Systems And Methods For Identifying Non-Corrupted Signal Segments For Use In Determining Physiological Parameters

• US 20110077484A1
• Filed: 09/30/2009
• Published: 03/31/2011
• Est. Priority Date: 09/30/2009
• Status: Abandoned Application

• Current Assignee: Nellcor Puritan Bennett Ireland
• Original Assignee: Nellcor Puritan Bennett Ireland
• Inventors: Van Slyke, Braddon M., McGonigle, Scott, Addison, Paul Stanley, Watson, James Nicholas
• Application Number:
  US12/570,388
• Publication Number:
  US 20110077484A1
• Time in Patent Office: Days
• US Class Current: 600/324
• CPC Class Codes: A61B 5/02416 using photoplethysmograph signals, e.g. generated by infrared radiation A61B5/14552 takes precedence
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/7207 of noise induced by motion artifacts
• CPC Class Codes: A61B 5/7221 Determining signal validity, reliability or quality A61B5/7207 and A61B5/7217 take precedence
• CPC Class Codes: A61B 5/7225 Details of analog processing, e.g. isolation amplifier, gain or sensitivity adjustment, filtering, baseline or drift compensation A61B5/30, A61B5/316 take precedence; transformers for specific applications H01F38/00; amplifiers H03F
• CPC Class Codes: A61B 5/726 using Wavelet transforms
• 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 5/7267 involving training the classification device
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

According to embodiments, non-corrupted signal segments are detected by a data modeling processor implementing an artificial neural network. The neural network may be trained to detect artifact in the signal (e.g., a PPG signal or some wavelet representation of a PPG signal) and gate valid signal segments for use in determining physiological parameters, such as, for example, pulse rate, oxygen saturation, pulse rate, respiration rate, and respiratory effort. When an artifact is detected, previously received known-good signal segments may be buffered and replace the signal segment or segments containing artifact. A regression analysis may also be performed in order to extrapolate new data from previously received known-good signal segments. In this way, more accurate and reliable physiological parameters may be determined.

Claims

1. A method for determining a physiological parameter, comprising:

receiving, from a sensor, a PPG signal;

using processing circuitry to;

transform the received PPG signal using a continuous wavelet transform,

pass a representation of the transformed signal to a neural network,

detect, with the neural network, a region of artifact in the representation of the transformed signal, and

determine a physiological parameter based at least in part on the representation of the transformed signal and information regarding the region of artifact; and

outputting to an output device the physiological parameter.

2. The method of claim 1 wherein the representation of the transformed signal comprises a scalogram of the transformed signal.

3. The method of claim 1 wherein the representation of the transformed signal comprises a three-dimensional ratio surface of the transformed signal.

4. The method of claim 1 wherein the neural network detects a region of artifact in the representation of the transformed signal by accessing a model for the neural network, the model based, at least in part, on the representation of the transformed signal.

5. The method of claim 1 wherein the neural network detects a region of artifact in the representation of the transformed signal by selecting a learning algorithm for the neural network, the learning algorithm implementing at least one of supervised learning, unsupervised learning, and reinforcement learning.

6. The method of claim 5 further comprising training the neural network to detect artifact in the representation of the transformed signal using the learning algorithm.

7. The method of claim 1 further comprising using the processing circuitry to modify the representation of the transformed signal by removing the detected region of artifact from the representation of the transformed signal.

8. The method of claim 1 further comprising using the processing circuitry to modify the representation of the transformed signal by replacing the detected region of artifact in the representation of the transformed signal with extrapolated data.

9. The method of claim 1 further comprising using the processing circuitry to modify the representation of the transformed signal by replacing the detected region of artifact with previously received buffered data.

10. The method claim 1 wherein the processing circuitry determines a pulse rate from the representation of the transformed signal.

11. A system for determining a physiological parameter, comprising:

a sensor configured to receive a PPG signal; and

processing circuitry configured to;

transform the received PPG signal using a continuous wavelet transform;

pass a representation of the transformed signal to a neural network;

detect, with the neural network, a region of artifact in the representation of the transformed signal; and

determine a physiological parameter based at least in part on the representation of the transformed signal and information regarding the region of artifact.

12. The system of claim 11 further comprising an output device to output the physiological parameter.

13. The system of claim 11 wherein the representation of the transformed signal comprises a scalogram of the transformed signal.

14. The system of claim 11 wherein the representation of the transformed signal comprises a three-dimensional ratio surface of the transformed signal.

15. The system of claim 11 wherein the neural network is configured to detect a region of artifact in the representation of the transformed signal by accessing a model for the neural network, the model based, at least in part, on the representation of the transformed signal.

16. The system of claim 11 wherein the neural network is configured to detect a region of artifact in the representation of the transformed signal by selecting a learning algorithm for the neural network, the learning algorithm implementing at least one of supervised learning, unsupervised learning, and reinforcement learning.

17. The system of claim 16 wherein the processing circuitry is configured to train the neural network to detect artifact in the representation of the transformed signal using the learning algorithm.

18. The system of claim 11 wherein the processing circuitry is configured to modify the representation of the transformed signal by removing the detected region of artifact from the representation of the transformed signal.

19. The system of claim 11 wherein the processing circuitry is configured to modify the representation of the transformed signal by replacing the detected region of artifact in the representation of the transformed signal with extrapolated data.

20. The system of claim 11 wherein the processing circuitry is configured to modify the representation of the transformed signal by replacing the detected region of artifact with previously received buffered data.