SYSTEMS AND METHODS FOR ANALYZING A RESPIRATORY PARAMETER

Methods and systems are provided that determine whether a patient is breathing irregularly. A system may receive a physiological signal, such as a plethysmographic signal or an end-tidal carbon dioxide signal, from a sensor. The system may analyze the signal for one or more features indicative of irregular breathing, which may be a result of a patient talking, moving, yawning, coughing, sneezing, or the like. The system may also be configured to provide an indication of the irregular breathing.

BACKGROUND

The present disclosure relates generally to techniques for monitoring physiological parameters of a patient and, more particularly, to techniques for determining a respiration rate of a patient.

In the field of medicine, doctors often desire to monitor certain physiological characteristics of their patients. Accordingly, a wide variety of systems and devices have been developed for monitoring many of these physiological characteristics. Generally, these patient monitoring systems provide doctors and other healthcare personnel with the information they need to provide the best possible healthcare for their patients. Consequently, such monitoring systems have become an indispensable part of modern medicine.

In some cases, clinicians may wish to monitor a patient's respiration rate. Respiration rate may be assessed using a wide variety of monitoring devices. For example, respiration rate may be monitored non-invasively via capnography using a carbon dioxide sensor. Additionally, respiration rate may be monitored non-invasively via photoplethysmography using a pulse oximetry sensor. However, signals obtained by the carbon dioxide sensor and/or by the pulse oximetry sensor may be adversely affected by certain events, such as the patient talking, moving, yawning, coughing, or the like. Thus, the signals may not always accurately reflect the patient's respiration rate.

DETAILED DESCRIPTION OF SPECIFIC EMBODIMENTS

As noted above, clinicians may wish to monitor a patient's respiration rate. Respiration rate may be determined using a wide variety of medical monitoring techniques, such as, for example, capnography and/or photoplethysmography. However, signals acquired using capnography and/or photoplethysmography may be adversely affected by certain events, such as a patient talking, moving, coughing, sneezing, yawning, or the like, which may result in artifacts or noise in the signals. For example, in some embodiments, respiration rate may be determined based at least in part upon modulations in a waveform (e.g., a plethysmographic waveform, an end-tidal carbon dioxide waveform, or any other suitable waveform), and the presence of certain events, such as talking, motion, coughing, sneezing, yawning, or the like, may alter the modulations in the waveform. As such, a calculated respiration rate may be adversely affected during such events. In particular, portions of a respiration waveform corresponding to such events may not contain clinically useful information for calculating respiration rate. However, it may be difficult for a caregiver to identify such events from the calculated respiration rate and/or the displayed respiration waveform.

Accordingly, the present embodiments provide techniques for detecting events that may adversely affect the calculated respiration rate and for alerting the caregiver of such events. For example, a monitor may be configured to analyze a waveform (e.g., a plethysmographic waveform, an end-tidal carbon dioxide waveform, or any other suitable waveform) for one or more features (e.g., characteristics of the waveform) indicative of the presence of events that may affect the determination of respiration rate (e.g., talking, motion, body movement, coughing, sneezing, yawning, or the like). As used herein, motion may include any action that cause a change in position of at least a portion of a patient's body and may include talking, body movement, coughing, sneezing, yawning, or the like. Additionally, as used herein, body movement may include abduction, adduction, extension, flexion, rotation, and/or circumduction of any portion of a patient's body. In certain embodiments, the one or more features indicative of the presence of events that may affect the determination of respiration rate may include a spread (e.g., variation) in the distribution of breath periods of the waveform, a ratio of the inhalation periods of the waveform to the exhalation periods of the waveform, and/or irregularity (e.g., asymmetry) of the peaks of the waveform. Additionally, in certain embodiments, the monitor may be configured to provide one or more indications of the presence of events that may affect the determination of respiration rate and/or more remove portions of the waveform corresponding to such events for the determination of respiration rate.

With the foregoing in mind,FIG. 1illustrates a schematic diagram of a system10for implementing techniques for monitoring physiological parameters of a patient12, such as respiration. The system10may include a patient monitor14operatively coupled to one or more plethysmographic sensors16. The one or more plethysmographic sensors16may be pulse oximetry sensors or any other suitable sensors. The plethysmographic sensors16may be configured to generate physiological signals, which may include a plethysmographic waveform, a pulse oximetry signal, or any other signal corresponding to blood flow in the patient12. As will be described in more detail below, the patient monitor14may be configured to determine physiological characteristics of the patient12based on the generated physiological signals, such as, for example, respiration rate, respiratory effort, blood oxygen saturation, heart rate, or the like. The patient monitor14may be a pulse oximeter monitor, such as those available from Covidien LP, or any other suitable monitor, such as a vital signals monitor, a critical care monitor, an obstetrical care monitor, or the like.

In certain embodiments, the system10may be configured to implement capnography techniques for determining physiological parameters (e.g., respiration rate) of the patient12. For example, the system10may include a capnograph18operatively coupled to one or more carbon dioxide sensors20. As will be described in more detail below, the capnograph18may be configured to determine physiological characteristics of the patient12using signals generated from the carbon dioxide sensor20, such as, for example, end tidal carbon dioxide concentration, respiration rate, respiratory effort, or the like. The carbon dioxide sensor20may be any suitable sensor for measuring carbon dioxide in respiratory gases or the tissue of the patient12. For example, the carbon dioxide sensor20may include chemical, electrical, optical, non-optical, quantum-restricted, electrochemical, enzymatic, spectrophotometric, fluorescent, or chemiluminescent indicators or transducers. In embodiments in which the carbon dioxide sensor20is configured to measure carbon dioxide in respiratory gases of the patient12, the carbon dioxide sensor20may be disposed within, integrated with, or generally coupled to an interface device22. The interface device22may be any suitable device for collecting respiratory gases of the patient12, such as a breathing mask (e.g., a nasal mask, a nasal/oral mask, a nasal prong, a full-face mask, or the like). In some embodiments, the interface device22may be a nebulizer, tracheostomy tube, or an endotracheal tube. In certain embodiments, the interface device22may be coupled to a ventilator or other device configured to support or supplement the respiratory efforts of the patient12.

