PhysioEx: Visual Analysis of Physiological Event Streams
In this work, we introduce a novel visualization technique, the Temporal Intensity Map, which visually integrates data values over time to reveal the frequency, duration, and timing of significant features in streaming data. We combine the Temporal Intensity Map with several coordinated visualizatio...
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| Published in | Computer graphics forum Vol. 35; no. 3; pp. 331 - 340 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Oxford
Blackwell Publishing Ltd
01.06.2016
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0167-7055 1467-8659 |
| DOI | 10.1111/cgf.12909 |
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| Abstract | In this work, we introduce a novel visualization technique, the Temporal Intensity Map, which visually integrates data values over time to reveal the frequency, duration, and timing of significant features in streaming data. We combine the Temporal Intensity Map with several coordinated visualizations of detected events in data streams to create PhysioEx, a visual dashboard for multiple heterogeneous data streams. We have applied PhysioEx in a design study in the field of neonatal medicine, to support clinical researchers exploring physiologic data streams. We evaluated our method through consultations with domain experts. Results show that our tool provides deep insight capabilities, supports hypothesis generation, and can be well integrated into the workflow of clinical researchers. |
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| AbstractList | In this work, we introduce a novel visualization technique, the Temporal Intensity Map, which visually integrates data values over time to reveal the frequency, duration, and timing of significant features in streaming data. We combine the Temporal Intensity Map with several coordinated visualizations of detected events in data streams to create PhysioEx, a visual dashboard for multiple heterogeneous data streams. We have applied PhysioEx in a design study in the field of neonatal medicine, to support clinical researchers exploring physiologic data streams. We evaluated our method through consultations with domain experts. Results show that our tool provides deep insight capabilities, supports hypothesis generation, and can be well integrated into the workflow of clinical researchers. |
| Author | Kamaleswaran, Rishikesan Collins, Christopher McGregor, Carolyn James, Andrew |
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| Cites_doi | 10.2217/pme.11.7 10.1007/s10115-003-0086-9 10.1109/TVCG.2009.84 10.1007/978-1-4899-3324-9 10.1145/1978942.1979196 10.1109/VAST.2014.7042572 10.1109/TVCG.2014.2346433 10.1016/S0140-6736(12)60820-4 10.1016/j.chemgeo.2012.04.021 10.1109/TVCG.2007.70415 10.1109/PACIFICVIS.2011.5742371 10.1109/TVCG.2011.185 10.1203/01.PDR.0000064904.05313.D2 10.1088/0967-3334/32/11/S08 10.1109/TVCG.2014.2346922 10.1258/ebm.2010.009336 10.1109/MCG.2009.6 10.1109/INFVIS.2000.885098 10.1111/cgf.12129 10.1109/MC.2013.157 10.1093/bioinformatics/btg001 10.1097/MOP.0b013e32835e8fe6 10.1109/TVCG.2011.179 10.1007/978-3-642-17274-8_51 10.1016/S0022-3476(86)80685-0 10.1109/TVCG.2013.227 |
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| Copyright | 2016 The Author(s) Computer Graphics Forum © 2016 The Eurographics Association and John Wiley & Sons Ltd. Published by John Wiley & Sons Ltd. 2016 The Eurographics Association and John Wiley & Sons Ltd. |
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| References_xml | – reference: Vermeesch P.: On the visualisation of detrital age distributions. Chemical Geology 312 (2012), 190-194. 2 – reference: Kett J.C.: Anemia in infancy. Blood 1599 (2012), 1678. 2 – reference: Lillie E.O., Patay B., Diamant J., Issell B., Topol E.J., Schork N.J.: The n-of-1 clinical trial: the ultimate strategy for individualizing medicine? Personalized medicine 8, 2 (2011), 161-173. 1 – reference: Moorman J.R., Delos J.B., Flower A.A., Cao H., Kovatchev B.P., Richman J.S., Lake D.E.: Cardiovascular oscillations at the bedside: early diagnosis of neonatal sepsis using heart rate characteristics monitoring. Physiological measurement 32, 11 (2011), 1821. 