Omer Inan
Associate Professor, Electrical and Computer Engineering Georgia Tech College of Engineering
- Atlanta GA
Omer Inan's research focuses on non-invasive physiologic monitoring for human health and performance.
Georgia Tech College of Engineering
View more experts managed by Georgia Tech College of Engineering
Media
Social
Biography
Areas of Expertise
Selected Accomplishments
Office of Naval Research Young Investigator Award (ONR YIP)
2018
National Science Foundation CAREER Award
2018
Roger P. Webb ECE Outstanding Junior Faculty Member Award, Georgia Tech
2018
Sigma Xi Young Faculty Award, Georgia Tech
2017
Lockheed Dean's Excellence in Teaching Award, Georgia Tech
2016
Education
Stanford University
Ph.D.
Electrical Engineering
2009
Stanford University
M.S.
Electrical Engineering
2005
Stanford University
B.S.
Electrical Engineering
2004
Links
Selected Media Appearances
This is what your knee sounds like: 'Chhh, chhh, chhh'
CNN online
2017-11-02
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
Motherboard online
2016-05-29
"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
Classification of Decompensated Heart Failure from Clinical and Home Ballistocardiography
IEEE Transactions on Biomedical EngineeringVB 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 stress
Brain StimulationNil 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 Testing
Journal of Cardiac FailureMobashir 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 practice
IEEE Transactions on Biomedical EngineeringRamakrishna 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 features
IEEE transactions on Biomedical EngineeringOmer 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.
