detects seizures based on feature extraction process once seizure detection is done enables the analysis circuit that process the data based Uridva Triyabhakyam based 128 point FFT and transmits energy and frequency contents of EEG data. All proposed blocks are simulated and synthesized using Xilinx ISE and coding is done in Verilog.

Automatic Seizure Detection using Fully Convolutional Nested LSTM

The automatic seizure detection system can effectively help doctors to monitor and diagnose epilepsy thus reducing their workload. Many outstanding studies have given good results in the two-class seizure detection problems, but most of them are based on hand-wrought feature extraction. This study proposes an end-to-end automatic seizure detection system based on deep learning, which does not require heavy preprocessing on the EEG data or feature engineering. The fully convolutional network with three convolution blocks is first used to learn the expressive seizure characteristics from EEG data. Then these robust EEG features pertinent to seizures are presented as an input to the Nested Long Short-Term Memory (NLSTM) model to explore the inherent temporal dependencies in EEG signals. Lastly, the high-level features obtained from the NLSTM model are fed into the softmax layer to output predicted labels. The proposed method yields an accuracy range of 98.44-100% in 10 different experiments based on the Bonn University database. A larger EEG database is then used to evaluate the performance of the proposed method in real-life situations. The average sensitivity of 97.47%, specificity of 96.17%, and false detection rate of 0.487 per hour are yielded. For CHB-MIT Scalp EEG database, the proposed model also achieves a segment-level sensitivity of 94.07% with a false detection rate of 0.66per hour. The excellent results obtained on three different EEG databases demonstrate that the proposed method has good robustness and generalization power under ideal and real-life conditions.

Visual seizure annotation and automated seizure detection using behind‐the‐ear electroencephalographic channels

ObjectiveSeizure diaries kept by patients are unreliable. Automated electroencephalography (EEG)‐based seizure detection systems are a useful support tool to objectively detect and register seizures during long‐term video‐EEG recording. However, this standard full scalp‐EEG recording setup is of limited use outside the hospital, and a discreet, wearable device is needed for capturing seizures in the home setting. We are developing a wearable device that records EEG with behind‐the‐ear electrodes. In this study, we determined whether the recognition of ictal patterns using only behind‐the‐ear EEG channels is possible. Second, an automated seizure detection algorithm was developed using only those behind‐the‐ear EEG channels.MethodsFifty‐four patients with a total of 182 seizures, mostly temporal lobe epilepsy (TLE), and 5284 hours of data, were recorded with a standard video‐EEG at University Hospital Leuven. In addition, extra behind‐the‐ear EEG channels were recorded. First, a neurologist was asked to annotate behind‐the‐ear EEG segments containing selected seizure and nonseizure fragments. Second, a data‐driven algorithm was developed using only behind‐the‐ear EEG. This algorithm was trained using data from other patients (patient‐independent model) or from the same patient (patient‐specific model).ResultsThe visual recognition study resulted in 65.7% sensitivity and 94.4% specificity. By using those seizure annotations, the automated algorithm obtained 64.1% sensitivity and 2.8 false‐positive detections (FPs)/24 hours with the patient‐independent model. The patient‐specific model achieved 69.1% sensitivity and 0.49 FPs/24 hours.SignificanceVisual recognition of ictal EEG patterns using only behind‐the‐ear EEG is possible in a significant number of patients with TLE. A patient‐specific seizure detection algorithm using only behind‐the‐ear EEG was able to detect more seizures automatically than what patients typically report, with 0.49 FPs/24 hours. We conclude that a large number of refractory TLE patients can benefit from using this device.

Nonlinear vector decomposed neural network based EEG signal feature extraction and detection of seizure

Electroencephalography is one of the important medical methods to evaluate and treat neurophysiology to combat disease related to seizure. The automatic seizure detection system aims to provide a balanced mechanism by excavating deep knowledge of the basic signal and kinetic domains. Contingent alert is given to initiate treatment to reduce the risk of injury in patients with epilepsy and to overcome contingencies. Moreover, the multi-channel Electroencephalogram (EEG) data of seizure detection in conventional machine learning algorithms cannot effectively accommodate both global and spatial information. Therefore in this work proposed a Nonlinear Vector Decomposed Neural Network (NVDN) to detect the seizure from EEG signal. The proposed NVDN based seizure detection system consist of three major stages they are as follows i) EEG preprocessing ii) Feature Extraction and iii) NVDN Classification. In this work, the NVDN technique is applied to improve the accuracy of seizure detection after using frequency domain feature to obtain the results from EEG waves. The performance of the proposed system is validate through MATLAB simulation. The results of the simulation show that the proposed NVDN method is able to effectively detect the seizure with a sensitivity of 94.7%. Specificity of 94.1% and accuracy 95.60%. As compared with conventional methods the proposed system achieve high detecting ratio.

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.