Method for continuously monitoring a patient using a body-worn device and associated system for alarms/alerts

• US 9,492,092 B2
• Filed: 05/20/2009
• Issued: 11/15/2016
• Est. Priority Date: 05/20/2009
• Status: Active Grant

• Current Assignee: Sotera Wireless Incorporated
• Original Assignee: Sotera Wireless Incorporated
• Inventors: McCombie, Devin, Dhillon, Marshal, Banet, Matt
• Primary Examiner(s): Matthews, Christine H
• Assistant Examiner(s): LANNU, JOSHUA DARYL DEANON
• Application Number:
  US12/469,192
• Publication Number:
  US 20100298657A1
• Time in Patent Office: 2,736 Days
• Field of Search: 600/300, 600/301, 600/484, 600/500, 600/595, 600508-528, 705/2, 705/3, 607/6, 607/19
• US Class Current: 1/1
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/0059 using light, e.g. diagnosis by transillumination, diascopy, fluorescence A61B5/0093, A61B5/1455, A61B5/02416 and A61B5/0261 take precedence
• 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 5/02028 Determining haemodynamic parameters not otherwise provided for, e.g. cardiac contractility or left ventricular ejection fraction
• CPC Class Codes: A61B 5/0205 Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
• 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 5/02125 of pulse wave propagation time
• CPC Class Codes: A61B 5/022 by applying pressure to close blood vessels, e.g. against the skin; Ophthalmodynamometers
• CPC Class Codes: A61B 5/02416 using photoplethysmograph signals, e.g. generated by infrared radiation A61B5/14552 takes precedence
• CPC Class Codes: A61B 5/02438 with portable devices, e.g. worn by the patient
• CPC Class Codes: A61B 5/0245 by using sensing means generating electric signals, i.e. ECG signals
• CPC Class Codes: A61B 5/0816 Measuring devices for examining respiratory frequency measuring frequency of electric signals G01R23/00
• CPC Class Codes: A61B 5/11 Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb for measuring pulse A61B5/02 ;A61B5/1038 takes precedence; motion detection to correct for motion artifacts in physiological signals A61B5/721
• CPC Class Codes: A61B 5/1114 Tracking parts of the body
• CPC Class Codes: A61B 5/1116 Determining posture transitions
• CPC Class Codes: A61B 5/1117 Fall detection
• CPC Class Codes: A61B 5/1118 Determining activity level
• CPC Class Codes: A61B 5/1123 Discriminating type of movement, e.g. walking or running A61B5/1116, A61B5/112 take precedence
• CPC Class Codes: A61B 5/145 : Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissuemeasuring of blood pressure or blood flow A61B5/02; non-radiation detecting or locating of foreign bodies in blood A61B5/06
• CPC Class Codes: A61B 5/316 : Modalities, i.e. specific diagnostic methods
• CPC Class Codes: A61B 5/318 : Heart-related electrical modalities, e.g. electrocardiography [ECG]
• CPC Class Codes: A61B 5/322 : Physical templates or devices for measuring ECG waveforms, e.g. electrocardiograph rulers or calipers by template matching A61B5/35
• CPC Class Codes: A61B 5/332 : Portable devices specially adapted therefor
• CPC Class Codes: A61B 5/349 : Detecting specific parameters of the electrocardiograph cycle
• CPC Class Codes: A61B 5/352 : Detecting R peaks, e.g. for synchronising diagnostic apparatus; Estimating R-R interval
• CPC Class Codes: A61B 5/6824 : Arm or wrist
• CPC Class Codes: A61B 5/721 : using a separate sensor to detect motion or using motion information derived from signals other than the physiological signal to be measured
• CPC Class Codes: A61B 5/7246 : using correlation, e.g. template matching or determination of similarity
• CPC Class Codes: A61B 5/7257 : using Fourier transforms
• CPC Class Codes: A61B 5/7278 : Artificial waveform generation or derivation, e.g. synthesising signals from measured signals A61B5/327 takes precedence
• CPC Class Codes: A61B 5/742 : using visual displays A61B5/339 takes precedence
• 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: G08B 21/0446 : worn on the body to detect changes of posture, e.g. a fall, inclination, acceleration, gait
• CPC Class Codes: G08B 21/0453 : worn on the body to detect health condition by physiological monitoring, e.g. electrocardiogram, temperature, breathing detecting, measuring or recording for diagnostic purposes A61B5/00
• CPC Class Codes: G16H 15/00 : ICT specially adapted for medical reports, e.g. generation or transmission thereof
• CPC Class Codes: G16H 50/20 : for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/50 : for simulation or modelling of medical disorders

Abstract

The invention provides a body-worn monitor that measures a patient'"'"'s vital signs (e.g. blood pressure, SpO2, heart rate, respiratory rate, and temperature) while simultaneously characterizing their activity state (e.g. resting, walking, convulsing, falling). The body-worn monitor processes this information to minimize corruption of the vital signs by motion-related artifacts. A software framework generates alarms/alerts based on threshold values that are either preset or determined in real time. The framework additionally includes a series of ‘heuristic’ rules that take the patient'"'"'s activity state and motion into account, and process the vital signs accordingly. These rules, for example, indicate that a walking patient is likely breathing and has a regular heart rate, even if their motion-corrupted vital signs suggest otherwise.

