Real-time Epileptic Seizure Detection Research Articles

Automatic epileptic seizure detection using MSA-DCNN and LSTM techniques with EEG signals

To identify epilepsy, Electroencephalography (EEG) is an important and common tool used to study the electrical activity of the human brain. The machine learning-based classifier is utilized to detect the seizure by manually extracting the features from the EEG signals in previous works. Though, these effective benefits have been proved already with automatic feature extraction, they are unable to achieve the classification of multiple classes. Meanwhile, real-time epileptic seizure detection is unable to keep the capacity because the identifiable EGG is too long. Hence, this paper is decided to develop an enhanced deep learning architecture with EEG signal for performing automatic epileptic seizure detection. The EEG signal is collected from the standard datasets. The Fourier-Bessel Series Expansion-Based Empirical Wavelet Transform (FBSE-EWT) method is used to decompose the signal from the gathered data. In feature extraction, the decomposed signals are used and while extracting the signal features, the autoencoder is involved in that process. To reduce the computational overload, the relief-F feature ranking method is used for choosing the important signal features. The Long Short Term Memory (LSTM) and Multi-Scale Atrous-based Deep Convolutional Neural Networks (MSA-DCNN) are used that is named as Hybrid Deep Scheme (HDS) to detect epileptic seizures with the top-ranked features for epileptic seizure classification. The Black Widow Optimization (BWO) and the Spider Monkey Optimization (SMO) are combined to generate Adaptive Spider Monkey Black Widow Optimization (ASMBWO) that is employed to perform the parameter tuning into a classification technique. Using different measures, the experimental analysis is done between the conventional epileptic seizure detection and the proposed model to establish an enhanced performance of the proposed model.

Shorter latency of real-time epileptic seizure detection via probabilistic prediction

Although recent studies have proposed seizure detection algorithms with good sensitivity performance, there is a remained challenge that they were hard to achieve significantly short detection latency in real-time scenarios. In this manuscript, we propose a novel deep learning framework intended for shortening epileptic seizure detection latency via probabilistic prediction. We are the first to convert the seizure detection task from traditional binary classification to probabilistic prediction by introducing a crossing period from seizure-oriented EEG recording and proposing a labeling rule using soft-label for crossing period samples. And, a novel multiscale STFT-based feature extraction method combined with 3D-CNN architecture is proposed to accurately capture predictive probabilities of samples. Furthermore, we also propose rectified weighting strategy to enhance predictive probabilities, and accumulative decision-making rule to achieve significantly shorter detection latency. We implement the proposed framework on two prevalent datasets — CHB-MIT scalp EEG dataset and SWEC-ETHZ intracranial EEG dataset in patient-specific leave-one-seizure-out cross-validation scheme. Eventually, the proposed algorithm successfully detected 94 out of 99 seizures during crossing period and 100% seizures detected after EEG onset, averaged 14.84% rectified predictive ictal probability (RPIP) errors of crossing samples, 2.3 s detection latency, 0.08/h false detection rate (FDR) on CHB-MIT dataset. Meanwhile, 84 out of 89 detected seizures during crossing period, 100% detected seizures after EEG onset, 16.17% RPIP errors, 4.7 s detection latency, and 0.08/h FDR are achieved on SWEC-ETHZ dataset. The obtained detection latencies are at least 50% shorter than state-of-the-art results reported in previous studies.

