ABSTRACT
The Electrocardiogram (ECG) is commonly used to detect arrhythmias. Traditionally, a single ECG observation is used for diagnosis, making it difficult to detect irregular arrhythmias. Recent technology developments, however, have made it cost-effective to collect large amounts of raw ECG data over time. This promises to improve diagnosis accuracy, but the large data volume presents new challenges for cardiologists. This paper introduces ECGLens, an interactive system for arrhythmia detection and analysis using large-scale ECG data. Our system integrates an automatic heartbeat classification algorithm based on convolutional neural network, an outlier detection algorithm, and a set of rich interaction techniques. We also introduce A-glyph, a novel glyph designed to improve the readability and comparison of ECG signals. We report results from a comprehensive user study showing that A-glyph improves the efficiency in arrhythmia detection, and demonstrate the effectiveness of ECGLens in arrhythmia detection through two expert interviews.
Supplemental Material
Available for Download
The auxiliary material contains a pdf document to provide more details about our deep learning model, including its architecture, the choice of parameters, and a comprehensive evaluation of the model (Section titled Heartbeat Classification?).
- U Rajendra Acharya, P Subbanna Bhat, and UC Niranjan. 2002. Comprehensive visualization of cardiac health using electrocardiograms. Computers in Biology and Medicine 32, 1 (2002), 49--54.Google ScholarCross Ref
- AMPS. 2008. A. M. P. S. llc. www.amps-llc.com/Home.php. (NOV 2008).Google Scholar
- Rodrigo Varejao Andreao, Bernadette Dorizzi, and Jérôme Boudy. 2006. ECG signal analysis through hidden Markov models. IEEE Transactions on Biomedical Engineering 53, 8 (2006), 1541--1549.Google ScholarCross Ref
- Muhammad Arif and others. 2008. Robust electrocardiogram (ECG) beat classification using discrete wavelet transform. Physiological Measurement 29, 5 (2008), 555.Google ScholarCross Ref
- American Heart Association. 2014. Target Heart Rates. http://www.heart.org/HEARTORG/HealthyLiving/ PhysicalActivity/Target-Heart-Rates_UCM_434341_Article.jsp#.WbI8stMjFBw. (MAY 2014).Google Scholar
- Raymond Robert Bond, Dewar Darren Finlay, Chris Desmond Nugent, and George Moore. 2010. A web-based visualization tool for transforming the 12-lead ECG into a body surface potential map. In Computing in Cardiology, 2010. IEEE, 285--288.Google Scholar
- Raymond R Bond, Dewar D Finlay, Chris D Nugent, George Moore, and Daniel Guldenring. 2013. Methods for presenting and visualising electrocardiographic data: from temporal signals to spatial imaging. Journal of Electrocardiology 46, 3 (2013), 182--196.Google ScholarCross Ref
- Markus M Breunig, Hans-Peter Kriegel, Raymond T Ng, and Jörg Sander. 2000. LOF: identifying density-based local outliers. In ACM Sigmod Record, Vol. 29. ACM, 93--104. Google ScholarDigital Library
- Huihua Kenny Chiang, Chao-Wei Chu, Gau-Yang Chen, and Cheng-Deng Kuo. 2001. A new 3-D display method for 12-lead ECG. IEEE Transactions on Biomedical Engineering 48, 10 (2001), 1195--1202.Google ScholarCross Ref
- Zhicheng Cui, Wenlin Chen, and Yixin Chen. 2016. Multi-scale convolutional neural networks for time series classification. arXiv preprint arXiv:1603.06995 (2016).Google Scholar
- Philip De Chazal, Maria O'Dwyer, and Richard B Reilly. 2004. Automatic classification of heartbeats using ECG morphology and heartbeat interval features. IEEE Transactions on Biomedical Engineering 51, 7 (2004), 1196--1206.Google ScholarCross Ref
- AAMI ECAR. 1987. Recommended practice for testing and reporting performance results of ventricular arrhythmia detection algorithms. Association for the Advancement of Medical Instrumentation (1987).Google Scholar
