Methods, devices, and storage media for rhythm analysis and decision-making in defibrillators

Abstract

A method, apparatus, and storage medium for rhythm analysis and decision-making in a defibrillator are disclosed. The method includes: acquiring the electrocardiogram (ECG) signal of a target subject during cardiopulmonary resuscitation (CPR); dividing the ECG signal into multiple analysis regions in a time series and determining the state corresponding to each analysis region; for each analysis region, determining a segment rhythm analysis mode based on the corresponding state and performing rhythm analysis to obtain the segment rhythm state of the analysis region; performing long-term rhythm analysis based on the segment rhythm states of multiple analysis regions within a first preset time period from the rhythm decision time to obtain long-term rhythm state characteristics; performing short-term rhythm analysis based on the segment rhythm states of at least one analysis region within a second preset time period from the rhythm decision time to obtain short-term rhythm state characteristics; and determining and outputting a rhythm decision based on the long-term and short-term rhythm state characteristics. This application can obtain more reliable rhythm decisions.

Description

Technical Field

[0001] This application relates to the field of defibrillator technology, and more specifically to a rhythm analysis and decision-making method, apparatus, and storage medium for a defibrillator. Background Technology

[0002] A defibrillator is a medical device that uses a strong pulse of electrical current to pass through the heart to eliminate arrhythmias and restore sinus rhythm. It is an essential piece of emergency equipment in the operating room. Defibrillation is a crucial step in cardiopulmonary resuscitation (CPR). During CPR compressions, rhythm analysis must be performed in conjunction with electrocardiogram (ECG) signals. Reliable rhythm analysis in CPR relies primarily on reliable segmental rhythm analysis and a comprehensive decision-making process based on the rhythmic states of multiple time segments.

[0003] Currently, rhythmic decision-making methods in CPR mainly focus on two aspects: one is to use short-term rhythmic analysis to synthesize rhythmic decisions, and the other is to use long-term rhythmic analysis to synthesize rhythmic decisions. However, using only short-term rhythmic analysis for comprehensive decision-making is easily affected by local disturbances, leading to rhythm misjudgment; relying solely on long-term rhythmic analysis reflects a long-term stable rhythmic state, but is not sensitive to local rhythmic fluctuations and has a slow response time to rhythm transitions. Summary of the Invention

[0004] This application provides a rhythm analysis and decision-making method for a defibrillator, comprising: acquiring the electrocardiogram (ECG) signal of the target object during cardiopulmonary resuscitation (CPR); dividing the ECG signal into multiple analysis regions in a time series and determining the state corresponding to each analysis region, the state including emergency treatment state and / or filtering state; for each analysis region, determining a segment rhythm analysis pattern of the analysis region based on the state corresponding to the analysis region, and performing rhythm analysis on the analysis region based on the segment rhythm analysis pattern to obtain the segment rhythm state of the analysis region; performing long-term rhythm analysis based on the segment rhythm states of multiple analysis regions within a first preset time period from the rhythm decision time to obtain long-term rhythm state features; performing short-term rhythm analysis based on the segment rhythm states of at least one analysis region within a second preset time period from the rhythm decision time to obtain short-term rhythm state features, wherein the first preset time period is longer than the second preset time period; determining a rhythm decision based on the long-term rhythm state features and the short-term rhythm state features, and outputting the rhythm decision.

[0005] In another aspect, this application provides a rhythm analysis and decision-making method for a defibrillator, the method comprising: acquiring a reference signal and the original electrocardiogram (ECG) signal of the target object during cardiopulmonary resuscitation (CPR); performing compression detection on the reference signal to obtain a time-domain compression event marker of the reference signal; determining an instantaneous compression interval based on the time-domain compression event marker, and filtering the original ECG signal based on the instantaneous compression interval to obtain a filtered ECG signal; dividing the ECG signal of the target object into multiple analysis regions in a time series, and performing rhythm analysis on each analysis region to obtain the segment rhythm state of each analysis region, wherein the target object... The target object's electrocardiogram (ECG) signal includes only the filtered ECG signal, or the target object's ECG signal includes both the original ECG signal and the filtered ECG signal; long-term rhythm analysis is performed based on the segment rhythm states of multiple analysis regions within a first preset time period from the rhythm decision time to obtain long-term rhythm state features; short-term rhythm analysis is performed based on the segment rhythm states of at least one analysis region within a second preset time period from the rhythm decision time to obtain short-term rhythm state features, wherein the first preset time period is longer than the second preset time period; a rhythm decision is determined based on the long-term rhythm state features and the short-term rhythm state features, and the rhythm decision is output.

[0006] In another aspect, this application provides a rhythm analysis and decision-making apparatus, which includes a memory and a processor. The memory stores a computer program executed by the processor, which, when run by the processor, performs the above-described rhythm analysis and decision-making method for a defibrillator.

[0007] In another aspect, this application provides a storage medium storing a computer program that, when running, executes the above-described rhythm analysis and decision-making method for a defibrillator.

[0008] The rhythm analysis and decision-making method and apparatus for defibrillators according to embodiments of this application combine long-term and short-term rhythm state characteristics to determine rhythm decisions. This avoids both local interference and insensitivity to local rhythm fluctuations, thereby obtaining more reliable rhythm decisions. Furthermore, since different rhythm analysis modes are selected based on different ECG signal states, the accuracy of the rhythm analysis results, i.e., the segment rhythm state, can be further improved, thereby further enhancing the reliability of rhythm decisions. Attached Figure Description

[0009] Figure 1 A schematic flowchart of a rhythm analysis and decision-making method for a defibrillator according to an embodiment of this application is shown.

[0010] Figure 2An exemplary flowchart of a rhythm analysis and decision-making method for a defibrillator according to an embodiment of this application is shown.

[0011] Figure 3 A schematic diagram of a rhythm analysis and decision-making method for a defibrillator according to an embodiment of this application is shown.

[0012] Figure 4 A schematic flowchart of a rhythm analysis and decision-making method for a defibrillator according to another embodiment of this application is shown.

[0013] Figure 5 An exemplary flowchart of a rhythm analysis and decision-making method for a defibrillator according to another embodiment of this application is shown.

[0014] Figure 6 An exemplary flowchart of a rhythm analysis and decision-making method for a defibrillator according to yet another embodiment of this application is shown.

[0015] Figure 7 A schematic block diagram of a rhythm analysis and decision-making apparatus for a defibrillator according to an embodiment of this application is shown. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this application more apparent, exemplary embodiments according to this application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are merely a part of the embodiments of this application, and not all of the embodiments of this application. It should be understood that this application is not limited to the exemplary embodiments described herein. Based on the embodiments of this application described herein, all other embodiments obtained by those skilled in the art without inventive effort should fall within the protection scope of this application.

[0017] The following description provides numerous specific details to offer a more thorough understanding of this application. However, it will be apparent to those skilled in the art that this application can be practiced without one or more of these details. In other instances, certain technical features well-known in the art have not been described to avoid confusion with this application.

[0018] It should be understood that this application can be implemented in various forms and should not be construed as being limited to the embodiments set forth herein. Rather, providing these embodiments will make the disclosure thorough and complete, and will fully convey the scope of this application to those skilled in the art.

[0019] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. When used herein, the singular forms “a,” “an,” and “the” are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the terms “compose” and / or “comprising,” when used in this specification, identify the presence of the stated features, integers, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups. When used herein, the term “and / or” includes any and all combinations of the associated listed items.

[0020] To fully understand this application, detailed steps and structures will be presented in the following description to illustrate the technical solutions proposed in this application. Preferred embodiments of this application are described in detail below; however, in addition to these detailed descriptions, this application may have other implementation methods.