A novel wearable device for automated real-time detection of epileptic seizures

BackgroundEpilepsy is a neurological disorder that has a variety of origins. It is caused by hyperexcitability and an imbalance between excitation and inhibition, which results in seizures. The World Health Organization (WHO) and its partners have classified epilepsy as a major public health concern. Over 50 million individuals globally are affected by epilepsy which shows that the patient’s family, social, educational, and vocational activities are severely limited if seizures are not controlled. Patients who suffer from epileptic seizures have emotional, behavioral, and neurological issues. Alerting systems using a wearable sensor are commonly used to detect epileptic seizures. However, most of the devices have no multimodal systems that increase sensitivity and lower the false discovery rate for screening and intervention of epileptic seizures. Therefore, the objective of this project was, to design and develop an efficient, economical, and automatically detecting epileptic seizure device in real-time.MethodsOur design incorporates different sensors to assess the patient’s condition such as an accelerometer, pulsoxymeter and vibration sensor which process body movement, heart rate variability, oxygen denaturation, and jerky movement respectively. The algorithm for real-time detection of epileptic seizures is based on the following: acceleration increases to a higher value of 23.4 m/s2 or decreases to a lower value of 10 m/s2 as energy is absorbed by the body, the heart rate increases by 10 bpm from the normal heart rate, oxygen denaturation is below 90% and vibration should be out of the range of 3 Hz -17 Hz. Then, a pulsoxymeter device was used as a gold standard to compare the heart rate variability and oxygen saturation sensor readings. The accuracy of the accelerometer and vibration sensor was also tested by a fast-moving and vibrating normal person’s hand.ResultsThe prototype was built and subjected to different tests and iterations. The proposed device was tested for accuracy, cost-effectiveness and ease of use. An acceptable accuracy was achieved for the accelerometer, pulsoxymeter, and vibration sensor measurements, and the prototype was built only with a component cost of less than 40 USD excluding design, manufacturing, and other costs. The design is tested to see if it fits the design criteria; the results of the tests reveal that a large portion of the scientific procedures utilized in this study to identify epileptic seizures is effective.ConclusionThis project is objectively targeted to design a medical device with multimodal systems that enable us to accurately detect epileptic seizures by detecting symptoms commonly associated with an episode of epileptic seizure and notifying a caregiver for immediate assistance. The proposed device has a great impact on reducing epileptic seizer mortality, especially in low-resource settings where both expertise and treatment are scarce.

LightSeizureNet: A Lightweight Deep Learning Model for Real-Time Epileptic Seizure Detection.

The monitoring of epilepsy patients in non-hospital environment is highly desirable, where ultra-low power wearable seizure detection devices are essential in such a system. The state-of-the-art epileptic seizure detection algorithms targeting such devices either rely on manual feature extractions, which can be biased due to the experience of experts, or deep neural networks, which suffer from high computation complexity. In this paper, we propose a lightweight deep learning model, LightSeizureNet (LSN), for real-time epileptic seizure detection based on raw EEG data in ultra-low power wearable seizure detection devices. The proposed LSN model includes a patient-independent version and a patient-specific version, both of which avoids manual feature extractions and high computation complexity, while maintaining good classification accuracy. Dilated one-dimensional (1D) convolution, global average pooling, and kernel-wise pruning are adopted to compress the LSN model. The proposed models are evaluated on the CHB-MIT scalp EEG database. The patient-independent LSN model achieves 97.09% accuracy with 6.2M MACs, while the patient-specific LSN model achieves 99.77% accuracy with 3.7 M MACs, which are competitive compared to the state of the art in terms of accuracy and complexity. Furthermore, the proposed model is highly interpretable, which is missing in many previous works. By using a uniform approach to explore the interpretability of the proposed model, fine-grained information such as the activated brain region and the frequency of brainwave during seizures is obtained for clinical diagnosis.

Detecting Epileptic Seizures via Non-Uniform Multivariate Embedding of EEG Signals.

Efficient real-time detection of epileptic seizures remains a challenging task in clinical practice. In this study, we introduce a new thresholding method to monitor brain activities via a non-uniform multivariate (NUM) embedding of multi-channel electroencephalogram (EEG) signals. Specifically, we present a NUM embedding optimization problem to identify the best embedding parameters. We originate one feature, named non-uniform multivariate multiscale entropy (NUMME), which is extracted from the NUM embedded EEG data. Finally, the extracted feature, compared to an individualized threshold, is used for monitoring and detecting seizure onsets. Experimental results on the real CHB-MIT Scalp EEG database show that our approach achieves a comparable performance to the state-of-art methods. Moreover, it is important to note that we accomplish this without using any sophisticated machine learning algorithms.

Deep C-LSTM Neural Network for Epileptic Seizure and Tumor Detection Using High-Dimension EEG Signals

Electroencephalography (EEG) is a common and significant tool for aiding in the diagnosis of epilepsy and studying the human brain electrical activity. Previously, the traditional machine learning (ML)-based classifier are used to identify the seizure by extracting features from the EEG signals manually. Although the effectiveness of these contributions have already been proved, they cannot achieve multiple class classification with automatic feature extraction. Meanwhile, the identifiable EEG segment is too long to limit the capability of real-time epileptic seizure detection. In this paper, a novel deep convolutional long short-term memory (C-LSTM) model is proposed for detecting seizure and tumor in human brain and identifying two eyes statuses (open and close). It achieves to predict a result in every 0.006 seconds with a short detection duration (one second). By comparing with other two types deep learning approaches (DCNN and LSTM), the presented deep C-LSTM obtains the best performance for classifying these five classes. All of the obtained total accuracy are over 98.80%.

