Uncertainty in Artificial Intelligence
First Name   Last Name   Password   Forgot Password   Log in!
    Proceedings   Proceeding details   Article details         Authors         Search    
Modeling Waveform Shapes with Random Eects Segmental Hidden Markov Models
Seyoung Kim, Padhraic Smyth, Stefan Luther
Abstract:
In this paper we describe a general probabilistic framework for modeling waveforms such as heartbeats from ECG data. The model is based on segmental hidden Markov models (as used in speech recognition) with the addition of random effects to the generative model. The random effects component of the model handles shape variability across different waveforms within a general class of waveforms of similar shape. We show that this probabilistic model provides a unified framework for learning these models from sets of waveform data as well as parsing, classification, and prediction of new waveforms. We derive a computationally efficient EM algorithm to fit the model on multiple waveforms, and introduce a scoring method that evaluates a test waveform based on its shape. Results on two real-world data sets demonstrate that the random effects methodology leads to improved accuracy (compared to alternative approaches) on classification and segmentation of real-world waveforms.
Keywords: null
Pages: 309-316
PS Link:
PDF Link: /papers/04/p309-kim.pdf
BibTex:
@INPROCEEDINGS{Kim04,
AUTHOR = "Seyoung Kim and Padhraic Smyth and Stefan Luther",
TITLE = "Modeling Waveform Shapes with Random Eects Segmental Hidden Markov Models",
BOOKTITLE = "Proceedings of the Twentieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-04)",
PUBLISHER = "AUAI Press",
ADDRESS = "Arlington, Virginia",
YEAR = "2004",
PAGES = "309--316"
}


hosted by DSL   •   site info   •   help