In certain embodiments, the system10may also include a multi-parameter monitor24operatively coupled to the patient monitor14and/or the capnograph18. In addition to the patient monitor14and/or the capnograph18, or alternatively, the multi-parameter monitor24may be configured to calculate physiological characteristics of the patient12. That is, in some embodiments, the multi-parameter monitor24may be configured to receive signals from the plethysmographic sensor16and/or signals from the carbon dioxide sensor20and may calculate respiration rate using signals from the plethysmographic sensor16, signals from the carbon dioxide sensor20, or both. Additionally, the multi-parameter monitor24may provide a central display for information from the patient monitor14, the capnograph18, and/or other medical monitoring devices or systems. For example, the multi-parameter monitor24may display a plethysmographic waveform from the patient monitor14, an end tidal carbon dioxide concentration waveform from the capnograph18, and/or the patient's respiration rate from the patient monitor14and/or the capnograph18. In one embodiment, the multi-parameter monitor24may be configured to analyze the values of the respiration rate received from the patient monitor14and the capnograph18and may determine which value of the respiration rate to display (e.g., which value is determined to be more accurate). In other embodiments, the multi-parameter monitor24may be configured to average the values of the respiration rate received from the patient monitor14and the capnograph18and may display the averaged respiration rate. Additionally, the multi-parameter monitor24may indicate an alarm condition via a display and/or a speaker if the patient's physiological characteristics are determined to be outside of an expected threshold or range. In certain embodiments, the multi-parameter monitor24, the patient monitor14, and/or the capnograph18may be connected to a network to enable the sharing of information with servers or other workstations.

While the illustrated embodiment of the system10includes components for implementing photoplethysmography techniques (e.g., the patient monitor14and the plethysmographic sensor16) and components for implementing capnography techniques (e.g., the capnograph18and the carbon dioxide sensor20), it should be noted that, in certain embodiments, the system10may not include the patient monitor14and the plethysmographic sensor16and/or may not include the capnograph18and the carbon dioxide sensor20. That is, in some embodiments, the present techniques for determining respiration rate and/or determining whether the patient12is breathing irregularly may be implemented by the patient monitor14using signals from the plethysmographic sensor16, without the use of the capnograph18. Further, in other embodiments, the present techniques for determining respiration rate and/or determining whether the patient12is breathing irregularly may be implemented by the capnograph18using signals from the carbon dioxide sensor20, without the use of the patient monitor14. Additionally, in other embodiments, the present techniques for determining respiration rate and/or determining whether the patient12is breathing irregularly may be implemented by the multi-parameter monitor24, or any other suitable processor-based device, using signals from the plethysmographic sensor16, signals from the carbon dioxide sensor20, or signals from both, without the use of the patient monitor14or the capnograph18. In some embodiments, the system10may additionally or alternatively include technologies configured to determine respiration rate and/or to detect events that may adversely affect the determination of respiration rate (e.g., talking, coughing, motion, body movement, sneezing, yawning, or the like) using any suitable signal. By way of example, suitable signals may include trans-thoracic impedance (TTI) signals, electrocardiography (ECG) signals, arterial line signals, blood flow signals, ultrasound signals, airflow signals, humidity signals, microphone signals, bed pressure sensor signals, accelerometer signals, remote sensing signals (e.g., video, infrared, radar, etc.), thoracic volume signals (e.g., from a chest band), and/or temperature signals (e.g., from a nasal thermistor). Accordingly, the system10may include any other suitable sensor, monitor, medical device, or any combinations thereof for acquiring signals for the determination of respiration rate and/or detecting events that may adversely affect the determination of respiration rate.

Turning toFIG. 2, a simplified block diagram of the patient monitor14and the plethysmographic sensor16of the system10is illustrated in accordance with an embodiment. As provided herein, the plethysmographic sensor16may be a sensor suitable for detection of one or more physiological parameters. The plethysmographic sensor16may include optical components, such as one or more emitters40and one or more detectors42. In one embodiment, the sensor16may be configured for photo-electric detection of blood and tissue constituents. For example, the plethysmographic sensor16may include pulse oximetry sensing functionality for determining the oxygen saturation of blood as well as other parameters (e.g., respiration rate, arrhythmia detection) from the plethysmographic waveform detected by the oximetry technique. An oximetry system may include a light sensor (e.g., the plethysmographic sensor16) that is placed at a site on a patient, typically a fingertip, toe, forehead or earlobe, or in the case of a neonate, across a foot. The plethysmographic sensor16may pass light using the emitter40through blood perfused tissue and photoelectrically sense the absorption of light in the tissue. For example, the patient monitor14may measure the intensity of light that is received at the light sensor as a function of time. A signal representing light intensity versus time or a mathematical manipulation of this signal (e.g., a scaled version thereof, a log taken thereof, a scaled version of a log taken thereof, etc.) may be referred to as the photoplethysmograph (photoplethysmography) signal. The light intensity or the amount of light absorbed may then be used to calculate the amount of the blood constituent (e.g., oxyhemoglobin) being measured and other physiological parameters such as the pulse rate and when each individual pulse occurs. Generally, the light passed through the tissue is selected to be of one or more wavelengths that are absorbed by the blood in an amount representative of the amount of the blood constituent present in the blood. The amount of light passed through the tissue varies in accordance with the changing amount of blood constituent in the tissue and the related light absorption. At least two, e.g., red and infrared (IR), wavelengths may be used because it has been observed that highly oxygenated blood will absorb relatively less red light and more infrared light than blood with a lower oxygen saturation. However, it should be understood that any appropriate wavelengths, e.g., green, etc., may be used as appropriate. Further, photoplethysmography measurements may be determined based on one, two, or three or more wavelengths of light.

The emitter40and the detector42may be arranged in a reflectance or transmission-type configuration with respect to one another. However, in embodiments in which the plethysmographic sensor16is configured for use on a patient's forehead (e.g. either alone or in conjunction with a hat or headband), the emitters40and detectors42may be in a reflectance configuration. The emitter40may also be a light emitting diode, superluminescent light emitting diode, a laser diode or a vertical cavity surface emitting laser (VCSEL). The emitter40and the detector42may also include optical fiber sensing elements. The emitter40may include a broadband or “white light” source, in which case the detector42could include any of a variety of elements for selecting specific wavelengths, such as reflective or refractive elements, absorptive filters, dielectric stack filters, or interferometers. These kinds of emitters and/or detectors would typically be coupled to the plethysmographic sensor16via fiber optics.

In certain embodiments, the emitter40and detector42may be configured for pulse oximetry. It should be noted that the emitter40may be capable of emitting at least two wavelengths of light, e.g., red and infrared (IR) light, into the tissue of a patient, where the red wavelength may be between about 600 nanometers (nm) and about 700 nm, and the IR wavelength may be between about 800 nm and about 1000 nm. The emitter40may include a single emitting device, for example, with two LEDs, or the emitter40may include a plurality of emitting devices with, for example, multiple LEDs at various locations. In some embodiments, the LEDs of the emitter40may emit three or more different wavelengths of light. Regardless of the number of emitting devices, light from the emitter40may be used to measure, as provided herein, a physiological parameter, such as a pulse rate, oxygen saturation, respiration rate, respiration effort, continuous non-invasive blood pressure, cardiac output, fluid responsiveness, perfusion, pulse rhythm type, hydration level, or any combination thereof. In certain embodiments, the sensor measurements may also be used for determining water fraction, hematocrit, or other physiologic parameters of the patient. It should be understood that, as used herein, the term “light” may refer to one or more of ultrasound, radio, microwave, millimeter wave, infrared, visible, ultraviolet, gamma ray or X-ray electromagnetic radiation, and may also include any wavelength within the radio, microwave, infrared, visible, ultraviolet, or X-ray spectra, and that any suitable wavelength of light may be appropriate for use with the present disclosure.

