The Experts below are selected from a list of 2442 Experts worldwide ranked by ideXlab platform
Levin Kuhlmann - One of the best experts on this subject based on the ideXlab platform.
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Human Intracranial EEG Quantitative Analysis and Automatic Feature Learning for Epileptic Seizure Prediction
arXiv: Neural and Evolutionary Computing, 2019Co-Authors: Ramy Hussein, Levin Kuhlmann, Mohamed Osama Ahmed, Rabab K. Ward, Z. Jane Wang, Yi GuoAbstract:Objective: The aim of this study is to develop an efficient and reliable epileptic Seizure Prediction system using intracranial EEG (iEEG) data, especially for people with drug-resistant epilepsy. The Prediction procedure should yield accurate results in a fast enough fashion to alert patients of impending Seizures. Methods: We quantitatively analyze the human iEEG data to obtain insights into how the human brain behaves before and between epileptic Seizures. We then introduce an efficient pre-processing method for reducing the data size and converting the time-series iEEG data into an image-like format that can be used as inputs to convolutional neural networks (CNNs). Further, we propose a Seizure Prediction algorithm that uses cooperative multi-scale CNNs for automatic feature learning of iEEG data. Results: 1) iEEG channels contain complementary information and excluding individual channels is not advisable to retain the spatial information needed for accurate Prediction of epileptic Seizures. 2) The traditional PCA is not a reliable method for iEEG data reduction in Seizure Prediction. 3) Hand-crafted iEEG features may not be suitable for reliable Seizure Prediction performance as the iEEG data varies between patients and over time for the same patient. 4) Seizure Prediction results show that our algorithm outperforms existing methods by achieving an average sensitivity of 87.85% and AUC score of 0.84. Conclusion: Understanding how the human brain behaves before Seizure attacks and far from them facilitates better designs of epileptic Seizure predictors. Significance: Accurate Seizure Prediction algorithms can warn patients about the next Seizure attack so they could avoid dangerous activities. Medications could then be administered to abort the impending Seizure and minimize the risk of injury.
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Seizure Prediction ready for a new era
Nature Reviews Neurology, 2018Co-Authors: Levin Kuhlmann, Klaus Lehnertz, Mark P. Richardson, Björn Schelter, Hitten P. ZaveriAbstract:Epilepsy is a common disorder characterized by recurrent Seizures. An overwhelming majority of people with epilepsy regard the unpredictability of Seizures as a major issue. More than 30 years of international effort have been devoted to the Prediction of Seizures, aiming to remove the burden of unpredictability and to couple novel, time-specific treatment to Seizure Prediction technology. A highly influential review published in 2007 concluded that insufficient evidence indicated that Seizures could be predicted. Since then, several advances have been made, including successful prospective Seizure Prediction using intracranial EEG in a small number of people in a trial of a real-time Seizure Prediction device. In this Review, we examine advances in the field, including EEG databases, Seizure Prediction competitions, the prospective trial mentioned and advances in our understanding of the mechanisms of Seizures. We argue that these advances, together with statistical evaluations, set the stage for a resurgence in efforts towards the development of Seizure Prediction methodologies. We propose new avenues of investigation involving a synergy between mechanisms, models, data, devices and algorithms and refine the existing guidelines for the development of Seizure Prediction technology to instigate development of a solution that removes the burden of the unpredictability of Seizures. In this Review, the authors consider advances over the past decade that have set the stage for a resurgence in attempts to predict Seizures in epilepsy, and they propose new avenues of investigation that combine mechanisms, models, data, devices and algorithms.
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Seizure Prediction - ready for a new era
Nature Reviews Neurology, 2018Co-Authors: Levin Kuhlmann, Klaus Lehnertz, Mark P. Richardson, Björn Schelter, Hitten P. ZaveriAbstract:Epilepsy is a common disorder characterized by recurrent Seizures. An overwhelming majority of people with epilepsy regard the unpredictability of Seizures as a major issue. More than 30 years of international effort have been devoted to the Prediction of Seizures, aiming to remove the burden of unpredictability and to couple novel, time-specific treatment to Seizure Prediction technology. A highly influential review published in 2007 concluded that insufficient evidence indicated that Seizures could be predicted. Since then, several advances have been made, including successful prospective Seizure Prediction using intracranial EEG in a small number of people in a trial of a real-time Seizure Prediction device. In this Review, we examine advances in the field, including EEG databases, Seizure Prediction competitions, the prospective trial mentioned and advances in our understanding of the mechanisms of Seizures. We argue that these advances, together with statistical evaluations, set the stage for a resurgence in efforts towards the development of Seizure Prediction methodologies. We propose new avenues of investigation involving a synergy between mechanisms, models, data, devices and algorithms and refine the existing guidelines for the development of Seizure Prediction technology to instigate development of a solution that removes the burden of the unpredictability of Seizures.
