PPT-Abstract Automated seizure detection using clinical electroencephalograms (EEGs) is a

Commercially available seizure detection systems suffer from unacceptably high false alarm rates Deep learning algorithms like Convolutional Neural Networks CNNs have not previously been effective due to the lack of big data resources . Status . Epilepticus. Emergency – pathological condition which is life threatening or which can lead to organ failure requiring prompt treatment in order to avoid severe worsening and/or severe sequels. Dan Coughlin. Kevin McCabe. Bob McCarthy. Steve Moffett. Background. Epilepsy is a brain disease that triggers seizures. Electroencephalograms (EEGs) read electrical impulses from the brain. Prediction. L. Veloso, J. McHugh, E. von Weltin, S. Lopez, I. Obeid and J. Picone. The Neural Engineering Data Consortium, Temple University. College of Engineering. Temple University. www.nedcdata.org. Introduction. A Thesis Proposal by:. Silvia . López de Diego. Neural Engineering Data Consortium. College of Engineering. Temple University. Philadelphia, Pennsylvania, USA. Abstract. The interpretation of electroencephalograms (EEGs) is a process that is still dependent on the subjective analysis of the examiners. Though inter-rater agreement on critical events such as seizures can be high, it is much lower on subtler events (e.g., when there are benign variants). The focus of this study is to automatically classify normal and abnormal EEGs to provide neurologists with real-time decision support.. Deep Learning Approaches to Automate Seizure Detection . Submitted to:. Dr. Joseph . Picone. , Dept. of Electrical and Computer Engineering. Dr. . Iyad. Obeid, Dept. of Electrical and Computer Engineering. A Machine Learning Perspective. Christian Ward, Dr. Iyad Obeid and . Dr. . Joseph Picone. Neural Engineering Data Consortium. College of Engineering. Temple University. Philadelphia, Pennsylvania, USA. using Channel Dependent Posteriors. Presented By:. Vinit Shah. Neural Engineering Data Consortium,. Temple University. 1. Abstract. An important factor of seizure detection problem, known as segmentation: defined as the ability to detect start and stop times within a fraction of a second, is a challenging and under-researched problem.. require . a highly trained . neurologist for interpretation. Current utilization of EEGs in Epilepsy Monitoring Units and Intensive Care Units require long-term monitoring with data collected over hours or days. However, having certified staff on-site to provide 24/7. SecOps Solutions Team. Customer Presentation . Agenda. Packages – What | Why. Business Challenges & Solutions. Market Opportunity. Solution Package Summary. Package Description – Value Proposition, Deployment. Subset of the publicly available TUH EEG Corpus (. www.isip.piconepress.com/projects/tuh_eeg). .. Evaluation Data:. 50 patients, 239 sessions, 1015 files. 171 hours of data including 16 hours of seizures.. THE TUH EEG SEIZURE . CORPUS. M. Golmohammadi. 1. , V. Shah. 2. , S. Lopez. 2. , S. Ziyabari. 2. , S. Yang. 2. , J. Camaratta. 1. , I. Obeid. 2. and J. Picone. 2. 1. Biosignal Analytics, Inc.. 2. The Neural Engineering Data Consortium, Temple University. This study establishes a baseline for automated classification of abnormal adult EEGs using machine learning and a big data resource.. 2,785 and 280 files from TUH EEG Abnormal were used for training and evaluation respectively. The first . Dr . Jeevan. . Silwal. MD,DNB,FIPN. Pediatric Neurologist. Siliguri. . . SEIZURES: . A seizure is defined as:. “Transient occurrence of signs and/or symptoms due to abnormal excessive or synchronous neuronal activity in the brain.”. Laura A. Rice, PhD, MPT, ATP; Alexander . Fliflet. , MS; Mikaela Frechette, MS; Rachel Brokenshire; . Libak. . Abou. ,. MPT, PT; Peter . Presti. , MS; . Harshal. Mahajan, PhD; Jacob . Sosnoff. , PhD; Wendy A. Rogers, PhD.