Modeling Waveform Shapes with Random Eects Segmental Hidden Markov Models

TL;DR

This paper introduces a probabilistic framework using segmental hidden Markov models with random effects to effectively model, classify, and predict waveform data like ECG signals, improving accuracy over existing methods.

Contribution

It develops a novel probabilistic model incorporating random effects into segmental HMMs for waveform analysis, with an efficient EM algorithm for learning from data.

Findings

Improved classification accuracy on real-world ECG data.

Effective segmentation and prediction of waveform shapes.

Demonstrated computational efficiency of the proposed EM algorithm.

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.