In any suitable configuration of the plethysmographic sensor16, the detector42may be an array of detector elements that may be capable of detecting light at various intensities and wavelengths. The detector42may convert the received light at a given intensity, which may be directly related to the absorbance and/or reflectance of light in the tissue of the patient12, into an electrical signal. That is, when more light at a certain wavelength is absorbed, less light of that wavelength is typically received from the tissue by the detector42, and when more light at a certain wavelength is reflected, more light of that wavelength is typically received from the tissue by the detector42. The detector42may receive light that has not entered the tissue to be used as a reference signal. After converting the received light to an electrical signal, the detector42may send the signal to the patient monitor14, where physiological characteristics may be calculated based at least in part on the absorption and/or reflection of light by the tissue of the patient.

In certain embodiments, the plethysmographic sensor16may also include an encoder44that may provide signals indicative of the wavelength of one or more light sources of the emitter40, which may allow for selection of appropriate calibration coefficients for calculating a physical parameter such as blood oxygen saturation or respiration rate. The encoder44may, for instance, be a coded resistor, EEPROM or other coding devices (such as a capacitor, inductor, PROM, RFID, parallel resident currents, or a colorimetric indicator) that may provide a signal to a processor46of the patient monitor14related to the characteristics of the plethysmographic sensor16to enable the processor46to determine the appropriate calibration characteristics of the plethysmographic sensor16. In some embodiments, the encoder44and/or the detector/decoder48may not be present.

Signals from the detector42and/or the encoder44may be transmitted to the patient monitor14. The patient monitor14may include one or more processors46coupled to an internal bus50. Also connected to the bus50may be a ROM memory52, a RAM memory54, a display58, control inputs60, and a speaker62. A time processing unit (TPU)64may provide timing control signals to light drive circuitry66, which may control when the emitter40is activated, and if multiple light sources are used, the multiplexed timing for the different light sources. The TPU64may also control the gating-in of signals from detector42through a switching circuit68. These signals are sampled at the proper time, depending at least in part upon which of multiple light sources is activated, if multiple light sources are used. The received signal from the detector42may be passed through one or more amplifiers (e.g., amplifiers70and72), a low pass filter74, and an analog-to-digital converter76for amplifying, filtering, and digitizing the electrical signals from the plethysmographic sensor16. The digital data may then be stored in a queued serial module (QSM)78, for later downloading to RAM54as QSM78fills up. In an embodiment, there may be multiple parallel paths for separate amplifiers, filters, and A/D converters for multiple light wavelengths or spectra received.

Based at least in part upon the received signals corresponding to the light received by optical components of the plethysmographic sensor16, the processor46may calculate oxygen saturation, respiration rate, and/or heart rate using various algorithms. It should be noted that, in order to measure respiration rate, embodiments of the present disclosure may utilize systems and methods such as those disclosed in U.S. Pat. No. 7,035,679, filed Jun. 21, 2002, U.S. Pat. No. 8,255,029, filed Feb. 27, 2004, and U.S. Publication Application No. 2013/0079606, filed Sep. 23, 2011, which are each incorporated herein by reference in their entirety for all purposes. In addition, the processor46may detect events (e.g., artifacts or noise in the plethysmographic waveform) that may adversely affect the determination of respiration rate, such as talking, motion, body movement, coughing, sneezing, yawning, or the like, and may display one or more indications of such events and/or remove the artifacts for the determination of respiration rates using various methods, such as those provided herein. These algorithms may employ certain coefficients, which may be empirically determined, and may correspond to the wavelengths of light used. The algorithms and coefficients may be stored in the ROM52or other suitable computer-readable storage medium and accessed and operated according to processor46instructions.

As noted above, the system10may also include components for implementing capnography techniques (e.g., the capnograph18and the carbon dioxide sensor20) to acquire signals for determining respiration rate and/or for detecting events that may adversely affect the determination of respiration rate. For example,FIG. 3illustrates a simplified block diagram of the capnograph18and the carbon dioxide sensor20of the system10. The carbon dioxide sensor20may include any appropriate sensor or sensor element for assessing expired carbon dioxide, including chemical, electrical optical, non-optical, quantum-restricted, electrochemical, enzymatic, spectrophotometric, fluorescent, or chemiluminescent indicators or transducers. Generally, the carbon dioxide sensor20may include any indicator that is sensitive to the presence of carbon dioxide and that is capable of being calibrated to give a response signal corresponding to a given predetermined concentration of carbon dioxide. In certain embodiments, the carbon dioxide sensor20may monitor the partial pressure or concentration of carbon dioxide in the respiratory gases. By monitoring the carbon dioxide changes during the breath cycle, the number of breaths per minute (i.e., the respiration rate) may be determined.

In certain embodiments, the carbon dioxide sensor20may include optical components, such as an emitter100and a detector102. For example, the emitter100may be one or more light emitting diodes adapted to transmit one or more wavelengths of light in the red to infrared range, and the detector102may be one or more photodetectors selected to receive light in the range or ranges emitted from the emitter100. Alternatively, the emitter100may also be a laser diode or a vertical cavity surface emitting laser (VCSEL). The emitter100and detector102may also include optical fiber sensing components. The emitter100may include a broadband or “white light” source, in which case the detector102could include any of a variety of elements for selecting specific wavelengths, for example, reflective or refractive elements or interferometers. These kinds of emitters100and/or detectors102would typically be coupled to the rigid or rigidified sensor20via fiber optics. Alternatively, the carbon dioxide sensor20may sense light detected through the respiratory gas at a different wavelength from the light emitted into the respiratory gas. Such sensors may be adapted to sense fluorescence, phosphorescence, Raman scattering, Rayleigh scattering and multi-photon events or photoacoustic effects. It should be understood that, as used herein, the term “light” may refer to one or more of ultrasound, radio, microwave, millimeter wave, infrared, visible, ultraviolet, gamma ray or X-ray electromagnetic radiation, and may also include any wavelength within the ultrasound, radio, microwave, millimeter wave, infrared, visible, ultraviolet, gamma ray or X-ray spectra.

The emitter100and the detector102may be arranged in a reflectance or transmission-type configuration with respect to one another. For example, in embodiments in which the carbon dioxide sensor20is integrated with the interface device22(e.g., embedded within a wall of the interface device22), the emitter100and the detector102may be arranged in a reflectance-type configuration. Alternatively, in embodiments in which the carbon dioxide sensor20is disposed about the interface device22(e.g., surrounding a portion of tubing of the interface device22), the emitter100and the detector102may be arranged in a transmission-type configuration.

