Track 3: Machine Learning/Deep Learning | Room: 1205
Emily Wefelmeyer, Research Fellow, Harrisburg University
Roozbeh Sadeghian, Assistant Professor, Harrisburg University
Alzheimer’s disease (AD) is a progressive and irreversible brain disease that occurs in older adults and is the sixth most common cause of death in the United States. In recent years, researchers have begun to suspect that the deterioration of the brain begins years, maybe even decades, before it is diagnosed with today’s diagnostic criteria. Since AD affects the speech and language centers of the brain, there is hope that earlier detection will come through analyzing speech and language patterns to predict an AD diagnosis. An earlier diagnosis will provide the option for earlier treatment to slow the deterioration of the brain. Currently, we have built a model using random forests to predict an AD diagnosis using the transcripts of speech samples obtained from subjects with and without AD. The final goal of this project is to diagnosis AD in the earliest stages.
Ms. Wefelmeyer earned her B.S. in Mathematics from University of Maryland Baltimore County in 2005. She spent twelve years teaching secondary mathematics in Maryland. In 2015, Ms. Wefelmeyer presented research on the lack of consistency with readability measures at the Harrisburg University Data Analytics Summit II. She earned her M.S. in Analytics from Harrisburg University of Science & Technology in 2017. Since September 2017, she has been a research fellow and PhD student in Data Science at Harrisburg University of Science & Technology. Ms. Wefelmeyer is currently leading the Susquehanna River Basin Commission’s signal-to-noise project and the HU Alzheimer’s Research group. Her dissertation research is looking at combining various analytical methods, including deep learning, to diagnosis Alzheimer’s disease using speech samples.
After earning his M.S. in electrical engineering, Dr. Sadeghian worked for several years in various industries as a senior industrial automation engineer. Later, he pursued his PhD in signal processing at SUNY Binghamton where he focused on using Machine Learning and speech recognition techniques for diagnosing human health disorders. Dr. Sadeghian worked on speech features as the baseline to diagnose the speech delay of children and Alzheimer’s disease in elderly people. He is currently working on developing algorithms in diagnosing health disorders in early stages.
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