- EcgSoft. 2008. EcgSoft home page. http://www.ecg-soft.com/. (NOV 2008).Google Scholar
- M Escalona-Morán, MC Soriano, J García-Prieto, I Fischer, and CR Mirasso. 2014. Multivariate nonlinear time-series estimation using delay-based reservoir computing. The European Physical Journal Special Topics 223, 13 (2014), 2903--2912.Google ScholarCross Ref
- Miguel Angel Escalona-Morán, Miguel C Soriano, Ingo Fischer, and Claudio R Mirasso. 2015. Electrocardiogram classification using reservoir computing with logistic regression. IEEE Journal of Biomedical and health Informatics 19, 3 (2015), 892--898.Google ScholarCross Ref
- Himanshu Gothwal, Silky Kedawat, and Rajesh Kumar. 2011. Cardiac arrhythmias detection in an ECG beat signal using fast fourier transform and artificial neural network. Journal of Biomedical Science and Engineering 4, 04 (2011), 289.Google ScholarCross Ref
- Alfredo Illanes-Manriquez, Raúl Jiménez, Gustavo Dinamarca, Claudia Jiménez, and Eduardo Lecannelier. 2010. Visualizing the electrocardiogram through orbital transform. In Annual International Conference of the IEEE on Engineering in Medicine and Biology Society (EMBC). IEEE, 5290--5293.Google ScholarCross Ref
- Wei Jiang and Seong G Kong. 2007. Block-based neural networks for personalized ECG signal classification. IEEE Transactions on Neural Networks 18, 6 (2007), 1750--1761. Google ScholarDigital Library
- S Karpagachelvi, M Arthanari, and M Sivakumar. 2010. ECG feature extraction techniques-a survey approach. arXiv preprint arXiv:1005.0957 (2010).Google Scholar
- John A Kastor. 2000. Arrhythmias. WB Saunders Company.Google Scholar
- Jan A Kors and Gerard van Herpen. 2008. Mirror image electrocardiograms and additional electrocardiographic leads: new wine in old wineskins? Journal of Electrocardiology 41, 3 (2008), 245--250.Google ScholarCross Ref
- Hanfei Lin, Siyuan Gao, David Gotz, Fan Du, Jingrui He, and Nan Cao. 2017. RCLens: Interactive Rare Category Exploration and Identification. IEEE Transactions on Visualization and Computer Graphics (2017).Google Scholar
- Jessica Lin, Eamonn Keogh, Li Wei, and Stefano Lonardi. 2007. Experiencing SAX: a novel symbolic representation of time series. Data Mining and knowledge discovery 15, 2 (2007), 107--144. Google ScholarDigital Library
- Eduardo José da S Luz, William Robson Schwartz, Guillermo Cámara-Chávez, and David Menotti. 2016. ECG-based heartbeat classification for arrhythmia detection: A survey. Computer Methods and Programs in Biomedicine 127 (2016), 144--164. Google ScholarDigital Library
- John E Madias. 2004. The 13th multiuse ECG lead: Shouldn't we use it more often, and on the same hard copy or computer screen, as the other 12 leads? Journal of Electrocardiology 37, 4 (2004), 285--287.Google ScholarCross Ref
- Farah Magrabi, Nigel H Lovell, and Branko G Celler. 1999. A web-based approach for electrocardiogram monitoring in the home. International Journal of Medical Informatics 54, 2 (1999), 145--153.Google ScholarCross Ref
- Juan Pablo Martínez, Rute Almeida, Salvador Olmos, Ana Paula Rocha, and Pablo Laguna. 2004. A wavelet-based ECG delineator: evaluation on standard databases. IEEE Transactions on Biomedical Engineering 51, 4 (2004), 570--581.Google ScholarCross Ref
- George B Moody and Roger G Mark. 2001. The impact of the MIT-BIH arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20, 3 (2001), 45--50.Google ScholarCross Ref
- Jalal A Nasiri, Mahmoud Naghibzadeh, H Sadoghi Yazdi, and Bahram Naghibzadeh. 2009. ECG arrhythmia classification with support vector machines and genetic algorithm. In Third UKSim European Symposium on Computer Modeling and Simulation. IEEE, 187--192. Google ScholarDigital Library
