short-paper

Conditional random fields for morphological analysis of wireless ECG signals

Authors:

Annamalai Natarajan,

Gustavo Angarita,

Robert Malison,

Deepak Ganesan,

Benjamin MarlinAuthors Info & Claims

BCB '14: Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics

Pages 370 - 379

https://doi.org/10.1145/2649387.2649414

Published: 20 September 2014 Publication History

Abstract

Thanks to advances in mobile sensing technologies, it has recently become practical to deploy wireless electrocardiograph sensors for continuous recording of ECG signals. This capability has diverse applications in the study of human health and behavior, but to realize its full potential, new computational tools are required to effectively deal with the uncertainty that results from the noisy and highly non-stationary signals collected using these devices. In this work, we present a novel approach to the problem of extracting the morphological structure of ECG signals based on the use of dynamically structured conditional random field (CRF) models. We apply this framework to the problem of extracting morphological structure from wireless ECG sensor data collected in a lab-based study of habituated cocaine users. Our results show that the proposed CRF-based approach significantly out-performs independent prediction models using the same features, as well as a widely cited open source toolkit.

References

[1]

F. Alamudun, J. Choi, R. Gutierrez-Osuna, H. Khan, and B. Ahmed. Removal of subject-dependent and activity-dependent variation in physiological measures of stress. In Pervasive Computing Technologies for Healthcare, Proceedings of the 6th International Conference on, pages 115--122, 2012.

[2]

E. Billauer. Peak detection. http://billauer.co.il/peakdet.html.

[3]

R. Bouhenguel and I. Mahgoub. A risk and incidence based atrial fibrillation detection scheme for wearable healthcare computing devices. In Pervasive Computing Technologies for Healthcare, Proceedings of the 6th International Conference on, pages 97--104, 2012.

[4]

R. Bouhenguel, I. Mahgoub, and M. llyas. An energy efficient model for monitoring and detecting atrial fibrillation in wearable computing. In Body Area Networks, Proceedings of the 7th International Conference on, pages 59--65, 2012.

Digital Library

[5]

P. De Chazal, M. O'Dwyer, and R. B. Reilly. Automatic classification of heartbeats using ecg morphology and heartbeat interval features. Biomedical Engineering, IEEE Transactions on, 51(7):1196--1206, 2004.

[6]

G. de Lannoy, A. De Decker, M. Verleysen, et al. A supervised learning approach based on the continuous wavelet transform for r spike detection in ecg. In BIOSIGNALS (1), pages 140--145, 2008.

[7]

G. de Lannoy, D. François, J. Delbeke, and M. Verleysen. Weighted conditional random fields for supervised interpatient heartbeat classification. Biomedical Engineering, IEEE Transactions on, 59(1):241--247, 2012.

[8]

G. de Lannoy, B. Frénay, M. Verleysen, and J. Delbeke. Supervised ecg delineation using the wavelet transform and hidden markov models. In 4th European Conference of the International Federation for Medical and Biological Engineering, pages 22--25, 2009.

[9]

W. Einthoven. Ueber die form des menschlichen electrocardiogramms. Archiv fÃijr die gesamte Physiologie des Menschen und der Tiere, 60(3-4):101--123, 1895.

[10]

E. Ertin, N. Stohs, S. Kumar, A. Raij, M. al'Absi, and S. Shah. Autosense: unobtrusively wearable sensor suite for inferring the onset, causality, and consequences of stress in the field. In Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems, pages 274--287, 2011.

Digital Library

[11]

A. L. Goldberger. Clinical electrocardiography: a simplified approach. Elsevier Health Sciences, 2012.

[12]

E. Haapalainen, S. Kim, J. F. Forlizzi, and A. K. Dey. Psycho-physiological measures for assessing cognitive load. In Ubiquitous computing, Proceedings of the 12th ACM international conference on, pages 301--310, 2010.

Digital Library

[13]

M. C. Haigney, S. Alam, S. Tebo, G. Marhefka, A. Elkashef, R. Kahn, C. Chiang, F. Vocci, and L. Cantilena. Intravenous cocaine and qt variability. Journal of cardiovascular electrophysiology, 17(6):610--616, 2006.

[14]

J.-H. Hong, J. Ramos, and A. K. Dey. Understanding physiological responses to stressors during physical activity. In Ubiquitous Computing, Proceedings of the 2012 ACM Conference on, pages 270--279, 2012.

