Omer T. Inan received the B.S., M.S., and Ph.D. degrees in electrical engineering from Stanford University, Stanford, CA, in 2004, 2005, and 2009, respectively. He joined ALZA Corporation (A Johnson and Johnson Company) in 2006 as an Engineering Intern in the Drug Device Research and Development Group, where he designed micropower, high efficiency circuits for iontophoretic drug delivery, and researched options for closed-loop drug delivery systems. In 2007, he joined Countryman Associates, Inc., Menlo Park, CA where he was Chief Engineer, involved in designing and developing high-end professional audio circuits and systems. From 2009-2013, he was also a Visiting Scholar in the Department of Electrical Engineering, Stanford University. Since 2013, Dr. Inan is an Assistant Professor of Electrical and Computer Engineering, and Program Faculty in Bioengineering, at the Georgia Institute of Technology. Since 2015, he is also an Adjunct Assistant Professor in Biomedical Engineering. His research focuses on non-invasive physiologic monitoring for human health and performance, and applying novel sensing systems to chronic disease management and pediatric care. He is an Associate Editor of the IEEE Journal of Biomedical and Health Informatics, and is Guest Editor for the same journal on a Special Issue entitled, “Unobtrusive Assessment of the Mechanical Aspects of Cardiovascular Performance.” He has published more than 65 technical articles in peer-reviewed international journals and conferences, and has four issued and four pending patents. His group’s research is currently funded by DARPA (DoD), NIH (NIA and NIBIB), US Army (CDMRP) Children’s Healthcare of Atlanta, and Texas Instruments. Dr. Inan, a Senior Member of IEEE, received the Gerald J. Lieberman Fellowship (Stanford University) in 2008-‘09 for outstanding scholarship, teaching and service. He received the NASA Ames Research Center Tech Briefs Award in 2011. He is a Three-Time National Collegiate Athletic Association All-American in the discus throw, and a former co-captain of the Stanford University Track and Field Team.
Areas of Expertise (6)
Home Monitoring of Chronic Disease
Medical Devices for Clinically-Relevant Applications
Non-invasive Physiological Monitoring
Selected Accomplishments (5)
Office of Naval Research Young Investigator Award (ONR YIP)
National Science Foundation CAREER Award
Roger P. Webb ECE Outstanding Junior Faculty Member Award, Georgia Tech
Sigma Xi Young Faculty Award, Georgia Tech
Lockheed Dean's Excellence in Teaching Award, Georgia Tech
Stanford University: Ph.D., Electrical Engineering 2009
Stanford University: M.S., Electrical Engineering 2005
Stanford University: B.S., Electrical Engineering 2004
Selected Media Appearances (2)
This is what your knee sounds like: 'Chhh, chhh, chhh'
Doctors then can listen to those recordings to pinpoint noticeable changes in the sounds, which could help them evaluate damage after a knee injury and track improvements or setbacks in recovery, said Omer Inan, an assistant professor at Georgia Tech who has led research on the new knee band technology. "We would not want to look at a snapshot of the sounds and try to diagnose if someone has a particular injury. But what we are interested in is looking at a person who already has been diagnosed and then tracking them over time to see if they're getting better or worse," Inan said.
This Gross Sound Your Knee Makes Could Be a Sign of Health
"I actually feel like there's some real information in [the noises] that can be exploited for the purposes of helping people with rehab," said Omer Inan, assistant professor of electrical and computer engineering, former discus thrower, and knee injury patient, who pitched the knee band idea. "It's a little bit like some kind of Halloween stuff happening. You're listening to your bones rubbing on each other, or maybe cartilage."
Selected Articles (5)
Classification of Decompensated Heart Failure from Clinical and Home BallistocardiographyIEEE Transactions on Biomedical Engineering
VB Aydemir, S Nagesh, M Shandhi, J Fan, L Klein, M Etemadi, JA Heller, O Inan, JM Rehg
2019 To improve home monitoring of heart failure patients so as to reduce emergencies and hospital readmissions. We aim to do this by analyzing the ballistocardiogram (BCG) to evaluate the clinical state of the patient.
