Respiratory stress detection
First Claim
1. 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.
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Abstract
Embodiments of the disclosure are directed to a system for analysis of respiratory distress in hospitalized patients. The system performs multi-parametric simultaneous analysis of respiration rate (RR) and pulse oximetry (SpO2) data trends in order to gauge patterns of patient instability pertaining to respiratory distress. Three patterns in SpO2 and RR are used along with LOWESS algorithm and Chauvenets criteria for outlier rejection to obtain robust short term and long term trends in RR and SpO2. Pattern analysis detects the presence of any one of three pattern types proposed. Further, a learning paradigm is introduced to find unknown instances of respiratory distress. This algorithm in conjunction with the learning model allows early detection of respiratory distress in hospital ward and ICU patients.
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Citations
21 Claims
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1. A computer-implemented method of detecting respiratory distress in a patient, the computer including a processor, said method comprising acts of:
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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. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
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13. A computerized system for early detection of respiratory distress comprising:
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one or more sensors attached to a patient to monitor a plurality of vital signs; a monitoring system connected to said one or more sensors; a storage component connected to said monitoring system to record patient data, wherein patient data comprises measures of the plurality of vital signs including respiratory rate and SpO2 of the patient; and a processor interconnected with said monitoring system and said storage component, wherein said processor is configured to; learn a first pattern including a trending classification of respiratory rate and SpO2 using patient history data; analyze the patient data over a time period; analyze least squares regression models of each of the measures of respiratory rate and SpO2 to identify segmented trends in the measures of respiratory rate and SpO2 simultaneously; remove a noisy deviation from the measures of respiratory rate and SpO2 by using the segmented trends; determine a second pattern based on the segmented trends in the measures of respiratory rate and SpO2; predict a potential patient distress by correlating the first pattern to the second pattern; and trigger 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. - View Dependent Claims (14, 15, 16, 17, 18, 19, 20, 21)
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Specification