Signals from the detector102may be transmitted to the capnograph18. The capnograph18may include one or more processors104coupled to an internal bus106. Also connected to the bus106may be a ROM memory108, a RAM memory110, control inputs112, a display114, and a speaker116. Light drive circuitry118may control when the emitter100is activated. The received signal from the detector102may be passed through one or more amplifiers (e.g., amplifier120), a filter122, and an analog-to-digital converter124for amplifying, filtering, and digitizing the electrical signals from the carbon dioxide sensor20. The digital data may then be stored in a queued serial module (QSM)126for later downloading to the RAM110as the QSM126fills up. In one embodiment, there may be multiple parallel paths for separate amplifiers, filters, and A/D converters for multiple light wavelengths or data received.

Based at least in part upon the received signals from the carbon dioxide sensor20, the processor104may calculate the partial pressure of carbon dioxide in the inhaled and/or exhaled breaths, the concentration of carbon dioxide in the inhaled and/or exhaled breaths, end tidal carbon dioxide, respiration rate, expiratory pH, and/or any other suitable parameters using various algorithms. In addition, the processor104may detect events (e.g., artifacts or noise in the carbon dioxide waveform) that may adversely affect the determination of respiration rate, such as talking, motion, body movement, coughing, sneezing, yawning, or the like, and may display one or more indications of such events and/or remove the artifacts for the determination or respiration rates using various methods, such as those provided herein. These algorithms may employ certain coefficients, which may be empirically determined, and may correspond to the wavelengths of light used. The algorithms and coefficients may be stored in the ROM108or other suitable computer-readable storage medium and accessed and operated according to processor104instructions.

FIG. 4illustrates a plethysmographic waveform and a carbon dioxide waveform that may be displayed and/or analyzed by the patient monitor14, the capnograph18, the multi-parameter monitor24, or any other suitable processor-based device. As noted above, in some embodiments, the plethysmographic waveform and/or the carbon dioxide waveform may be analyzed by only one processor-based device (e.g., the patient monitor14, the capnograph18, or the multi-parameter monitor24), using the techniques as described below, to determine respiration rate and to determine whether the patient12is breathing irregularly. For example, in one embodiment, the patient monitor14may receive signals from both the plethysmographic sensor16and the carbon dioxide sensor20and may determine whether the patient14is breathing irregularly based on the signals from both the plethysmographic sensor16and the carbon dioxide sensor20. Further, in another embodiment, the capnograph18may receive signals from both the plethysmographic sensor16and the carbon dioxide sensor20and may determine whether the patient14is breathing irregularly based on the signals from both the plethysmographic sensor16and the carbon dioxide sensor20. Additionally, in another embodiment, the multi-parameter monitor24may receive signals from both the plethysmographic sensor16and the carbon dioxide sensor20and may determine whether the patient14is breathing irregularly based on the signals from both the plethysmographic sensor16and the carbon dioxide sensor20.

In particular,FIG. 4Aillustrates a first plot130, which shows the amplitude (on y-axis132) of an example plethysmographic waveform134over time (x-axis136), andFIG. 4Billustrates a second plot138, which shows the amplitude (on y-axis140) of an example carbon dioxide waveform142over time (x-axis144). The plethysmographic waveform134may be generated by the plethysmographic sensor16and analyzed by the processor46to determine respiration rate. Additionally, the carbon dioxide waveform142may be generated by the carbon dioxide sensor20and analyzed by the processor104to determine respiration rate. Further, the processor46and the processor104may analyze the plethysmographic waveform134and the carbon dioxide waveform142, respectively, for one or more features that may be indicative of irregular breathing, which may be caused by one or more events, such as talking, motion, coughing, sneezing, yawning, or the like. Additionally, in certain embodiments, the processor46and/or the processor104may be configured to identify such events (e.g., talking, motion, body movement, coughing, sneezing, yawning, or the like) based at least in part upon the detection of the one or more features.

The plethysmographic waveform134and the carbon dioxide waveform142each generally rise and fall over the course of a breath period (e.g., breath periods146of the plethysmographic waveform134and breath periods148of the carbon dioxide waveform142). In particular, the amplitude of the plethysmographic waveform134increases (e.g., rises) during inhalation and an inspiratory upstroke150is observed. During exhalation, the amplitude of the plethysmographic waveform134decreases (e.g., falls) and an expiratory downstroke152is observed. In contrast, the amplitude of the carbon dioxide waveform142increases during exhalation and decreases during inhalation. In particular, the carbon dioxide waveform142includes expiratory upstrokes154and inspiratory downstrokes156. More specifically, the carbon dioxide waveform142may include an inspiratory baseline158that is indicative of inspired gas, which is generally devoid of or includes a minimal amount of carbon dioxide. The inspiratory baseline158is followed by the expiratory upstroke154. The carbon dioxide waveform142may include an alveolar plateau160between the expiratory upstroke154and the inspiratory downstroke156.

As illustrated, the plethysmographic waveform134and the carbon dioxide waveform142each include a first portion (e.g., a first portion162of the plethysmographic waveform134and a first portion170of the carbon dioxide waveform142) that may be indicative of regular (e.g., normal) breathing. Specifically, periods of regular breathing may be periods when the patient12is not talking, moving, coughing, sneezing, yawning, or the like. Periods of regular breathing may provide clinically useful information for the calculation of respiration rate and, in particular, may provide a more accurate calculation of respiration rate as compared to periods when the patient is12is not talking, moving, coughing, sneezing, yawning, or the like.

The first portion162of the plethysmographic waveform134and the first portion170of the carbon dioxide waveform142may each include generally periodic breath periods. In particular, the spread (e.g., variance, standard deviation) of a distribution of the breath periods146and148in the first portion162and the first portion170, respectively, may be less than a predetermined threshold for the spread of the breath distribution. That is, the patient12may inhale and exhale with a generally constant frequency. In certain embodiments, the predetermined threshold for the spread of the breath distribution may be based at least in part upon a mean respiration rate of the patient12. Accordingly, in certain embodiments, the processor46and the processor104may be configured to analyze the plethysmographic waveform134and the carbon dioxide waveform142, respectively, for one or more features indicative of normal breathing (e.g., generally periodic breath periods) and may be configured to determine that the patient12is breathing normally (e.g., not talking, moving, coughing, yawning, sneezing, etc.) based at least in part upon one or more detected features indicative of normal breathing and/or based at least in part upon a determination that the spread of the breath periods is less than a predetermined threshold.

Additionally, each breath period146in the first portion162of the plethysmographic waveform134may be generally symmetrical. That is, the inspiratory upstroke150of each breath period146of the first portion162may have a generally similar duration and slope (e.g., absolute value of the slope) to that of the respective expiratory downstroke152. For example, the slope172of the inspiratory upstroke150for a breath period174of the first portion162may be within a predetermined range of an absolute value of the slope176of the expiratory downstroke152for the same breath period174. Additionally, the period178(e.g., duration) of the inspiratory upstroke150may be within a predetermined range of the period180of the expiratory downstroke152. Accordingly, in certain embodiments, the processor46may be configured to analyze the plethysmographic waveform134for generally symmetrical breath periods and may be configured to determine that the patient12is breathing normally (e.g., not talking, moving, coughing, yawning, sneezing, etc.) based at least in part upon the determination that plethysmographic waveform134includes generally symmetrical breath periods.

