SeizureBank: A Repository of Analysis-ready Seizure Signal Data

Approximately 60 million people worldwide suffer from epileptic seizures. A key challenge in machine learning ap proaches for epilepsy research is the lack of a data resource of analysis-ready (no additional preprocessing is needed when using the data for developing computational methods) seizure signal datasets with associated tools for seizure data management and visualization. We introduce SeizureBank, a web-based data management and visualization system for epileptic seizures. SeizureBank comes with a built-in seizure data preparation pipeline and web-based interfaces for querying, exporting and visualizing seizure-related signal data. In this pilot study, 224 seizures from 115 patients were extracted from over one terabyte of signal data and deposited in SeizureBank. To demonstrate the value of this approach, we develop a feature-based seizure identification approach and evaluate the performance on a variety of data sources. The results can serve as a cross-dataset evaluation benchmark for future seizure identification studies.

[1]  Orrin Devinsky,et al.  Nonseizure SUDEP: Sudden unexpected death in epilepsy without preceding epileptic seizures , 2016, Epilepsia.

[2]  Guo-Qiang Zhang,et al.  SpindleSphere: A Web-based Platform for Large-scale Sleep Spindle Analysis and Visualization , 2017, AMIA.

[3]  Ram Bilas Pachori,et al.  A Multivariate Approach for Patient-Specific EEG Seizure Detection Using Empirical Wavelet Transform , 2017, IEEE Transactions on Biomedical Engineering.

[4]  M. Goldenberg,et al.  Overview of drugs used for epilepsy and seizures: etiology, diagnosis, and treatment. , 2010, P & T : a peer-reviewed journal for formulary management.

[5]  B. Hjorth EEG analysis based on time domain properties. , 1970, Electroencephalography and clinical neurophysiology.

[6]  Jeffrey Heer,et al.  SpanningAspectRatioBank Easing FunctionS ArrayIn ColorIn Date Interpolator MatrixInterpola NumObjecPointI Rectang ISchedu Parallel Pause Scheduler Sequen Transition Transitioner Transiti Tween Co DelimGraphMLCon IData JSONCon DataField DataSc Dat DataSource Data DataUtil DirtySprite LineS RectSprite , 2011 .

[7]  Xiang Zhang,et al.  HyCLASSS: A Hybrid Classifier for Automatic Sleep Stage Scoring , 2018, IEEE Journal of Biomedical and Health Informatics.

[8]  K Lehnertz,et al.  Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[9]  Sheng-Fu Liang,et al.  A rule-based automatic sleep staging method , 2012, Journal of Neuroscience Methods.

[10]  I. Rezek,et al.  Stochastic complexity measures for physiological signal analysis , 1998, IEEE Transactions on Biomedical Engineering.