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Epilepsyecosystem.org: crowd-sourcing reproducible Seizure Prediction with long-term human intracranial EEG.
Brain, 2018Co-Authors: Levin Kuhlmann, Dean R. Freestone, Philippa J. Karoly, Andriy Temko, Alexandre Barachant, Gilberto Titericz, Brian W. Lang, Benjamin H. Brinkmann, Daniel LaveryAbstract:Accurate Seizure Prediction will transform epilepsy management by offering warnings to patients or triggering interventions. However, state-of-the-art algorithm design relies on accessing adequate long-term data. Crowd-sourcing ecosystems leverage quality data to enable cost-effective, rapid development of predictive algorithms. A crowd-sourcing ecosystem for Seizure Prediction is presented involving an international competition, a follow-up held-out data evaluation, and an online platform, Epilepsyecosystem.org, for yielding further improvements in Prediction performance. Crowd-sourced algorithms were obtained via the 'Melbourne-University AES-MathWorks-NIH Seizure Prediction Challenge' conducted at kaggle.com. Long-term continuous intracranial electroencephalography (iEEG) data (442 days of recordings and 211 lead Seizures per patient) from Prediction-resistant patients who had the lowest Seizure Prediction performances from the NeuroVista Seizure Advisory System clinical trial were analysed. Contestants (646 individuals in 478 teams) from around the world developed algorithms to distinguish between 10-min inter-Seizure versus pre-Seizure data clips. Over 10 000 algorithms were submitted. The top algorithms as determined by using the contest data were evaluated on a much larger held-out dataset. The data and top algorithms are available online for further investigation and development. The top performing contest entry scored 0.81 area under the classification curve. The performance reduced by only 6.7% on held-out data. Many other teams also showed high Prediction reproducibility. Pseudo-prospective evaluation demonstrated that many algorithms, when used alone or weighted by circadian information, performed better than the benchmarks, including an average increase in sensitivity of 1.9 times the original clinical trial sensitivity for matched time in warning. These results indicate that clinically-relevant Seizure Prediction is possible in a wider range of patients than previously thought possible. Moreover, different algorithms performed best for different patients, supporting the use of patient-specific algorithms and long-term monitoring. The crowd-sourcing ecosystem for Seizure Prediction will enable further worldwide community study of the data to yield greater improvements in Prediction performance by way of competition, collaboration and synergism.10.1093/brain/awy210_video1awy210media15817489051001.
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Semi-supervised Seizure Prediction with Generative Adversarial Networks
arXiv: Computer Vision and Pattern Recognition, 2018Co-Authors: Nhan Duy Truong, Levin Kuhlmann, Mohammad Reza Bonyadi, Omid KaveheiAbstract:In this article, we propose an approach that can make use of not only labeled EEG signals but also the unlabeled ones which is more accessible. We also suggest the use of data fusion to further improve the Seizure Prediction accuracy. Data fusion in our vision includes EEG signals, cardiogram signals, body temperature and time. We use the short-time Fourier transform on 28-s EEG windows as a pre-processing step. A generative adversarial network (GAN) is trained in an unsupervised manner where information of Seizure onset is disregarded. The trained Discriminator of the GAN is then used as feature extractor. Features generated by the feature extractor are classified by two fully-connected layers (can be replaced by any classifier) for the labeled EEG signals. This semi-supervised Seizure Prediction method achieves area under the operating characteristic curve (AUC) of 77.68% and 75.47% for the CHBMIT scalp EEG dataset and the Freiburg Hospital intracranial EEG dataset, respectively. Unsupervised training without the need of labeling is important because not only it can be performed in real-time during EEG signal recording, but also it does not require feature engineering effort for each patient.
Jens Timmer - One of the best experts on this subject based on the ideXlab platform.
