PPT-Automated Identification of Abnormal Adult EEGs

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 interrater agreement on critical events such as seizures can be high it is much lower on subtler events eg when there are benign variants The focus of this study is to automatically classify normal and abnormal EEGs to provide neurologists with realtime decision support. From Phrenology to Anthropometry. HI269. Week . 3. Phrenology and the bumpy path to the embodied mind…. Phrenology. Key points. Propounded by Franz Joseph Gall (1758-1828), phrenology was based in the idea that the brain had approximately 26 ‘organs’ or ‘mental faculties’, each of which represented a particular trait or propensity (such as intelligence, acquisitiveness, and self-esteem).. Department Of Emergency Services and Public Protection. Division of State Police. AFIS Project Description. Modernize . Connecticut’s . Automated Fingerprint Identification System (AFIS). , replacing the current system with a new, state of the art . “Fingerprints cannot lie, . but liars can make fingerprints.”. —. Unknown. 2. Fingerprinting Merit Badge. Requirement #1. Give . a short history of fingerprinting. . Tell . the difference between civil and criminal identification. . 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. Objectives. To define normal and abnormal. To decide between methods of deciding normality. To recognize the use of the bell-shaped curve showing normal distribution. To develop and analyze surveys to determine normal personality qualities and behaviors. 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. CAUSES OF ABNORMAL BEHAVIOR. Biological/Genetic . View as mental disorder – similar to physical disorders . Diagnosis and treatment . Nervous system and the brain. CAUSES OF ABNORMAL BEHAVIOR. Cognitive –Emotional . HI269. Week . 3. Phrenology and the bumpy path to the embodied mind…. Phrenology. Key points. Propounded by Franz Joseph Gall (1758-1828), phrenology was based in the idea that the brain had approximately 26 ‘organs’ or ‘mental faculties’, each of which represented a particular trait or propensity (such as intelligence, acquisitiveness, and self-esteem).. 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. 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. . José Ignacio Orlando. 1,2. , Elena Prokofyeva. 3,4. , Mariana del Fresno. 1,5. and Matthew B. Blaschko. 6. 1 . Instituto. . Pladema. , UNCPBA, . Tandil. , Argentina. 2. . Consejo. Nacional de . Investigaciones. 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. SCREENING. PICK UP ON EARLY SIGNS OF SERIOUS CONDITIONS (SUCH AS CANCER). BEFORE PATIENT HAS SYMPTOMS. ACCEPTABLY DETECTED & TREATED WITH POSITIVE OUTCOMES. SCREENING IS NOT PERFECT & CAN HAVE FALSE POSITIVES & NEGATIVES. 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 .