Additionally or alternatively, the processor46and the processor104may be configured to analyze the plethysmographic waveform134and the carbon dioxide waveform142, respectively, for one or more features indicative of irregular breathing, such as talking, motion, body movement, coughing, sneezing, yawning, or the like. As will be described in more detail below, talking, motion, body movement, coughing, sneezing, and/or yawning, may result in irregular periodicity of breath periods, asymmetric breath periods, short inhalations relative to exhalations, sharp inhalations (e.g., steep inspiratory upstrokes), and/or irregular peaks on the waveform. As illustrated, the plethysmographic waveform134and the carbon dioxide waveform142include a period of irregular breathing192and194, respectively. The periods of irregular breathing192and194may each be indicative of talking, motion, body movement, coughing sneezing, yawning, and/or any other action that may alter the patient's breathing. The periods of irregular breathing192and194may not provide clinically useful information and/or may decrease the accuracy of the determination of respiration rate and/or other physiological parameters. Thus, it may be desirable to identify periods of irregular breathing, to provide an indication to a user of the periods of irregular breathing, and/or to exclude data during the periods of irregular breathing from the calculation of respiration rate.

In contrast to the first portions162and170, the periods of irregular breathing192and194include breath periods196and198, respectively, which are irregular (e.g., inconstant) over time. In particular, the spread (e.g., variance, standard deviation) of a distribution of the breath periods196and198in the period of irregular breathing192and194, respectively, may be greater than a predetermined threshold. For example, as illustrated inFIG. 4A, the period of irregular breathing192of the plethysmographic waveform134includes a first breath period200and a second breath period202, and the first breath period200is longer than the second breath period202, which may increase the spread of the distribution of the breath periods196. Similarly, the period of irregular breathing194of the carbon dioxide waveform142includes a first breath period204and a second breath period206, and the first breath period204is longer than the second breath period206.

Accordingly, the processor46and the processor104may be configured to analyze the plethysmographic waveform134and the carbon dioxide waveform142, respectively, for irregular breath periods and, in some embodiments, may be configured to calculate the spread (e.g., standard deviation) of a distribution of breath periods. Further, the processor46and the processor104may be configured to determine that the patient12is breathing irregularly based at least in part upon the detection of irregular breath periods (e.g., irregular breath periods196and/or198) and/or a determination that the spread of the distribution of breath periods (e.g., irregular breath periods196and/or198) is greater than a predetermined threshold.

Additionally, one or more breath periods196in the period of irregular breathing192of the plethysmographic waveform134may be asymmetrical. That is, the inspiratory upstroke150of one or more breath periods196in the period of irregular breathing192may have a duration (e.g., period) and/or a slope (e.g., absolute value of the slope) that is substantially different from (e.g., outside of a predetermined range of) that of the respective expiratory downstroke152. By way of example, the slope210of the inspiratory upstroke150for a breath period212in the period of irregular breathing192may be outside of a predetermined range of the slope216of the expiratory downstroke152for the same breath period212. Additionally, the period218(e.g., duration) of the inspiratory upstroke150of the breath period212may be outside of a predetermined range of the period220of the expiratory downstroke152for the breath period212.

Accordingly, in certain embodiments, the processor46may be configured to analyze the plethysmographic waveform134for asymmetrical breath periods (e.g., breath periods196). For example, the processor46may be configured to compare the slope of the inspiratory upstroke of each breath period to the slope of the expiratory downstroke of the respective breath period. Additionally, the processor46may be configured to compare the period of the inspiratory upstroke of each breath period to the period of the expiratory downstroke of the respective breath period. Furthermore, the processor46may be configured to determine that the patient12is breathing irregularly based at least in part upon a determination that the slopes of one or more inspiratory upstrokes of one or more breath periods are outside of a predetermined range of the slopes of one or more expiratory downstrokes of the respective one or more breath periods, a determination that periods of one or more inspiratory upstrokes of one or more breath periods are outside of a predetermined range of the periods of one or more expiratory downstrokes of the respective one or more breath periods, and/or the detection of any other features indicative of asymmetric breath periods.

Furthermore, irregular breathing, and in particular, irregular breathing due to talking or yawning, may result in sharp inhalations. For example, irregular breathing may include inhalations that are short (e.g., a shorter period or duration) and/or rapid (e.g., a steeper or greater slope) relative to inhalations of normal breathing and/or relative to exhalations of the respective breath period. This may occur because the patient12may take a quick, deep breath before talking or in between talking (e.g., vocal pauses) and may slowly exhale over the course of the talking. For example, as illustrated inFIG. 4A, the plethysmographic waveform134may include one or more steep inspiratory upstrokes222that have a slope greater than a predetermined threshold. In some embodiments, the predetermined threshold may be based at least in part upon an average slope of the inspiratory upstrokes150of the first portion162. Similarly, as illustrated inFIG. 4B, the carbon dioxide waveform142may include one or more steep inspiratory downstrokes224that have a slope greater than a predetermined threshold, which may be based at least in part upon an average slope of the inspiratory downstrokes156of the first portion170. In certain embodiments, the predetermined thresholds for the slope of the inspiratory upstrokes150and the inspiratory downstrokes156may be determined based upon a predetermined deviation from the respective average slope value. Accordingly, the processor46and/or the processor104may be configured to compare the slope of the inspiratory upstrokes150and the slope of the inspiratory downstrokes156, respectively, to a respective predetermined threshold. Furthermore, the processor46and/or the processor104may be configured to determine that the patient12is breathing irregularly based at least in part upon a determination that the slope of the inspiratory upstroke150is greater than a predetermined threshold and/or a determination that the slope of the inspiratory downstroke154is greater than a predetermined threshold.

Additionally, the plethysmographic waveform134and the carbon dioxide waveform142may include one or more short inspiratory upstrokes226and inspiratory downstrokes228, respectively. For example, the short inspiratory upstroke226of the plethysmographic waveform134may have a period230that is less than a predetermined threshold, which may be based at least in part upon an average period of the inspiratory upstrokes of the first portion162. Additionally, the short inspiratory downstroke228of the carbon dioxide waveform142may have a period232that is less than a predetermined threshold, which may be based at least in part upon an average period of the inspiratory downstroke of the first portion170. Accordingly, the processor46and/or the processor104may be configured to compare the period of the inspiratory upstroke150and the inspiratory downstroke156, respectively, to a respective predetermined threshold. Furthermore, the processor46and/or the processor104may be configured to determine that the patient12is breathing irregularly based at least in part upon a determination that the period of the inspiratory upstroke150is greater than a predetermined threshold and/or a determination that the period of the inspiratory downstroke154is greater than a predetermined threshold.

Additionally, irregular breathing may result in long exhalations. In particular, irregular breathing may include long exhalations relative to exhalations during normal breathing and/or relative to inhalations of the same. For example, as noted above, the patient12may exhale slowly over the course of talking or may exhale slowly while yawning, which may result in long exhalations. In some embodiments, the processor46and/or the processor104may be configured to calculate a ratio of the inspiratory periods to the expiratory periods for one or more breath periods. The processor46and/or the processor104may be configured to determine that the patient12is breathing irregularly based upon a determination that the ratio of the inspiratory periods to the expiratory periods is below a predetermined threshold. In certain embodiments, the processor46and/or the processor104may be configured to determine that the patient12is talking based upon a determination that the ratio of the inspiratory periods to the expiratory periods is below a predetermined threshold. Additionally, in some embodiments, the processor46and/or the processor104may be configured to characterize the variability of the ratio of the inspiratory periods to the expiratory periods over time and may be configured to determine that the patient12is breathing irregularly based upon a determination that the variability (e.g., the standard deviation) of the ratio is greater than a predetermined threshold.

Furthermore, the plethysmographic waveform134and/or the carbon dioxide waveform142may include features having a high variability during the periods of irregular breathing (e.g., the period of irregular breathing192and the period of irregular breathing194, respectively). For example, the slope of the plethysmographic waveform134and the slope of the carbon dioxide waveform142may vary over time during the period of irregular breathing192and the period of irregular breathing194, respectively. In certain embodiments, the slope of the inspiratory upstroke150over different breath periods196of the plethysmographic waveform134may vary over time during the period of irregular breathing192. Additionally, the slope of the expiratory upstroke154over different breath periods198may vary over time during the period of irregular breathing194. Accordingly, in certain embodiments, the processor46and the processor104may be configured to analyze the plethysmographic waveform134and the carbon dioxide waveform142, respectively, for high variability and may be configured to determine that the patient12is breathing irregularly based upon the detection of high variability. For example, the processor46and the processor104may be configured to quantify the gradient of the slope of the plethysmographic waveform134(e.g., the slope of the inspiratory upstroke150) and the gradient of the slope of the carbon dioxide waveform142(e.g., the slope of the expiratory upstroke154), respectively. In certain embodiments, the processor46and/or the processor104may be configured to determine that the patient12is breathing irregularly based upon a determination that the gradient of the upstroke slope of the plethysmographic waveform134and/or of the carbon dioxide waveform142, respectively, is greater than a predetermined threshold. Further, the processor46and/or the processor104may be configured to determine that the patient12is breathing irregularly based upon a determination that the variation (e.g., spread, standard deviation) of the gradient of the upstroke slope of the plethysmographic waveform134and/or of the carbon dioxide waveform142, respectively, is greater than a predetermined threshold.

Additionally, the periods of irregular breathing192and194may include irregularity in the peak portions of the respective waveforms. For example, as illustrated inFIG. 4A, a peak portion240of the plethysmographic waveform134includes irregular peaks (e.g., ripples). Similarly, a peak portion242of the carbon dioxide waveform142may include irregular peaks. The processor46and/or the processor104may be configured to analyze the plethysmographic waveform134and/or the carbon dioxide waveform142for irregularity. In certain embodiments, the processor46and/or the processor104may be configured to quantify irregular peaks of the respective waveforms based on the number, size, and/or variability of the ripples in the peak portions240and242, respectively. The processor46and/or the processor104may determine that the patient12is breathing irregularly based upon a determination that the value of the irregularity exceeds a predetermined threshold. In certain embodiments, the predetermined threshold may be based at least in part upon historical data for the respective waveform.

In certain embodiments, the processor46and/or the processor104may be configured to perform signal processing techniques to analyze the plethysmographic waveform134and/or the carbon dioxide waveform142, respectively, to detect events such as talking, motion, coughing, sneezing, yawning, or the like. That is, rather than detecting such events by identifying features in identified breath periods, as described above, the processor46and/or the processor104may also be configured to detect the events directly from the plethysmographic waveform134and/or the carbon dioxide waveform142, respectively. For example, the processor46and/or the processor104may be configured to implement various techniques, such as, for example, piecewise linear approximation, linear regression, linear combination, multivariate analysis, principal component analysis (PCA), other suitable matrix techniques, independent component analysis (ICA), linear discriminate analysis (LDA), and/or any suitable signal transform methods (e.g., fast Fourier transform (FFT), continuous wavelet transform (CWT), Hilbert transform, or Laplace transform). Furthermore, signal processing techniques may include use of neural networks (e.g., multilayer perception networks (MLP) or radial basis networks), stochastic or probabilistic classifiers (e.g., Bayesian, Hidden Markov Model (HMM), or fuzzy logic classifiers), genetic-based algorithms, propositional or predicate logics (e.g., non-monotonic or modal logics), nearest neighbor classification methods (e.g., kth nearest neighbor or learning vector quantization (LVQ) methods), or any other learning-based algorithms.

Additionally, the signal processing techniques may include the combination of the plethysmographic waveform134and/or the carbon dioxide waveform142with additional sensors, including plethysmographic sensors (e.g., the plethysmographic sensor16), carbon dioxide sensors (e.g., the carbon dioxide sensor20), motion sensors, pressure sensors, temperature sensors, and/or ultrasound sensors. The additional sensors may provide data to be used with the plethysmographic waveform134and/or the carbon dioxide waveform142, which may aid in distinguishing physiological signals from artifacts or other non-physiological components, which may be caused by talking, motion, coughing, sneezing, yawning, or the like. Furthermore, the additional sensors may provide data to be used with the plethysmographic waveform134and/or the carbon dioxide waveform142, which may aid in the identification (e.g., classification) of artifacts or other non-physiological components that may result in irregular breathing, such as talking, motion, coughing, sneezing, yawning, or the like. For example, a plethysmographic sensor (e.g., the plethysmographic sensor16) may be configured to detect patient motion and/or to determine the state of the sensor, such as a sensor off state, which may indicate that the sensor is not properly coupled to the patient12, and/or a disconnect state, which may indicate that the sensor is not connected to the patient monitor. In certain embodiments, in order to determine the state of the plethysmographic sensor, embodiments of the present disclosure may utilize systems and methods such as those disclosed in U.S. Pat. No. 6,035,223, filed Nov. 19, 1997, which is incorporated herein by reference in its entirety for all purposes.

With the foregoing in mind,FIG. 5illustrates a method250for providing an indication of irregular breathing. The method250may be performed as an automated procedure by a system, such as the system10. In addition, certain steps of the method250may be performed by a processor or a processor-based device, such as the patient monitor14, the capnograph18, and/or the multi-parameter monitor24, which includes instructions for implementing certain steps of the method250. As noted above, in one embodiment, the method250may be performed using only the patient monitor14, the capnograph18, the multi-parameter monitor24, or any other suitable processor-based device. Further, the method250may be performed using signals from only the plethysmographic sensor16or using signals from only the carbon dioxide sensor20.