[0021] First, refer to Figure 1 This application describes a rhythm analysis and decision-making method for a defibrillator according to one embodiment. Figure 1 A schematic flowchart of a rhythm analysis and decision-making method 100 for a defibrillator according to one embodiment of this application is shown. Figure 1 As shown, the rhythm analysis and decision-making method 100 for defibrillators may include the following steps:

[0022] In step S110, the electrocardiogram signal of the target object is acquired during cardiopulmonary resuscitation.

[0023] In step S120, the electrocardiogram signal is divided into multiple analysis regions in time series, and the state corresponding to each analysis region is determined, including emergency treatment state and / or filtering state.

[0024] In step S130, for each analysis region, a segment rhythm analysis mode for the analysis region is determined based on the state corresponding to the analysis region, and rhythm analysis is performed on the analysis region based on the segment rhythm analysis mode to obtain the segment rhythm state of the analysis region.

[0025] In step S140, long-term rhythm analysis is performed on the fragment rhythm states of multiple analysis regions within the first preset time period of the distance rhythm decision time to obtain long-term rhythm state characteristics.

[0026] In step S150, short-term rhythm analysis is performed on the segment rhythm state of at least one of the analysis regions within the second preset time period of the distance rhythm decision time to obtain short-term rhythm state characteristics, wherein the first preset time period is longer than the second preset time period.

[0027] In step S160, a rhythm decision is determined based on the long-term rhythm state characteristics and the short-term rhythm state characteristics, and the rhythm decision is output.

[0028] In the embodiments of this application, the electrocardiogram (ECG) signal of the target subject during cardiopulmonary resuscitation (CPR) is divided into multiple analysis regions in time series. The state corresponding to each analysis region (emergency treatment state and / or filtering state) is determined. Different segment rhythm analysis modes are used for analysis regions with different states to obtain the segment rhythm state of each analysis region. Finally, long-term rhythm state analysis and long-short rhythm state analysis are performed based on the segment rhythm states of each analysis region. Combining the characteristics of long-term and short-term rhythm states, rhythm decisions are determined. This avoids both local interference and insensitivity to local rhythm fluctuations, thus obtaining more reliable rhythm decisions. Moreover, since different rhythm analysis modes are selected based on ECG signals in different states, the accuracy of the rhythm analysis results, i.e., the segment rhythm state, can be further improved, thereby further enhancing the reliability of rhythm decisions.

[0029] In the embodiments of this application, the ECG signal of the target object obtained in step S110 can be the original ECG signal and / or the filtered ECG signal. The filtered ECG signal is obtained based on reference signals related to chest compressions during cardiopulmonary resuscitation (CPR) and the original ECG signal. Specifically, the reference signals (such as chest impedance signals acquired via defibrillation chest electrodes, blood oxygen signals, respiratory signals, and signals sensed by CPR sensors) can be used for time-domain compression event detection to identify compression events. Alternatively, the frequency domain components of the reference signals can be combined to assist in compression event detection. An adaptive filtering model can then be used to filter out CPR interference from the original ECG signal waveform, thus achieving CPR chest compression detection and filtering to obtain the filtered ECG signal.

[0030] In the embodiments of this application, in step S120, the electrocardiogram signal is divided into multiple analysis regions in time series, each analysis region may have the same time length; or, the time length of each analysis region may be changed as needed. Furthermore, two adjacent analysis regions may be continuous or discontinuous in time. Additionally, two adjacent analysis regions may partially overlap or not overlap in time.

[0031] In the embodiments of this application, the state of each analysis region includes an emergency treatment state and / or a filtering state. The emergency treatment state reflects the emergency medical personnel's emergency response to the target object during the time period corresponding to that analysis region; the filtering state reflects whether the electrocardiogram (ECG) signal during the time period corresponding to that analysis region needs filtering. Since different emergency medical response states and whether the ECG signal needs filtering both reflect whether the ECG signal is interfered with by CPR during a time period, and whether the ECG signal is interfered with by CPR affects rhythm analysis and decision-making, in the embodiments of this application, determining the state of each analysis region of the ECG signal and determining a suitable (corresponding) segment rhythm analysis mode based on the state of each analysis region of the ECG signal is beneficial for more accurate rhythm analysis of each analysis region, obtaining more accurate segment rhythm states, and thus facilitating more reliable rhythm decisions.

[0032] In embodiments of this application, the emergency treatment state may further include a compression in progress state (also simply referred to as a compression state), a compression transition state, and a compression pause state. The filtering state may further include a filtering required state and a filtering-free state. In embodiments of this application, determining the segment rhythm analysis mode of the analysis region based on the state corresponding to the analysis region may include: when the emergency treatment state of the analysis region is a compression pause state and / or the filtering state of the analysis region is a filtering-free state, determining the segment rhythm analysis mode of the analysis region as an interference-free mode; when the emergency treatment state of the analysis region is a compression in progress state or a compression transition state, and the filtering state of the analysis region is a filtering required state, determining the segment rhythm analysis mode of the analysis region as an interference-prone mode.

[0033] As mentioned earlier, different emergency response states and whether ECG signals require filtering reflect whether the ECG signal is interfered with by CPR over a given time period. This interference, in turn, affects rhythm analysis and decision-making. Therefore, when the emergency response state of an analysis region is determined to be chest compression pause, it indicates that the ECG signal is minimally interfered with by CPR, thus allowing for segment rhythm analysis using an interference-free mode. Similarly, when the filtering state of an analysis region is determined to be no filtering required, it indicates that the ECG signal is minimally interfered with by CPR, allowing for segment rhythm analysis using an interference-free mode. In interference-free mode, both the raw and filtered ECG signals can be used for rhythm analysis; therefore, rhythm analysis can be performed based on the raw and / or filtered ECG signals using an interference-free rhythm strategy.

[0034] When an analysis region is identified as being in a CPR-induced or transitional state, it indicates that the ECG signal is significantly affected by CPR interference, necessitating the use of an interference-enabled mode for segment rhythm analysis. Similarly, when an analysis region is identified as requiring filtering, it indicates that the ECG signal is significantly affected by CPR interference, again requiring the use of an interference-enabled mode for segment rhythm analysis. In interference-enabled mode, due to CPR interference, rhythm analysis can be performed using an interference-enabled rhythm strategy based solely on the filtered ECG signal; alternatively, it can combine the original and filtered ECG signals for a combined interference-enabled rhythm strategy.

[0035] In embodiments of this application, the emergency treatment status may further include an electric shock status. The step of determining the segment rhythm analysis mode of the analysis region based on the status corresponding to the analysis region may further include: when the emergency treatment status of the analysis region is an electric shock status, determining the segment rhythm analysis mode of the analysis region as an initialization mode. In the electric shock status, a new round of chest compressions can be initiated by default, rhythm analysis is initialized, and the first analysis after the electric shock is used as the initial state of the rhythm analysis, without considering the rhythm information prior to the electric shock. For example, the electric shock status can be sensed when the emergency responder presses the electric shock confirmation button.

[0036] In an embodiment of this application, determining the state corresponding to each analysis region in step S120 may further include: acquiring reference signals related to chest compressions during cardiopulmonary resuscitation (CPR) on the target object; dividing the reference signals into multiple sub-analysis regions in a time series and determining the compression status corresponding to each sub-analysis region; for each analysis region of the electrocardiogram (ECG) signal, determining the emergency treatment status of each analysis region based on the compression status of one or more of the sub-analysis regions corresponding to the analysis region. In this embodiment, reference signals (such as chest impedance signals, blood oxygen signals, respiratory signals, and signals sensed by CPR sensors acquired via defibrillation chest electrodes) are acquired and divided into multiple sub-analysis regions. One or more sub-analysis regions of the reference signals correspond to one analysis region of the ECG signal. Therefore, the emergency treatment status of an analysis region is determined based on the compression status of one or more sub-analysis regions corresponding to an analysis region.