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Seizure Prediction in epilepsy from circadian concepts via probabilistic forecasting to statistical evaluation
International Conference of the IEEE Engineering in Medicine and Biology Society, 2011Co-Authors: Björn Schelter, Andreas Schulzebonhage, Matthias Ihle, Hinnerk Feldwischdrentrup, Jens TimmerAbstract:Seizure Prediction performance is hampered by high numbers of false Predictions. Here we present an approach to reduce the number of false Predictions based on circadian concepts. Based on eight representative patients we demonstrate that this approach increases the performance considerably. The fraction of patients for whom we found a significant Seizure Prediction performance was increased from 25% to 38% by accounting for circadian dependencies.
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EMBC - Seizure Prediction in epilepsy: From circadian concepts via probabilistic forecasting to statistical evaluation
2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2011Co-Authors: Björn Schelter, H. Feldwisch-drentrup, Andreas Schulze-bonhage, Matthias Ihle, Jens TimmerAbstract:Seizure Prediction performance is hampered by high numbers of false Predictions. Here we present an approach to reduce the number of false Predictions based on circadian concepts. Based on eight representative patients we demonstrate that this approach increases the performance considerably. The fraction of patients for whom we found a significant Seizure Prediction performance was increased from 25% to 38% by accounting for circadian dependencies.
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Joining the benefits: Combining epileptic Seizure Prediction methods
Epilepsia, 2010Co-Authors: H. Feldwisch-drentrup, Bjoern Schelter, Jens Timmer, Michael Jachan, Jakob Nawrath, Andreas Schulze-bonhageAbstract:Summary Purpose: In recent years, a variety of methods developed in the field of linear and nonlinear time series analysis have been used to obtain reliable Predictions of epileptic Seizures. Because individual methods for Seizure Prediction so far have shown statistical significance but insufficient performance for clinical applications, we investigated possible improvements by combining algorithms capturing different aspects of electroencephalogram (EEG) dynamics. Methods: We applied the mean phase coherence and the dynamic similarity index to long-term continuous intracranial EEG data. The predictive performance of both methods was assessed and statistically evaluated separately, as well as by using logical “AND” and “OR” combinations. Results: Used independently, either method resulted in a statistically significant Prediction performance in only a few patients. Particularly the “AND” combination led to improved Prediction performances, leading to an increase in sensitivity and/or specificity. For a maximum false Prediction rate of 0.15/h, the mean sensitivity improved from about 25% for the individual methods to 43.2% for the “AND” and to 35.2% for the “OR” combination. Discussion: This study shows that combinations of Prediction methods are promising new approaches to enhance Seizure Prediction performance considerably. It allows merging the individual benefits of Prediction methods in a complementary manner. Because either sensitivity or specificity of Seizure Prediction methods can be improved depending on the needs of the desired clinical application, the combination opens a new window for future use in a clinical setting.
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Seizure Prediction: the impact of long Prediction horizons.
Epilepsy Research, 2007Co-Authors: Björn Schelter, Matthias Winterhalder, Andreas Schulze-bonhage, Jakob Nawrath, Armin Brandt, Hinnerk Feldwisch Genannt Drentrup, J. Wohlmuth, Jens TimmerAbstract:Several procedures have been proposed to be capable of predicting the occurrence of epileptic Seizures. Up to now, all proposed algorithms are far from being sufficient for a clinical application. This is, however, often not obvious when results of Seizure Prediction performance are reported. Here, we discuss impacts of long Prediction horizons with respect to clinical needs and the strain on patients by analyzing long-term continuous intracranial electroencephalography data.
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testing statistical significance of multivariate time series analysis techniques for epileptic Seizure Prediction
Chaos, 2006Co-Authors: Bjoern Schelter, Matthias Winterhalder, Thomas Maiwald, Andreas Schulzebonhage, Armin Brandt, Ariane Schad, Jens TimmerAbstract:Nonlinear time series analysis techniques have been proposed to detect changes in the electroencephalography dynamics prior to epileptic Seizures. Their applicability in practice to predict Seizure onsets is hampered by the present lack of generally accepted standards to assess their performance. We propose an analytic approach to judge the Prediction performance of multivariate Seizure Prediction methods. Statistical tests are introduced to assess patient individual results, taking into account that Prediction methods are applied to multiple time series and several Seizures. Their performance is illustrated utilizing a bivariate Seizure Prediction method based on synchronization theory.
David W Browne - One of the best experts on this subject based on the ideXlab platform.
