ACOUSTIC RESPIRATORY MONITORING SENSOR WITH PROBE-OFF DETECTION

• US 20180085068A1
• Filed: 09/01/2017
• Published: 03/29/2018
• Est. Priority Date: 01/02/2013
• Status: Active Application

• Current Assignee: Masimo Corporation
• Original Assignee: Masimo Corporation
• Inventors: Telfort, Valery G.
• Application Number:
  US15/693,854
• Publication Number:
  US 20180085068A1
• Time in Patent Office: Days
• CPC Class Codes: A61B 2560/0266 for monitoring or limiting apparatus function
• 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 5/02416 using photoplethysmograph signals, e.g. generated by infrared radiation A61B5/14552 takes precedence
• CPC Class Codes: A61B 5/0261 using optical means, e.g. infrared light
• CPC Class Codes: A61B 5/0295 using plethysmography, i.e. measuring the variations in the volume of a body part as modified by the circulation of blood therethrough, e.g. impedance plethysmography
• CPC Class Codes: A61B 5/0816 Measuring devices for examining respiratory frequency measuring frequency of electric signals G01R23/00
• CPC Class Codes: A61B 5/33 specially adapted for cooperation with other devices
• CPC Class Codes: A61B 5/7221 Determining signal validity, reliability or quality A61B5/7207 and A61B5/7217 take precedence
• CPC Class Codes: A61B 5/7246 using correlation, e.g. template matching or determination of similarity
• CPC Class Codes: A61B 5/725 using specific filters therefor, e.g. Kalman or adaptive filters A61B5/316 take precedence
• CPC Class Codes: A61B 5/7257 using Fourier transforms
• CPC Class Codes: A61B 5/726 using Wavelet 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 7/003 Detecting lung or respiration noise
• CPC Class Codes: A61B 7/026 comprising more than one sound collector
• CPC Class Codes: A61B 7/04 Electric stethoscopes microphones, acoustic transducers therefor H04R

Abstract

Embodiments described herein include sensors and sensor systems having probe-off detection features. For example, sensors and physiological monitors described herein include hardware and/or software capable of providing an indication of the integrity of the connection between the sensor and the patient. In various embodiments, the physiological monitor is configured to output an indication of a probe-off condition for an acoustic sensor (or other type of sensor). For example, in an embodiment, a signal from an acoustic sensor is compared with a signal from a second sensor to determine a probe-off condition.

Claims

1-25. -25. (canceled)

26. A method of automating detection of whether an acoustic sensor is properly attached to a patient, the method comprising:

receiving an acoustic physiological signal from an acoustic sensor;

transforming the acoustic physiological signal into a frequency domain;

extracting energy of low frequency components in the frequency transformed acoustic physiological signal;

comparing the extracted energy with a threshold that indicates a likelihood of good quality connection of the acoustic sensor with the patient; and

in response to said comparison, outputting an indication of automated detection of whether the acoustic sensor is properly attached to the patient.

27. The method of claim 26, wherein the low frequency bins include frequencies corresponding to a heart rate.

28. The method of claim 26, wherein the extraction of energy comprises adding magnitude of low frequency components of the transformed acoustic physiological signal.

29. The method of claim 26, wherein the extraction of energy comprises computing a power spectral density of the acoustic physiological signal.

30. The method of claim 26, wherein the indication comprises an alarm.

31. The method of claim 26, wherein the transform comprises wherein the transform comprises one or more of the following:

a wavelet transform, a short-time Fourier transform, a chirplet transform, and a Gabor transform.

32. A system of automating detection of whether an acoustic sensor is properly attached to a patient, the system comprising one or more hardware processors configured to:

receive an acoustic physiological signal from an acoustic sensor;

transform the acoustic physiological signal into a frequency domain;

extract energy of low frequency components in the frequency transformed acoustic physiological signal;

compare the extracted energy with a threshold that indicates a likelihood of good quality connection of the acoustic sensor with the patient; and

in response to said comparison, output an indication of automated detection of whether the acoustic sensor is properly attached to the patient.

33. The system of claim 32, wherein the low frequency bins include frequencies corresponding to a heart rate.

34. The system of claim 32, wherein the extraction of energy comprises adding magnitude of low frequency components of the transformed acoustic physiological signal.

35. The system of claim 32, wherein the extraction of energy comprises computing a power spectral density of the acoustic physiological signal.

36. The system of claim 32, wherein the indication comprises an alarm.

37. The system of claim 32, wherein the transform comprises wherein the transform comprises one or more of the following:

a wavelet transform, a short-time Fourier transform, a chirplet transform, and a Gabor transform.

38. A system of automating detection of whether an acoustic sensor is properly attached to a patient, the system comprising one or more hardware processors configured to:

receive an acoustic physiological signal from an acoustic sensor;

determine a magnitude of at least one of a plurality of low frequency components in the received acoustic physiological signal, and

in response to said determination, output an indication of automated detection of whether the acoustic sensor is properly attached to the patient.

39. The system of claim 38, wherein the at least one of the plurality of low frequency components include a frequency corresponding to a heart rate.

40. The system of claim 38, wherein the one or more hardware processors are further configured to transform the acoustic physiological signal into a frequency domain for the determination of the magnitude.

41. The system of claim 40, wherein the indication comprises an alarm.

42. The system of claim 41, wherein the transform comprises wherein the transform comprises one or more of the following:

a wavelet transform, a short-time Fourier transform, a chirplet transform, and a Gabor transform.