[0037] In one example, determining the first aid status of each analysis area based on the compression status of one or more sub-analysis areas corresponding to the analysis area may include: when the compression status of each sub-analysis area corresponding to the analysis area is a compression in progress state; when some sub-analysis areas corresponding to the analysis area are a compression in progress state and the compression status of the remaining sub-analysis areas is a compression outage state; and when the compression status of each sub-analysis area corresponding to the analysis area is a compression pause state.

[0038] In embodiments of this application, determining the pressing status corresponding to each sub-analysis region may include: performing time-domain analysis and / or frequency-domain analysis on the reference signal to obtain time-domain pressing features and / or frequency-domain pressing features; and performing pressing detection on each sub-analysis region of the reference signal based on the time-domain pressing features and / or the frequency-domain pressing features to determine whether a pressing event exists in each sub-analysis region. In this embodiment, the determination of whether a pressing event exists in each sub-analysis region can be based on time-domain pressing features, frequency-domain pressing features, or a combination of time-domain and frequency-domain pressing features to more accurately determine whether a pressing event exists in each sub-analysis region.

[0039] The above is an example description of determining the emergency treatment status of each analysis region. The determination of the filtering status of each analysis region is described below. In embodiments of this application, determining the status corresponding to each analysis region may include: acquiring a reference signal during cardiopulmonary resuscitation (CPR) of the target object; dividing the reference signal into multiple sub-analysis regions in a time series; for each analysis region of the electrocardiogram (ECG) signal, performing correlation analysis between the reference signal of one or more sub-analysis regions corresponding to the analysis region and the noise of the analysis region; determining the filtering method of the analysis region based on the result of the correlation analysis; and determining the filtering status of the analysis region based on the filtering method of the analysis region.

[0040] In this embodiment, a reference signal (such as chest impedance signal acquired via defibrillation chest electrodes, blood oxygen signal, respiratory signal, and signal sensed by a CPR sensor) is acquired and divided into multiple sub-analysis regions. One or more sub-analysis regions of the reference signal correspond to one analysis region of the ECG signal. Therefore, by performing correlation analysis based on the reference signal portion corresponding to one or more sub-analysis regions of an analysis region and the noise level of that analysis region, the filtering method for that analysis region can be determined, thereby determining the filtering state of that analysis region. For example, when correlation analysis determines that the ECG filtering method for an analysis region is low-order or high-order filtering, the filtering state of that analysis region can be determined as requiring filtering. When correlation analysis determines that the ECG filtering method for an analysis region is not requiring filtering, the filtering state of that analysis region can be determined as not requiring filtering.

[0041] In the embodiments of this application, after determining the state of each analysis region and thereby determining the segment rhythm analysis mode of each analysis region, segment rhythm analysis is performed on each analysis region using the segment rhythm analysis mode corresponding to each analysis region, thereby obtaining the segment rhythm state of each analysis region, as described in step S130. Afterwards, based on the obtained segment rhythm state, long-term rhythm analysis and short-term rhythm analysis are performed in steps S140 and S150 respectively, which are described in detail below.

[0042] In the embodiments of this application, long-term rhythm analysis is performed based on the segment rhythm states of multiple analysis regions within a first preset time period from the rhythm decision time to obtain long-term rhythm state characteristics. That is, the long-term rhythm state is obtained by rhythm analysis of the segment rhythm states of multiple analysis regions within a first preset time period from the rhythm decision time. Here, the first preset time period refers to the time period from the decision time back to the rhythm decision time point. The endpoint of this time period can be the rhythm decision time, or it can be a time interval from the decision time. In one example, the analysis duration of the long-term rhythm analysis is a time range of more than 10 seconds from the nearest rhythm decision time, but not exceeding the current compression cycle (generally 2 or 3 minutes), and includes the segment rhythm states of at least 5 analysis regions. Typically, multiple segment rhythm states within the nearest 2 minutes from the rhythm decision time can be analyzed to calculate the long-term rhythm state characteristics. In this example, the aforementioned first preset time period is equal to 2 minutes.

[0043] In embodiments of this application, long-term rhythm state features may include at least one of the following: the proportion of each segment rhythm state in the set of segment rhythm states of each of the plurality of analysis regions; the proportion of consecutive identical segment rhythm states when the segment rhythm states of each of the plurality of analysis regions are sorted by time; the time proportion of different states and / or the proportion of each segment rhythm state in different states in the corresponding state set of each of the plurality of analysis regions; and the weighted score of each segment rhythm state in the set of segment rhythm states of each of the plurality of analysis regions, wherein the weight of the segment rhythm state of each analysis region depends on the time distance of the analysis region from the rhythm decision time and / or the state corresponding to the analysis region.

[0044] In embodiments of this application, for an analysis region: the closer the analysis region is to the rhythm decision moment, the greater the weight assigned to the segment rhythm state of the analysis region; and when the emergency treatment state of the analysis region is a compression pause state or a filtering state that does not require filtering, a first weight is assigned to the segment rhythm state of the analysis region; when the emergency treatment state of the analysis region is a compression in progress state and the filtering state that requires filtering, a second weight is assigned to the segment rhythm state of the analysis region, wherein the second weight is less than the first weight; when the emergency treatment state of the analysis region is a compression transition state and the filtering state that requires filtering, a third weight is assigned to the segment rhythm state of the analysis region, wherein the third weight is less than the second weight.

[0045] In this embodiment, from a time dimension, the closer to the rhythm decision moment, the greater the weight assigned to the segment rhythm state at the corresponding moment. From the perspective of signal reliability, if the emergency treatment status is compression paused or filtering status is no filtering required, the ECG signal analyzed is free of CPR interference, and the segment rhythm status analysis results are relatively reliable. A first weighting coefficient is assigned to the segment rhythm status at the corresponding time. If the emergency treatment status is compression in progress and filtering status is required, the ECG signal analyzed is affected by CPR interference, and the reliability of the segment rhythm status analysis results is reduced. A second weighting coefficient (less than the first weighting coefficient) is assigned to the segment rhythm status at the corresponding time. If the compression status in the current analysis area is the compression transition state and filtering status is required, the analyzed signal includes both CPR-interfered and CPR-free ECG signals. Due to the influence of CPR filtering, the CPR-filtered ECG signal in the compression transition area may differ significantly in amplitude and even morphology from the unfiltered ECG signal. The segment rhythm status analysis results are unreliable, and a third weighting coefficient (less than the second weighting coefficient) is assigned to the segment rhythm status at the corresponding time. In the embodiments of this application, the rhythmic state weight allocation criteria of both signal reliability and time dimensions can be combined to assign weights to the segment rhythmic states of different analysis regions, perform weighted combinations, and obtain the weighted score for each rhythmic state. If the emergency treatment state of the current analysis region is electric shock, the initialization mode is entered, long-term rhythmic analysis is initialized, and a new round of compression cycle is started by default. The first segment rhythmic analysis after electric shock is used as the initial segment rhythmic state for long-term rhythmic analysis, and the rhythmic information before electric shock is no longer considered.

