BIC

This modular, self-contained software program automatically determines the reliability of respiratory rates (RR) derived from respiratory waveforms generated by vital signs monitors (left side of figure). While enabling the selection of high-quality RRs from archived data for study, it also applies to data-driven decision-support systems for military and civilian medical triage, diagnostics, and prognostics. It can identify reliable RRs to automated care systems or to caregivers either at the bedside, or monitoring from a remote location. It is implemented with three components as shown in the figure: an original algorithm that evaluates the respiratory waveform quality (green box), a unique breath identification algorithm (blue box), and a decision logic algorithm that compares the newly-calculated and original monitor-derived (reference) respiratory rates, and the waveform quality, to formulate the final QI (orange box in figure).

The invention is designed to flag poor-quality respiratory rate data that are prone to occur in the setting of degraded biosignal waveforms; to identify poor quality data from waveforms expressing ambiguous breaths that affect reliable calculation of RR by vital signs monitors; and to eliminate manual review and rejection of low-reliability RR data. In the future, remote users could apply it as an adjunct system to judge the quality of RR data received from patients in the field.

Features and advantages:

• Automatically distinguishes reliable from unreliable RR data

• High throughput, unbiased review of data with no variation over time

• Breath identification algorithm separates true breaths from noisy, irregular waveforms

• Can screen large biosignal databases for good quality data

• Could provide on-the-fly monitoring of firefighters and warfighters who are at risk of sudden injury