Work

Conditional random fields for morphological analysis of wireless ECG signals

Year: 2014

Type: conference-paper

Abstract: Thanks to advances in mobile sensing technologies, it has recently become practical to deploy wireless electrocardiograph sensors for continuous recording of ECG signals. This capability has diverse applications in the study of human health and behavior, but to realize its full potential, new computational tools are required to effectively deal with the uncertainty that results from the noisy and highly non-stationary signals collected using these devices. In this work, we present a novel approach to the problem of extracting the morphological structure of ECG signals based on the use of dynamically structured conditional random field (CRF) models. We apply this framework to the problem of extracting morphological structure from wireless ECG sensor data collected in a lab-based study of habituated cocaine users. Our results show that the proposed CRF-based approach significantly out-performs independent prediction models using the same features, as well as a widely cited open source toolkit.

Source: ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB)

DOI: 10.1145/2649387.2649414

Authors: Annamalai Natarajan, Edward Gaiser, Gustavo Angarita, Robert Malison, Deepak Ganesan, Benjamin Marlin

Institutions: University of Massachusetts Amherst, Yale University

Language: English

Topic: ECG Monitoring and Analysis

Subfield: Cardiology and Cardiovascular Medicine

Field: Medicine

Domain: Health Sciences

Open Access status: green

Funders: National Institute on Drug Abuse, National Institute of Biomedical Imaging and Bioengineering, National Center for Research Resources

Awards: Center of Excellence for Mobile Sensor Data-to-Knowledge (MD2K) - OVERALL, Patient-Oriented Research and Mentoring in the Translational Neurobiology/Genetic, Translational Research of Cocaine, Striatum, and Impulsivities, Yale University Clinical and Translational Science Award Program, Validation of a Remote Wireless Sensor Network (WSN) Approach to the Individualiz