title: Conditional Random Fields for Morphological Analysis of Wireless ECG Signals.
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.
authors.affiliation: School of Computer Science, University of Massachusetts, Amherst, MA.
authors.forename: Annamalai
authors.initials: A
authors.lastname: Natarajan
authors.affiliation: Department of Psychiatry, Yale University, New Haven, CT.
authors.forename: Edward
authors.initials: E
authors.lastname: Gaiser
authors.affiliation: Department of Psychiatry, Yale University, New Haven, CT.
authors.forename: Gustavo
authors.initials: G
authors.lastname: Angarita
authors.affiliation: Department of Psychiatry, Yale University, New Haven, CT.
authors.forename: Robert
authors.initials: R
authors.lastname: Malison
authors.affiliation: School of Computer Science, University of Massachusetts, Amherst, MA.
authors.forename: Deepak
authors.initials: D
authors.lastname: Ganesan
authors.affiliation: School of Computer Science, University of Massachusetts, Amherst, MA.
authors.forename: Benjamin
authors.initials: B
authors.lastname: Marlin
keywords: Electrocardiogram; Machine Learning; Mobile Health