Real-Time Extraction and Analysis of Key Morphological Features in the Electrocardiogram, for Data Compression and Clinical Decision Support
Massive amounts of clinical data can now be collected by stand-alone or wearable monitors over extended periods of time. One key challenge is to convert the volumes of raw data into clinically relevant and actionable information, ideally in real-time. This becomes imperative especially in the domai...
Main Authors: | Gordhandas, Ankit, Heldt, Thomas, Verghese, George C. |
---|---|
Other Authors: | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
Format: | Article |
Language: | en_US |
Published: |
American Association for Artificial Intelligence
2013
|
Online Access: | http://hdl.handle.net/1721.1/79056 https://orcid.org/0000-0002-5930-7694 https://orcid.org/0000-0002-2446-1499 |
Similar Items
-
Relating Noninvasive Cardiac Output and Total Peripheral Resistance Estimates to Physical Activity in an Ambulatory Setting
by: Haslam, Bryan Todd, et al.
Published: (2013) -
Distilling clinically interpretable information from data collected on next-generation wearable sensors
by: Haslam, Bryan Todd, et al.
Published: (2014) -
Denoising and feature extraction of electrocardiogram (ECG) signals
by: Manoj, Leona Ann
Published: (2017) -
Identification of myocardial infarction using morphological features of electrocardiogram and vectorcardiogram
by: Nastaran Jafari Hafshejani, et al.
Published: (2021-12-01) -
An Algorithm for Filtering Electrocardiograms to Improve Nonlinear Feature Extraction
by: Mohammad Bahmanyar, et al.
Published: (2007-04-01)