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epileptic Seizure Prediction using the spatiotemporal correlation structure of intracranial eeg
International Conference on Acoustics Speech and Signal Processing, 2011Co-Authors: James R Williamson, Daniel W Bliss, David W BrowneAbstract:A patient-specific Seizure Prediction algorithm is proposed that extracts novel multivariate signal coherence features from ECoG recordings and classifies a patient's pre-Seizure state. The algorithm uses space-delay correlation and covariance matrices at several delay scales to extract the spatiotemporal correlation structure from multichannel ECoG signals. Eigenspectra and amplitude features are extracted from the correlation and covariance matrices, followed by dimensionality reduction using principal components analysis, classification using a support vector machine, and temporal integration to produce a Seizure Prediction score. Evaluation on the Freiburg EEG database produced a sensitivity of 90.8% and false positive rate of .094.
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ICASSP - Epileptic Seizure Prediction using the spatiotemporal correlation structure of intracranial EEG
2011 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2011Co-Authors: James R Williamson, Daniel W Bliss, David W BrowneAbstract:A patient-specific Seizure Prediction algorithm is proposed that extracts novel multivariate signal coherence features from ECoG recordings and classifies a patient's pre-Seizure state. The algorithm uses space-delay correlation and covariance matrices at several delay scales to extract the spatiotemporal correlation structure from multichannel ECoG signals. Eigenspectra and amplitude features are extracted from the correlation and covariance matrices, followed by dimensionality reduction using principal components analysis, classification using a support vector machine, and temporal integration to produce a Seizure Prediction score. Evaluation on the Freiburg EEG database produced a sensitivity of 90.8% and false positive rate of .094.
Philippa J. Karoly - One of the best experts on this subject based on the ideXlab platform.
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Ensembling crowdsourced Seizure Prediction algorithms using long-term human intracranial EEG.
Epilepsia, 2019Co-Authors: Chip Reuben, Dean R. Freestone, Philippa J. Karoly, Andriy Temko, Alexandre Barachant, Gilberto Titericz, Brian W. Lang, Daniel Lavery, Kelly RomanAbstract:Seizure Prediction is feasible, but greater accuracy is needed to make Seizure Prediction clinically viable across a large group of patients. Recent work crowdsourced state-of-the-art Prediction algorithms in a worldwide competition, yielding improvements in Seizure Prediction performance for patients whose Seizures were previously found hard to anticipate. The aim of the current analysis was to explore potential performance improvements using an ensemble of the top competition algorithms. The results suggest that minor increments in performance may be possible; however, the outcomes of statistical testing limit the confidence in these increments. Our results suggest that for the specific algorithms, evaluation framework, and data considered here, incremental improvements are achievable but there may be upper bounds on machine learning-based Seizure Prediction performance for some patients whose Seizures are challenging to predict. Other more tailored approaches that, for example, take into account a deeper understanding of preictal mechanisms, patient-specific sleep-wake rhythms, or novel measurement approaches, may still offer further gains for these types of patients.
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Epilepsyecosystem.org: crowd-sourcing reproducible Seizure Prediction with long-term human intracranial EEG.
Brain, 2018Co-Authors: Levin Kuhlmann, Dean R. Freestone, Philippa J. Karoly, Andriy Temko, Alexandre Barachant, Gilberto Titericz, Brian W. Lang, Benjamin H. Brinkmann, Daniel LaveryAbstract:Accurate Seizure Prediction will transform epilepsy management by offering warnings to patients or triggering interventions. However, state-of-the-art algorithm design relies on accessing adequate long-term data. Crowd-sourcing ecosystems leverage quality data to enable cost-effective, rapid development of predictive algorithms. A crowd-sourcing ecosystem for Seizure Prediction is presented involving an international competition, a follow-up held-out data evaluation, and an online platform, Epilepsyecosystem.org, for yielding further improvements in Prediction performance. Crowd-sourced algorithms were obtained via the 'Melbourne-University AES-MathWorks-NIH Seizure Prediction Challenge' conducted at kaggle.com. Long-term continuous intracranial electroencephalography (iEEG) data (442 days of recordings and 211 lead Seizures per patient) from Prediction-resistant patients who had the lowest Seizure Prediction performances from the NeuroVista Seizure Advisory System clinical trial were analysed. Contestants (646 individuals in 478 teams) from around the world developed algorithms to distinguish between 10-min inter-Seizure versus pre-Seizure data clips. Over 10 000 algorithms were submitted. The top algorithms as determined by using the contest data were evaluated on a much larger held-out dataset. The data and top algorithms are available online for further investigation and development. The top performing contest entry scored 0.81 area under the classification curve. The performance reduced by only 6.7% on held-out data. Many other teams also showed high Prediction reproducibility. Pseudo-prospective evaluation demonstrated that many algorithms, when used alone or weighted by circadian information, performed better than the benchmarks, including an average increase in sensitivity of 1.9 times the original clinical trial sensitivity for matched time in warning. These results indicate that clinically-relevant Seizure Prediction is possible in a wider range of patients than previously thought possible. Moreover, different algorithms performed best for different patients, supporting the use of patient-specific algorithms and long-term monitoring. The crowd-sourcing ecosystem for Seizure Prediction will enable further worldwide community study of the data to yield greater improvements in Prediction performance by way of competition, collaboration and synergism.10.1093/brain/awy210_video1awy210media15817489051001.
