Modeling waveform shapes with random effects segmental hidden Markov models
Se Young Kim, Padhraic Smyth, Stefan Luther · 2004
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 e#ects to the generative model. The random e#ects component of the model handles shape variability across di#erent 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 e#cient 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 datasets demonstrate that the random e#ects methodology leads to improved accuracy (compared to alternative approaches) on classification and segmentation of real-world waveforms.