Digital Library

[15]

D. W. Hosmer Jr and S. Lemeshow. Applied logistic regression. John Wiley & Sons, 2004.

[16]

S. M. Hossain, A. A. Ali, M. M. Rahman, E. Ertin, D. Epstein, A. Kennedy, K. Preston, A. Umbricht, Y. Chen, and S. Kumar. Identifying drug (cocaine) intake events from acute physiological response in the presence of free-living physical activity. In Proceedings of the 13th international symposium on Information processing in sensor networks, pages 71--82, 2014.

Digital Library

[17]

S. Hu, Z. Shao, and J. Tan. A real-time cardiac arrhythmia classification system with wearable electrocardiogram. In Body Sensor Networks, Proceedings of the 2011 International Conference on, pages 119--124, 2011.

Digital Library

[18]

N. Hughes and L. Tarassenko. Automated qt interval analysis with confidence measures. In Computers in Cardiology, 2004, pages 765--768, 2004.

[19]

R. Jané, A. Blasi, J. García, and P. Laguna. Evaluation of an automatic threshold based detector of waveform limits in holter ecg with the qt database. In Computers in Cardiology 1997, pages 295--298, 1997.

[20]

D. Koller and N. Friedman. Probabilistic graphical models: principles and techniques. MIT press, 2009.

Digital Library

[21]

H. W. Kuhn. The hungarian method for the assignment problem. Naval research logistics quarterly, 2(1-2):83--97, 1955.

[22]

J. D. Lafferty, A. McCallum, and F. C. N. Pereira. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In Proceedings of the Eighteenth International Conference on Machine Learning, pages 282--289, 2001.

Digital Library

[23]

P. Laguna, R. G. Mark, A. Goldberg, and G. B. Moody. A database for evaluation of algorithms for measurement of qt and other waveform intervals in the ecg. In Computers in Cardiology 1997, pages 673--676, 1997.

[24]

K. Levin, M. Copersino, D. Epstein, S. Boyd, and D. Gorelick. Longitudinal ECG changes in cocaine users during extended abstinence. Drug Alcohol Depend, 95(1-2):160--163, 2008.

[25]

A. Magnano, N. Talathoti, R. Hallur, D. Jurus, J. Dizon, S. Holleran, B. D. M., E. Collins, and H. Garan. Effect of acute cocaine administration on the QTc interval of habitual users. The American journal of cardiology, 97(8):1244--1246, 2006.

[26]

J. Mairal, F. Bach, J. Ponce, and G. Sapiro. Online learning for matrix factorization and sparse coding. J. Mach. Learn. Res., 11:19--60, 2010.

Digital Library

[27]

J. P. Martínez, R. Almeida, S. Olmos, A. P. Rocha, and P. Laguna. A wavelet-based ecg delineator: evaluation on standard databases. Biomedical Engineering, IEEE Transactions on, 51(4):570--581, 2004.

[28]

A. Natarajan, A. Parate, E. Gaiser, G. Angarita, R. Malison, B. Marlin, and D. Ganesan. Detecting cocaine use with wearable electrocardiogram sensors. In Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing, pages 123--132, 2013.

Digital Library

[29]

B. A. Olshausen and D. J. Field. Sparse coding with an overcomplete basis set: A strategy employed by v1? Vision research, 37(23):3311--3325, 1997.

[30]

L. Pablo, J. Raimon, B. Eudald, and V. A. David. Qrs detection and waveform boundary recognition using ecgpuwave. http://physionet.org/physiotools/ecgpuwave/.

[31]

J. Pan and W. J. Tompkins. A real-time qrs detection algorithm. Biomedical Engineering, IEEE Transactions on, 32(3):230--236, 1985.

[32]

B. G. Schwartz, S. Rezkalla, and R. A. Kloner. Cardiovascular effects of cocaine. Circulation, 122(24):2558--2569, 2010.

[33]

F. W. Stallmann and H. V. Pipberger. Automatic recognition of electrocardiographic waves by digital computer. Circulation research, 9(6):1138--1143, 1961.

[34]

W. Vongpatanasin, A. J. Taylor, and R. G. Victor. Effects of cocaine on heart rate variability in healthy subjects. The American journal of cardiology, 93(3):385--388, 2004.

[35]

Zephyr. Bioharness 3. http://www.zephyr-technology.com/products/bioharness-3/.

[36]

W. Zong, G. Moody, and D. Jiang. A robust open-source algorithm to detect onset and duration of qrs complexes. In Computers in Cardiology, 2003, pages 737--740, 2003.

