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WO2017211814A1 - Système et procédés pour un support de détection d'impulsions basé sur la photopléthysmographie pendant des interruptions dans des compressions thoraciques - Google Patents

Système et procédés pour un support de détection d'impulsions basé sur la photopléthysmographie pendant des interruptions dans des compressions thoraciques Download PDF

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WO2017211814A1
WO2017211814A1 PCT/EP2017/063694 EP2017063694W WO2017211814A1 WO 2017211814 A1 WO2017211814 A1 WO 2017211814A1 EP 2017063694 W EP2017063694 W EP 2017063694W WO 2017211814 A1 WO2017211814 A1 WO 2017211814A1
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Prior art keywords
pulse
signal
ppg
cpr
normalized
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Ralph Wilhelm Christianus Gemma Rosa WIJSHOFF
Jens MÜHLSTEFF
Christoph HAARBURGER
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Koninklijke Philips NV
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Koninklijke Philips NV
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/4848—Monitoring or testing the effects of treatment, e.g. of medication
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
    • A61B5/024—Measuring pulse rate or heart rate
    • A61B5/02416—Measuring pulse rate or heart rate using photoplethysmograph signals, e.g. generated by infrared radiation
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
    • A61B5/7207—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal of noise induced by motion artifacts
    • A61B5/721—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal of noise induced by motion artifacts using a separate sensor to detect motion or using motion information derived from signals other than the physiological signal to be measured
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7271—Specific aspects of physiological measurement analysis
    • A61B5/7282—Event detection, e.g. detecting unique waveforms indicative of a medical condition
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2505/00—Evaluating, monitoring or diagnosing in the context of a particular type of medical care
    • A61B2505/01—Emergency care
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N1/00—Electrotherapy; Circuits therefor
    • A61N1/18—Applying electric currents by contact electrodes
    • A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
    • A61N1/38—Applying electric currents by contact electrodes alternating or intermittent currents for producing shock effects
    • A61N1/39—Heart defibrillators
    • A61N1/3904—External heart defibrillators [EHD]
    • A61N1/39044—External heart defibrillators [EHD] in combination with cardiopulmonary resuscitation [CPR] therapy
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems

Definitions

  • the invention relates to a method of and apparatus for processing
  • CPR cardiopulmonary resuscitation
  • CPR is the emergency procedure for people suffering from a cardiac arrest.
  • CPR cardiac resus .
  • chest compressions are delivered to artificially generate circulation of blood, and ventilations are given to supply blood with oxygen.
  • the goal of CPR is to achieve return of spontaneous circulation (ROSC).
  • ROSC spontaneous circulation
  • the heart of the patient has resumed beating and generates a spontaneous circulation which is life-sustaining.
  • CPR can be stopped after achieving ROSC.
  • FIGURE 1 presents a simplified schematic of the 30:2 CPR protocol 100 that is widely used during cardiac rescues.
  • CPR is delivered in 2-min blocks, in which series of thirty chest compressions are alternated by two ventilations. A compression rate of 100 to 120 min "1 is targeted. During the ventilations, compressions are stopped. At the end of each 2-min block, clinicians determine whether ROSC has been achieved. If so, CPR can be stopped. If ROSC has not been achieved, a new 2-min block of CPR is initiated.
  • International guidelines state that interruptions of the compressions for assessment of ROSC should last no longer than 10 s.
  • ROSC assessment involves pulse checks which are typically performed by manual palpation. Manual palpation interrupts the chest compressions, is time-consuming and is unreliable. Consequently, manual palpation can lead to long interruptions in the chest compressions which degrades the quality of CPR.
  • ECG electrocardiography
  • PPG PPG signal
  • PR pulse rate
  • Sp02 oxygen saturation
  • FIGURE 2 illustrates porcine data 200 obtained during a laboratory test.
  • the top trace 210 is the band-pass filtered PPG signal.
  • the bottom trace 212 is the invasive aortic blood pressure measured as a reference.
  • the PPG signal Before the defibrillation shock, pre-shock interval 214, the animal is in cardiac arrest.
  • the PPG signal directly shows that there is no spontaneous pulse, confirming cardiac arrest.
  • the heart of the animal resumes beating, as is confirmed by the rising aortic blood pressure.
  • the PPG signal directly shows presence of a spontaneous pulse.
  • Presence and absence of a spontaneous pulse in the PPG signal are indicated on a continuous scale, which can support the clinician in decision making. Furthermore, in case of 30:2 CPR, pauses in chest compressions are naturally available in the protocol, which can be used for the PPG-based pulse detection support.
  • compression reference signal such as trans-thoracic impedance, accelerometry, chest compression force, a radar signal, or a camera signal, or alternatively derived from the PPG signal itself. This is followed by an interpretation of the PPG signal during the pauses in the chest compressions to indicate to the rescuer one or more of the following indications, as well as the relative or absolute value of the indication: presence of a spontaneous pulse in the PPG signal, absence of a spontaneous pulse in the PPG signal, or artifacts in the PPG signal.
  • the method improves the functionality and efficiency of a signal processing computer and circuit in a medical device such as a defibrillator.
  • a medical device such as a defibrillator.
  • a third benefit is provided in that by rapidly detecting presence of a spontaneous pulse with PPG, the clinician can be supported to determine if further assessment of ROSC is adequate and compression should be stopped, which may reduce the risk of compression-induced refibrillation.
  • a fourth benefit is provided in that by rapidly detecting presence of a spontaneous pulse with PPG, the clinician can be supported to determine withholding or postponing administration of vasopressors, which may also reduce the risk of re-arrest.
  • a fifth benefit is that PPG is an easy-to-use and non-invasive technology, which is more convenient to use during CPR than, e.g., a continuous invasive blood pressure measurement. And by including artifact detection, the clinician or rescuer can be warned when the PPG signal should not be interpreted.
  • detecting pulse during CPR comprising the steps of providing a signal processing apparatus having a photoplethysmography (PPG) detector which outputs a PPG signal stream and a cardiopulmonary resuscitation (CPR) compressions detector which outputs a CPR compressions reference signal (CMP).
  • PPG photoplethysmography
  • CPR cardiopulmonary resuscitation
  • CMP CPR compressions reference signal
  • the method further describes the steps of automatically detecting a cessation of CPR compressions and a subsequent CPR compressions-free time period based on the CMP from the CPR compressions detector, acquiring a stream of normalized PPG signal values from the PPG detector over the time period, and deriving a normalized PPG signal autocorrelation-function-prominence and periodicity from the stream of normalized PPG signal values acquired over the time period.
  • the method further includes steps of calculating a normalized time derivative with respect to each of the normalized PPG signal values referred to as the normalized phase-space plot (nPSP), identifying and clustering zero- values of the normalized PPG signals in the nPSP, identifying and clustering zero-values of the normalized derivatives in the nPSP, and classifying a pulse condition consisting of one selected from the set of pulse, no pulse, and artifact based upon the deriving step and both of the identifying and clustering steps.
  • the method concludes by outputting a result of the classified pulse condition.
  • the classified pulse condition can be output on a continuous scale from, e.g., zero to one, where zero indicates pulse absence with a high likelihood and one indicates pulse presence with a high likelihood.
  • identifying and clustering steps which include a zero crossing (ZC) analysis wherein a proportion of ZCs of normalized PPG signals and normalized PPG signal time-derivatives in the nPSP greater than a predetermined value are identified, and used in the classifying step to determine the pulse condition.
  • the nPSP is a graphical representation of the PPG signal where one coordinate (say x) is the normalized PPG signal and the other coordinate (say y) is the normalized PPG signal time- derivative.
  • the area traversed by the traces in the nPSP can be analyzed rather than only the zero-values and zero-crossings, and a fraction of the traces can be required to traverse within set bounds in order to consider pulse to be present.
  • features derived from the normalized PPG signal and the time- derivate of the normalized PPG signal can be used in a logistical regression classifier to determine the pulse condition on a continuous scale from e.g., zero to one, where zero indicates a high likelihood of pulse absence and one indicates a high likelihood of pulse presence.
  • the range spanned by the continuous classifier output can be subdivided in various regions, where, e.g., a classifier output smaller than threshold Tl should be interpreted as pulse absence, a classifier output larger than T2 should be interpreted as pulse presence, and a classifier output larger than or equal to Tl or smaller than or equal to T2 should be interpreted as indeterminate.
  • Another embodiment of the invention is an apparatus for detecting spontaneous pulse during CPR, comprising an input port for a photoplethysmography (PPG) signal, and an input port for a CPR compressions sensor providing a CPR compressions reference signal (CMP).
  • the apparatus further includes a signal processing circuit having a processor configured to execute an algorithm for determining a cessation of CPR compressions from the CMP, then processing the PPG signal during compressions-free time period by identifying and clustering values of the PPG signal and the PPG signal derivative, classifying a pulse condition consisting of one selected from the set of pulse, no pulse, and artifact based upon the processing, and providing an output of the pulse condition, possibly on a continuous scale.
  • the apparatus further includes a user output including one of an aural and a visual indication of the pulse condition.
  • the apparatus may be a defibrillator.
  • Another embodiment of the invention may be a computer program product including a computer-readable storage medium storing instructions in non-volatile memory that when executed by a computer causes the computer to perform a method for using a computer system for detecting a spontaneous pulse during the application of cardiopulmonary resuscitation (CPR).
  • the method of the computer program product may be the method described above.
  • FIGURE 1 is an illustration of a typical prior art CPR protocol.
  • FIGURE 2 illustrates the detection of spontaneous pulse during a CPR event using a PPG detector.
  • FIGURE 3 illustrates a first embodiment of the main elements of the inventive apparatus.
  • FIGURE 4 illustrates a method step for determining an autocorrelation-function- prominence and periodicity of a pulse signal in a PPG signal stream.
  • FIGURE 5 illustrates a method step for determining the presence of a pulse signal in a PPG signal stream.
  • FIGURE 6 illustrates a method for classifying a pulse signal in a PPG signal stream.
  • FIGURE 7 is a method flow chart according to one embodiment of the invention.
  • FIGURE 3 a system 300 for detecting a spontaneous pulse during
  • the CPR is shown, which includes apparatus 350.
  • the apparatus 350 may be a patient monitoring device, an automatic CPR compressions device, a defibrillator, or another medical device for use in or out of the hospital.
  • apparatus 350 will be contained in a housing which contains signal processing circuitry that includes computer processors and controllers.
  • Input ports into the housing receive data signals from various external sensors.
  • An output port may contain audible annunciators, lights, and displays for providing information to the user.
  • apparatus 350 includes an input port 310 for a
  • a PPG signal is an optically obtained plethysmograph signal, which provides a measure of the variations of blood volume in the illuminated tissue. It can be obtained by a pulse oximeter which illuminates the skin and measures changes in light absorption. A conventional pulse oximeter monitors the perfusion of blood to the dermis and subcutaneous tissue of the skin. Besides the ECG, the PPG signal is one of the most often acquired signals in clinics, especially in anesthesia or intensive care.
  • the PPG input thus may be from a contact sensor measured from the finger, ear, nose or forehead.
  • the PPG signal can also be obtained from a non-contact sensor, such as a camera to remotely detect variations in red, infrared, or green light emanating from a subject's skin.
  • a non-contact sensor such as a camera to remotely detect variations in red, infrared, or green light emanating from a subject's skin.
  • an ultrasound or radar transducer signal may be substituted for the PPG input signal, and further used as a substitute for the PPG input signal.
  • Apparatus 350 also includes a second input port 320 for a CPR compressions sensor which provides a CPR compressions reference signal (CMP).
  • CMP CPR compressions reference signal
  • the CMP reference signal can be of several types of compression sensors, based on for example accelerometry, trans-thoracic impedance from ECG or defibrillator electrodes placed on the patient, force, camera, ultrasound transducer, or a radar signal sensor.
  • the CMP reference signal can be the PPG signal or a derived signal relating to the movement of the chest during CPR compressions.
  • the CMP reference signal can be a control signal provided by an automatic chest compression robot.
  • the PPG signal and CMP reference signal inputs are provided to a signal processing circuit 330 which is configured to determine a pulse condition of "pulse", "no pulse”, or “artifact” from the signal input data.
  • the signal processing circuit 330 executes computer program instructions residing in non-volatile memory in accordance with the algorithm and method described in detail in the following paragraphs. In general the circuit executes the algorithmic functions of determining a cessation of CPR compressions by the CMP and then processing the PPG signal during compressions-free time period by identifying and clustering values of the PPG signal and the PPG signal derivative.
  • Signal processing circuit 330 conveys the output of the pulse condition to a user output 340 for information and control purposes.
  • User output 340 is preferably one or more of an aural and a visual indication of the pulse condition.
  • FIGURE 3 shows a simple light and text display clearly indicating one of "pulse” 360, "No pulse” 370, or "Artifact” 380. Pulse presence and absence can be indicated on a continuous scale as illustrated in FIGURE 3.
  • the continuous scale can range from e.g., zero to one, where zero indicates a high likelihood of pulse absence and one indicates a high likelihood of pulse presence. Furthermore, the range spanned by the continuous classifier output can be subdivided in various regions, where, e.g., a classifier output smaller than threshold Tl should be interpreted as pulse absence, a classifier output larger than T2 should be interpreted as pulse presence, and a classifier output larger than or equal to Tl or smaller than or equal to T2 should be interpreted as indeterminate.
  • user output 340 may be configured to provide guidance instructions that are appropriate to the determined pulse condition. For example, output 340 may provide an instruction to "further assess pulse presence" for a "pulse” condition, or to provide an instruction to "continue CPR" in the case of a "no pulse” or "indeterminate” condition.
  • apparatus 350 is embodied as a defibrillator
  • externally applied ECG electrodes may provide a concurrent stream of ECG signals via a third input port (not shown) to signal processing circuit 330.
  • an analysis of the ECG signal data completely determines whether the defibrillator is to be armed for providing electrotherapy or not.
  • PEA and normal ECG rhythm cannot be distinguished. But in an
  • Signal processing circuitry 330 may control the arming of the defibrillator for electrotherapy based on both of the analysis of the ECG signal and of the pulse condition. For example, if the ECG signal is analyzed as shockable, the circuit 330 may initiate arming of the apparatus/defibrillator 350 for delivery of electrotherapy only if the pulse condition is also classified as "no pulse” or "indeterminate". But if the ECG signal is analyzed as shockable and the pulse condition is classified as "pulse", circuit 330 may prevent arming, or dis-arm high voltage circuitry to prevent an inappropriate delivery of electrotherapy.
  • processor As used herein for purposes of the present disclosure, the term "processor" is used
  • a processor can be implemented in numerous ways (e.g., such as with dedicated hardware) to perform various functions discussed herein.
  • a processor is also one example of a controller which employs one or more microprocessors that may be programmed using software (e.g., microcode) to perform various functions discussed herein.
  • a controller may be
  • controller components that may be employed in various embodiments of the present disclosure include, but are not limited to, conventional microprocessors, application specific integrated circuits (ASICs), and field-programmable gate arrays (FPGAs).
  • ASICs application specific integrated circuits
  • FPGAs field-programmable gate arrays
  • a processor or controller may be associated with one or more computer storage media (generically referred to herein as "memory,” e.g., volatile and nonvolatile computer memory such as RAM, PROM, EPROM, and EEPROM, floppy disks, compact disks, optical disks, magnetic tape, etc.).
  • the storage media may be encoded with one or more programs that, when executed on one or more processors and/or controllers, perform at least some of the functions discussed herein.
  • Various storage media may be fixed within a processor or controller or may be transportable, such that the one or more programs stored thereon can be loaded into a processor or controller so as to implement various aspects of the present invention discussed herein.
  • program or “computer program” are used herein in a generic sense to refer to any type of computer code (e.g., software or microcode) that can be employed to program one or more processors or controllers.
  • the processor or controller may be a computer that is
  • FIGURE 7 illustrates the preferred method 700 for detecting the pulse condition in the presence of CPR.
  • FIGURE 4 refers in detail to a method step 750 for deriving a PPG signal prominence and periodicity.
  • FIGURE 5 refers in detail to a method step 770 for identifying and clustering zero-values of normalized PPG signals and their time derivatives.
  • FIGURE 6 refers in detail to a method step 780 of classifying a pulse condition based on the derived prominence and periodicity and the identifying and clustering.
  • FIGURES illustrate in detail a preferred algorithm for detecting a pulse condition in the presence of CPR.
  • Method 700 for detecting a pulse condition in the presence of CPR begins with a step of providing 710 of a PPG signal stream and a CMP as inputs.
  • the inputs may be provided in accordance with a defibrillator as described with regards to FIGURE 3, although different types of apparatus' may be used.
  • the PPG and CMP signal inputs to the defibrillator may optionally be disposed as a camera-based sensor.
  • the CMP signal input can be provided by a compression robot, and can be derived from a compression control signal used in the compression robot.
  • the CMP input signal stream is then monitored at monitoring step 720 for the particular purpose of identifying periods of signal that represent ongoing chest compressions, and periods of relative low signal or noise that represent a lack of chest compression activity.
  • One method of detecting compression signal is by analysing the power in an accelerometer or a trans-thoracic impedance signal.
  • Another method of detection compression signal in an accelerometer or a trans-thoracic impedance signal is by analysing the shape of the signal and determining the periodicity of the signal.
  • the PPG signal stream may be captured at all times, with values stored in computer memory. Non-usable PPG signals, i.e. during chest compressions periods, may optionally be discarded, while usable PPG signals, i.e. during pauses in chest compressions, may be retrieved for subsequent analysis.
  • Method 700 continues at step 730 by automatically detecting pauses in, or cessations of, ongoing chest compressions as indicated by the CMP.
  • a pause may be initiated in order to ventilate a non-intubated patient and per protocol may last up to ten seconds when assessing ROSC.
  • Step 730 further determines whether the pause duration exceeds a minimum analysis time period.
  • a preferable minimum time period is four seconds of pause, which correlates to the size of sequential analysis time windows. If the time period is too short, the method returns and continues to monitor for a subsequent cessation of CPR at step 720.
  • PPG signal artifact detection may for example be detected by comparing predetermined thresholds to one or more of the PPG signal transmission ratios, e.g. the detected photocurrent versus the applied LED current [nA/mA], that is higher than would be expected with tissue between the LEDs and photodiode.
  • the PPG signal can also be considered to be in artifact when the PPG signal saturates or clips, or too large a signal range is spanned by the raw PPG signal in too short a period of time. PPG signals with too much artifact may be non-indicative of a pulse condition.
  • the pulse condition is classified as "artifact" at step 744 and is provided to the output display at step 790.
  • the PPG signal is analysed for prominence and periodicity at step 750.
  • the normalized PPG signal data is grouped into sequential time windows for analysis, preferably 4 seconds long.
  • One method 400 for determining prominence and periodicity is illustrated in FIGURE 4 in a normalized
  • the PPG signal prominence 430 is determined as the amplitude of the signal relative to the largest of the two adjacent minima in the surrounding intervals, here shown as minima 432 and 434.
  • the location of the prominence peak at 440 indicates periodicity of the signal, which further may be used to determine rate, i.e. pulse rate in beats per minute (BPM).
  • rate i.e. pulse rate in beats per minute (BPM).
  • Other features of the nACF analysis may be determined, such as the number or sum of the prominent peaks in the ACF window, given a threshold for the prominence.
  • pulse rate can be determined from other methods than the nACF, such as via Fourier Analysis or other types of spectral analysis.
  • a pulsatility-derived measure of the PPG signal may also be determined at step 750 by root mean square (RMS) analysis.
  • the RMS pulsatility can for instance be obtained as
  • ppgbp[n] the band-pass filtered PPG signal
  • ppgbl[n] the baseline of the PPG signal extracted via a low-pass filter
  • ppgn[n] the normalized PPG signal
  • Nwdw the window duration in samples.
  • the unit of the pulsatility, pit, is mNP which stands for milli-Normalized- Pulsatility.
  • the PPG pulse rate is further assessed at rate decision step 752 to ascertain whether the calculated pulse is outside of an expected range.
  • One expected range of rates is from about 30 BPM to about 300 BPM. Artifact-free rate values outside of this range may indicate that the PPG signal is not associated with pulse, and therefore a "no pulse” or "indeterminate” classification is set at step 754, which is further provided to the output/display step 790.
  • calculating step 760 calculates a normalized time-derivative with respect to each of the normalized PPG signal values.
  • the sets of normalized PPG signal values and their derivatives are then processed at identifying step 770 in a normalized phase-space plot (nPSP) analysis, which analyses "zero-crossings", i.e. zero-values, of PPG signals versus their derivatives and vice-versa.
  • nPSP normalized phase-space plot
  • the PPG signals and their derivatives over each time period are plotted on the signal axis 510 and the derivative axis 520 respectively.
  • Signals and derivatives are normalized by dividing by their absolute maxima in the window.
  • ZCs zero-crossings
  • the ZCs 530 are then clustered according to the quadrant and analysed for their distance from the origin, i.e. magnitude of derivative values, and for the distance of each ZC from its cluster center.
  • ZCs zero-crossings
  • FIGURE 6 Although four total clusters are indicated in FIGURE 6, more or less clusters may be identified. For example, two separate clusters in the top quadrant of FIGURE 6 might be identified by the particular ZCs.
  • Various ZC statistical parameters may be developed from the data in each window. For example a proportion of ZCs farther than a particular fraction from the origin, e.g. 0.65 from the origin may be used as a factor to assess absence or presence of pulse. Other parameters that may be useful for classifying pulse/no-pulse are:
  • Pulse rate determined from the number of zero-crossings per unit of time in one quadrant of the phase-space plot.
  • the desired parameters from deriving step 750 and identifying/clustering step 770 are provided to a classifying step 780 for final determination of a pulse condition of "pulse", "no pulse”, or "artifact.”
  • classifying step 770 conducts a logistical regression of the parameters to arrive at a "no-pulse” or “pulse” decision on a continuous scale from, e.g., zero (high likelihood of pulse absence) to one (high likelihood of pulse presence), and for conflicting or confounded parameters to arrive at an "artifact" decision.
  • the parameters of ZC zero-value spread (nPSP_kmeansclusterdistance), proportion of ZCs clustered outside a numerical threshold from the origin (nPSP_zerocrossings), and the PPG signal prominence (max_prominence) combine to determine a classifier output (classifier_output) by the following calculation: %% The classifier output (interpreted as posterior likelihood)
  • h theta_0 + theta_l * max_prominence + theta_2 * nPSP_zerocrossings + theta_3 * nPSP_kmeansclusterdistance
  • classifier_output 1 / (1 + exp(-h) );
  • the classifier output from step 780 is selected accordingly to indicate absence of spontaneous pulse, or to indicate presence of pulse on a continuous scale from, e.g., zero (high likelihood of pulse absence) to one (high likelihood of pulse presence).
  • the output is provided to the user at display step 790.
  • FIGURE 6 illustrates the performance and output of the method 700 described above.
  • the parameters of prominence 630, distance from cluster center 640, and proportion of ZC outside a distance from the origin 650 are plotted for each time window segment tested.
  • the respective classifier output on a continuous scale 610 is shown in the lower part of FIGURE 6.
  • prominence 630 increases, distance to cluster centers 640 decreases, and the fraction of distant ZCs 650 increases.
  • the resulting classifier output 610 correctly follows the transition from a "no-pulse" condition to a "pulse” condition.
  • the range spanned by the continuous classifier output can be subdivided in various regions, where, e.g., a classifier output smaller than threshold Tl should be interpreted as pulse absence, a classifier output larger than T2 should be interpreted as pulse presence, and a classifier output larger than or equal to Tl or smaller than or equal to T2 should be interpreted as indeterminate.
  • the classifier is able to detect presence or absence of a spontaneous pulse in a single four-second window, a quicker time than manual palpation. Thus, the rescuer or clinician can rapidly decide whether or not a further assessment of ROSC is adequate, and whether or not CPR chest compressions should be resumed.