A Self-Aware Epilepsy Monitoring System for Real-Time Epileptic Seizure Detection

Epilepsy is one of the most prevalent paroxystic neurological disorders that can dramatically degrade the quality of life and may even lead to death. Therefore, real-time epilepsy monitoring and seizure detection has become important over the past decades. In this context, wearable technologies offer a promising solution to pervasive epilepsy monitoring by removing the constraints with respect to time and location. In this paper, we propose a self-aware wearable system for real-time detection of epileptic seizures on a long-term basis. First, we propose a multi-parametric machine learning technique to detect seizures by analyzing both cardiac and respiratory responses to seizures, which are obtained using only the ECG signal. Second, in order to enable long-time epilepsy detection, we introduce the notion of self-awareness in our real-time wearable system. We evaluate the performance of our proposed solution based on an epilepsy database of more than 211 hours of recording, provided by the Lausanne University Hospital (CHUV), on the INYU wearable sensor. Our proposed system achieves a sensitivity of 88.66% and a specificity of 85.65% before applying self-awareness. Moreover, by controlling the energy-quality trade-offs using our self-aware energy-management technique, we can tune the battery lifetime of the wearable system to last between 67.55 and 136.91 days while, still outperforming the state-of-the-art techniques for wearable seizure detection, by achieving from 85.54% to 79.33% geometric mean of specificity and sensitivity.

Detecting Seizures From EEG Signals Using the Entropy of Visibility Heights of Hierarchical Neighbors

Epilepsy is a chronic disorder that causes unprovoked, recurrent sudden abnormal reactions of the brain. Characterizing electroencephalogram (EEG) signals of the patient is an effective way for the early prediction of epileptic seizures. In this paper, a new method called the entropy of visibility heights of hierarchical neighbors (EVHHN) is proposed to detect seizures from the EEG signals. First, the visibility relationships of three nearest neighbors are determined by a visibility criterion. Then, we compute the visibility heights of three nearest neighbors for each data point. Next, the four different kinds of entropy associated with neighbor visibility states are calculated to characterize the EEG signals and finally these features are validated by LS-SVM classifiers. In the experiment, the normal and ictal EEG signals are classified with the accuracy of 99.6%, meanwhile, the interictal and ictal EEG signals are distinguished with the accuracy of 98.35%, which proves the effectiveness of our proposed method. Notably, the computational time of extracting features for each set is 1.751 s, which is largely reduced compared with other weighted visibility graph-based methods. In conclusion, the EVHHN can potentially be an effective method for characterizing complicated EEG signals and real-time detection of epileptic seizures.

Hardware Design of Real Time Epileptic Seizure Detection Based on STFT and SVM

Closed-loop stimulation of many neurological disorders, such as epilepsy, is an emerging technology and regarded as a promising alternative for surgical and drug treatment. In this paper, a real-time seizure detection algorithm based on STFT and support vector machine (SVM) and its field-programmable gate array (FPGA) implementation are proposed. With a two-stage patient-specific channel selection and feature selection mechanism, those redundant and uncorrelated spectral features are removed from the entire feature set. The evaluation results on CHB-MIT epilepsy database show that the mean detection latency of the proposed algorithm is 6 s, the sensitivity is 98.4%, and the false detection rate is 0.356/h. The performance of our proposed algorithm is comparable to other existing seizure detection algorithms. Moreover, we implement the proposed seizure detection algorithm on Xilinx Zynq-7000 XC7Z020 with high level synthesis. Each classification of the input electroencephalography signal can be finished within 313 $\mu \text{s}$ , and the power consumption of the programmable logic is only 380 mW at 100 MHz. In hardware implementation, an optimization strategy for the nested-loop structure within nonlinear SVM is proposed to improve pipeline efficiency. Compared with existing method, the experimental result shows that our method can speed up the nonlinear SVM by $1.70\times $ , $1.53\times $ , $1.37\times $ , and $1.26\times $ with the unroll factor equal to 1–4 at the same DSP utilization rate. The evaluation results affirm the possibility of integrating the proposed algorithm and FPGA implementation into a wearable seizure control device.