[0046] In the embodiments of this application, short-term rhythm analysis is performed based on the segment rhythm states of at least one analysis region within a second preset time period from the rhythm decision time to obtain short-term rhythm state characteristics. That is, long-term rhythm states are obtained by rhythm analysis of the segment rhythm states of at least one analysis region within a second preset time period from the rhythm decision time. Here, the second preset time period refers to the time period from the decision time back to the rhythm decision time point. The endpoint of this time period can be the rhythm decision time, or it can be something other than the rhythm decision time, but can be a certain time away from the decision time. In one example, the analysis duration of the short-term rhythm analysis is a time range within the nearest 10 seconds from the rhythm decision time, including the segment rhythm states of at least one analysis region. Typically, one or more segment rhythm states within the nearest 10 seconds from the rhythm decision time can be analyzed to calculate the short-term rhythm state characteristics. In this example, the aforementioned second preset time period is equal to 10 seconds.

[0047] In embodiments of this application, short-term rhythm state features may include at least one of the following: the proportion of each segment rhythm state in the set of segment rhythm states of each of the plurality of analysis regions; the proportion of consecutive identical segment rhythm states when the segment rhythm states of each of the plurality of analysis regions are sorted by time; the time proportion of different states and / or the proportion of each segment rhythm state in different states in the corresponding state set of each of the plurality of analysis regions; the weighted score of each segment rhythm state in the set of segment rhythm states of each of the plurality of analysis regions, wherein the weight of the segment rhythm state of each analysis region depends on the time distance of the analysis region from the rhythm decision time and / or the state corresponding to the analysis region; in analysis regions where the state is a press-to-pause state or a no-filter state, the time distance between the analysis region closest to the rhythm decision time and the rhythm decision time, and the percentage of its segment rhythm state.

[0048] In the embodiments of this application, similar to the long-term rhythm analysis described above, in the short-term rhythm analysis, for an analysis region: the closer the analysis region is to the rhythm decision time, the greater the weight assigned to the segment rhythm state of the analysis region; and when the emergency treatment state of the analysis region is a compression pause state or a filtering state that does not require filtering, a first weight is assigned to the segment rhythm state of the analysis region; when the emergency treatment state of the analysis region is a compression in progress state and the filtering state that requires filtering, a second weight is assigned to the segment rhythm state of the analysis region, wherein the second weight is less than the first weight; when the emergency treatment state of the analysis region is a compression transition state and the filtering state that requires filtering, a third weight is assigned to the segment rhythm state of the analysis region, wherein the third weight is less than the second weight.

[0049] In this embodiment, from a time dimension, the closer to the rhythm decision moment, the greater the weight assigned to the segment rhythm state at the corresponding moment. From the perspective of signal reliability, if the emergency treatment status is compression paused or filtering status is no filtering required, the ECG signal analyzed is free of CPR interference, and the segment rhythm status analysis results are relatively reliable. A first weighting coefficient is assigned to the segment rhythm status at the corresponding time. If the emergency treatment status is compression in progress and filtering status is required, the ECG signal analyzed is affected by CPR interference, and the reliability of the segment rhythm status analysis results is reduced. A second weighting coefficient (less than the first weighting coefficient) is assigned to the segment rhythm status at the corresponding time. If the compression status in the current analysis area is the compression transition state and filtering status is required, the analyzed signal includes both CPR-interfered and CPR-free ECG signals. Due to the influence of CPR filtering, the CPR-filtered ECG signal in the compression transition area may differ significantly in amplitude and even morphology from the unfiltered ECG signal. The segment rhythm status analysis results are unreliable, and a third weighting coefficient (less than the second weighting coefficient) is assigned to the segment rhythm status at the corresponding time. In the embodiments of this application, the rhythmic state weight allocation criteria of both signal reliability and time dimensions can be combined to assign weights to the rhythmic states of different analysis regions, perform weighted combinations, and obtain the weighted score for each rhythmic state. If the emergency treatment state of the current analysis region is electric shock, the system enters the initialization mode, performs short-term rhythmic analysis initialization, and starts a new round of chest compressions by default, no longer considering the rhythmic information before electric shock.

[0050] After obtaining the long-term and short-term rhythm state features, a rhythm decision is determined by combining these two features. In embodiments of this application, determining the rhythm decision based on the long-term and short-term rhythm state features may include: when the distribution of segment rhythm states embodied by the long-term and short-term rhythm state features is consistent, a rhythm decision is determined based on the distribution of segment rhythm states embodied by either the long-term or short-term rhythm state features; when the distribution of segment rhythm states embodied by the long-term and short-term rhythm state features is inconsistent, a rhythm decision is determined based on the more reliable of the two distributions, or a rhythm decision is determined based on the distribution of segment rhythm states embodied by the short-term rhythm state features, or an uncertain rhythm decision is output. As described above regarding the signal reliability dimension, the ECG signals corresponding to different analysis regions are different, and the reliability of their segment rhythm state analysis results also differs. Therefore, the reliability of the distribution of segment rhythm states reflected by the long-term rhythm state features and the short-term rhythm state features is related to the state of the analysis regions used in the long-term rhythm analysis and the short-term rhythm analysis, respectively.

[0051] In this embodiment, a defibrillation decision is made based on a criterion established according to the distribution characteristics of long-term and short-term rhythm states. If the distribution characteristics of the long-term and short-term rhythm states are consistent, a defibrillation decision is output based on the rhythm state represented by the distribution characteristics. If the distribution characteristics of the long-term and short-term rhythm states are inconsistent, a defibrillation decision is output based on the rhythm state represented by the more reliable distribution characteristics measured from both time and signal reliability dimensions. If the distribution characteristics of the long-term and short-term rhythm states are inconsistent, and both distribution characteristics are unreliable, an uncertain rhythm decision is output. For example, if the weighted score feature in the long-term rhythm analysis indicates that the long-term rhythm state is defibrillable, and at the same time, there are no time periods in the short-term rhythm analysis where compressions are paused or no filtering is required, and the proportion of defibrillable rhythm states is large, then a defibrillable rhythm decision is output. If the rhythm state percentage, weighted score, and other features in the long-term rhythm analysis are not very distinguishable in terms of whether a rhythm state is defibrillable, but there are time periods in the short-term rhythm analysis where compressions are paused or no filtering is required, and the proportion of defibrillable rhythm states in the time period closest to the rhythm decision time is large, then a defibrillable rhythm decision can also be output.

[0052] In another embodiment of this application, determining the rhythm decision based on the long-term rhythm state features and the short-term rhythm state features may include: for each segment rhythm state, obtaining a weighted score for the segment rhythm state by weighted combination of the long-term rhythm state features corresponding to the segment rhythm state and the short-term rhythm state features corresponding to the segment rhythm state; and determining the rhythm decision based on the weighted score for each segment rhythm state. In this embodiment, the rhythm decision based on the weighted score obtained by weighted combination of the distribution characteristics of the long-term and short-term rhythm states can be expressed as the following formula:

[0053] RhythmScore=A*LongTimeScore+B*ShortTimeScore+C,

[0054] RhythmScore is a weighted score for a certain rhythmic state, used to determine whether defibrillation is possible, not possible, or uncertain. LongTimeScore measures the long-term distribution characteristics of a certain rhythmic state, and can be a single value or a combination of values ​​measuring multiple long-term rhythmic state distribution characteristics. For example, LongTimeScore can be the distribution value of different rhythmic states within a long-term rhythmic state feature (e.g., percentage), or it can be derived from the distribution values ​​of different rhythmic states within a long-term rhythmic state feature through some numerical transformation (e.g., normalized weighted scores). Combination methods can include normalizing, averaging, or weighted averaging multiple long-term rhythmic state distribution characteristics to obtain the combined value. ShortTimeScore measures the short-term distribution characteristics of a certain rhythmic state, and can be a single value or a combination of values ​​measuring multiple short-term rhythmic state distribution characteristics. A, B, and C are weighting coefficients obtained from regression analysis.

