TY - GEN
T1 - Parsing wireless electrocardiogram signals with context free grammar conditional random fields
AU - Nguyen, Thai
AU - Adams, Roy J.
AU - Natarajan, Annamalai
AU - Marlin, Benjamin M.
N1 - Funding Information:
This work was partially supported by the National Institutes of Health under awards 1U54EB020404-01, and the National Science Foundation under Grant No. 1350522.
Publisher Copyright:
© 2016 IEEE.
PY - 2016/12/1
Y1 - 2016/12/1
N2 - 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 focus on ECG morphology extraction because it is a key step in higher-level detection tasks such as arrhythmia detection and the detection of drug use. We introduce a robust context free grammar for parsing noisy ECG data, and show significantly improved performance on the ECG morphological labeling task.
AB - 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 focus on ECG morphology extraction because it is a key step in higher-level detection tasks such as arrhythmia detection and the detection of drug use. We introduce a robust context free grammar for parsing noisy ECG data, and show significantly improved performance on the ECG morphological labeling task.
UR - http://www.scopus.com/inward/record.url?scp=85011066586&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85011066586&partnerID=8YFLogxK
U2 - 10.1109/WH.2016.7764570
DO - 10.1109/WH.2016.7764570
M3 - Conference contribution
AN - SCOPUS:85011066586
T3 - 2016 IEEE Wireless Health, WH 2016
SP - 149
EP - 156
BT - 2016 IEEE Wireless Health, WH 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE Wireless Health, WH 2016
Y2 - 25 October 2016 through 27 October 2016
ER -