2 – reference: Krstajic M., Bertini E., Keim D.A.: Cloudlines: Compact display of event episodes in multiple time-series. IEEE Trans. on Visualization and Computer Graphics 17, 12 (2011), 2432-2439. 2 – reference: Martin R.J., Miller M.J., Carlo W.A.: Pathogenesis of apnea in preterm infants. The Journal of Pediatrics 109, 5 (1986), 733-741. 2 – reference: Toyoda T., Mochizuki Y., Konagaya A.: GSCope: a clipped fisheye viewer effective for highly complicated biomolecular network graphs. Bioinformatics 19, 3 (2003), 437-438. 2 – reference: Flower A.A., Moorman J.R., Lake D.E., Delos J.B.: Periodic heart rate decelerations in premature infants. Experimental Biology and Medicine 235, 4 (2010), 531-538. 2 – reference: Bostock M., Ogievetsky V., Heer J.: D3 data-driven documents. IEEE Trans. on Visualization and Computer Graphics 17, 12 (2011), 2301-2309. 3 – reference: Liu Z., Jiang B., Heer J.: immens: Real-time visual querying of big data. Computer Graphics Forum (Proc. EuroVis) 32, 3 (June 2013), 421-430. doi:10.1111/cgf.12129. 2 – reference: Hinneburg A., Keim D.A.: A general approach to clustering in large databases with noise. Knowledge and Information Systems 5, 4 (2003), 387-415. 2 – reference: Griffin M.P., O'shea T.M., Bissonette E.A., Harrell F.E., Lake D.E., Moorman J.R.: Abnormal heart rate characteristics preceding neonatal sepsis and sepsis-like illness. Pediatric Research 53, 6 (2003), 920-926. 2 – reference: Aigner W., Miksch S., Muller W., Schumann H., Tominski C.: Visual methods for analyzing time-oriented data. IEEE Trans. on Visualization and Computer Graphics 14, 1 (2008), 47-60. 2 – reference: SILVERMAN B. W.: Density estimation for statistics and data analysis, vol. 26. CRC press, 1986. 2 – reference: McGergor C.: Big data in neonatal intensive care. IEEE Computer 46, 6 (2013), 54-59. 2 – reference: Turlach B.A., et al.: Bandwidth selection in kernel density estimation: A review. Université catholique de Louvain, 1993. 2 – reference: Blencowe H., Cousens S., Oestergaard M.Z., Chou D., Moller A.-B., Narwal R., Adler A., Garcia C.V., Rohde S., Say L., et al.: National, regional, and worldwide estimates of preterm birth rates in the year 2010 with time trends since 1990 for selected countries: a systematic analysis and implications. The Lancet 379, 9832 (2012), 2162-2172. 2 – reference: Chen M., Ebert D., Hagen H., Laramee R.S., Van Liere R., Ma K.-L., Ribarsky W., Scheuermann G., Silver D.: Data, information, and knowledge in visualization. IEEE Computer Graphics and Applications 29, 1 (2009), 12-19. 2 – reference: Cui W., Liu S., Wu Z., Wei H.: How hierarchical topics evolve in large text corpora. IEEE Trans. on Visualization and Computer Graphics 20, 12 (Dec. 2014), 2281-2290. doi:10.1109/TVCG.2014.2346433. 2 – reference: Fairchild K.D.: Predictive monitoring for early detection of sepsis in neonatal ICU patients. Current Opinion in Pediatrics 25, 2 (2013), 172-179. 2 – reference: Pedregosa F., Varoquaux G., Gramfort A., Michel V., Thirion B., Grisel O., Blondel M., Prettenhofer P., Weiss R., Dubourg V., et al.: Scikit-learn: Machine learning in python. The Journal of Machine Learning Research 12 (2011), 2825-2830. 4 – reference: Elmqvist N., Fekete J.-D.: Hierarchical aggregation for information visualization: Overview, techniques, and design guidelines. IEEE Trans. on Visualization and Computer Graphics 16, 3 (2010), 439-454. 2 – reference: Huron S., Vuillemot R., Fekete J.-D.: Visual sedimentation. IEEE Trans. on Visualization and Computer Graphics 19, 12 (2013), 2446-2455. 2 – reference: Zhao J., Cao N., Wen Z., Song Y., Lin Y.-R., Collins C.: #FluxFlow: Visual analysis of anomalous information spreading on social media. 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| SubjectTerms | Analysis Anatomy & physiology Categories and Subject Descriptors (according to ACM CCS) Computer graphics Consultancy services Data transmission Design analysis H.5.2 [Information Interfaces and Presentation]: User Interfaces-Graphical user interfaces J.3 [Life and Medical Sciences]: Medical information systems Streaming media Streams Studies Time measurements Visual Visualization Workflow |
| Title | PhysioEx: Visual Analysis of Physiological Event Streams |
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