Cited By

Angarita GPittman BNararajan AMayerson TParate AMarlin BGueorguieva RPotenza MGanesan DMalison R(2023)Discriminating cocaine use from other sympathomimetics using wearable electrocardiographic (ECG) sensorsDrug and Alcohol Dependence10.1016/j.drugalcdep.2023.110898250(110898)Online publication date: Oct-2023
https://doi.org/10.1016/j.drugalcdep.2023.110898
Gullapalli BNatarajan AAngarita GMalison RGanesan DRahman T(2019)On-body Sensing of Cocaine Craving, Euphoria and Drug-Seeking Behavior Using Cardiac and Respiratory SignalsProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/33289173:2(1-31)Online publication date: 21-Jun-2019
https://dl.acm.org/doi/10.1145/3328917
Natarajan AGanesan DMarlin B(2019)Hierarchical Active Learning for Model Personalization in the Presence of Label Scarcity2019 IEEE 16th International Conference on Wearable and Implantable Body Sensor Networks (BSN)10.1109/BSN.2019.8771081(1-4)Online publication date: May-2019
https://doi.org/10.1109/BSN.2019.8771081
Show More Cited By

Index Terms

Conditional random fields for morphological analysis of wireless ECG signals
1. Applied computing
  1. Life and medical sciences
    1. Consumer health
    2. Health informatics
2. Computing methodologies
  1. Artificial intelligence
    1. Philosophical/theoretical foundations of artificial intelligence

Recommendations

Ventricular activity morphological characterization: Ectopic beats removal in long term atrial fibrillation recordings

Ectopic beats are early heart beats remarkably different to the normal beat morphology that provoke serious disturbances in electrocardiographic analysis. These beats are very common in atrial fibrillation (AF), causing important residua when ...
Detection of cardiac abnormalities in ECG signal using time-based signal processing algorithm

Electrocardiograms are bioelectrical signals that provide information regarding the normal and abnormal physiology of the heart. Early detection of cardiac irregularities in cardiac patients prevents strokes and sudden deaths. The effective functioning ...
ECG-grained Cardiac Monitoring Using UWB Signals

With the development of wireless sensing, researchers have proposed many contactless vital sign monitoring systems, which can be used to monitor respiration rates, heart rates, cardiac cycles and etc. However, these vital signs are ones of coarse ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

BCB '14: Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics

September 2014

851 pages

ISBN:9781450328944

DOI:10.1145/2649387

General Chairs:
Pierre Baldi
University of California, Irvine
,
Wei Wang
University of California, Los Angeles

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGBio: ACM Special Interest Group on Bioinformatics

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 September 2014

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper

Conference

BCB '14

Sponsor:

SIGBio

BCB '14: ACM-BCB '14

September 20 - 23, 2014

California, Newport Beach

Acceptance Rates

Overall Acceptance Rate 254 of 885 submissions, 29%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

5
Total Citations
View Citations
155
Total Downloads

Downloads (Last 12 months)5
Downloads (Last 6 weeks)0

Reflects downloads up to 28 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Angarita GPittman BNararajan AMayerson TParate AMarlin BGueorguieva RPotenza MGanesan DMalison R(2023)Discriminating cocaine use from other sympathomimetics using wearable electrocardiographic (ECG) sensorsDrug and Alcohol Dependence10.1016/j.drugalcdep.2023.110898250(110898)Online publication date: Oct-2023
https://doi.org/10.1016/j.drugalcdep.2023.110898
Gullapalli BNatarajan AAngarita GMalison RGanesan DRahman T(2019)On-body Sensing of Cocaine Craving, Euphoria and Drug-Seeking Behavior Using Cardiac and Respiratory SignalsProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/33289173:2(1-31)Online publication date: 21-Jun-2019
https://dl.acm.org/doi/10.1145/3328917
Natarajan AGanesan DMarlin B(2019)Hierarchical Active Learning for Model Personalization in the Presence of Label Scarcity2019 IEEE 16th International Conference on Wearable and Implantable Body Sensor Networks (BSN)10.1109/BSN.2019.8771081(1-4)Online publication date: May-2019
https://doi.org/10.1109/BSN.2019.8771081
Natarajan AAngarita GGaiser EMalison RGanesan DMarlin BLukowicz PKrüger ABulling ALim YPatel S(2016)Domain adaptation methods for improving lab-to-field generalization of cocaine detection using wearable ECGProceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing10.1145/2971648.2971666(875-885)Online publication date: 12-Sep-2016
https://dl.acm.org/doi/10.1145/2971648.2971666
Nguyen Adams Natarajan Marlin (2016)Parsing wireless electrocardiogram signals with context free grammar conditional random fields2016 IEEE Wireless Health (WH)10.1109/WH.2016.7764570(1-8)Online publication date: Oct-2016
https://doi.org/10.1109/WH.2016.7764570

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten