Source: https://patents.google.com/patent/US9801561B2/en
Timestamp: 2018-06-20 18:10:41
Document Index: 669890229

Matched Legal Cases: ['Application No. 61', 'Application No. 61', 'Application No. 61', 'Application No. 61', 'Application No. 61', 'Application No. 61', 'Application No. 61', 'Application No. 61', 'Application No. 61', 'art 85', 'art 2100', 'art 2200', 'art 2000', 'art 2100', 'Application No. 16178129']

US9801561B2 - System and method for electrocardiogram analysis and optimization of cardiopulmonary resuscitation and therapy delivery - Google Patents
US9801561B2
US9801561B2 US15395780 US201615395780A US9801561B2 US 9801561 B2 US9801561 B2 US 9801561B2 US 15395780 US15395780 US 15395780 US 201615395780 A US201615395780 A US 201615395780A US 9801561 B2 US9801561 B2 US 9801561B2
US15395780
US20170105644A1 (en )
This application is a continuation of and claims priority to U.S. Non-Provisional application Ser. No. 14/656,666 entitled “System and Method for Electrocardiogram Analysis and Optimization of Cardiopulmonary Resuscitation and Therapy Delivery,” filed Mar. 12, 2015, which claims the benefit of U.S. Provisional Application No. 61/952,039 entitled “Pause Coordination for CPR Artifact Filtering,” filed Mar. 12, 2014, and U.S. Provisional Application No. 61/952,074 entitled “Compression Rate Assessment for Accurate Comb Filtering,” filed Mar. 12, 2014, and which is further a continuation-in-part of U.S. patent application Ser. No. 14/558,610 entitled “System and Method for Electrocardiogram Analysis and Optimization of Cardiopulmonary Resuscitation and Therapy Delivery,” filed Dec. 2, 2014, which issued Mar. 15, 2016 as U.S. Pat. No. 9,283,400 and is a continuation of U.S. patent application Ser. No. 13/836,062 entitled “System and Method for Electrocardiogram Analysis and Optimization of Cardiopulmonary Resuscitation and Therapy Delivery,” filed Mar. 15, 2013, which issued Dec. 2, 2014 as U.S. Pat. No. 8,903,498 and is a continuation-in-part of U.S. patent application Ser. No. 13/676,593, entitled “Filter Mechanism for Removing ECG Artifact from Mechanical Chest Compressions,” filed Nov. 14, 2012, which issued Jul. 21, 2015 as U.S. Pat. No. 9,084,545 and claims the benefit of: U.S. Provisional Application No. 61/616,874 entitled “Visual Rhythm Assessment Meter,” filed Mar. 28, 2012; U.S. Provisional Application No. 61/616,727, entitled “ECG Frequency Analysis during CPR,” filed Mar. 28, 2012; U.S. Provisional Application No. 61/616,973 entitled “An Analysis during CPR Algorithm Utilizing Shock History,” filed Mar. 28, 2012; U.S. Provisional Application No. 61/616,660 entitled “Guiding Therapy with Real-Time VF Quality Measurement,” filed Mar. 28, 2012; U.S. Provisional Application No. 61/616,372 entitled “AED Operation Dependent on Previous Analysis Results,” filed Mar. 27, 2012; U.S. Provisional Application No. 61/616,847 entitled “Method of Integrating Cardiac Rhythm Analysis during CPR into an AED Algorithm” filed Mar. 28, 2012; and U.S. Provisional Application No. 61/642,407 entitled “Real-Time Filter for Removing ECG Artifact from Mechanical Compression,” filed May 3, 2012, all of which are hereby incorporated by reference herein in their entirety.
Ventricular Fibrillation can often be reversed using a life-saving device called a defibrillator. A defibrillator, if applied properly, can administer an electrical shock to the heart. The shock may terminate the VF, thus, giving the heart the opportunity to resume pumping blood. If VF is not terminated, the shock may be repeated, often at escalating energies.
The challenge of defibrillating early after the onset of VF is being met in a number of ways. First, for some people who are considered to be at a higher risk of VF or other heart arrhythmias, an Implantable Cardioverter Defibrillator (ICD) can be implanted surgically. An ICD can monitor the person's heart, and administer an electrical shock as needed. As such, an ICD reduces the need to have the higher-risk person be monitored constantly by medical personnel. For individuals who are not an ICD candidates but still in need of monitoring, a portable defibrillator that can be worn by the individual at risk can be used.
In this race against time for human life, being able to, in real-time, understand the optimal amount, durations, pauses, administration frequency of CPR in combination of shock therapy, as well as how to improve and what to do when the CPR quality is poor, is highly desirable. Being able to monitor and analyze, and customize the CPR and the rhythm at the same time and in real-time, determine when to start with a CPR or a shock first, whether to stop altogether, or continue for a longer than routine/protocol-prescribed period to resuscitate successfully, is highly desirable and highly sought after. However, prior attempts, due to issues largely related to noise artifact, have failed to providing a system and method capable of successful monitoring and analyzing of rhythms, and other physiological signals and parameters, while performing chest compressions.
When analyzing a relatively noisy data, such as an Electrocardiogram (ECG) during a Cardiopulmonary Resuscitation (CPR), definitive recommendations are not always possible or most helpful. In certain embodiments, an external medical device may include a housing, an energy storage module within the housing for storing an electrical charge, and a defibrillation port for guiding via electrodes the stored electrical charge to a person. The device may also include a processor in the housing configured to receive a signal from a patient receiving chest compressions and apply at least one filter to remove from the signal chest compression artifacts resulting from the chest compressions being delivered to the patient. An advantage over the prior art is that an external medical device in accordance with the disclosed technology can present to a user a cleaner signal than would otherwise be provided in situations where a patient is receiving chest compressions. Also, the device may determine from chest compression artifacts in the patient signal-a chest compression signature that corresponds to at least one particular type of chest compression device.
FIG. 12A is a lime diagram of a VF signal having no chest compression artifacts.
FIG. 23 is a time diagram showing unfiltered and filtered ECG signals during CPR with a pause, according to embodiments.
FIG. 24 is a lime diagram showing unfiltered and filtered ECG signals during CPR with a shock, according to embodiments.
FIG. 25 is an example block diagram of a filter mechanism with a protection switch according to embodiments.
A portable external defibrillator 100 has been brought close to person 82. The portable external defibrillator can also be a wearable or hybrid defibrillator 82. At least two defibrillation electrodes 104, 108 are usually provided with external defibrillator 100, and are sometimes called electrodes 104,108. Electrodes 104, 108 are coupled with external defibrillator 100 via respective electrode leads 105, 109. A rescuer (not shown) has attached electrodes 104, 108 to the skin of person 82. Defibrillator 100 is administering, via electrodes 104, 108, a brief, strong electric pulse 111 through the body of person 82. Pulse 111, also known as a defibrillation shock, goes also through heart 85, in an attempt to restart it, for saving the life of person 82.
Defibrillator 300 also includes a processor 330. Processor 330 may be implemented in any number of ways. Such ways include, byway of example and not of limitation, digital and/or analog processors such as microprocessors and digital-signal processors (DSPs); controllers such as microcontrollers; software running in a machine; programmable circuits such as Field Programmable Gate Arrays (FPGAs), Field-Programmable Analog Arrays (FPAAs), Programmable Logic Devices (PLDs), Application Specific Integrated Circuits (ASICs), any combination of one or more of these, and so on.
In the example, the system also includes a mechanical chest compression device 485. The mechanical chest compression device 485 may deliver compressions at 100+/−0.01 compressions/minute, which is 1⅔+−0.00017 Hz. Such precise frequency control is unusual for typical chest compression devices. An ECG signal may thus be corrupted by chest compression artifacts corresponding to chest compressions delivered by the chest compression device 485 to the patient 482. Such artifacts may have an artifact fundamental frequency of 1⅔ Hz, and the artifact signal may also contain harmonics of 1⅔ Hz, which will show up at multiples of 1⅔ Hz, e.g., 3⅓ Hz, 5.0 Hz, and 6⅔ Hz. The spectral content of these frequency components is generally extremely narrow.
Certain conventional CPR artifact filters may be adaptive in nature. As used herein, an adaptive filter generally refers to a filter whose transfer function is dependent on the input signal. An adaptive filter may adjust its filter coefficients, center frequency, roll off, notch width, Q, or other characteristic based on the input signal. Non-adaptive filters according to embodiments generally use predetermined coefficients that may precisely set the transfer function independent of the input signal.
In certain embodiments, the processor 430 is further configured to analyze the filtered ECG data. In these embodiments, the processor 430 may be further configured to determine a shock/no-shock decision based on the analysis of the filtered ECG data.
Conventional CPR artifact filters have been unsuccessful at removing CPR artifacts, in part, because they typically focus on removing the fundamental frequency while paying little, if any, attention to the harmonic frequencies. In the example illustrated by FIG. 5, the 12th harmonic is only about 11 dB down from the fundamental frequency. In one example, to produce a clean ECG signal, CPR artifacts usually need to be attenuated by at least 20 dB, and possibly as much as 40 dB. In order to clean up the signal, frequencies up to at least the harmonic must typically be removed.
In one embodiment, to effectively remove CPR artifacts resulting from application of a conventional chest compression device, a very low-Q filler is preferable. Assuming that at least 20 dB of attenuation is needed, even a filter having Q=2 may not be effective in removing the artifact from the signal due to the spectral peaks of the artifact being too tall and too broad.
Because the spectral content of a mechanical CPR device according to embodiments is generally narrow, a high-Q filter is used to remove the compression artifact and retain the cardiac ECG signal with little distortion. Because a mechanical CPR device according to embodiments generally produces compressions at a precisely known frequency, the artifact may be filtered using a non-adaptive filter. Combining these two aspects (narrow frequency content and precise frequency control) according to embodiments thus enabled a high-Q comb filter to be used as an effective filler for removing CPR artifacts from the input signal.
FIG. 10B is a time diagram of an ECG signal having QRS complexes and chest compression artifacts with no filtering. For example, the ECG signal of FIG. 1 OB may generally correspond to a patient, such as the patient 482 of FIG. 4, that is not necessarily experiencing a cardiac event but is presently receiving chest compressions, e.g., from a chest compression device such as the mechanical chest compression device 485 of FIG. 4. As can be readily ascertained by even a causal viewer, the QRS complexes in the ECG signal are at least partially, if not fully, obscured by the chest compression artifacts.
FIG. 10C is a time diagram of an ECG signal having QRS complexes and chest compression artifacts with a filter mechanism, such as the filter mechanism 425 of FIG. 4, applied thereto. The effect of such application is readily apparent. Indeed, the lime diagram of FIG. 10C is significantly closer in appearance to the time diagram of FIG. 10A than to the time diagram of FIG. 10B. One can even readily discern P-waves and inverted T-waves in the time diagram. Further, a QRS detector could use the filtered waveform to provide an accurate intrinsic heart rate indication during delivery, of chest compressions to the patient.
Performing the steps or instructions of a program requires physical device, physical manipulations of physical quantities. Usually, though not necessarily, these quantities may be transferred, combined, compared, and otherwise manipulated or processed according to the instructions, and they may also be stored in a computer-readable medium. These quantities include, for example electrical, magnetic, and electromagnetic signals, and also states of matter that can be queried by such signals. It Ls convenient at times, principally for reasons of common usage, to refer to these quantities as bits, data bits, samples, values, symbols, characters, images, terms, numbers, or the like. It should be borne in mind, however, that all of these and similar terms are associated with the appropriate physical quantities, and that these terms are merely convenient labels applied to these physical quantities, individually or in groups.
Often, for the sake of convenience only, it is preferred to implement and describe a program as various interconnected distinct software modules or features or algorithm, individually and collectively also known as software. This is not necessary, however, and there may be cases where modules are equivalently aggregated into a single program with unclear boundaries. In any event, the software modules or features of this description may be implemented by themselves, or in combination with others. Even though it is said that the program may be stored in a computer-readable medium, it should be clear to a person skilled in the art that it need not be a single memory, or even a single machine. Various portions, modules or features of it may reside in separate memories, or even separate machines. The separate machines may be connected directly, or through a network, such as a local access network (LAN), or a global network, such as the Internet.
According to the operation at 1506, a filter mechanism, such as the filter mechanism 425 of FIG. 4, is selected based on the existing chest compression signature. In certain embodiments, information corresponding to the pattern may be merged with information corresponding to the predetermined pattern. According to the operation at 1508, a new chest compression signature is generated based on the pattern.
While a correct shock decision can be properly made during CPR, there is a subset of patient signals that contains excessive noise, which may prevent analysis. This subset can be automatically identified and excluded from analysis during CPR but this approach also carries the risk of excluding cases in which the automatic analysis would have been successful. Amplitude of CPR artifact is much greater in patients who have not received a defibrillation shock than in those patients who have been shocked. The task of analyzing during CPR is easier alter a shock has been delivered than before.
In a further embodiment, certain parameters, which are useful post-shock are not be reliable pre-shock if the noise sensitivity of different measured parameters varies. Some parameters are so sensitive to noise that they are not useful for patients that have not been previously shocked. To compensate, this method will invoke different parameters for patients who have been previously shocked than for those that have not been previously shocked.
In a further embodiment, there is the noise on the impedance signal of an unshocked patient versus a shocked patient is not higher. In other words, there is no reason to adjust the processing of the impedance signal based on the shock history. An analysis module that utilizes both the ECG signal and the impedance signal and the ECG signal processing parameters are adjusted based on the shock history but not adjust the impedance parameters. Shock history is used as an indicator of the amount of expected noise on an ECG signal. Other ways of anticipating the amount of noise include a low-frequency (<kHz) impedance measurement used as an indicator of the amount of expected noise.
To-date, defibrillators measure the patient impedance at a high frequency (10 kHz-100 kHz). A high frequency carrier signal is advantageous because such signal helps to separate the high-frequency impedance carrier signal from the ECG signal, which has a relatively low bandwidth. Also, AC signals in the range of 30 kHz-60 kHz have been shown to be useful for predicting the high voltage defibrillation shock impedance. On the other hand, a low-frequency impedance measurement is used when anticipating the amount of noise that might be expected on an ECG signal. When measured at a low frequency, a high impedance patient is expected to have a noisier ECG signal than a low impedance patient. Thus, an impedance measurement can be used to adjust an ECG analysis algorithm in a manner similar to the patient's shock status.
In contrast to prior attempts in the field, which as described above generally exhibit the same behavior each CPR period regardless of the results of previous rhythm analysis results, in the present disclosure, a defibrillator behavior in a given CPR period is dependent on previous rhythm analysis results. One aspect of this embodiment varies the CPR period and the associated prompts as a function of previous rhythm analysis results. In this embodiment, if the first AED analysis yields a “no shock advised,” the CPR period can be different than if the first analysis result is “shock advised.” In one embodiment, after an initial “no shock advised,” the CPR period is modified such that CPR is performed indefinitely. No further rhythm analyses is then performed on that patient. Alternatively, the CPR period is lengthened. For example, the CPR period is lengthened from two minutes to five minutes. Alternatively, the AED is set up to prompt for CPR until another event is detected. Such an event can be a user action, such as a button press, or can be a device-detected event, such as electrode disconnection.
On the other hand, if the first rhythm analysis result is “shock advised,” the device operates with normal CPR periods (i.e. an analysis every 2 minutes). These CPR periods can continue as long as the patient is treated by the defibrillator or they could be altered when another event is detected. Such an event might be, byway of an example, a user action, such as a button press, or it could be a device-detected event, such as electrode disconnection.
In a further embodiment, a medical device or a defibrillator alters the analysis module's algorithm based on a patient analysis other than a shock/no-shock decision. For example, if a patient has an initially non-shockable rhythm that contains QRS complexes, the algorithm used for subsequent analyses can be different than for an initially asystolic patient. Both rhythms are non-shockable, but different algorithms can be used for subsequent analyses. Similarly, different algorithms can be invoked for patients with initial bradycardia, normal sinus rhythm, supra-ventricular tachycardia, or other non-shockable rhythms. Along the same lines, different algorithms can be employed for subsequent analyses for a patient with initial “course” ventricular fibrillation (VF) as opposed to “fine” VF. Course VF can be distinguished from fine VF based on the peak-peak signal amplitude, or can be based a frequency analysis of the VF signal, an amplitude-frequency analysis such as AMSA, or other VF analysis method. The analysis algorithm can also be chosen based on previous patient hemodynamic information. A subsequent analysis in a patient that had previously exhibited a pulse can be different from a patient who never had a pulse detected. The algorithm selection can be based on the patient analysis at the time the medical device, such as and AED, is initially applied, on a patient analysis immediately prior to a given analysis, or an analysis performed at another point in time. For these purposes, the patient analysis can be an ECG analysis, or it can be an analysis of another patient signal or combination of signals. An analysis module includes an algorithm for quantitative evaluation of patient data and leads to a decision about a patient condition.
Shock Index=A*VFScore+InitialShockable+B*SubsequentVF+C, where
Shock Index=An overall numerical rating of likelihood the patient needs a shock
2 Minutes: Initial rhythm analysis is performed. If SHOCK
ADVISED, analysis continues every 2 minutes.
If NO SHOCK ADVISED, analysis interval
changes to 4 minutes.
6 Minutes: Rhythm analysis in “Initially Non-Shockable” mode.
This mode is biased toward higher specificity because
of the initial non-shockable rhythm. If SHOCK ADVISED,
analysis interval would switch back to 2 minutes.
If NO SHOCK ADVISED, analysis interval stays at 4
10 Minutes:  Rhythm analysis in Initially Non-Shockable mode with
2 previous no-shock results.This mode is biased toward
even higher specificity. If SHOCK ADVISED, analysis
interval switches back to 2 minutes (as below).
12 Minutes:  Rhythm analysis in Initially Non-Shockable mode with
subsequent VF.
In a further embodiment, analysis-during-CPR algorithms include a “continuous mode” which analyzes continuously during CPR and interrupts the CPR period if VF is detected. A continuous mode is recommended for patients with a high likelihood refibrillating, and not recommended if a patient has a very low probability of going into VF. If the odds of VF are low, a continuous mode can increase the likelihood of incorrectly indicating “shock advised” while providing little chance of detecting and terminating VF sooner. In such cases, rhythm analysis may carry a risk of an incorrect result. Thus, over-analyzing a patient unlikely to be in VF may do more harm than good.
To mitigate this concern, activation of continuous mode is made contingent on the patient history. An AED treating a patient with an initially non-shockable rhythm performs a rhythm analysis during CPR at the regular intervals (e.g. 2 minutes). However, an AED treating a patient with an initially shockable rhythm can switch to continuous mode because re fibrillation is likely and the patient will benefit from earlier VF termination.
The present embodiment oilers a real-time VF quality indication to a rescuer/user during CPR. The indication might be a comparative scale of some type, such as a continuum scale, a gauge or a bar graph, a trend line, a pie chart, or a colorimetric scale, a digital scale, etc. The indication may also be tactile or auditory. The visual indication illustrates the quality of a VF in real time and allows the user to quickly assess the status and trajectory of the cardiac rhythm, allowing, for example, the CPR to continue as long as the quality of the cardiac rhythm, such as VF, is improving. One skilled in the art will realize that there are many ways of displaying an analog value. The examples shown here are graphical, but it is possible that numbers could be displayed as well, or combinations of numbers and graphics. The Shock Index value could be displayed, or the probability of VF could be displayed, or some other numerical indicator that relates to the waveform. This approach is superior to doing CPR for a fixed period or until a fixed level of quality disregarding the actual individual real-time patient data and status. If, for example, the VF quality is observed as not improving, other interventions are engaged rather than a continued CPR.
If VF quality continues to improve, the rescuer continues CPR with a higher degree of confidence and without unnecessary slopping or pausing at a predetermined threshold as may be recommended by existing guidelines. If VF quality is not improving it is possible that CPR quality is poor and that the caregiver should change the depth, rate or other parameter. It is possible that optimal CPR depth and rate may vary from one individual to another. This approach allows CPR to be adjusted dynamically based on the results with a given patient. VF quality indications given to the user allow assessment of the current VF quality and of whether it is improving.
Further, FIGS. 18A-C illustrate embodiments of representations of shock recommendation using a logistic regression. The inputs for logical regression comprise numerical measurements of the ECG and impedance signals. The output of the logistic regression approach is a number such as a shock index number as illustrated in FIG. 18A. A positive shock index value indicates a shockable rhythm, a negative shock index indicates a non-shockable rhythm, and a value in the middle is indeterminate. A shock index value of zero means there is a 50-50 chance that the patient has VF. By way of an example, for “accuracy emphasis” mode all values between −2.5 and +2.5 are considered indeterminate; shockable rhythms are >2.5 non-shockable rhythms are <−2.5. Such analog meter, as illustrated in FIGS. 18B and 18C, exemplifies a “Rhythm Assessment Meter” and is, for example, displayed on a manual defibrillator screen. The far left of the meter scale is −5, the far right is +5, and the indeterminate zone goes from −2.5 to +2.5.
FIG. 19 is an example of waveform assessment trend line. Here, the probability of VF in a patient starts at time T0:00 with a non-shockable rhythm, and then transitions to a shockable rhythm after approximately 43 seconds of CPR. The trend line illustrates the probability of VF (as shown in FIG. 19), the shock index, or another numerical value relating to the patient waveform. The trend line illustrates the regions that should be considered shockable, non-shockable, and indeterminate, as presented in FIG. 17. The trend line illustrates when an individual is refibrillated and how long he/she had been in fibrillation. Also, if the trend line is steadily in the shockable or non-shockable zone, then the operator's confidence increases as to correctness of the rhythm analysis, whereas a number that is not consistently displaying is an indicator of uncertainty. The shock index can be calculated continuously, continually or at discrete intervals. The intervals could be based on the CPR interval, on the artifact level on the signal, the Central Processing Unit (CPU) burden for calculation, and/or other parameters, there may also be intervals during which no data is available.
A large sudden change on the rhythm assessment meter may indicate the onset of VF, while a small change may simply be the result of remaining, unfiltered noise. The device is further capable of voice prompts, flashing lights, signals, etc. when a certain zone with a certain confidence level is reached. The meter can further facilitate answers when an operator does not trust a filtered ECG signal/The meter is most valuable when an operator/rescuer has no way of knowing whether to trust the filtered ECG signal and when resuscitation needs to stop CPR to obtain a clean signal.
By way of an example, when the meter is in the indeterminate zone, the waveform may not be trusted. If the meter is in the shockable zone, the filtered waveform display may be helpful by providing an indication of the VF amplitude, a feature that provides an indication of the health of the patient. If the meter is in the non-shockable zone the rescuer may find it useful to know whether the patient is in asystole or pulseless electrical activity (PEA). If the patient appears to have regular, normal-rate QRS complexes the rescuer may choose to/stop CPR to check for a pulse. Conversely, if they are confident that no QRS complexes are present they may choose to skip their normal pulse check.
In a further embodiment, a defibrillator, such as an AED, integrates a CPR prompting sequence. FIGS. 20, 21 and 22 illustrate steps for flexibly incorporating CPR prompting sequence into another device such as a defibrillator. In one embodiment, an AED user or a remote device is prompted to setup or flexibly adjust with setup options. Setup options can be decided upon, by way of an example, by a Medical Director of the person operating the defibrillator to conform to the treatment protocol he or she orders for all persons operating under his or her directorship. As such, the device can have provision to store the elected setup options so that the device prompts according to the ordered treatment protocol and/or algorithm. Memory storage may be made by nonvolatile memory, flash memory, disk memory or similar device, in other devices and communicated to the device by a wired or wireless communication channel, including the Internet. Other options are possible.
FIGS. 20, 21 and 22 detail the three general options, termed herein Periodic Mode, Continuous Mode and Minimum CPR Time, starting with prompts in 2001 2101 or 2201, respectively, according to the choice made by the Medical Director, for example. The prompting may start after the device is turned on, or following additionally an analysis without CPR or following additionally an Initial CPR period.
FIG. 20 is a flowchart illustrating the method 2000 for prompting and interacting with the analysis algorithm if Periodic Mode has been chosen. According to operation 2001 the user is prompted to perform CPR. According to an operation at 2002 the rhythm analysis begins, silently in the preferred embodiment. This begins toward the end of the CPR period. The amount of time before the end of the CPR period to begin the rhythm analysis is determined by the time it will take various operations to take place such that the device is ready to shock at the end of the CPR period without pause. This will be determined by such factors as the amount of data necessary for rhythm analysis and the time necessary to acquire that data, the computation time of the algorithm, and the time necessary to charge the defibrillator.
According to a decision step at 2003 the result of the analysis algorithm determines the operations subsequently taken.
According to a next operation at 2005 the device waits until the end of the CPR period. This might be necessary, for example, if the time it takes to perform various operations before operation 2005 is variable, and the longest possible must be taken into account in operation 2002. Alternatively, the device could prompt for shock delivery as soon as the charging is complete.
FIG. 21 is a flowchart 2100 for illustrating the method of prompting and interacting with the analysis algorithm if Continuous Mode has been chosen. According to operation 2101 the user is prompted to perform CPR and the rhythm analysis starts at the same time. The rhythm analysis is silent in the preferred embodiment.
According to a decision step at 2102 the flowchart the result of the analysis algorithm determines the operations subsequently taken. The CPR period is nearly over if the amount of time before the end of the CPR period is such that the device will be ready to shock at the end of the CPR period without pause. This will be determined by such factors as the amount of data necessary for rhythm analysis and the time necessary to acquire that data, the computation time of the algorithm and the time necessary to charge the defibrillator.
According to a decision step at 2103 the result of the analysis algorithm determines the operations subsequently taken.
If the result of the analysis algorithm is no or indeterminate, the device continues the CPR period and continuous to perform the rhythm analysis according to decision step 2101.
If the decision step at 2102 is that the CPR period is nearly over, then according a decision step at 2107, the subsequent operation steps are determined by the analysis algorithm result, but in a different fashion than is outlined in decision step 2103. It is possible that the algorithm works slightly or completely differently in step 2107 than in 2103. For example, there may be an advantage to a different tradeoff between sensitivity and specificity in the algorithm because the impact to the patient of an incorrect nonshockable result in processing step 2103 is for the user to perform more CPR, but in processing step 2107 it would result in failure to deliver therapy to a patient who needed it.
If the analysis algorithm determination in step 2107 is indeterminate, according to an operation at 2108, the user is then prompted to slop CPR.
According to an operation at 2009, the device then performs a rhythm analysis using an algorithm which is appropriate for patients who are not receiving CPR, as is common in the state of the art.
FIG. 22 is a flowchart 2200 for illustrating the method of prompting and interacting with the analysis algorithm if Minimum CPR Time has been chosen. This option is like Continuous Mode as illustrated in FIG. 21, but ensures that the patient receives a minimum amount of CPR.
According to an operation 2201 the user is prompted to perform CPR.
According to a decision step at 2202 the result of the analysis algorithm determines the operations subsequently taken. The step tests to see if a minimum amount of CPR has been given. The amount of CPR can be determined by either the duration of one CPR period or by separate input from the Medical Director or by some equivalent means.
If the minimum amount of CPR has not been given, according to an operation at 2203 operation proceeds as outlined in operation 2001 in flowchart 2000. If the minimum amount of CPR has been given, according to an operation at 2204 operation proceeds as outlined in operation 2101 in flowchart 2100.
In a further embodiment the system and method for electrocardiogram analysis for optimization of chest compressions and therapy and delivery include the rhythm assessment meter device and the filtered waveform display where the meter and the display complement and corroborate results of one another and the system arms a rescuer/operator with results based on a certain confidence level. For example, the waveform changes its appearance when the rhythm assessment meter is in the indeterminate zone. In another example, if the waveform can not be trusted, the waveform changes to a specific color, gray—for example, or perhaps to a dashed line. Alternatively, a visual or audible indication is given when the filtered waveform provides low confidence level to the rescuer.
In FIG. 23, the signal 2301 represents the ECG signal taken from a cardiac arrest patient who was receiving mechanical chest compressions with a pause. The signal 2303 is same signal that has been filtered with at least one filter mechanism (not shown) comprising a comb filler, such as an embodiment of the filter mechanism 425 described above. In this embodiment, at least one filter mechanism is implemented using an IIR filter, which incorporates a “memory” that takes lime to respond to changes in the ECG signal 2301.
During a compressions period 2302, the start of the ECG signal 2301 shows a significant amount of compression artifact that would interfere with rhythm interpretation. The at least one filter mechanism has removed compression artifact in a portion of the ECG signal 2301 during the compressions period 2302. This filtering revealed that the patient is experiencing VF (ventricular fibrillation), as shown in a portion of the filtered signal 2303 during the compressions period 2302. As can be seen, that portion of the ECG signal 2301 is noisy and that portion of the filtered signal 2303 is relatively clean during the compressions period 2302.
During a pause period 2311 (i.e., when compressions are paused) the situation is reversed—a portion of the ECG signal 2301 is relatively clean and a portion of the filtered signal 2303 contains artifact. Due to the nature of the IIR filter used in this embodiment of at least one filter mechanism, the amount of artifact on that portion of the filtered signal 2303 diminishes over time during the pause period 2311. This delay in reducing the artifact can be problematic for EMS rescuers who may be used to rapidly assessing the patient's rhythm during a compression pause. This behavior of the at least one filter mechanism (i.e., injecting artifact during a compression pause) is inherent in a comb filter structure and may also occur with other filters such as notch filters and adaptive filters.
After compressions restart in a compressions period 2321, there is artifact on both the ECG signal 2301 and the filtered signal 2303. Over time the at least one filter mechanism “re-learns” the shape of the artifact, but the response delay can be undesirable. Pause periods such as the pause period 2311 can occur when the rescuer stops and restarts the compression device. Pause periods may also happen periodically if the device is programmed to include automatic ventilation pauses in a pattern such as a 30:2 compression/ventilation ratio. The artifact that would be inserted during a compression pause could be annoying to rescuers because they may be looking at the ECG during a pause. In this situation the at least one filter mechanism may not perform as they would likely expect—the at least one filter mechanism removes the artifact during the compressions and inserts it during an early portion of a CPR pause.
In FIG. 24, the signal 2401 represents the ECG signal taken from the same cardiac arrest patient who was receiving mechanical chest compressions at a different time period in which a defibrillation shock was delivered to the patient. The signal 2403 is same signal that has been filtered with the at least one filter mechanism (not shown); During a non-shock period 2411, a portion of the ECG signal 2401 shows compression artifact and a portion of the filtered signal 2403 shows the ECG signal with the artifact removed.
During a time period 2421, a defibrillation shock is delivered to the patient. No ECG data is available during the shock, so a portion of the ECG signal 2401 is represented as a flat line in the period 2421. During the time period 2421, a portion of the filtered signal 2403 has some compression artifact because the at least one Filter mechanism must “re-learn” the artifact.
In a post shock time period 2431, eventually the at least one filter mechanism learns the artifact and begins to show a cleaned-up signal later in the time period 2431, but the delay can be undesirable in some applications. Filter delays such as this are particularly undesirable after a defibrillation shock because they interfere with the ability of rescuers to determine whether the shock succeeded and deciding whether another shock may be necessary.
The examples shown above use a comb filter for the purposes of illustration, similar problems are likely to be observed with any IIR filter or FIR filter with a long memory. Similarly, adaptive filters may take time to respond to signal changes. In general, any filtering technique that has a “learning” aspect may experience similar problems.
Further, there may be other types of disturbances that can cause similar artifacts as the at least one filter mechanism relearns the artifact. Various embodiments described below can be used to detect these disturbances.
In one embodiment, a monitoring device (e.g., a monitor) is aware of events such as a monitoring lead falling off or a change in the monitoring lead vector. This information is communicated to the monitoring device so that appropriate compensation can be provided as will be described below. For example, in some embodiments, the information is provided to the at least one filter mechanism so that it can make the appropriate compensation. If the monitor is an external defibrillator, then the at least one filter mechanism can also be configured to compensate for shock disturbances.
In other embodiments, pauses in compressions can be detected by the monitoring device using signal analysis of the ECG signal. For example, mechanical compressions have a very specific frequency signature. The absence of that signature could be taken as evidence that the compressions have stopped. Another method of detecting mechanical compressions is to run an inverse comb filter and detect the amplitude of the resulting signal. Both of these techniques may take time to acquire enough data and may be appropriate in less time urgent applications.
In other embodiments, pauses in compressions can be detected by the monitoring device using the impedance signal if it is available. One method of detecting the presence of compressions is to take the RMS value of the impedance signal over a 600 ms period. If the RMS value is above a threshold then it is likely that compressions are occurring. This method may have less delay than ECG signal analysis, but also may be less specific. Other types of motion may affect the RMS value, causing erroneous detection of compressions.
In still other embodiments, the CPR machine can communicate with the monitoring device when compressions are going to be started or stopped. The monitoring device can then compensate for the disturbance. In one particular embodiment, the CPR machine would provide advance notice that compressions are going to be started or stopped so that the monitoring device can compensate before the memory of the at least one filtering mechanism is disturbed.
In yet another embodiment, the monitoring device may be able to anticipate some signal disturbances simply by knowing what kind of CPR machine that is being used. For example, some CPR machines may provide precisely-controlled liming for a 30:2 compression/ventilation ratio. The monitoring device may then anticipate the ventilation pause simply based on the elapsed time since the last known pause (e.g., detecting using the ECG or impedance signal). This method is particularly advantageous because it requires no communication between the CPR machine and the monitoring device but still allows the pause locations to be precisely identified.
In response to detecting a disturbance (e.g., via any of the various embodiments described above), the monitoring device is configured to selectively compensate for the detected disturbance. Various embodiments described below can be used to provide the compensation.
In one embodiment, once a pause in compressions is identified the monitoring device can choose to automatically switch from displaying the filtered signal to displaying the unfiltered signal. This would avoid the problem seen in FIG. 23 in which the filtered signal has more artifact than the original ECG signal during a compression pause. However, when compressions are restarted the at least one filtering mechanism still needs to re-learn the artifact.
In another embodiment, after a disturbance it is possible to make the at least one filtering mechanism learn more quickly by temporarily reducing the quality factor (i.e. the “Q”) of the at least one filtering mechanism. Note, quality factor of the at least one filtering mechanism is described above in conjunction with FIG. 7. In one embodiment, the at least one filtering mechanism implemented using a comb filter has a relatively high Q value of 16. The Q of the comb filter can be changed relatively easily by changing the value of “b” in the comb filter transfer function. For example, the Q could be reduced to a value of 2 for one second alter a disturbance, and then switched back to 16. This would allow the filter to recover relatively quickly when compressions are started, a monitoring lead is attached, or after a shock has been delivered.
In still another embodiment, the disturbance to the filtered ECG signal is reduced by controlling the CPR machine to restart the compressions after a pause in a manner in which the new compression group is in-phase with the previous compression group. For example, if the compression rate is 100 compressions per minute, each compression requires 0.6 seconds. If compression pauses are arranged to be an integer number of 0.6 second intervals, then the new compression group will be in-phase with the previous compression group. This reduces the amount of re-learning that the at least one filtering mechanism needs to do.
In yet another embodiment, if the CPR machine restarts compressions in-phase with the previous compression group and the monitoring device knows where the compression pause starts and ends, then it is possible for the monitor device to “protect” the at least one filtering mechanism during the pause. This would allow the at least filtering mechanism to retain its memory of the artifact during the pause rather than “unlearning.” Then, when compressions are restarted the at least one filtering mechanism could be re-engaged without having to reclaim the artifact again. This would avoid a significant disturbance of the filter for every compression pause.
One method of protecting the at least one filter mechanism during a pause is simply to stop the at least one filtering mechanism (i.e. prevent it from accepting any new inputs, prevent it from shifting the delay lines, and prevent it from calculating any new outputs). Then the at least one filtering mechanism is restarted at the appropriate time-when compressions are resumed. The at least one filtering mechanism is restarted at substantially the same point in the compression cycle as it stopped. As described above, this could be done if there was communication between the CPR machine and the monitor device, or if the monitoring device had advance knowledge of the length of the compression pause.
FIG. 25 shows one embodiment of a filter mechanism 2500 with a protection switch 2502. The switch 2502 allows the filter mechanism 2500 to operate normally during chest compressions but to transition to a protected mode during a pause. In the protected mode the input to the numerator delay line is taken from the output of the numerator delay line is rather than the input signal. This recirculates the existing data (which contains compression artifact) through the filter mechanism 2500 during a pause, which prevents the unlearning effect. When compressions are restarted the input to the numerator delay line is switched back to the input ECG and the filter mechanism 2500 begins removing the CPR artifact without having to relearn the artifact shape. This method has the advantage that the filter mechanism 2500 could be restarted at any point in the compression cycle and the filter would still be in sync with the artifact.
In another embodiment, the at least one filter mechanism is protected during a compression pause by changing the Q of the filter. A filter with a high Q learns the new signal very slowly. If the Q of the filter was made extremely high during a compression pause it is possible that little or no new learning would happen during the pause. If the Q was switched back to the normal value when compressions are restarted the filter would still be in sync with the artifact and the need for learning would be avoided. A similar technique could be used with an adaptive filter that avoids relearning during compression pauses.
1. A system for treating a cardiac arrest patient, the system comprising:
an EGG monitor; and
a mechanical chest compression device configured to automatically deliver a series of chest compressions and ventilation pauses, wherein the ECG monitor is configured to filter the ECG signal to remove the compression artifact and produce a filtered ECG signal, wherein the ECG monitor is further configured to display the filtered signal when the mechanical chest compression device is delivering chest compressions and displays the unfiltered ECG signal during ventilation pauses, wherein the ECG monitor includes a filter configured to perform the filtering of the ECG signal by attenuating a plurality of regularly spaced bands, and wherein the ECG monitor includes a filter protection switch configured to switch to a protected mode during pauses in the chest compressions to protect the filter during the pauses in the chest compressions.
2. The system of claim 1, wherein an input to a numerator delay line of the filter is taken from an output of the numerator delay line when the filter protection switch is in the protected mode.
3. The system of claim 2, wherein the filter protection switch is further configured to switch away from the protected mode to allow the filter to operate normally responsive to a resuming of the chest compressions after the ventilation pauses.
4. The system of claim 3, wherein the input to the numerator delay line of the filter is the ECG signal when the filter protection switch is no longer in the protected mode.
5. The system of claim 1, wherein the filter comprises a comb filter.
6. The system of claim 1, wherein the ECG monitor is configured to identify the ventilation pauses.
7. The system of claim 6, wherein the ECG monitor is further configured to automatically switch from displaying the filtered ECG signal to displaying the unfiltered ECG signal responsive to identifying the ventilation pauses.
8. The system of claim 1, wherein an input to a numerator delay line of the filter is the ECG signal.
9. The system of claim 1, wherein the mechanical chest compression device is configured to transmit a signal to the ECG monitor indicative of the ventilation pauses.
10. The system of claim 1, wherein the ECG monitor is configured to change a quality factor (Q) of the filter responsive to an event.
11. The system of claim 10, wherein the event includes an attachment of a monitoring lead to the ECG monitor, a delivery of a shock, or a beginning of the delivery of chest compressions.
12. The system of claim 10, wherein the filter has a first value of Q for a first period of time and a second value of Q for a second period of time.
13. The system of claim 12, wherein the ECG monitor is configured to temporarily reduce the Q of the filter from the first value to the second value responsive to the event.
14. The system of claim 13, wherein the ECG monitor is further configured to restore the Q of the filter from the second value back to the first value after a period of time.
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US20140088374A1 (en) 2012-09-24 2014-03-27 Physio-Control, Inc. Filtering patient signal also for ventilation artifacts
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US9545211B2 (en) 2017-01-17 grant
US20150297107A1 (en) 2015-10-22 application
US20170105644A1 (en) 2017-04-20 application
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