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epileptic Seizure Prediction using big data and deep learning toward a mobile system
EBioMedicine, 2017Co-Authors: Isabell Kiralkornek, Philippa J. Karoly, Subhrajit Roy, Ewan S Nurse, Benjamin S Mashford, Thomas L Carroll, Daniel PayneAbstract:Abstract Background Seizure Prediction can increase independence and allow preventative treatment for patients with epilepsy. We present a proof-of-concept for a Seizure Prediction system that is accurate, fully automated, patient-specific, and tunable to an individual's needs. Methods Intracranial electroencephalography (iEEG) data of ten patients obtained from a Seizure advisory system were analyzed as part of a pseudoprospective Seizure Prediction study. First, a deep learning classifier was trained to distinguish between preictal and interictal signals. Second, classifier performance was tested on held-out iEEG data from all patients and benchmarked against the performance of a random predictor. Third, the Prediction system was tuned so sensitivity or time in warning could be prioritized by the patient. Finally, a demonstration of the feasibility of deployment of the Prediction system onto an ultra-low power neuromorphic chip for autonomous operation on a wearable device is provided. Results The Prediction system achieved mean sensitivity of 69% and mean time in warning of 27%, significantly surpassing an equivalent random predictor for all patients by 42%. Conclusion This study demonstrates that deep learning in combination with neuromorphic hardware can provide the basis for a wearable, real-time, always-on, patient-specific Seizure warning system with low power consumption and reliable long-term performance.
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A forward-looking review of Seizure Prediction.
Current Opinion in Neurology, 2017Co-Authors: Dean R. Freestone, Philippa J. Karoly, Mark J. CookAbstract:Purpose of reviewSeizure Prediction has made important advances over the last decade, with the recent demonstration that prospective Seizure Prediction is possible, though there remain significant obstacles to broader application. In this review, we will describe insights gained from long-term trial
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Seizure Prediction: Science Fiction or Soon to Become Reality?
Current Neurology and Neuroscience Reports, 2015Co-Authors: Dean R. Freestone, Levin Kuhlmann, Philippa J. Karoly, Andre D. H. Peterson, Alan Lai, Farhad Goodarzy, Mark J. CookAbstract:This review highlights recent developments in the field of epileptic Seizure Prediction. We argue that Seizure Prediction is possible; however, most previous attempts have used data with an insufficient amount of information to solve the problem. The review discusses four methods for gaining more information above standard clinical electrophysiological recordings. We first discuss developments in obtaining long-term data that enables better characterisation of signal features and trends. Then, we discuss the usage of electrical stimulation to probe neural circuits to obtain robust information regarding excitability. Following this, we present a review of developments in high-resolution micro-electrode technologies that enable neuroimaging across spatial scales. Finally, we present recent results from data-driven model-based analyses, which enable imaging of Seizure generating mechanisms from clinical electrophysiological measurements. It is foreseeable that the field of Seizure Prediction will shift focus to a more probabilistic forecasting approach leading to improvements in the quality of life for the millions of people who suffer uncontrolled Seizures. However, a missing piece of the puzzle is devices to acquire long-term high-quality data. When this void is filled, Seizure Prediction will become a reality.
Andreas Schulze-bonhage - One of the best experts on this subject based on the ideXlab platform.
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Brainatic: A System for Real-Time Epileptic Seizure Prediction
Brain-Computer Interface Research, 2014Co-Authors: Cesar Teixeira, Bruno Direito, H. Feldwisch-drentrup, Björn Schelter, Matthias Ihle, Gianpietro Favaro, Mojtaba Bandarabadi, Catalina Alvarado, Michel Le Van Quyen, Andreas Schulze-bonhageAbstract:A new system developed for real-time scalp EEG-based epileptic Seizure Prediction is presented, based on real time classification by machine learning methods, and named Brainatic. The system enables the consideration of previously trained classifiers for real-time Seizure Prediction. The software facilitates the computation of 22 univariate measures (features) per electrode, and classification using support vector machines (SVM), multilayer perceptron (MLP) neural networks and radial basis functions (RBF) neural networks. Brainatic was able to operate in real-time on a dual Intel® AtomTM netbook with 2GB of RAM, and was used to perform the clinical and ambulatory tests of the EU project EPILEPSIAE.
