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Proceedings Paper

LAM: A landscape matching algorithm for respiratory data alignment
Author(s): Liangyou Chen; Thomas McKenna; Andrei Gribok; Jaques Reifman
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Paper Abstract

Respiratory waveforms and their derived respiratory rate time-series data can become misaligned from each other when they are collected by vital signs monitors under sub-optimal field conditions. The monitor-provided waveforms and rates can be re-aligned by independently calculating respiratory rates from the waveforms and then aligning them with the monitor-provided rates. However, substantially different rates may be generated from the same waveform due to the presence of ambiguous breaths at noisy positions in the waveform. This paper reports a landscape matching (LAM) algorithm to align respiratory rate time-series data with the waveform that they are derived from by using rates that are calculated by different means. The algorithm exploits the intermittent matches between two respiratory rate time series to generate a matching score for an alignment. The best alignment exhibits the highest matching score. The alignment performance of the LAM algorithm is compared to that of a correlation matching (CM) algorithm using field-collected respiratory data. Alignment performance is evaluated by: (1) comparing the ability of the two algorithms to return a shifted waveform to its original, known position; and (2) comparing the percent of points that match between the monitor-provided and calculated respiratory rate time-series data after re-alignment. The LAM alignment algorithm outperforms the CM algorithm in both comparisons at a statistically significant level (p<0.05). Out of 67 samples with shifted time-series data, on average, the LAM aligns respiratory rates within 44 seconds of the original position, which is significantly better the CM-calculated alignment (136 seconds). Out of 465 samples, the LAM performs better, worse, and equal to the CM algorithm in percentage of points matching in 73%, 11%, and 16% of the cases, respectively. This robust alignment algorithm supports the use of reliable post-hoc monitor-provided respiratory rates for data mining purposes.

Paper Details

Date Published: 17 May 2006
PDF: 10 pages
Proc. SPIE 6235, Signal Processing, Sensor Fusion, and Target Recognition XV, 62351B (17 May 2006); doi: 10.1117/12.664908
Show Author Affiliations
Liangyou Chen, U.S. Army Medical Research and Materiel Command (United States)
Thomas McKenna, U.S. Army Medical Research and Materiel Command (United States)
Andrei Gribok, U.S. Army Medical Research and Materiel Command (United States)
Jaques Reifman, U.S. Army Medical Research and Materiel Command (United States)


Published in SPIE Proceedings Vol. 6235:
Signal Processing, Sensor Fusion, and Target Recognition XV
Ivan Kadar, Editor(s)

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