Quantifying acute physiological biomarkers of transcutaneous cervical vagal nerve stimulation in the context of psychological stressBrain Stimulation
Nil Z Gurel, Minxuan Huang, Matthew T Wittbrodt, Hewon Jung, Stacy L Ladd, Md Mobashir H Shandhi, Yi-An Ko, Lucy Shallenberger, Jonathon A Nye, Bradley Pearce, Viola Vaccarino, Amit J Shah, J Douglas Bremner, Omer T Inan
2019 Stress is associated with activation of the sympathetic nervous system, and can lead to lasting alterations in autonomic function and in extreme cases symptoms of posttraumatic stress disorder (PTSD). Vagal nerve stimulation (VNS) is a potentially useful tool as a modulator of autonomic nervous system function, however currently available implantable devices are limited by cost and inconvenience.
Seismocardiography and Machine Learning Algorithms to Assess Clinical Status of Patients with Heart Failure in Cardiopulmonary Exercise TestingJournal of Cardiac Failure
Mobashir Md Hasan Shandhi, Joanna Fan, J Alex Heller, Mozziyar Etemadi, Omer T Inan, Liviu Klein
2019 Cardiopulmonary exercise testing (CPET) is an important risk stratification tool in patients (pts) with heart failure (HF); measures such as peak VO2, VE/VCO2 slope have prognostic value in HF pts to determine whether a patient needs advanced heart therapy or not. In our previous studies, we have shown that wearable chest patch based seismocardiogram (SCG) signals can be used to estimate features from CPET and SCG can be used to differentiate between compensated (C) and decompensated (D) pts with HF following exercise (6 minute walk test).
Toward ubiquitous blood pressure monitoring via pulse transit time: theory and practiceIEEE Transactions on Biomedical Engineering
Ramakrishna Mukkamala, Jin-Oh Hahn, Omer T Inan, Lalit K Mestha, Chang-Sei Kim, Hakan Töreyin, Survi Kyal
2015 Ubiquitous blood pressure (BP) monitoring is needed to improve hypertension detection and control and is becoming feasible due to recent technological advances such as in wearable sensing. Pulse transit time (PTT) represents a well-known potential approach for ubiquitous BP monitoring. The goal of this review is to facilitate the achievement of reliable ubiquitous BP monitoring via PTT. We explain the conventional BP measurement methods and their limitations; present models to summarize the theory of the PTT-BP relationship; outline the approach while pinpointing the key challenges; overview the previous work toward putting the theory to practice; make suggestions for best practice and future research; and discuss realistic expectations for the approach.
Robust neural-network-based classification of premature ventricular contractions using wavelet transform and timing interval featuresIEEE transactions on Biomedical Engineering
Omer T Inan, Laurent Giovangrandi, Gregory TA Kovacs
2006 Automatic electrocardiogram (ECG) beat classification is essential to timely diagnosis of dangerous heart conditions. Specifically, accurate detection of premature ventricular contractions (PVCs) is imperative to prepare for the possible onset of life-threatening arrhythmias. Although many groups have developed highly accurate algorithms for detecting PVC beats, results have generally been limited to relatively small data sets. Additionally, many of the highest classification accuracies (>90%) have been achieved in experiments where training and testing sets overlapped significantly. Expanding the overall data set greatly reduces overall accuracy due to significant variation in ECG morphology among different patients. As a result, we believe that morphological information must be coupled with timing information, which is more constant among patients, in order to achieve high classification accuracy for larger data sets. With this approach, we combined wavelet-transformed ECG waves with timing information as our feature set for classification. We used select waveforms of 18 files of the MIT/BIH arrhythmia database, which provides an annotated collection of normal and arrhythmic beats, for training our neural-network classifier. We then tested the classifier on these 18 training files as well as 22 other files from the database. The accuracy was 95.16% over 93,281 beats from all 40 files, and 96.82% over the 22 files outside the training set in differentiating normal, PVC, and other beats.