[0055] After determining the rhythm decision, it can be output, for example, at the end of the compression cycle. Based on the rhythm decision result, the emergency personnel are instructed to perform emergency treatment. If the rhythm decision output is a defibrillation rhythm decision, the emergency personnel are instructed to deliver a shock. If the rhythm decision output is a non-defibrillation rhythm decision, the emergency personnel are instructed to continue compressions. If the rhythm decision output is an uncertain rhythm decision, the emergency personnel are instructed to pause compressions and confirm the rhythm of the ECG signal without compression interference.

[0056] The above description uses a fixed CPR operation mode as an example to illustrate the rhythm analysis and decision-making method 100 for a defibrillator according to an embodiment of this application. In the fixed CPR operation mode, the emergency responder performs cardiopulmonary resuscitation according to a fixed compression cycle set by the system. After the compression cycle ends, a rhythm decision is given, instructing the emergency responder to perform emergency treatment. This method 100 can also be used in a continuous CPR operation mode. In the continuous CPR operation mode, the emergency responder does not have a fixed compression cycle during CPR; rhythm decisions are continuously made during the CPR process. Once a defibrillation rhythm is output by the rhythm decision, the emergency responder is immediately instructed to perform defibrillation, or the emergency responder actively initiates a rhythm analysis request, receives a rhythm decision, and is instructed to perform emergency treatment.

[0057] In an embodiment for continuous CPR operation mode, in one example, the analysis duration of long-duration rhythm analysis can be a time range of more than 10 seconds from the rhythm decision moment, but not exceeding 3 minutes, and includes at least 5 segment rhythm states from the analysis region; the analysis duration of short-duration rhythm analysis is a time range of less than 10 seconds from the rhythm decision moment, and includes at least 1 segment rhythm state from the analysis region. Rhythm decision-making is performed continuously during CPR. If the analysis duration or number of analysis regions before the rhythm decision moment does not meet the analysis conditions of long-duration rhythm analysis, the long-duration rhythm state remains in the default initial state (uncertain rhythm). During rhythm decision-making, the long-duration default initial rhythm state and the short-duration rhythm state characteristics are combined, and a rhythm decision-making strategy is used to output a defibrillable, non-defibrillable, or uncertain rhythm decision. The rhythm decision-making strategy at this time includes, but is not limited to, the following methods: directly outputting uncertain rhythm decisions; making defibrillation decisions based on judgment criteria established according to short-term rhythm state characteristics; if the short-term rhythm state is relatively reliable from the perspectives of time and signal reliability, outputting defibrillation decisions based on the rhythm state represented by the corresponding distribution characteristics; if the short-term rhythm state is unreliable, outputting uncertain rhythm decisions (for example, if there are periods of compression pause or no filtering required in the short-term rhythm analysis, and the proportion of defibrillable rhythm states in the period closest to the rhythm decision time is relatively large, outputting defibrillable rhythm decisions); making rhythm decisions based on a weighted score obtained by weighted combination of long-term default initial rhythm state and short-term rhythm state characteristics, which can be expressed as the following formula:

[0058] RhythmScore=A*LongTimeScore+B*ShortTimeScore+C,

[0059] Wherein, RhythmScore is the weighted score of a certain rhythm state, and the decision of whether a rhythm is defibrillable, undefibrillable, or indeterminate is made based on the weighted score of the rhythm state; LongTimeScore is the value that measures the long-term default initial rhythm state; ShortTimeScore is the value that measures the short-term rhythm state distribution characteristics of a certain rhythm state, which can be a value that measures the distribution characteristics of one short-term rhythm state or a combination of values ​​that measures the distribution characteristics of multiple short-term rhythm states; A, B, and C are the weight coefficients obtained from regression analysis.

[0060] Based on the rhythm decision result, instruct emergency personnel to perform first aid. If the rhythm decision does not actively initiate a rhythm analysis request, and the rhythm decision outputs a defibrillable rhythm, immediately instruct the emergency personnel to deliver a shock. If the rhythm decision outputs another rhythm, do not instruct or instruct the emergency personnel to continue compressions. If the emergency personnel actively initiate a rhythm analysis request and provide a rhythm decision, and the rhythm decision outputs a defibrillable rhythm, immediately instruct the emergency personnel to deliver a shock. If the rhythm decision outputs a non-defibrillable rhythm, instruct the emergency personnel to continue compressions. If the rhythm decision outputs an uncertain rhythm, instruct the emergency personnel to pause compressions and confirm the rhythm of the ECG signal without compression interference.

[0061] The above exemplarily illustrates a rhythm analysis and decision-making method 100 for a defibrillator according to an embodiment of this application. To better understand this method, Figure 2 An exemplary flowchart of rhythm analysis and decision-making according to an embodiment of this application is shown (primarily illustrating the various stages of the entire process and their progression). Figure 3 This diagram illustrates the process of rhythm analysis and decision-making according to an embodiment of this application (primarily showing various signals, states at different times, and factors considered in rhythm state analysis, weight allocation, etc.). It can be used to... Figure 2 and Figure 3 To better understand the content mentioned above, it will not be repeated here.

[0062] Overall, the rhythm analysis and decision-making method 100 for defibrillators according to the embodiments of this application considers not only the short-term rhythm state before the rhythm decision moment, but also the long-term rhythm state before the rhythm decision moment. The rhythm state is generally relatively stable within the CPR compression cycle. By focusing on the rhythm state over a long period before the rhythm decision moment within the CPR compression cycle, a stable rhythm state within the CPR compression cycle is obtained. At the same time, in order to respond promptly to local rhythm fluctuations, the rhythm state over a short period before the rhythm decision moment within the CPR compression cycle is focused on to obtain a short-term instantaneous rhythm state. By combining the long-term stable rhythm state and the short-term instantaneous rhythm state, a rhythm decision is made to instruct emergency personnel to perform emergency treatment.

[0063] Based on the above description, the rhythm analysis and decision-making method 100 for a defibrillator according to the embodiments of this application combines long-term rhythm state characteristics and short-term rhythm state characteristics to determine the rhythm decision. This avoids both local interference and insensitivity to local rhythm fluctuations, thereby obtaining a more reliable rhythm decision. Moreover, since different rhythm analysis modes are selected based on ECG signals in different states, the accuracy of the rhythm analysis results, i.e., the segment rhythm state, can be further improved, thereby further improving the reliability of the rhythm decision.

[0064] The following is combined with Figure 4 A rhythm analysis and decision-making method 400 for a defibrillator according to another embodiment of this application is described. For example... Figure 4 As shown, the rhythm analysis and decision-making method 400 for defibrillators may include the following steps:

[0065] In step S410, reference signals and the original electrocardiogram signals of the target object during cardiopulmonary resuscitation are acquired.

[0066] In step S420, the reference signal is pressed to obtain the time-domain press event marker of the reference signal.

[0067] In step S430, the instantaneous compression interval is determined based on the time-domain compression event marker, and the original ECG signal is filtered based on the instantaneous compression interval to obtain a filtered ECG signal.

[0068] In step S440, the electrocardiogram (ECG) signal of the target object is divided into multiple analysis regions in time series, and rhythm analysis is performed on each analysis region to obtain the segment rhythm state of each analysis region. The ECG signal of the target object includes only the filtered ECG signal, or the ECG signal of the target object includes the original ECG signal and the filtered ECG signal.

[0069] In step S450, long-term rhythm analysis is performed on the fragment rhythm states of multiple analysis regions within the first preset time period of the distance rhythm decision time to obtain long-term rhythm state characteristics.

[0070] In step S460, short-term rhythm analysis is performed on the segment rhythm state of at least one of the analysis regions within the second preset time period of the distance rhythm decision time to obtain short-term rhythm state characteristics, wherein the first preset time period is longer than the second preset time period.