The method250may include receiving one or more signals from one or more sensors (block252). In certain embodiments, the one or more signals may be acquired by plethysmographic sensors (e.g., the plethysmographic sensor16), carbon dioxide sensors (e.g., the carbon dioxide sensors20), motion sensors, temperature sensors, pressure sensors, or any other suitable sensor. The one or more signals may include, for example, a plethysmographic waveform (e.g., the plethysmographic waveform134), a carbon dioxide waveform (e.g., the carbon dioxide waveform142), and/or any other suitable waveform.

The method250may also include determining if one or more features indicative of irregular breathing are present in the one or more waveforms of the one or more received signals (block254). As described above, irregular breathing may result from talking, moving, coughing, sneezing, and/or yawning. In certain embodiments, detecting the one or more features indicative of irregular breathing may include detecting irregular periodicity of breath periods, asymmetric breath periods, short inhalations relative to exhalations, sharp inhalations (e.g., steep inspiratory upstrokes), and/or irregular peaks on the waveform of the received signal. In particular, the one or more features indicative of irregular breathing may be detected by analyzing the waveform using the techniques as described above with respect toFIG. 4. In some embodiments, the method250may include obtaining (e.g., selecting) a segment of the received signal and analyzing the segment to detect the one or more features indicative of irregular breathing. For example, the segment may correspond to data to be used to calculate respiration rate. Thus, it may be desirable to determine whether the selected segment includes features indicative of irregular breathing to determine whether to use the segment to calculate respiration rate and/or to determine whether to display a calculated respiration rate, as will be described in more detail below. In other embodiments, the method250may include analyzing the waveform of the signal directly using the above-described signal processing techniques.

The method250may also include determining respiration rate (block256) based at least in part upon the received signal. Respiration rate may be determined using data obtained from a plethysmographic waveform134and/or a carbon dioxide waveform142, as described above with respect toFIG. 2andFIG. 3, respectively. In some embodiments, respiration rate may be determined using a segment of the signal (e.g., one or more data points of the signal). In certain embodiments, determining respiration rate (block256) may occur in response to a determination that features indicative of irregular breathing are not present. That is, the determination that the signal or signal segment does not includes one or more features indicative of irregular breathing may indicate that the signal or signal segment includes clinically useful information that may result in an accurate calculation of respiration rate. In one embodiment, the method250may not determine respiration rate using a signal segment that includes one or more features indicative of irregular breathing. The determination that the signal segment includes one or more features indicative of irregular breathing may indicate that the signal segment includes one or more artifacts that may adversely affect the accuracy of the calculation of respiration rate. Thus, it may be desirable to omit signal segments including features indicative of irregular breathing from the calculation of respiration rate.

The method250may also include displaying the determined respiration rate (block258). The respiration rate may be displayed on the patient monitor14, the capnograph18, and/or the multi-parameter monitor24. In certain embodiments, the respiration rate may be displayed based on a determination that the signal or signal segment does not include one or more features indicative of irregular breathing. In one embodiment, respiration rate may not be displayed based on a determination that the signal or signal segment includes one or more features indicative of irregular breathing. For example, it may be desirable to prevent the display of respiration rate based upon a determination that the respiration rate was calculated using data that may include one or more artifacts that may adversely affect the accuracy of the calculation.

In other embodiments, the method250may include providing an indication of irregular breathing (block260) based upon a determination that one or more features indicative of irregular breathing are present. For example, in certain embodiments, the indication of irregular breathing may be provided instead of displaying the respiration rate. Thus, the method250may provide information to the user regarding the absence of the calculated respiration rate. In one embodiment, the absence of the calculated respiration rate may be the indication of irregular breathing. In other embodiments, the indication of irregular breathing may be provided in combination with the displayed respiration rate. In this manner, the indication of irregular breathing may inform the user that the calculated respiration rate may not be accurate as a result of the patient breathing irregularly.

In certain embodiments, providing the indication of irregular breathing may include displaying text, a symbol, graphic, and/or any other suitable display on a display of the patient monitor14, the capnograph18, and/or the multi-parameter monitor24. In some embodiments, providing the indication of irregular breathing may include altering the displayed waveform (e.g., the plethysmographic waveform134and/or the carbon dioxide waveform142). For example, the patient monitor14, the capnograph18, and/or the multi-parameter monitor24may be configured to remove a portion of the waveform corresponding to the signal segment including the one or more features indicative of irregular breathing, to change the color and/or line quality of the portion of the waveform, to shade the portion of the waveform, to add text and/or a graphic to the portion of the waveform, or any other suitable technique. Further, in some embodiments, providing the indication of irregular breathing may include providing an audible alarm and/or an indicator light via the patient monitor14, the capnograph18, and/or the multi-parameter monitor24.

As noted above, the patient monitor14, the capnograph18, and/or the multi-parameter monitor24may be configured to detect one or more features indicative of irregular breathing and/or to determine the cause of the irregular breathing (e.g., the type of artifact), such as talking, moving, coughing, sneezing, and/or yawning. For example,FIG. 6illustrates a method270for determining the cause of the presence of one or more features indicative of irregular breathing in a waveform (e.g., the plethysmographic waveform134and/or the carbon dioxide waveform142). The method270may include receiving one or more signals from one or more sensors (block252) and determining if one or more features indicative of irregular breathing are present in the one or more waveforms of the one or more received signals (block254), as described above with respect toFIG. 5. Additionally, as noted above, the method270includes determining respiration rate (block256) and displaying the respiration rate (block258) in response to a determination that features indicative of irregular breathing are not present in the signal segment.

Further, the method270may include classifying (e.g., identifying) the cause of the irregular breathing (block272). In some embodiments, classifying the cause of the irregular breathing may include identifying one or more features that are indicative of a certain type of irregular breathing, such as talking or motion. For example, classifying the cause of the irregular breathing may include determining a characteristic of the one or more features, and the characteristic may be an association or relationship between a type of feature or a combination of certain features and a type of irregular breathing. As noted above, talking may result in sharp inhalations and/or slow exhalations. Accordingly, detecting such features in the waveform may facilitate the classification of the cause of the irregular breathing as talking. Additionally, in certain embodiments, detecting irregular peak portions (e.g., ripples) in the waveform in the absence of sharp inhalations and/or slow exhalations may indicate that the patient is moving. Accordingly, detecting such features in the waveform may facilitate the classification of the cause of the irregular breathing as motion. In some embodiments, a memory (e.g., the ROM52and/or the RAM54of the patient monitor14and/or the ROM108and/or the RAM110of the capnograph18) may be configured to store the characteristics for one or more features indicative of irregular breathing. In one embodiment, the characteristics may be stored as a look-up table. For example, the processor46and/or the processor104may be configured to access the memory and determine the characteristic of the feature or the features based on the type of feature (e.g., sharp inhalation, slow exhalation, irregular peak portions, etc.) or the combination of features. Furthermore, as noted above, the system10may be configured to analyze signals generated by two or more sensors, such as plethysmographic sensors, carbon dioxide sensors, motion sensors, pressure sensors, temperature sensors, and the like, to aid in the identification of the cause of the irregular breathing. For example, in some embodiments, the patient monitor14, the capnograph18, and/or the multi-parameter monitor24may be configured to compare signals generated by two or more sensors to facilitate the classification of the cause of the irregular breathing.