Convolutional neural networks for real-time epileptic seizure detection

Epileptic seizures constitute a serious neurological condition for patients and, if untreated, considerably decrease their quality of life. Early and correct diagnosis by semiological seizure analysis provides the main approach to treat and improve the patients’ condition. To obtain reliable and quantifiable information, medical professionals perform seizure detection and subsequent analysis using expensive video-EEG systems in specialized epilepsy monitoring units. However, the detection of seizures, especially under difficult circumstances such as occlusion by the blanket or in the absence of predictive EEG patterns, is highly subjective and should therefore be supported by automated systems. In this work, we conjecture that features learned via a convolutional neural network provide the ability to distinctively detect seizures from video, and even allow our system to generalize to different seizure types. By comparing our method to the state of the art we show the superior performance of learned features for epileptic seizure detection.

Automated Diagnosis of Epilepsy Using Key-Point-Based Local Binary Pattern of EEG Signals.

The electroencephalogram (EEG) signals are commonly used for diagnosis of epilepsy. In this paper, we present a new methodology for EEG-based automated diagnosis of epilepsy. Our method involves detection of key points at multiple scales in EEG signals using a pyramid of difference of Gaussian filtered signals. Local binary patterns (LBPs) are computed at these key points and the histogram of these patterns are considered as the feature set, which is fed to the support vector machine (SVM) for the classification of EEG signals. The proposed methodology has been investigated for the four well-known classification problems namely, 1) normal and epileptic seizure, 2) epileptic seizure and seizure free, 3) normal, epileptic seizure, and seizure free, and 4) epileptic seizure and nonseizure EEG signals using publically available university of Bonn EEG database. Our experimental results in terms of classification accuracies have been compared with existing methods for the classification of the aforementioned problems. Further, performance evaluation on another EEG dataset shows that our approach is effective for classification of seizure and seizure-free EEG signals. The proposed methodology based on the LBP computed at key points is simple and easy to implement for real-time epileptic seizure detection.

Coarse Grained Reconfigurable Array Based Architecture for Low Power Real-Time Seizure Detection

There is increasing research and commercial interest in miniature on-body and implantable devices for continuous real-time biosignal monitoring. A key challenge in realizing this vision is in implementation of biosignal processing algorithms with acceptably low energy consumption. In this article, we investigate implementation of the REACT algorithm for real-time epileptic seizure detection on a Coarse Grained Reconfigurable Array (CGRA) based architecture. Computationally expensive biosignal processing tasks are offloaded from a conventional Digital Signal Processor (DSP) to the CGRA. The CGRA is designed to support low power biosignal processing by means of a systolic architecture, flexible interconnect and low resource usage. The CGRA architecture is shown to provide 38% and 60% improvements in energy consumption and in performance, respectively, for the REACT system, without the use of voltage scaling or increased clock frequency.

Miniaturized wireless ECG monitor for real-time detection of epileptic seizures

Recent advances in miniaturization of ultra-low power components allow for more intelligent wearable health monitors. The development and evaluation of a wireless wearable electrocardiogram (ECG) monitor to detect epileptic seizures from changes in the cardiac rhythm is described. The ECG data are analyzed by embedded algorithms: a robust beat-detection algorithm combined with a real-time epileptic seizure detector. In its current implementation, the proposed prototype is 52× 36× 15mm 3 , and has an autonomy of one day. Based on data collected on the first three epilepsy patients, preliminary clinical results are provided. Wireless, miniaturized and comfortable, this prototype opens new perspectives for health monitoring.

A hierarchical approach for online temporal lobe seizure detection in long-term intracranial EEG recordings