- M Oefinger, W Zong, M Krieger, and RG Mark. 2004. An interactive web-based tool for multiscale physiological data visualization. In Computers in Cardiology, 2004. IEEE, 569--571.Google Scholar
- OFFIS. 2011. Discussion forum for OFFIS DICOM tools. http://forum.dcmtk.org/2011. (JAN 2011).Google Scholar
- Stanislaw Osowski, Linh Tran Hoai, and Tomasz Markiewicz. 2004. Support vector machine-based expert system for reliable heartbeat recognition. IEEE Transactions on Biomedical Engineering 51, 4 (2004), 582--589.Google ScholarCross Ref
- Yüksel Özbay, Rahime Ceylan, and Bekir Karlik. 2006. A fuzzy clustering neural network architecture for classification of ECG arrhythmias. Computers in Biology and Medicine 36, 4 (2006), 376--388. Google ScholarDigital Library
- Alex Page, Tolga Soyata, Jean-Philippe Couderc, and Mehmet Aktas. 2015b. An open source ECG clock generator for visualization of long-term cardiac monitoring data. IEEE Access 3 (2015), 2704--2714.Google ScholarCross Ref
- Alex Page, Tolga Soyata, Jean-Philippe Couderc, Mehmet Aktas, Burak Kantarci, and Silvana Andreescu. 2015a. Visualization of health monitoring data acquired from distributed sensors for multiple patients. In Global Communications Conference (GLOBECOM). IEEE, 1--7.Google ScholarCross Ref
- Saurabh Pal and Swanirbhar Majumder. 2010. ECG Data Analysis. In Intelligent Medical Technologies and Biomedical Engineering: Tools and Applications. IGI Global, 122--144.Google Scholar
- Pranav Rajpurkar, Awni Y Hannun, Masoumeh Haghpanahi, Codie Bourn, and Andrew Y Ng. 2017. Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks. arXiv preprint arXiv:1707.01836 (2017).Google Scholar
- EM Tamil, NH Kamarudin, R Salleh, M Yamani Idna Idris, MN Noorzaily, and AM Tamil. 2008. Heartbeat electrocardiogram (ECG) signal feature extraction using discrete wavelet transforms (DWT). Proceedings of CSPA (2008), 1112--1117.Google Scholar
- A Teeramongkonrasmee, C Tangwongsan, and S Sitthisook. 2009. Development of a real-time cardiac arrhythmia analyzer. In Proceedings of 32nd Electrical Engineering Conference (EECON-32), Vol. 2. 1367--1370.Google Scholar
- Markos G Tsipouras, Dimitrios I Fotiadis, and D Sideris. 2005. An arrhythmia classification system based on the RR-interval signal. Artificial Intelligence in Medicine 33, 3 (2005), 237--250. Google ScholarDigital Library
- Edward Tufte and P Graves-Morris. 2014. The visual display of quantitative information.; 1983. (2014). Google ScholarDigital Library
- Galen S Wagner. 2001. Marriott's practical electrocardiography. Lippincott Williams & Wilkins.Google Scholar
- Can Ye, BVK Vijaya Kumar, and Miguel Tavares Coimbra. 2012. Combining general multi-class and specific two-class classifiers for improved customized ECG heartbeat classification. In 21st International Conference on Pattern Recognition (ICPR). IEEE, 2428--2431.Google Scholar
- Yun-Chi Yeh, Wen-June Wang, and Che Wun Chiou. 2009. Cardiac arrhythmia diagnosis method using linear discriminant analysis on ECG signals. Measurement 42, 5 (2009), 778--789.Google ScholarCross Ref
- Qibin Zhao and Liqing Zhang. 2005. ECG feature extraction and classification using wavelet transform and support vector machines. In International Conference on Neural Networks and Brain, Vol. 2. IEEE, 1089--1092.Google Scholar
- Muhammad Zubair, Jinsul Kim, and Changwoo Yoon. 2016. An automated ECG beat classification system using convolutional neural networks. In 6th International Conference on IT Convergence and Security (ICITCS). IEEE, 1--5.Google ScholarCross Ref
Index Terms
- ECGLens: Interactive Visual Exploration of Large Scale ECG Data for Arrhythmia Detection
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