Claims

1. A method for continuously monitoring a patient, comprising the following steps:

detecting a first time-dependent physiological waveform indicative of one or more contractile properties of the patient'"'"'s heart with a first sensor comprising a first detector configured to be worn on the patient'"'"'s body;

detecting a second time-dependent physiological waveform indicative of one or more contractile properties of the patient'"'"'s heart with a second sensor comprising a second detector configured to be worn on the patient'"'"'s body;

detecting a first time-dependent motion waveform with a first motion-detecting sensor adapted to be positioned on the patient'"'"'s wrist, detecting a second time-dependent motion waveform with a second motion-detecting sensor adapted to be positioned on the patient'"'"'s upper arm, and detecting a third time-dependent motion waveform with a third motion-detecting sensor adapted to be positioned on the patient'"'"'s chest, wherein each time-dependent motion waveform is indicative of motion of the location on the patient'"'"'s body to which the corresponding motion detecting sensor is affixed;

processing the first and second time-dependent physiological waveforms to determine at least one vital sign from the patient;

analyzing at least a portion of the first, second, and third time-dependent motion waveforms with a motion-determining algorithm;

determining the patient'"'"'s activity state by discriminating between resting, moving, sitting, standing, walking, running, falling, lying down, and convulsing states by using results of said analyzing at least a portion of the first, second, and third time-dependent motion waveforms; and

generating an alarm by processing the patient'"'"'s activity state and comparing the at least one vital sign to a predetermined alarm criteria corresponding to the activity state.

2. The method of claim 1, wherein the analyzing step further comprises calculating a mathematical transform of at least one of the first, second, and third time-dependent motion waveforms to determine a frequency-dependent motion waveform.

3. The method of claim 2, wherein the analyzing step further comprises analyzing frequencies in the frequency-dependent motion waveform within a band between 0-3 Hz to determine if the patient is walking.

4. The method of claim 3, wherein the analyzing step further comprises analyzing frequencies in the frequency-dependent motion waveform within a band between 0-10 Hz to determine if the patient is convulsing.

5. The method of claim 4, wherein the analyzing step further comprises analyzing frequencies in frequency-dependent motion waveform within a band between 0-15 Hz to determine if the patient is falling.

6. The method of claim 1, wherein the analyzing step further comprises calculating a time-dependent change in at least two of the first, second, and third time-dependent motion waveforms.

7. The method of claim 6, wherein the analyzing step further comprises calculating a standard deviation of at least one of the first, second, and third time-dependent motion waveforms.

8. The method of claim 6, wherein the analyzing step further comprises calculating a mathematical derivative of at least one of the first, second, and third time-dependent motion waveforms.

9. The method of claim 6, wherein the analyzing step further comprises comparing at least one of the first, second, and third time-dependent motion waveforms to a mathematical function.

10. The method of claim 1, wherein the analyzing step further comprises calculating a logit variable from at least the first and second time-dependent motion waveforms, or waveforms calculated therefrom, and comparing the logit variable to a predetermined ROC curve to determine the patient'"'"'s activity state.

11. The method of claim 10, wherein the analyzing step further comprises comparing the logit variable calculated from at least the first and second time-dependent motion waveforms, or a waveform calculated therefrom, to different ROC curves corresponding to activity states selected from resting, moving, sitting, standing, walking, running, falling, lying down, and convulsing.

12. The method of claim 1, wherein the processing step further comprises calculating a time difference between a feature in the first time-dependent physiological waveform and a feature in the second time-dependent physiological waveform.

13. The method of claim 12, wherein the processing step further comprises calculating a blood pressure from the time difference.

14. The method of claim 13, wherein the first time-dependent physiological waveform is an electrical waveform, and the second time-dependent physiological waveform is an optical waveform.

15. The method of claim 13, wherein each of the first and second time-dependent physiological waveforms are selected from a group consisting of an optical waveform, an acoustic waveform, an ECG waveform, and a pressure waveform.

16. The method of claim 1, wherein the first time-dependent physiological waveform is an electrical waveform, and the at least one vital sign determined during the processing step is heart rate.

17. The method of claim 1, wherein the first time-dependent physiological waveform is an electrical waveform, and the at least one vital sign determined during the processing step is respiratory rate.

18. The method of claim 1, wherein the first time-dependent physiological waveform is an optical waveform, and the at least one vital sign determined during the processing step is SpO2.