Parsing Wireless Electrocardiogram Signals with the CRF-CFG Model

Citation:
Nguyen, T., R. J. Adams, A. Natarajan, and B. M. Marlin, "Parsing Wireless Electrocardiogram Signals with the CRF-CFG Model", Conference on Uncertainty in Artificial Intelligence Machine Learning for Health Workshop, 2016.

Abstract:

Recent advances in wearable sensor technology have made it possible to simultaneously collect multiple streams of physiological and context data from individuals as they go about their daily activities in natural environments. However, extracting reliable higher-level inferences from these raw data streams remains a key data analysis challenge. In this paper, we focus on the specific case of the analysis of data from wireless electrocardiogram (ECG) sensors. We present a new robust probabilistic approach to ECG morphology extraction using conditional random field context free grammar models, which have traditionally been applied to parsing problems in natural language processing. We introduce a robust context free grammar for parsing noisy ECG data, and show significantly improved performance on the ECG morphological labeling task.

Notes:

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