[0071] In step S470, a rhythm decision is determined based on the long-term rhythm state characteristics and the short-term rhythm state characteristics, and the rhythm decision is output.

[0072] In this embodiment, a time-domain compression event marker is first obtained by compression detection of a reference signal. Based on the time-domain compression event marker, the instantaneous compression interval can be determined. The original ECG signal is then filtered based on the instantaneous compression interval to obtain a filtered ECG signal. Similar to the previous embodiment, in this embodiment, the ECG signal of the target object during cardiopulmonary resuscitation (wherein, the target object's ECG signal may only include the filtered ECG signal, or the target object's ECG signal may include both the original ECG signal and the filtered ECG signal) is divided into multiple analysis regions in time series. Long-term rhythm state analysis and long-short rhythm state analysis are performed based on the segment rhythm state of each analysis region. Rhythm decisions are determined by combining long-term and short-term rhythm state characteristics. This avoids both local interference and insensitivity to local rhythm fluctuations, resulting in more reliable rhythm decisions. The difference is that in this embodiment, it is not necessary to determine the state corresponding to each analysis region; instead, rhythm analysis is directly performed on each analysis region to obtain the segment rhythm state of each analysis region. Therefore, this embodiment is similar to the embodiments described above, except that some steps are omitted. For the sake of brevity, only the operations in this embodiment are summarized here. For details of these operations, please refer to the contents of the foregoing embodiments.

[0073] In an embodiment of this application, step S420, which involves performing time-domain press detection on the reference signal to obtain a time-domain press event marker for the reference signal, may include: performing time-domain analysis and / or frequency-domain analysis on the reference signal to obtain time-domain press features and / or frequency-domain press features; and performing press detection on the reference signal based on the time-domain press features and / or the frequency-domain press features to obtain a time-domain press event marker for the reference signal. In this embodiment, press detection may be performed based on time-domain features, frequency-domain features, or a combination of both to obtain a time-domain press event marker for the reference signal.

[0074] In the embodiments of this application, similar to the embodiments described above, the long-term rhythm state features and short-term rhythm state features obtained in steps S450 and S460 may each include at least one of the following: the proportion of each segment rhythm state in the set of segment rhythm states of each of the multiple analysis regions; the proportion of consecutive identical segment rhythm states when the segment rhythm states of each of the multiple analysis regions are sorted by time; the time proportion of different states and / or the proportion of each segment rhythm state in different states in the corresponding state set of each of the multiple analysis regions; the weighted score of each segment rhythm state in the set of segment rhythm states of each of the multiple analysis regions, wherein the weight of the segment rhythm state of each analysis region depends on the time distance of the analysis region from the rhythm decision time and / or the state corresponding to the analysis region.

[0075] In embodiments of this application, similar to the embodiments described above, step S470, which determines the rhythm decision based on the long-term rhythm state features and the short-term rhythm state features, may include: when the distribution of the segment rhythm states reflected by the long-term rhythm state features and the short-term rhythm state features is consistent, determining the rhythm decision based on the distribution of the segment rhythm states reflected by the long-term rhythm state features or the short-term rhythm state features; when the distribution of the segment rhythm states reflected by the long-term rhythm state features and the short-term rhythm state features is inconsistent, determining the rhythm decision based on the one with higher reliability among the two distributions, or determining the rhythm decision based on the distribution of the segment rhythm states reflected by the short-term rhythm state features, or outputting an uncertain rhythm decision. Alternatively, step S470, which determines the rhythm decision based on the long-term rhythm state features and the short-term rhythm state features, may include: for each segment rhythm state, obtaining a weighted score for the segment rhythm state by weighting the long-term rhythm state features corresponding to the segment rhythm state and the short-term rhythm state features corresponding to the segment rhythm state; and determining the rhythm decision based on the weighted score for each segment rhythm state.

[0076] In the embodiments of this application, similar to the embodiments described above, method 400 can be applied to fixed cardiopulmonary resuscitation operation mode and continuous cardiopulmonary resuscitation operation mode. When applied to continuous cardiopulmonary resuscitation operation mode, if the analysis area before the rhythm decision time is insufficient for long-term rhythm analysis, long-term rhythm analysis is not performed, and rhythm decision is determined according to the preset long-term default initial rhythm state and the short-term rhythm state characteristics.

[0077] The above exemplarily illustrates a rhythm analysis and decision-making method 400 for a defibrillator according to an embodiment of this application. To better understand this method, Figure 5An exemplary flowchart of rhythm analysis and decision-making according to this embodiment is shown (primarily illustrating the various stages and their progression throughout the process). This flowchart provides a better understanding of the content of the method 400 described above, which will not be repeated here. Furthermore, in another embodiment of this application, steps S410 to S430 of the above method 400 can be omitted, and execution can begin directly from step S440. In this case, the ECG signal in step S440 can refer to a conventional ECG bandpass filtered ECG signal or a CPR filtered ECG signal. In this embodiment, the ECG signal can be used as a compression reference signal for analysis, such as... Figure 6 As shown in the exemplary flowchart, it can serve as a variation of method 400.

[0078] Based on the above description, the rhythm analysis and decision-making method 400 for defibrillators according to the embodiments of this application and its variations combine long-term rhythm state characteristics and short-term rhythm state characteristics to determine rhythm decisions, which can avoid being affected by local interference and avoid being insensitive to local rhythm fluctuations, thereby obtaining more reliable rhythm decisions.

[0079] The above exemplarily describes a rhythm analysis and decision-making method for a defibrillator according to embodiments of this application. The following, in conjunction with... Figure 7 This application describes a rhythm analysis and decision-making apparatus for a defibrillator, provided in another aspect of the present application. Figure 7 A schematic block diagram of a rhythm analysis and decision-making apparatus 700 for a defibrillator according to an embodiment of this application is shown. Figure 7 As shown, the rhythm analysis and decision-making device 700 for a defibrillator may include a memory 710 and a processor 720. The memory 710 stores a computer program executed by the processor 720. When executed by the processor 720, the computer program performs the rhythm analysis and decision-making method for a defibrillator described above according to the embodiments of this application. In a further embodiment of this application, the rhythm analysis and decision-making device 700 for a defibrillator may further include a signal acquisition component 730, which can be used to acquire electrocardiogram signals and / or reference signals related to chest compressions during cardiopulmonary resuscitation on a target subject, and transmit them to the processor 720 so that it can execute the rhythm analysis and decision-making method for a defibrillator described above according to the embodiments of this application. In the embodiments of this application, the above-mentioned device 700 may be a defibrillator. Those skilled in the art can understand the structure and operation of each component of the rhythm analysis and decision-making device 700 for a defibrillator according to the embodiments of this application in conjunction with the foregoing description. For the sake of brevity, it will not be described again here.

[0080] Furthermore, according to embodiments of this application, a storage medium is also provided, on which program instructions are stored. When the program instructions are executed by a computer or processor, they are used to perform corresponding steps of the rhythm analysis and decision-making method for a defibrillator according to embodiments of this application. The storage medium may, for example, include a memory card of a smartphone, a storage component of a tablet computer, a hard disk of a personal computer, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a portable compact disc read-only memory (CD-ROM), a USB memory, or any combination of the above storage media. A computer-readable storage medium may be any combination of one or more computer-readable storage media.

[0081] Furthermore, according to embodiments of this application, a computer program is also provided, which can be stored on a cloud or local storage medium. When this computer program is run by a computer or processor, it performs the corresponding steps of the rhythm analysis and decision-making method for a defibrillator according to embodiments of this application.