Objective. Around 1% of the world's population is affected by epilepsy, and nearly 25% of patients cannot be treated effectively by available therapies. The presence of closed-loop seizure-triggered stimulation provides a promising solution for these patients. Realization of fast, accurate, and energy-efficient seizure detection is the key to such implants. In this study, we propose a two-stage on-line seizure detection algorithm with low-energy consumption for temporal lobe epilepsy (TLE). Approach. Multi-channel signals are processed through independent component analysis and the most representative independent component (IC) is automatically selected to eliminate artifacts. Seizure-like intracranial electroencephalogram (iEEG) segments are fast detected in the first stage of the proposed method and these seizures are confirmed in the second stage. The conditional activation of the second-stage signal processing reduces the computational effort, and hence energy, since most of the non-seizure events are filtered out in the first stage. Main results. Long-term iEEG recordings of 11 patients who suffered from TLE were analyzed via leave-one-out cross validation. The proposed method has a detection accuracy of 95.24%, a false alarm rate of 0.09/h, and an average detection delay time of 9.2 s. For the six patients with mesial TLE, a detection accuracy of 100.0%, a false alarm rate of 0.06/h, and an average detection delay time of 4.8 s can be achieved. The hierarchical approach provides a 90% energy reduction, yielding effective and energy-efficient implementation for real-time epileptic seizure detection. Significance. An on-line seizure detection method that can be applied to monitor continuous iEEG signals of patients who suffered from TLE was developed. An IC selection strategy to automatically determine the most seizure-related IC for seizure detection was also proposed. The system has advantages of (1) high detection accuracy, (2) low false alarm, (3) short detection latency, and (4) energy-efficient design for hardware implementation.

Real-time detection of epileptic seizures in animal models using reservoir computing

In recent years, an increasing number of studies have investigated the effects of closed-loop anti-epileptic treatments. Most of the current research still is very labour intensive: real-time treatment is manually triggered and conclusions can only be drawn after multiple days of manual review and annotation of the electroencephalogram (EEG). In this paper we propose a technique based on reservoir computing (RC) to automatically and in real-time detect epileptic seizures in the intra-cranial EEG (iEEG) of epileptic rats in order to immediately trigger seizure treatment. The performance of the system is evaluated in two different seizure types: absence seizures from genetic absence epilepsy rats from Strasbourg (GAERS) and limbic seizures from post status epilepticus (PSE) rats. The dataset consists of 452 hours iEEG from 23 GAERS and 2083 hours iEEG from 22 PSE rats. In the default set-up the system detects 0.09 and 0.13 false positives per seizure and misses 0.07 and 0.005 events per seizure for GAERS and PSE rats respectively. It achieves an average detection delay below 1s in GAERS and less than 10s in the PSE data. This detection delay and the number of missed seizures can be further decreased when a higher false positive rate is allowed. Our method outperforms state-of-the-art detection techniques and only a few parameters require optimization on a limited training set. It is therefore suited for automatic seizure detection based on iEEG and may serve as a useful tool for epilepsy researchers. The technique avoids the time-consuming manual review and annotation of EEG and can be incorporated in a closed-loop treatment strategy.

Automatic epileptic seizure detection in EEGs based on optimized sample entropy and extreme learning machine

Epilepsy is one of the most common neurological disorders – approximately one in every 100 people worldwide are suffering from it. The electroencephalogram (EEG) is the most common source of information used to monitor, diagnose and manage neurological disorders related to epilepsy. Large amounts of data are produced by EEG monitoring devices, and analysis by visual inspection of long recordings of EEG in order to find traces of epilepsy is not routinely possible. Therefore, automated detection of epilepsy has been a goal of many researchers for a long time. This paper presents a novel method for automatic epileptic seizure detection. An optimized sample entropy (O-SampEn) algorithm is proposed and combined with extreme learning machine (ELM) to identify the EEG signals regarding the existence of seizure or not. To the knowledge of the authors, there exists no similar work in the literature. A public dataset was utilized for evaluating the proposed method. Results show that the proposed epilepsy detection approach achieves not only high detection accuracy but also a very fast computation speed, which demonstrates its huge potential for the real-time detection of epileptic seizures.

An Implantable Closedloop Asynchronous Drug Delivery System for the Treatment of Refractory Epilepsy

In this paper, we present an implantable device for intra-cerebral electroencephalography (icEEG) data acquisition and real-time epileptic seizure detection with simultaneous focal antiepileptic drug injection feedback. This implantable device includes a neural signal amplifier, an asynchronous seizure detector, a drug delivery system (DDS) including a micropump, and a hybrid subdural electrode (HSE). The asynchronous detection algorithm is based on data-dependent analysis and validated with Matlab tools. The detector and DDS have a power saving mode. The HSE contacts are made of Platinum (Pt) encapsulated with polydimethylsiloxane (PDMS). Given the heterogeneity of electrographic seizure signals and seizure suppression threshold, the implantable device provides tunable parameters facility through an external transmitter to adapt to each individual's neurophysiology prior to clinical deployment. The proposed detector and DDS were assembled in Ø 50 mm and Ø 30 mm circular printed circuit boards, respectively. The detector was validated using icEEG recordings of seven patients who had previously undergone an intracranial investigation for epilepsy surgery. The triggering of the DDS was tested and a predefined seizure suppression dose was delivered ~16 s after electrographical seizure onsets. The device's power consumption was reduced by 12% in active mode and 49% in power saving mode compared to similar seizure detection algorithms implemented with synchronous architecture.

