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EventDTW: An Improved Dynamic Time Warping Algorithm for Aligning Biomedical Signals of Nonuniform Sampling Frequencies

1
The Departments of Biomedical Engineering and Biostatistics & Bioinformatics, Duke University, Durham, NC 27708, USA
2
The Division of Cardiology and the Cardiovascular Research Institute, University of California San Francisco, San Francisco, CA 94143, USA
*
Author to whom correspondence should be addressed.
First co-authors.
Sensors 2020, 20(9), 2700; https://doi.org/10.3390/s20092700
Received: 19 March 2020 / Revised: 2 May 2020 / Accepted: 6 May 2020 / Published: 9 May 2020
(This article belongs to the Section Intelligent Sensors)
The dynamic time warping (DTW) algorithm is widely used in pattern matching and sequence alignment tasks, including speech recognition and time series clustering. However, DTW algorithms perform poorly when aligning sequences of uneven sampling frequencies. This makes it difficult to apply DTW to practical problems, such as aligning signals that are recorded simultaneously by sensors with different, uneven, and dynamic sampling frequencies. As multi-modal sensing technologies become increasingly popular, it is necessary to develop methods for high quality alignment of such signals. Here we propose a DTW algorithm called EventDTW which uses information propagated from defined events as basis for path matching and hence sequence alignment. We have developed two metrics, the error rate (ER) and the singularity score (SS), to define and evaluate alignment quality and to enable comparison of performance across DTW algorithms. We demonstrate the utility of these metrics on 84 publicly-available signals in addition to our own multi-modal biomedical signals. EventDTW outperformed existing DTW algorithms for optimal alignment of signals with different sampling frequencies in 37% of artificial signal alignment tasks and 76% of real-world signal alignment tasks. View Full-Text
Keywords: dynamic time warping; signal alignment; nonuniform sampling dynamic time warping; signal alignment; nonuniform sampling
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MDPI and ACS Style

Jiang, Y.; Qi, Y.; Wang, W.K.; Bent, B.; Avram, R.; Olgin, J.; Dunn, J. EventDTW: An Improved Dynamic Time Warping Algorithm for Aligning Biomedical Signals of Nonuniform Sampling Frequencies. Sensors 2020, 20, 2700. https://doi.org/10.3390/s20092700

AMA Style

Jiang Y, Qi Y, Wang WK, Bent B, Avram R, Olgin J, Dunn J. EventDTW: An Improved Dynamic Time Warping Algorithm for Aligning Biomedical Signals of Nonuniform Sampling Frequencies. Sensors. 2020; 20(9):2700. https://doi.org/10.3390/s20092700

Chicago/Turabian Style

Jiang, Yihang, Yuankai Qi, Will K. Wang, Brinnae Bent, Robert Avram, Jeffrey Olgin, and Jessilyn Dunn. 2020. "EventDTW: An Improved Dynamic Time Warping Algorithm for Aligning Biomedical Signals of Nonuniform Sampling Frequencies" Sensors 20, no. 9: 2700. https://doi.org/10.3390/s20092700

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