[0082] Based on the above description, the rhythm analysis and decision-making method and apparatus for defibrillators according to embodiments of this application combine long-term rhythm state characteristics and short-term rhythm state characteristics to determine rhythm decisions. This avoids both local interference and insensitivity to local rhythm fluctuations, thereby obtaining more reliable rhythm decisions. Furthermore, the rhythm analysis and decision-making method and apparatus for defibrillators according to embodiments of this application can select different rhythm analysis modes based on ECG signals in different states, further improving the accuracy of rhythm analysis results, i.e., segment rhythm states, and thus further enhancing the reliability of rhythm decisions.

[0083] Although exemplary embodiments have been described herein with reference to the accompanying drawings, it should be understood that the above exemplary embodiments are merely illustrative and are not intended to limit the scope of this application. Various changes and modifications can be made therein by those skilled in the art without departing from the scope and spirit of this application. All such changes and modifications are intended to be included within the scope of this application as claimed in the appended claims.

[0084] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0085] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another apparatus, or some features may be ignored or not executed.

[0086] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of this application may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0087] Similarly, it should be understood that, in order to streamline this application and aid in understanding one or more of the various inventive aspects, features of this application may sometimes be grouped together in a single embodiment, figure, or description thereof in the description of exemplary embodiments of this application. However, this approach should not be construed as reflecting an intention that the claimed application requires more features than are expressly recited in each claim. Rather, as reflected in the corresponding claims, its inventive point lies in solving the corresponding technical problem with features fewer than all features of a single disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into that detailed description, wherein each claim itself is a separate embodiment of this application.

[0088] Those skilled in the art will understand that, apart from the mutual exclusion of features, all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or apparatus so disclosed can be combined in any combination. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0089] Furthermore, those skilled in the art will understand that although some embodiments herein include certain features but not others included in other embodiments, combinations of features from different embodiments are intended to be within the scope of this application and form different embodiments. For example, in the claims, any one of the claimed embodiments can be used in any combination.

[0090] The various component embodiments of this application can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some modules according to the embodiments of this application. This application can also be implemented as an apparatus program (e.g., a computer program and computer program product) for performing part or all of the methods described herein. Such an implementation of this application can be stored on a computer-readable medium, or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0091] It should be noted that the above embodiments are illustrative of this application and not limiting of it, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. This application can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names.

[0092] The above are merely specific embodiments or descriptions of specific embodiments of this application. The scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. The scope of protection of this application shall be determined by the scope of the claims.

Claims

1. A rhythm analysis and decision-making method for defibrillators, characterized in that, The method includes: Acquire the electrocardiogram (ECG) signal of the target object during cardiopulmonary resuscitation (CPR). The electrocardiogram signal is divided into multiple analysis regions in time series, and the state corresponding to each analysis region is determined, including emergency treatment state and / or filtering state; For each analysis region, a segment rhythm analysis mode for the analysis region is determined based on the state corresponding to the analysis region, and rhythm analysis is performed on the analysis region based on the segment rhythm analysis mode to obtain the segment rhythm state of the analysis region. Long-term rhythm analysis is performed on the fragment rhythm states of multiple analysis regions within the first preset time period of the distance rhythm decision time to obtain long-term rhythm state characteristics. Short-term rhythm analysis is performed on the segment rhythm state of at least one of the analysis regions within the second preset time period of the distance rhythm decision time to obtain short-term rhythm state characteristics, wherein the first preset time period is longer than the second preset time period; Based on the long-term rhythm state characteristics and the short-term rhythm state characteristics, a rhythm decision is determined and the rhythm decision is output.

2. The method according to claim 1, characterized in that, The emergency treatment states include compression in progress, compression transition, and compression pause; the filtering states include filtering required and filtering not required; and determining the segment rhythm analysis mode of the analysis region based on the state corresponding to the analysis region includes: When the emergency treatment status of the analysis area is in the press-pause state and / or the filtering status of the analysis area is in the no-filter state, the segment rhythm analysis mode of the analysis area is determined to be the interference-free mode. When the emergency treatment status of the analysis area is in the compression in progress state or the compression transition state, and the filtering status of the analysis area is in the filtering required state, the segment rhythm analysis mode of the analysis area is determined to be the interference mode.

3. The method according to claim 2, characterized in that, The emergency treatment status also includes the electric shock status, and the step of determining the segment rhythm analysis mode of the analysis region based on the status corresponding to the analysis region further includes: When the emergency treatment status of the analysis area is electric shock, the segment rhythm analysis mode of the analysis area is determined to be the initialization mode.

4. The method according to claim 2, characterized in that, The electrocardiogram (ECG) signal includes the original ECG signal and the filtered ECG signal. In the interference-free mode, rhythm analysis is performed based on the original ECG signal and / or the filtered ECG signal using an interference-free rhythm strategy. In the interference mode, rhythm analysis is performed based solely on the filtered ECG signal, or based on the original ECG signal and the filtered ECG signal using an interference-involved rhythm strategy.

5. The method according to claim 2, characterized in that, Determining the state corresponding to each analysis region includes: Acquire reference signals related to chest compressions during cardiopulmonary resuscitation on a target subject; The reference signal is divided into multiple sub-analysis regions in time series, and the pressing situation corresponding to each sub-analysis region is determined; For each analysis region of the electrocardiogram signal, the emergency treatment status of each analysis region is determined based on the compression status of one or more sub-analysis regions corresponding to the analysis region.

6. The method according to claim 5, characterized in that, The determination of the emergency treatment status of each analysis area based on the pressure status of one or more sub-analysis areas corresponding to the analysis area includes: When the pressing status of each sub-analysis area corresponding to the analysis area is a pressing event, the emergency treatment status of the analysis area is determined to be a pressing in progress state. When, in a plurality of sub-analysis areas corresponding to the analysis area, some sub-analysis areas show a pressing event and the remaining sub-analysis areas show no pressing event, the emergency treatment status of the analysis area is determined to be a pressing transition state. When the pressing status of each sub-analysis area corresponding to the analysis area is that there is no pressing event, the emergency treatment status of the analysis area is determined to be the pressing paused state.

7. The method according to claim 5, characterized in that, Determining the pressure status corresponding to each sub-analysis region includes: Perform time-domain analysis and / or frequency-domain analysis on the reference signal to obtain time-domain pressing characteristics and / or frequency-domain pressing characteristics; Press detection is performed on each sub-analysis region of the reference signal based on the time-domain press features and / or the frequency-domain press features to determine whether a press event exists in each sub-analysis region.

8. The method according to claim 2, characterized in that, Determining the state corresponding to each analysis region includes: Acquire reference signals during cardiopulmonary resuscitation of the target object; The reference signal is divided into multiple sub-analysis regions in the time series; For each analysis region of the ECG signal, a correlation analysis is performed between the reference signal of one or more sub-analysis regions corresponding to the analysis region and the noise of the analysis region. Based on the result of the correlation analysis, the filtering method of the analysis region is determined, and the filtering state of the analysis region is determined based on the filtering method of the analysis region.

9. The method according to claim 2, characterized in that, Both the long-term circadian rhythm state characteristics and the short-term circadian rhythm state characteristics include at least one of the following: The proportion of each segment rhythm state in the set of segment rhythm states of each of the multiple analysis regions; When the segment rhythm states of each of the multiple analysis regions are sorted by time, the proportion of consecutive identical segment rhythm states is considered. In the state sets corresponding to each of the multiple analysis regions, the time proportion of different states and / or the proportion of each segment rhythm state in different states; The weighted score of each segment rhythm state in the set of segment rhythm states of the multiple analysis regions, wherein the weight of the segment rhythm state of each analysis region depends on the time distance of the analysis region from the rhythm decision time and / or the state corresponding to the analysis region.

