Shamim Nemati

Title: Innovations by Inventor Shamim Nemati

Introduction

Shamim Nemati is an accomplished inventor based in Atlanta, GA (US). He has made significant contributions to the field of medical technology, particularly in the area of cardiac monitoring and post-traumatic stress disorder (PTSD) classification. With a total of 2 patents, his work demonstrates a commitment to improving patient care through innovative solutions.

Latest Patents

One of Shamim Nemati's latest patents is focused on "Methods and systems for determining abnormal cardiac activity." This invention utilizes advanced techniques to accurately and efficiently identify abnormal cardiac activity from motion and cardiac data. The method employs machine learning, specifically a trained deep learning architecture, to classify periods of time into various classes, including abnormal and normal cardiac activity. This technology is particularly useful for long-term patient monitoring.

Another notable patent is related to "Using heart rate information to classify PTSD." This system and method involve receiving electrocardiography data from individuals and determining features that can be compared to a logistic regression classifier. The classifier is trained using features from individuals with and without PTSD, allowing for an assessment of PTSD severity based on heart rate variability metrics.

Career Highlights

Shamim Nemati has worked with prestigious institutions such as Emory University and Georgia Tech Research Corporation. His experience in these organizations has allowed him to collaborate with leading experts in the field and contribute to groundbreaking research.

Collaborations

Some of his notable coworkers include Gari Clifford and Amit Shah. Their collaboration has likely enhanced the quality and impact of his research endeavors.

Conclusion

Shamim Nemati's innovative work in cardiac monitoring and PTSD classification showcases his dedication to advancing medical technology. His patents reflect a deep understanding of machine learning and its applications in healthcare, ultimately aiming to improve patient outcomes.