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EMBC - Seizure Prediction in epilepsy: From circadian concepts via probabilistic forecasting to statistical evaluation
2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2011Co-Authors: Björn Schelter, H. Feldwisch-drentrup, Andreas Schulze-bonhage, Matthias Ihle, Jens TimmerAbstract:Seizure Prediction performance is hampered by high numbers of false Predictions. Here we present an approach to reduce the number of false Predictions based on circadian concepts. Based on eight representative patients we demonstrate that this approach increases the performance considerably. The fraction of patients for whom we found a significant Seizure Prediction performance was increased from 25% to 38% by accounting for circadian dependencies.
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Joining the benefits: Combining epileptic Seizure Prediction methods
Epilepsia, 2010Co-Authors: H. Feldwisch-drentrup, Bjoern Schelter, Jens Timmer, Michael Jachan, Jakob Nawrath, Andreas Schulze-bonhageAbstract:Summary Purpose: In recent years, a variety of methods developed in the field of linear and nonlinear time series analysis have been used to obtain reliable Predictions of epileptic Seizures. Because individual methods for Seizure Prediction so far have shown statistical significance but insufficient performance for clinical applications, we investigated possible improvements by combining algorithms capturing different aspects of electroencephalogram (EEG) dynamics. Methods: We applied the mean phase coherence and the dynamic similarity index to long-term continuous intracranial EEG data. The predictive performance of both methods was assessed and statistically evaluated separately, as well as by using logical “AND” and “OR” combinations. Results: Used independently, either method resulted in a statistically significant Prediction performance in only a few patients. Particularly the “AND” combination led to improved Prediction performances, leading to an increase in sensitivity and/or specificity. For a maximum false Prediction rate of 0.15/h, the mean sensitivity improved from about 25% for the individual methods to 43.2% for the “AND” and to 35.2% for the “OR” combination. Discussion: This study shows that combinations of Prediction methods are promising new approaches to enhance Seizure Prediction performance considerably. It allows merging the individual benefits of Prediction methods in a complementary manner. Because either sensitivity or specificity of Seizure Prediction methods can be improved depending on the needs of the desired clinical application, the combination opens a new window for future use in a clinical setting.
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Views of patients with epilepsy on Seizure Prediction devices.
Epilepsy & Behavior, 2010Co-Authors: Andreas Schulze-bonhage, Francisco Sales, Kathrin Wagner, Rute Teotónio, Astrid Carius, Annette Schelle, Matthias IhleAbstract:Abstract Patients´ views on the relevance, performance requirements, and implementation of Seizure Prediction devices have so far not been evaluated in a standardized form. We here report views of outpatients with uncontrolled epilepsy from the epilepsy centers at Freiburg, Germany, and Coimbra, Portugal, based on a questionnaire. Interest in the development of methods for Seizure Prediction both for warning and for closed-loop interventions is high. High sensitivity of Prediction is regarded as more important than specificity. Short Prediction time windows are preferred, but the indication of Seizure-prone periods is also considered worthwhile. Only a few patients are, however, willing to wear EEG electrodes for signal acquisition on a long-term basis. These data support the view that Seizure Prediction is of high interest to patients with uncontrolled epilepsy. Improvements in the performance of presently available Prediction algorithms and technical improvements in EEG recording will, however, be necessary to meet patients´ requirements.
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Seizure Prediction: the impact of long Prediction horizons.
Epilepsy Research, 2007Co-Authors: Björn Schelter, Matthias Winterhalder, Andreas Schulze-bonhage, Jakob Nawrath, Armin Brandt, Hinnerk Feldwisch Genannt Drentrup, J. Wohlmuth, Jens TimmerAbstract:Several procedures have been proposed to be capable of predicting the occurrence of epileptic Seizures. Up to now, all proposed algorithms are far from being sufficient for a clinical application. This is, however, often not obvious when results of Seizure Prediction performance are reported. Here, we discuss impacts of long Prediction horizons with respect to clinical needs and the strain on patients by analyzing long-term continuous intracranial electroencephalography data.