10. The method according to claim 9, characterized in that, The short-term rhythm state characteristics also include: in the analysis region where the state is a press-to-pause state or a no-filter state, the time distance between the analysis region closest to the rhythm decision time and the rhythm decision time, as well as the percentage of its segment rhythm state.

11. The method according to claim 9, characterized in that, For an analysis region: The closer the analysis region is to the rhythm decision time, the greater the weight assigned to the segment rhythm state of the analysis region; and When the emergency treatment status of the analysis area is in the compression pause state or the filtering status is in the no-filter state, the first weight is assigned to the segment rhythm status of the analysis area. When the emergency treatment status of the analysis area is in the compression state and the filtering status is in the filtering state, a second weight is assigned to the segment rhythm state of the analysis area, wherein the second weight is less than the first weight. When the emergency treatment status of the analysis area is in the compression transition state and the filtering status is in the filtering required state, a third weight is assigned to the segment rhythm status of the analysis area, wherein the third weight is less than the second weight.

12. The method according to claim 1, characterized in that, The step of determining rhythm decisions based on the long-term rhythm state characteristics and the short-term rhythm state characteristics includes: When the distribution of segment rhythm states reflected by the long-term rhythm state features and the short-term rhythm state features is consistent, the rhythm decision is determined according to the distribution of segment rhythm states reflected by the long-term rhythm state features or the short-term rhythm state features. When the distribution of segment rhythm states reflected by the long-term rhythm state features and the short-term rhythm state features is inconsistent, the rhythm decision is determined based on the more reliable of the two distributions, or the rhythm decision is determined based on the distribution of segment rhythm states reflected by the short-term rhythm state features, or an uncertain rhythm decision is output.

13. The method according to claim 12, characterized in that, The reliability of the distribution of segment rhythm states reflected by the long-term rhythm state features and the short-term rhythm state features is related to the state of the analysis regions used in the long-term rhythm analysis and the short-term rhythm analysis, respectively.

14. The method according to claim 1, characterized in that, The step of determining rhythm decisions based on the long-term rhythm state characteristics and the short-term rhythm state characteristics includes: For each segment rhythm state, a weighted score for the segment rhythm state is obtained by weighting the long-term rhythm state features and the corresponding features of the segment rhythm state, and the short-term rhythm state features and the corresponding features of the segment rhythm state. Rhythm decisions are determined based on the weighted scores of each segment's rhythmic state.

15. The method according to claim 1, characterized in that, The method can be applied to both fixed cardiopulmonary resuscitation (CPR) operation mode and continuous CPR operation mode. When applied to continuous CPR operation mode, if the analysis area before the rhythm decision time is insufficient for long-term rhythm analysis, long-term rhythm analysis is not performed, and the rhythm decision is determined based on the preset long-term default initial rhythm state and the short-term rhythm state characteristics.

16. The method according to claim 1, characterized in that, The analysis region has at least one of the following properties: Each of the analysis regions has the same time length or can change the time length as needed; Any two of the analyzed regions may be continuous or discontinuous in time; Any two of the analysis regions may partially overlap or not overlap in time.

17. A rhythm analysis and decision-making method for defibrillators, characterized in that, The method includes: Acquire reference signals and the original electrocardiogram signals of the target object during cardiopulmonary resuscitation; Press detection is performed on the reference signal to obtain the time-domain press event marker of the reference signal; The instantaneous compression interval is determined based on the time-domain compression event marker, and the original ECG signal is filtered based on the instantaneous compression interval to obtain a filtered ECG signal. The electrocardiogram (ECG) signal of the target object is divided into multiple analysis regions in time series, and rhythm analysis is performed on each analysis region to obtain the segment rhythm state of each analysis region. The ECG signal of the target object includes only the filtered ECG signal, or the ECG signal of the target object includes the original ECG signal and the filtered ECG signal. Long-term rhythm analysis is performed on the fragment rhythm states of multiple analysis regions within the first preset time period of the distance rhythm decision time to obtain long-term rhythm state characteristics. Short-term rhythm analysis is performed on the segment rhythm state of at least one of the analysis regions within the second preset time period of the distance rhythm decision time to obtain short-term rhythm state characteristics, wherein the first preset time period is longer than the second preset time period; Based on the long-term rhythm state characteristics and the short-term rhythm state characteristics, a rhythm decision is determined and the rhythm decision is output.

18. The method according to claim 17, characterized in that, Press detection is performed on the reference signal to obtain a time-domain press event marker for the reference signal, including: Perform time-domain analysis and / or frequency-domain analysis on the reference signal to obtain time-domain pressing characteristics and / or frequency-domain pressing characteristics; The reference signal is press-detected based on the time-domain press features and / or the frequency-domain press features to obtain the time-domain press event marker of the reference signal.

19. The method according to claim 17, characterized in that, Both the long-term circadian rhythm state characteristics and the short-term circadian rhythm state characteristics include at least one of the following: The proportion of each segment rhythm state in the set of segment rhythm states of each of the multiple analysis regions; When the segment rhythm states of each of the multiple analysis regions are sorted by time, the proportion of consecutive identical segment rhythm states is considered. In the state sets corresponding to each of the multiple analysis regions, the time proportion of different states and / or the proportion of each segment rhythm state in different states; The weighted score of each segment rhythm state in the set of segment rhythm states of the multiple analysis regions, wherein the weight of the segment rhythm state of each analysis region depends on the time distance of the analysis region from the rhythm decision time and / or the state corresponding to the analysis region.

20. The method according to claim 17, characterized in that, The step of determining rhythm decisions based on the long-term rhythm state characteristics and the short-term rhythm state characteristics includes: When the distribution of segment rhythm states reflected by the long-term rhythm state features and the short-term rhythm state features is consistent, the rhythm decision is determined according to the distribution of segment rhythm states reflected by the long-term rhythm state features or the short-term rhythm state features. When the distribution of segment rhythm states reflected by the long-term rhythm state features and the short-term rhythm state features is inconsistent, the rhythm decision is determined based on the more reliable of the two distributions, or the rhythm decision is determined based on the distribution of segment rhythm states reflected by the short-term rhythm state features, or an uncertain rhythm decision is output.

21. The method according to claim 17, characterized in that, The step of determining rhythm decisions based on the long-term rhythm state characteristics and the short-term rhythm state characteristics includes: For each segment rhythm state, a weighted score for the segment rhythm state is obtained by weighting the long-term rhythm state features and the corresponding features of the segment rhythm state, and the short-term rhythm state features and the corresponding features of the segment rhythm state. Rhythm decisions are determined based on the weighted scores of each segment's rhythmic state.

22. The method according to claim 17, characterized in that, The method can be applied to both fixed cardiopulmonary resuscitation (CPR) operation mode and continuous CPR operation mode. When applied to continuous CPR operation mode, if the analysis area before the rhythm decision time is insufficient for long-term rhythm analysis, long-term rhythm analysis is not performed, and the rhythm decision is determined based on the preset long-term default initial rhythm state and the short-term rhythm state characteristics.

23. A rhythm analysis and decision-making device, characterized in that, The device includes a memory and a processor, the memory storing a computer program executed by the processor, the computer program executing, when run by the processor, the rhythm analysis and decision-making method for a defibrillator as described in any one of claims 1-22.

24. The apparatus according to claim 23, characterized in that, The device also includes a signal acquisition component, which is used to acquire reference signals related to chest compressions during cardiopulmonary resuscitation of the target object.

25. The apparatus according to claim 24, characterized in that, The device is a defibrillator.

26. A storage medium, characterized in that, The storage medium stores a computer program that, when executed, performs the rhythm analysis and decision-making method for a defibrillator as described in any one of claims 1-22.

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