Dr. Jaques Reifman Profile

Dr. Jaques Reifman

at US Army Medical Research and Material Command

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Author

Publications (3)

Proceedings Article | 19 May 2006 Paper

Estimation of confidence levels for physiology variables measured by a vital signs detection system

Jingyu Liu, Thomas McKenna, Andrei Gribok, Beth Beidleman, William Tharion, Jaques Reifman

Proceedings Volume 6218, 621818 (2006) https://doi.org/10.1117/12.665965

KEYWORDS: Sensors, Algorithm development, Fuzzy logic, Heart, Physiology, Vital signs, Feature extraction, Nonlinear filtering, Digital filtering, Interference (communication)

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Proceedings Article | 17 May 2006 Paper

LAM: A landscape matching algorithm for respiratory data alignment

Liangyou Chen, Thomas McKenna, Andrei Gribok, Jaques Reifman

Proceedings Volume 6235, 62351B (2006) https://doi.org/10.1117/12.664908

KEYWORDS: Curium, Phase modulation, Electrocardiography, Algorithm development, Heart, Vital signs, Physiology, Data centers, Data archive systems, Distance measurement

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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.

Proceedings Article | 23 May 2005 Paper

Hybrid approaches to physiologic modeling and prediction

Nicholas Oleng', Jaques Reifman

Proceedings Volume 5797, (2005) https://doi.org/10.1117/12.605323

KEYWORDS: Data modeling, Neural networks, Autoregressive models, Performance modeling, Temperature metrology, Process modeling, Mathematical modeling, Injuries, Error analysis, Body temperature

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