Additionally, the method270may include providing an indication of the cause of the irregular breathing (block274) based on the classification. As noted above, the respiration rate may be calculated and displayed in response to a determination that one or more features indicative of irregular breathing are not present in the signal or signal segment. However, in other embodiments, the respiration rate may be calculated and displayed regardless of the presence of the one or more features indicative of irregular breathing, and the indication of the cause of the irregular breathing may be provided in combination with the displayed respiration rate. The providing the indication of the cause of irregular breathing may include displaying text (e.g., talking, motion, yawning, sneezing, coughing, and so forth), a symbol, graphic (e.g., an image of talking, motion, yawning, sneezing, coughing, and so forth), and/or any other suitable display that provides an indication of the cause on a display of the patient monitor14, the capnograph18, and/or the multi-parameter monitor24. In some embodiments, providing the indication of irregular breathing may include altering the displayed waveform (e.g., the plethysmographic waveform134and/or the carbon dioxide waveform142). For example, the patient monitor14, the capnograph18, and/or the multi-parameter monitor24may be configured to remove a portion of the waveform corresponding to the signal segment including the one or more features indicative of irregular breathing, to change the color and/or line quality of the portion of the waveform, to shade the portion of the waveform, to add text and/or a graphic to the portion of the waveform, or any other suitable technique. Further, in some embodiments, providing the indication of irregular breathing may include providing an audible alarm and/or an indicator light via the patient monitor14, the capnograph18, and/or the multi-parameter monitor24.

As noted above, the various indications of irregular breathing and the indications of the cause of irregular breathing may be provided using the patient monitor14, the capnograph18, and/or the multi-parameter monitor24. Accordingly, while the embodiments described below with respect toFIGS. 7 and 8are described in the context of the display114of the capnograph18, it should be noted that the embodiments may be displayed on any suitable display, such as the display58of the patient monitor14or a display of the multi-parameter monitor24. Furthermore, while the embodiments described below with respect toFIGS. 7 and 8are described in the context of the carbon dioxide waveform142, it should be noted that the present techniques may be implemented using the plethysmographic waveform134, any other suitable waveform or signal, or a combination thereof.

For example,FIG. 7is an illustration290of the display114of the capnograph18that may display the carbon dioxide waveform142, a calculated value of respiration rate292, and any other suitable waveforms, physiological parameters, and/or user indications. As illustrated, the carbon dioxide waveform142includes periods of irregular breathing. In response to detecting the periods of irregular breathing, the processor104may be configured to cause the display to display an indication of irregular breathing294. The indication of irregular breathing294may include a textual indication, such as “irregular breathing” or any other text suitable for conveying to a caregiver that the patient may be breathing irregularly and/or that the accuracy of the calculated respiration rate may be adversely affected. As illustrated, the indication of irregular breathing294may be displayed below the value of respiration rate292or in any other suitable location. Additionally, the indication of irregular breathing294may be displayed as a tab, a banner, a dialog box, or any other suitable type of display. Additionally or alternatively, the indication of irregular breathing294may include a symbol296, such as an exclamation point, an asterisk, a star, or a stop sign. In other embodiments, the processor104may be configured to alter the color, size, font, and/or shading of the value of respiration rate292in response to a determination that the patient is breathing irregularly. Additionally, in embodiments in which the processor104is configured to classify the cause of the irregular breathing, the indication of irregular breathing294may include an indication of the cause of the irregular breathing298, which may be a textual indication, such as “talking” or any other text suitable for conveying the determined cause of the irregular breathing, a symbol, a graphic, or the like.

Additionally, the processor104may be configured to alter the carbon dioxide waveform142to provide the indication of irregular breathing. In certain embodiments, the processor104may be configured to alter the carbon dioxide waveform142to identify the portions of the carbon dioxide waveform142corresponding to periods of irregular breathing300. For example, as illustrated inFIG. 7, the processor104may be configured to provide a shaded region302over portions of the carbon dioxide waveform142that the processor104has determined correspond to irregular breathing. However, it should be noted that other techniques may be used to identify the portions of the waveform, such as altering the color, thickness, and/or line quality of the waveform. Further, the processor104may be configured to cause the display of the indication of irregular breathing294in the shaded region302or proximate to the shaded region302. Additionally, the processor104may be configured to cause the display of the indication of the cause of the irregular breathing298in the shaded region302or proximate to the shaded region302.

In other embodiments, the processor104may be configured to remove portions of the carbon dioxide waveform142corresponding to periods of irregular breathing. For example, as illustrated inFIG. 8, the processor104may omit the periods of irregular breathing300from the displayed carbon dioxide waveform142. The omitted periods of irregular breathing300may be shaded regions302, as described above with respect toFIG. 7. In other embodiments, the omitted periods of irregular breathing300may not be shaded. In some embodiments, the processor104may cause the display of the indication of irregular breathing294in the shaded regions302and/or the display of the indication of the cause of the irregular breathing298. For example, as illustrated, the carbon dioxide waveform142includes a first indication of the irregular breathing298that identifies the cause of a first period of irregular breathing300as motion and includes a second indication of irregular breathing298that identifies the cause of a second period of irregular breathing300as talking.

The techniques provided herein have been illustrated with reference to the monitoring of a physiological signal (which may be a photoplethysmographic signal or an end-tidal carbon dioxide signal); however, it will be understood that the disclosure is not limited to monitoring physiological signals and is usefully applied within a number of signal monitoring settings. Those skilled in the art will recognize that the present disclosure has wide applicability to other signals including, but not limited to, other biosignals (e.g., electrocardiogram, electroencephalogram, electrogastrogram, electromyogram, heart rate signals, pathological sounds, ultrasound, or any other suitable biosignal), any other suitable signal, and/or any combination thereof.

While the disclosure may be susceptible to various modifications and alternative forms, specific embodiments have been shown by way of example in the drawings and will be described in detail herein. However, it should be understood that the disclosure is not intended to be limited to the particular forms disclosed. Rather, the disclosure is to cover all modifications, equivalents and alternatives falling within the spirit and scope of the disclosure as defined by the following appended claims. Further, it should be understood that elements of the disclosed embodiments may be combined or exchanged with one another.