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International Workshop on Advanced Analysis and Learning on Temporal Data

AALTD 2019: Advanced Analytics and Learning on Temporal Data pp 213–228Cite as

An Initial Study on Adapting DTW at Individual Query for Electrocardiogram Analysis

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 11986)

Abstract

This paper describes an initial investigation on adapting windowed Dynamic Time Warping (DTW) for enhancing the reliability of fast DTW for Electrocardiogram analysis in Cardiology, a domain where risks are especially important to avoid. The key question it explores is whether it is worthwhile to adapt the window size of DTW for every query temporal sequence, a factor critically determining the speed-accuracy tradeoff of DTW. It in addition extends the adaptation to cover also the order of sequences for lower bound calculations. Experiments on ECG temporal sequences show that the techniques help significantly reduce risks that windowed DTW algorithms are subject to and at the same time keeping a high speed.

Keywords

  • DTW
  • Time series analytics
  • Algorithm optimizations
  • Electrocardiogram

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  • DOI: 10.1007/978-3-030-39098-3_16
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Author information

Authors and Affiliations

  1. Enloe Magnet High School, Raleigh, NC, 27610, USA

    Daniel Shen

  2. Computer Science Department, North Carolina State University, Raleigh, NC, 27695, USA

    Min Chi

Authors
  1. Daniel Shen
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  2. Min Chi
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Corresponding author

Correspondence to Daniel Shen .

Editor information

Editors and Affiliations

  1. Orange Labs, Lannion, France

    Vincent Lemaire

  2. Inria, University of Rennes, Rennes, France

    Simon Malinowski

  3. University of East Anglia, Norwich, UK

    Prof. Anthony Bagnall

  4. Orange Labs, Châtillon, France

    Alexis Bondu

  5. Irisa, Agrocampus Ouest, Rennes, France

    Dr. Thomas Guyet

  6. University of Rennes 2, Rennes, France

    Romain Tavenard

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Shen, D., Chi, M. (2020). An Initial Study on Adapting DTW at Individual Query for Electrocardiogram Analysis. In: Lemaire, V., Malinowski, S., Bagnall, A., Bondu, A., Guyet, T., Tavenard, R. (eds) Advanced Analytics and Learning on Temporal Data. AALTD 2019. Lecture Notes in Computer Science(), vol 11986. Springer, Cham. https://doi.org/10.1007/978-3-030-39098-3_16

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  • DOI: https://doi.org/10.1007/978-3-030-39098-3_16

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