Method of analyzing and processing signals

Abstract

According to embodiments, a system for processing a physiological signals is disclosed. The system may comprise a sensor for generating the physiological signal. The system may comprise a processor configured to receive and process the physiological signal in order to improve interpretation and subsequent analysis of the physiological signal. The processor may be configured to generate a wavelet transform based on the physiological signal. The processor may be configured to determine phase values corresponding to the subject's respiration based on the wavelet transform. The processor may be configured to generate a sinusoidal waveform that is representative of the subject's breathing based on the phase values. The system may also comprise a display device configured to display the sinusoidal waveform.

Claims

1. A method for processing a photoplethysmograph signal of a subject, comprising: receiving, using a processor, the photoplethysmograph signal from a sensor comprising at least one light transmitter and a photodetector; generating, using the processor, a wavelet transform based on the photoplethysmograph signal, wherein the wavelet transform comprises phase information; determining, using the processor, phase values corresponding to the subject's respiration based on the wavelet transform; identifying a respiration ridge based on the wavelet transform of a pulse band, the pulse band corresponding to a pulse component of the photoplethysmograph signal; generating, using the processor, a representative sinusoidal waveform based on the phase values and the identified respiration ridge, wherein the representative sinusoidal waveform is representative of the subject's breathing; and displaying, using a display device, the representative sinusoidal waveform.

2. The method of claim 1, wherein determining the phase values comprises determining the phase values based on the wavelet transform and a secondary wavelet transform.

3. The method of claim 1, wherein determining the phase values comprises processing the phase information to remove erroneous phase information caused by movement artifact.

4. The method of claim 1, wherein generating the representative sinusoidal waveform comprises determining the cosine of the phase values.

5. The method of claim 1, further comprising determining, using the processor, a respiration parameter based on the representative sinusoidal waveform.

6. The method of claim 5, wherein the respiration parameter is indicative of a property of an individual breath.

7. The method of claim 1, further comprising determining, using the processor, respiration rate based on the photoplethysmograph signal.

8. A system for processing a photoplethysmograph signal of a subject comprising: a sensor comprising at least one light transmitter and a photodetector, wherein the sensor is configured to generate the photoplethysmograph signal; a processor configured to perform operations comprising: receiving the photoplethysmograph signal from the sensor; generating a wavelet transform based on the photoplethysmograph signal, wherein the wavelet transform comprises phase information; determining phase values corresponding to the subject's respiration based on the wavelet transform; identifying a respiration ridge based on the wavelet transform of a pulse band, the pulse band corresponding to a pulse component of the photoplethysmograph signal; and generating a representative sinusoidal waveform based on the phase values and the identified respiration ridge, wherein the representative sinusoidal waveform is representative of the subject's breathing; and a display device configured for displaying the representative sinusoidal waveform.

9. The system of claim 8, wherein the processor is configured to determine the phase values based on the wavelet transform and a secondary wavelet transform.

10. The system of claim 8, wherein determining the phase values comprises processing the phase information to remove erroneous phase information caused by movement artifact.

11. The system of claim 8, wherein generating the representative sinusoidal waveform comprises determining the cosine of the phase values.

12. The system of claim 8, wherein the processor is configured to perform operations further comprising determining a respiration parameter based on the representative sinusoidal waveform.

13. The system of claim 12, wherein the respiration parameter is indicative of a property of an individual breath.

14. The system of claim 8, wherein the processor is configured to perform operations further comprising determining respiration rate based on the photoplethysmograph signal.

Description

BRIEF DESCRIPTION OF DRAWINGS

(1) FIG. 1(a): A wavelet transform surface showing the pulse band (located between the dashed lines). (High to Low energy is graded from white to black in the grey scale plot.)

(2) FIG. 1(b): Three-dimensional view of the wavelet transform surface of FIG. 1(a) showing the maxima of the pulse band with respect to frequency (the ridge) superimposed as a black path across the band maxima. (High to Low energy is graded from white to black in the grey scale plot.)

(3) FIG. 2: 3-D Schematic of a wavelet transform surface containing two bands. The locus of the local maxima on the bands (the ridges) are shown by dashed lines.

(4) FIG. 3: Schematics of the RAP (top left) and RFP (top right) signals derived from ridge A in FIG. 1 together with their corresponding wavelet transforms shown below each (in 2D).

(5) FIG. 4(a): The SWFD method as applied to a pulse oximeter signalScalogram of Original Signal. (High to Low energy is graded from white to black in the grey scale plot.)

(6) FIG. 4 (b): The SWFD method as applied to a pulse oximeter signal3-D view of scalogram in (a) with the path of the pulse band ridge superimposed. (High to Low energy is graded from white to black in the grey scale plot.)

(7) FIG. 4 (c): The SWFD method as applied to a pulse oximeter signalRAP signal (Top: full signal. Lower: blow up of selected region)

(8) FIG. 4 (d): The SWFD method as applied to a pulse oximeter signalRFP signal (Top: full signal. Lower: blow up of selected region)

(9) FIG. 5(a): The SWFD method as applied to a pulse oximeter signalRAP scalogram. (High to Low energy is graded from white to black in the grey scale plot.)

(10) FIG. 5(b): The SWFD method as applied to a pulse oximeter signalRFP scalogram. (High to Low energy is graded from white to black in the grey scale plot.)

(11) FIG. 5(c): The SWFD method as applied to a pulse oximeter signal3-D view of RAP scalogram with breathing band ridge shown. (High to Low energy is graded from white to black in the grey scale plot.)

(12) FIG. 5(d): The SWFD method as applied to a pulse oximeter signal3-D view of RFP scalogram with ridge shown. (High to Low energy is graded from white to black in the grey scale plot.)