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  • Electrotherapy Devices (AREA)

Abstract

L'invention concerne un appareil et un procédé relatifs au domaine de la réanimation cardiopulmonaire (CPR), comprenant le traitement d'un signal de photopléthysmographie pendant la procédure de protocole de réanimation cardiopulmonaire afin de déterminer rapidement si une impulsion spontanée est présente ou non. L'utilisateur obtient ainsi des informations rapides quant à savoir s'il faut ou non poursuivre les compressions de réanimation cardiopulmonaire, s'il faut administrer ou non des vasopresseurs, et éventuellement, s'il faut ou non continuer un protocole de défibrillation.
PCT/EP2017/063694 2016-06-06 2017-06-06 Système et procédés pour un support de détection d'impulsions basé sur la photopléthysmographie pendant des interruptions dans des compressions thoraciques Ceased WO2017211814A1 (fr)

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EP4011286A1 (fr) 2020-12-11 2022-06-15 Koninklijke Philips N.V. Aide à la décision en réanimation cardiopulmonaire
EP4011287A1 (fr) 2020-12-11 2022-06-15 Koninklijke Philips N.V. Aide à la décision en réanimation cardiopulmonaire
EP4410216A1 (fr) * 2023-02-03 2024-08-07 Stryker Corporation Amélioration de la surveillance d'un patient au moyen de capteurs multiples

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CN111989034A (zh) * 2018-04-17 2020-11-24 皇家飞利浦有限公司 用于支持在心肺复苏期间检测自发循环的恢复的设备、系统和方法
CN112423658A (zh) * 2018-07-13 2021-02-26 皇家飞利浦有限公司 用于心肺复苏的光体积描记脉搏血氧计
JP2022530702A (ja) * 2019-07-18 2022-06-30 コーニンクレッカ フィリップス エヌ ヴェ Ｃｐｒ中の心拍検出及びパルスオキシメトリ測定のアクティブ化及び構成を制御するための装置、システム及び方法
WO2021008925A1 (fr) 2019-07-18 2021-01-21 Koninklijke Philips N.V. Dispositif, système et procédé pour commander l'activation et la configuration de mesures de pulsations et de mesures d'oxymétrie de pouls pendant une réanimation cardio-respiratoire
CN112739306A (zh) * 2019-07-18 2021-04-30 皇家飞利浦有限公司 在cpr期间控制对脉搏检测和脉搏血氧测定测量的激活和配置的设备、系统和方法
EP3766411A1 (fr) 2019-07-18 2021-01-20 Koninklijke Philips N.V. Dispositif, système et procédé de contrôle de l'activation et de la configuration des mesures de détection et d'oxymétrie de pouls pendant la réanimation cardio-pulmonaire
US11406562B2 (en) 2019-07-18 2022-08-09 Koninklijke Philips N.V. Device, system, and method to control activation and configuration of pulse detection and pulse oximetry measurements during CPR
JP7155444B2 (ja) 2019-07-18 2022-10-18 コーニンクレッカ フィリップス エヌ ヴェ Ｃｐｒ中の心拍検出及びパルスオキシメトリ測定のアクティブ化及び構成を制御するための装置、システム及び方法
EP4011286A1 (fr) 2020-12-11 2022-06-15 Koninklijke Philips N.V. Aide à la décision en réanimation cardiopulmonaire
EP4011287A1 (fr) 2020-12-11 2022-06-15 Koninklijke Philips N.V. Aide à la décision en réanimation cardiopulmonaire
WO2022122991A1 (fr) 2020-12-11 2022-06-16 Koninklijke Philips N.V. Aide à la décision en matière de réanimation cardio-pulmonaire
WO2022122539A1 (fr) 2020-12-11 2022-06-16 Koninklijke Philips N.V. Aide à la décision en matière de réanimation cardio-pulmonaire
EP4410216A1 (fr) * 2023-02-03 2024-08-07 Stryker Corporation Amélioration de la surveillance d'un patient au moyen de capteurs multiples

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