2014 ScalableNoiseMininginLongTermEl

• (Chia & Syed, 2014) ⇒ Chih-Chun Chia, and Zeeshan Syed. (2014). “Scalable Noise Mining in Long-term Electrocardiographic Time-series to Predict Death Following Heart Attacks.” In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2014) Journal. ISBN:978-1-4503-2956-9 doi:10.1145/2623330.2623702

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Cited By

• http://scholar.google.com/scholar?q=%222014%22+Scalable+Noise+Mining+in+Long-term+Electrocardiographic+Time-series+to+Predict+Death+Following+Heart+Attacks
• http://dl.acm.org/citation.cfm?id=2623330.2623702&preflayout=flat#citedby

Quotes

Author Keywords

• Adaptive downsampling; cardiovascular disease; electrocardiogram; general; noise mining; time-warping

Abstract

Cardiac disease is the leading cause of death around the world; with ischemic heart disease alone claiming 7 million lives in 2011. This burden can be attributed, in part, to the absence of biomarkers that can reliably identify high risk patients and match them to treatments that are appropriate for them. In recent clinical studies, we have demonstrated the ability of computation to extract information with substantial prognostic utility that is typically disregarded in time-series data collected from cardiac patients. Of particular interest are subtle variations in long-term electrocardiographic (ECG) data that are usually overlooked as noise but provide a useful assessment of myocardial instability. In multiple clinical cohorts, we have developed the pathophysiological basis for studying probabilistic variations in long-term ECG and demonstrated the ability of this information to effectively risk stratify patients at risk of dying following heart attacks. In this paper, we extend this work and focus on the question of how to reduce its computational complexity for scalable use in large datasets or energy constrained embedded devices. Our basic approach to uncovering pathological structure within the ECG focuses on characterizing beat-to-beat time-warped shape deformations of the ECG using a modified dynamic time-warping (DTW) and Lomb-Scargle periodogram-based algorithm. As part of our efforts to scale this work up, we explore a novel approach to address the quadratic runtime of DTW. We achieve this by developing the idea of adaptive downsampling to reduce the size of the inputs presented to DTW, and describe changes to the dynamic programming problem underlying DTW to exploit adaptively downsampled ECG signals. When evaluated on data from 765 patients in the DISPERSE2-TIMI33 trial, our results show that high morphologic variability is associated with an 8 - to 9-fold increased risk of death within 90 days of a heart attack. Moreover, the use of adaptive downsampling with a modified DTW formulation achieves a 7 - to almost 20-fold reduction in runtime relative to DTW, without a significant change in biomarker discrimination.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2014 ScalableNoiseMininginLongTermEl	Chih-Chun Chia Zeeshan Syed			Scalable Noise Mining in Long-term Electrocardiographic Time-series to Predict Death Following Heart Attacks				10.1145/2623330.2623702		2014