RESPIRATORY STRESS DETECTION

• US 20150157275A1
• Filed: 12/10/2013
• Published: 06/11/2015
• Est. Priority Date: 12/10/2013
• Status: Active Grant

• Current Assignee: General Electric Company
• Original Assignee: General Electric Company
• Inventors: Swamy, Gokul, Genc, Sahika, Ravishankar, Hariharan, Saha, Aditya
• Granted Patent: US 9,750,463 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: A61B 5/002 Monitoring the patient using a local or closed circuit, e.g. in a room or building A61B5/0017 takes precedence
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/01 Measuring temperature of body parts ; Diagnostic temperature sensing, e.g. for malignant or inflamed tissue clinical contact thermometers G01K13/20
• CPC Class Codes: A61B 5/02055 Simultaneously evaluating both cardiovascular condition and temperature
• CPC Class Codes: A61B 5/021 Measuring pressure in heart or blood vessels A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/024 Detecting, measuring or recording pulse rate or heart rate A61B5/0205, A61B5/021 take precedence
• CPC Class Codes: A61B 5/0816 Measuring devices for examining respiratory frequency measuring frequency of electric signals G01R23/00
• CPC Class Codes: A61B 5/0836 Measuring rate of CO2 production
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/14542 for measuring blood gases A61B5/14551 takes precedence
• CPC Class Codes: A61B 5/201 Assessing renal or kidney functions
• CPC Class Codes: A61B 5/202 Assessing bladder functions, e.g. incontinence assessment
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/7246 using correlation, e.g. template matching or determination of similarity
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: A61B 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: A61B 5/746 Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms alarms per se G08B
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment

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.

Claims

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

monitoring patient data over a time period, said patient data comprising measures of respiratory rate and SpO₂ recorded simultaneously in a storage component;

tracking said measures of respiratory rate and SpO₂ over said time period, individually, in corresponding least squares regression models;

analyzing said least squares regression models of each of said measures of respiratory rate and SpO₂ to remove noisy deviation from respective said measures of respiratory rate and SpO₂; and

identifying multiple segmented trends in each of said least squares regression models as one of an uptrend, downtrend, or neutral, wherein each of said trends in sequential combination establish a pattern;

wherein said pattern triggers an alarm as an early warning system of patient distress and prevents false alarm fatigue.

2. The computer-implemented method of claim 1, further comprising a step of correlating said measures of respiratory rate and SpO₂.

3. The computer-implemented method of claim 2, wherein said step of correlating, said pattern is recognized in real-time.

4. The computer-implemented method of claim 3, wherein said pattern is one of three types to include:

(a) Type I;

gradual decrease in SpO₂ with compensatory hyperventilation;

(b) Type II;

progressive minute decreases in minute ventilation and SpO₂;

or

(c) Type III;

Guarded rapid airflow including SpO₂ reductions followed by precipitous SpO₂ fall.

5. The computer-implemented method of claim 3, wherein said pattern is learned and unique to said patient.

6. The computer-implemented method of claim 1, wherein said early warning system of patient distress indicates early detection of at least one of cardiopulmonary arrest, respiratory failure, renal failure, sepsis, and re-intubation risk.

7. The computer-implemented method of claim 1, wherein said patient data comprises measures of a plurality of vital signs, in combination.

8. The computer-implemented method of claim 7, wherein said step of tracking includes said measures of said plurality of vital signs.

9. The computer-implemented method of claim 1, wherein said patterns comprise one of:

(a) hyperventilation compensated respiratory distress, (b) progressive unidirectional hypoventilation, or (c) sentinel rapid airflow with SpO₂ reductions.

10. The computer-implemented method of claim 1, wherein said measures of respiratory rate and SpO₂, individually, do not trigger an alarm, and wherein changes in said measures of respiratory rate and SpO₂, in combination, trigger an alarm

11. The computer-implemented method of claim 1, wherein said multiple segmented trends are estimated to predict patterns of progressing patient instability.

12. The computer-implemented method of claim 1, wherein said multiple segmented trends are represented by a 3-tuple time series T:

{TY, t, s} wherein TY denotes each segmented trend as uptrend, downtrend, or neutral;

wherein t is the time duration for which the trend component is active; and

wherein s denotes the strength as a magnitude of mild, moderate, or severe such that the trend is characterized as a label vector T_RR/SpO2=[T₁, T₂ T₃ . . . T_n].

13. The computer-implemented method of claim 12, wherein the label vector is generated in real-time and is continuously updated.

14. A computerized system for early detection of respiratory distress comprising:

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 said plurality of vital signs including respiratory rate and SpO₂ of said patient;

a processor interconnected with said monitoring system and said storage component to analyze said patient data over a time period such that segmented trends are identified in said measures of respiratory rate and SpO₂ and define one or more patterns to characterize patient status.

15. The computerized system of claim 14, wherein said segmented trends are identified as one of an uptrend, downtrend, or neutral using a least squares regression model.

16. The computerized system of claim 14, wherein each of said segmented trends in sequential combination establish said pattern in real-time.

17. The computerized system of claim 16, wherein said pattern triggers an alarm to provide an early warning system of patient distress.

18. The computerized system of claim 17, wherein said pattern triggers a critical alarm and prevents false alarm fatigue.

19. The computerized system of claim 14, wherein said measures of respiratory rate and SpO₂ are recorded and correlated simultaneously.

20. The computerized system of claim 14, wherein said plurality of vital signs further comprise body temperature, pulse or heart rate, blood pressure, blood glucose, urine production, urinary incontinence, and end-tidal CO₂, among others, which are correlated with said respiratory rate and SpO₂, alone or in combination, to characterize said one or more patterns that alert a clinician as to patient distress.

21. The computerized system of claim 19, further comprising patient history data integrated with said storage component to characterize said one or more patterns.

22. The computer-implemented system of claim 19, wherein said one or more patterns integrates patient use of any medications, sedatives, analgesics, vitamins, and supplements.

23. The computer-implemented system of claim 14, wherein the processor is a computing chip connected wirelessly to a secure network.

24. The computer-implemented system of claim 23, wherein a plurality of web-based personal devices can access said patient data and said patient status.