A Novel Low-Power-Implantable Epileptic Seizure-Onset Detector

A novel implantable low-power integrated circuit is proposed for real-time epileptic seizure detection. The presented chip is part of an epilepsy prosthesis device that triggers focal treatment to disrupt seizure progression. The proposed chip integrates a front-end preamplifier, voltage-level detectors, digital demodulators, and a high-frequency detector. The preamplifier uses a new chopper stabilizer topology that reduces instrumentation low-frequency and ripple noises by modulating the signal in the analog domain and demodulating it in the digital domain. Moreover, each voltage-level detector consists of an ultra-low-power comparator with an adjustable threshold voltage. The digitally integrated high-frequency detector is tunable to recognize the high-frequency activities for the unique detection of seizure patterns specific to each patient. The digitally controlled circuits perform accurate seizure detection. A mathematical model of the proposed seizure detection algorithm was validated in Matlab and circuits were implemented in a 2 mm(2) chip using the CMOS 0.18- μm process. The proposed detector was tested by using intracerebral electroencephalography (icEEG) recordings from seven patients with drug-resistant epilepsy. The seizure signals were assessed by the proposed detector and the average seizure detection delay was 13.5 s, well before the onset of clinical manifestations. The measured total power consumption of the detector is 51 μW.

Automated Real-Time Epileptic Seizure Detection in Scalp EEG Recordings Using an Algorithm Based on Wavelet Packet Transform

A novel wavelet-based algorithm for real-time detection of epileptic seizures using scalp EEG is proposed. In a moving-window analysis, the EEG from each channel is decomposed by wavelet packet transform. Using wavelet coefficients from seizure and nonseizure references, a patient-specific measure is developed to quantify the separation between seizure and nonseizure states for the frequency range of 1-30 Hz. Utilizing this measure, a frequency band representing the maximum separation between the two states is determined and employed to develop a normalized index, called combined seizure index (CSI). CSI is derived for each epoch of every EEG channel based on both rhythmicity and relative energy of that epoch as well as consistency among different channels. Increasing significantly during ictal states, CSI is inspected using one-sided cumulative sum test to generate proper channel alarms. Analyzing alarms from all channels, a seizure alarm is finally generated. The algorithm was tested on scalp EEG recordings from 14 patients, totaling approximately 75.8 h with 63 seizures. Results revealed a high sensitivity of 90.5%, a false detection rate of 0.51 h(-1) and a median detection delay of 7 s. The algorithm could also lateralize the focus side for patients with temporal lobe epilepsy.

EEG analysis with simulated neuronal cell models helps to detect pre-seizure changes

Objectives: To test if a method for real-time detection of epileptic seizures based on electroencephalographic (EEG) analysis with simulated neuronal cell models can be modified to identify pre-seizure changes. Methods: Our EEG analysis method consists of two simulated leaky integrate and fire units (LIFU) connected to a signal preprocessing stage that marks parts of the EEG signals with slopes larger than a preset threshold Hth with unit pulses. The LIFUs change their spiking frequency depending on the rate and the synchrony of the impinging pulse trains. Here, we use our method in a high-sensitivity mode by setting Hth to low values, which causes the LIFUs to continuously spike during the interictal state. We test if the LIFUs spiking rates change before seizure onset. Results: We used 9 long-term EEGs (16±7 h) of 7 patients with drug resistant epilepsy. Fifteen seizures were analyzed and all were preceded by an increase of the time-averaged spiking rates SR av of the LIFUs. We defined a function F Sz, which quantifies the changes of SR av. F Sz increased and stayed above an individually set and fixed threshold 83±91 min (range: 4–330 min) before EEG seizure onset. Only two false alarms occurred. Conclusions: We conclude that EEG analysis with simulated neuronal cell models may be used to detect pre-seizure changes with high sensitivity and specificity.