Continuously variable duration hidden Markov models for automatic speech recognition
Computer Speech and Language
Fundamentals of speech recognition
Fundamentals of speech recognition
Deformable Markov model templates for time-series pattern matching
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Fast Time Sequence Indexing for Arbitrary Lp Norms
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Modelling ECG signals with hidden Markov models
Artificial Intelligence in Medicine
Using vision, acoustics, and natural language for disambiguation
Proceedings of the ACM/IEEE international conference on Human-robot interaction
Segmental Hidden Markov Models with Random Effects for Waveform Modeling
The Journal of Machine Learning Research
Invariant time-series classification
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
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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.