Respiratory stress detection

A computer-implemented method of detecting respiratory distress in a patient, the computer including a processor, said method comprising acts of:

• learning, by the processor, a first pattern including a trending classification of respiratory rate and SpO2 using patient history data;
• monitoring, by the processor, patient data over a time period, the patient data comprising measures of respiratory rate and SpO2 recorded simultaneously in a storage component;
• tracking, by the processor, the measures of respiratory rate and SpO2 over said time period, individually, in corresponding least squares regression models, wherein tracking comprises collecting and storing the measures of respiratory rate and SpO2 in the storage component;
• analyzing, by the processor, the least squares regression models of each of the measures of respiratory rate and SpO2 to identify multiple segmented trends in each of the least squares regression models;
• removing, by the processor, a noisy deviation from the measures of respiratory rate and SpO2 by using the multiple segmented trends;
• identifying, by the processor, the multiple segmented trends in each of the least squares regression models as one of an uptrend, downtrend, or neutral;
• determining, by the processor, a second pattern based on the multiple segmented trends from the measures of respiratory rate and the multiple segmented trends from the measures of SpO2;
• predicting, by the processor, a potential patient distress by correlating the first pattern to the second pattern;
• and
• triggering, by the processor, an alarm based on the correlation of the first pattern and the second pattern, wherein the alarm is a warning system of patient distress and prevents non-actionable alarms.