(13) FIG. 6(a): PPG Signal

(14) FIG. 6(b): Pulse band and ridge corresponding to signal (a). (High to Low energy is graded from white to black in the grey scale plot.)

(15) FIG. 6(c): RAP signal derived from ridge in (b) with breathing switch (square waveform) superimposed.

(16) FIG. 6(d): RFP signal derived from ridge in (b)

(17) FIG. 7(a): Wavelet Transform of RAP signal. (High to Low energy is graded from white to black in the grey scale plot.)

(18) FIG. 7(b): Extracted ridges from wavelet transform in (a). (High to Low energy is graded from white to black in the grey scale plot.)

(19) FIG. 7(c): Wavelet Transform of RFP signal. (High to Low energy is graded from white to black in the grey scale plot.)

(20) FIG. 7(d): Extracted ridges from wavelet transform in (c). (High to Low energy is graded from white to black in the grey scale plot.)

(21) FIG. 8(a): Breathing ridges extracted from the original wavelet transform

(22) FIG. 8(b): Breathing ridges extracted from the secondary wavelet transform of the RAP signal

(23) FIG. 8(c): Breathing ridges extracted from the secondary wavelet transform of the RFP signal

(24) FIG. 8(d): Selected respiration path (SRP).

(25) FIG. 9: Transform Phase along the SRP

(26) FIG. 10: Filling in missing segments of the SRP

(27) FIG. 11: Wavelet Representations of the Red PPG (top) and Infrared PPG (bottom)

(28) FIG. 12: Schematic of the Sliding Window used to Obtain the Wavelet Components for the 3-D Lissajous

(29) FIG. 13(a): Wavelet-based 3-D Lissajous: 3-D View.

(30) FIG. 13(b): Wavelet-based 3-D Lissajous: End on View of (a).

(31) FIG. 13(c): Wavelet-based 3-D Lissajous: End on View of Selected Component.

(32) FIG. 14: Standard Deviation of Lissajous Components in FIG. 3. Top plot: SD of principle component; Middle plot: SD of minor component; Lower plot: Ratio of SD components. All three plots plotted against frequency in Hz.

(33) FIG. 15: Computed Oxygen Saturation Curves. Dotted line: Signal Amplitude Method; Dashed Line traditional Signal Lissajous Method; Solid Line: Wavelet-based 3-D Lissajous Method.

(34) FIG. 16: The red and infrared wavelet modulus surfaces corresponding to a 45 second segment of PPG signals. (High to Low energy is graded from white to black in the grey scale plot.)

(35) FIG. 17: The wavelet ratio surface derived from the division of the red by the infrared wavelet representations shown in FIG. 16.

(36) FIG. 18: An end view of the wavelet ratio surface shown in FIG. 17.

(37) FIG. 19: Computed Oxygen Saturation curves. Dotted line: Oxygen Saturation from Traditional Signal Amplitude Method; Dashed Line: Oxygen Saturation from Traditional Signal Lissajous Method; Solid Line: Oxygen Saturation from Traditional Wavelet-Ratio Surface Method

(38) FIG. 20(a): Wavelet transform plot of a PPG signal taken from a young baby showing a corresponding to patient movement. Low to high energy is depicted from black to white in the greyscale plot.

(39) FIG. 20(b): Three-dimensional view of (a). Low to high energy is depicted from black to white in the greyscale plot.

(40) FIG. 21(a): Transform plot of FIG. 20(a) with modulus maxima superimposed. Low to high energy is depicted from black to white in the greyscale plot.

(41) FIG. 21(b): Three-dimensional view of FIG. 21(a). Low to high energy is depicted from black to white in the greyscale plot.

(42) FIG. 22(a): End view of modulus maxima lines in FIG. 21(b).

(43) FIG. 22(b): Amplitude threshold method of identifying modulus maxima associated with movement artefact

(44) FIG. 22(c): Pulse ridge-based method of identifying modulus maxima associated with movement artefact

(45) FIG. 22(d): Respiration ridge-based method of identifying modulus maxima associated with movement artefact

(46) FIG. 23: Block diagram of device configuration

(47) FIG. 24: Block diagram of subcomponents of oxygen saturation component (16) shown in FIG. 23

(48) FIG. 25: Block diagram of subcomponents of respiration component (18) shown in FIG. 23

(49) FIG. 26: Block diagram of subcomponents of movement component (17) shown in FIG. 23

(50) FIG. 27(a): Schematic of foot cuff mounting: soft housing surrounding foot used to hold monitoring apparatus. 80 patient leg; 81 patient heel; 82 patient toes; 83 soft housing surrounding foot

(51) FIG. 27(b): View from both sides of the envisaged device: preferred embodiment for neonatal monitor. 84 connection cabling; 85 RF components attached to housing; 86 LEDs; 87 pulse oximeter components attached to housing; 88 photodetector. (Note LEDs and photodetector may also be located on toe using short cable length from cuff.)

(52) FIG. 28: Schematic of wrist cuff mounting: 90 electronic component housing; 91 wrist band; 92 connector cable; 93 finger probe

GENERAL

(53) The invention has been described and shown with specific reference to specific embodiments. However it will be understood by those skilled in the art that changes to the form and details of the disclosed embodiments may be made without departing from the spirit and scope of the invention. For example signal transforms other than the wavelet transform may be used. Other variations may include using a multiplexed arrangement which alternates measurements for pulse, oxygen saturation, respiration and movement artefact using variations of the acquisition equipment and transmission electronics. These variations may include but are not limited to the use of more than two wavelengths of light and variable power and/or variable duty cycle to the light transmitters.

REFERENCE

(54) Addison P. S., The Illustrated Wavelet Transform Handbook, Institute of Physics Publishing, 2002, Bristol, UK.