Abstract
With the advanced technology of medical devices and sensors, an abundance of medical data streams are available. However, data analysis techniques are very limited, especially for processing massive multiple physiological streams that may only be understood by medical experts. The state-of-the-art techniques only allow multiple medical devices to independently monitor different physiological parameters for the patient’s status, thus they signal too many false alarms, creating unnecessary noise, especially in the Intensive Care Unit (ICU). An effective solution which has been recently studied is to integrate information from multiple physiologic parameters to reduce alarms. But it is a challenge to detect abnormalities from high frequently changed physiological streams data, since abnormalities occur gradually due to the complex situation of patients. An analysis of ICU physiological data streams shows that many vital physiological parameters are changed periodically (such as heart rate, arterial pressure, and respiratory impedance) and thus abnormalities are generally abnormal period patterns. In this paper, we develop a Mining Abnormal Period Patterns from Multiple Physiological Streams (MAPPMPS) method to detect and rank abnormalities in medical sensor streams. The efficiency and effectiveness of the MAPPMPS method is demonstrated by a real-world massive database of multiple physiological streams sampled in ICU, comprising 250 patients’ streams (each stream involving over 1.3 million data points) with a total size of 28 GB data.
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Biot, L., Holzapfel, L., Becq, G., Melot, C., Baconnier, P.: Do we need a systematic activation of alarm soundings for blood pressure monitoring for the safety of ICU patients? J. Crit. Care 18(4), 212–216 (2003)
Charbonnier, S.: On line extraction of temporal episodes from ICU high-frequency data: a visual support for signal interpretation. Comput. Methods Prog. Biomed. 78, 115–132 (2005)
Charbonnier, S., Becq, G., Biot, L.: On-line segmentation algorithm for continuously monitored data in intensive care units. IEEE Trans. Biomed. Eng. 51(3), 484–492 (2004)
Charbonnier, S., Garcia-Beltan, C., Cadet, C., Gentil, S.: Trend extraction and analysis for complex system monitoring and decision support. Eng. Appl. Artif. Intell. 18(1), 21–36 (2005)
Charbonnier, S., Gentil, S.: On-line adaptive trend extraction of multiple physiological signals for alarm filtering in intensive care units. Int. J. Adapt. Control. Signal Proc. 24, 382–408 (2010)
Cvach, M., Dang, D., Foster, J., Irechukwu, J.: Clinical alarms and the impact on patient safety. Initiatives Safe Patient Care, 1–8 (2009)
Douglas, D.H., Peucker, T.K.: Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. Can. Cartogr. 10(2), 112–122 (1973)
Goldberger, A.L., Amaral, L.A.N., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.-K., Stanley, H.E.: PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23), e215–e220 (2000)
Haimowitz, I., Phillip, P.L., Kohane, I.: Managing temporal worlds for medical trend diagnosis. Artif. Intell. Med. 8, 299–321 (1996)
He, J., Zhang, Y., Huang, G., de Souza, P.: CIRCE: correcting imprecise readings and compressing excrescent points for querying common patterns in uncertain sensor streams. Information Systems, available online 25 Jan., 2012: http://dx.doi.org/10.1016/j.is.2012.01.003
Huang, G., Zhang, Y., He, J., Ding, Z.: Efficiently retrieving longest common route patterns of moving objects by summarizing turning regions. Proc. of the 15th Pacific-Asia Conference in Knowledge Discovery and Data Mining (PAKDD 2011), pp. 375–386, Shenzhen, China (2011)
Patel, V.L., Shortliffe, E.H., Stefanelli, M., Szolovits, P., Berthold, M.R., Bellazzi, R., Abu-Hanna, A.: The coming of age of artificial intelligence in medicine. Artif. Intell. Med. 46(1), 5–17 (2009). doi:10.1016/j.artmed.2008.07.017
Ramamohanarao, K., Fan, H.: Patterns based classifiers. World Wide Web 10, 71–83 (2007)
Sebastiao, R., Gama, J., Rodrigues, P.P., Bernardes, J.: Monitoring incremental histogram distribution for change detection in data streams. The 2nd International Workshop on Knowledge Discovery from Sensor Data (Sensor-KDD 2008), pp. 25–42, Las Vegas, USA, Aug. 24–27, 2008
Sebastião, R., Silva, M.M., Gama, J., Mendonça, T.: “Contributions to a decision support system based on depth of anesthesia signals”. Proc. 25th IEEE International Symposium on Computer-Based Medical Systems (CBMS’12), June 20–22, Rome, Italy (2012)
Sow, D., Biem, A., Blount, M., Ebling, M., Verscheure, O.: Body sensor data processing using stream computing. Proc. of the 11th ACM International Conference on Multimedia Information Retrieval (MIR’10), Philadelphia, Pennsylvania, USA, March 29–31, 2010
Weiss, Y.G., Maliar, A., Eidelman, L.A., Berlatzky, Y., Hanson III, C.W., Deutschman, C.S., Zajicek, G.: Computer assisted physiologic monitoring and stability assessment in vascular surgical patients undergoing general anesthesia–preliminary data. J. Clin. Monit. Comput. 16, 107–113 (2000)
Welch, J.P., Ford, P.J., Teplick, R.S., Rubsamen, R.M.: The Massachusetts General Hospital-Marquette Foundation Hemodynamic and Electrocardiographic Database–comprehensive collection of critical care waveforms. J. Clin. Monit. 7(1), 96–97 (1991)
White, E.R.: Assessment of line generalization algorithms using characteristic points. Am. Cartogr. 12, 17–27 (1985)
Zhou, X., Li, H., Liu, H., Li, M., Tang, L., Fan, Y., Hu, Z.: Monitoring abnormal patterns with complex semantics over ICU data streams. Proc of the International Workshop on Intelligent Computing in Pattern Analysis/Synthesis (IWICPAS’06), pp. 185–194 (2006)
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Huang, G., Zhang, Y., Cao, J. et al. Online mining abnormal period patterns from multiple medical sensor data streams. World Wide Web 17, 569–587 (2014). https://doi.org/10.1007/s11280-013-0203-y
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DOI: https://doi.org/10.1007/s11280-013-0203-y