CN109124605B - Method, device and equipment for reducing false alarm in ICU - Google Patents
Method, device and equipment for reducing false alarm in ICU Download PDFInfo
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Abstract
The application belongs to the field of medical equipment, and discloses a method for reducing false alarm in an ICU, which comprises the following steps: filtering noise interference in the electrocardiosignal and the arterial blood pressure signal; respectively determining the integral quality indexes of the electrocardiosignals and the arterial blood pressure signals according to the filtered electrocardiosignals and the filtered arterial blood pressure signals; performing data fusion calculation according to the overall signal quality index to obtain fusion heart rate estimation; and comparing the fusion heart rate estimation with a judgment threshold value, and determining whether to alarm or not according to a comparison result. When the electrocardiosignal or the arterial blood pressure signal comprises serious interference, the serious interference can be effectively inhibited by fusing heart rate estimation, so that the false alarm rate is effectively improved and reduced.
Description
Technical Field
The application belongs to the field of medical equipment, and particularly relates to a method, a device and equipment for reducing false alarms in an ICU.
Background
The critical illness medicine is a science for researching the generation and development rules of critical illness and diagnosis and treatment thereof, and emphasizes emergency treatment measures in treatment. The clinical discipline is a clinical discipline with multiple disciplines which are intersected and permeated with each other and gradually emerges in the end of the 50 s of the 20 th century, and aims to provide continuous organ function supporting treatment as early as possible when a life-threatening acute severe patient suffers from single or multiple organ failure, and meanwhile, to carry out active treatment aiming at the etiology so as to win the time and the condition and finally control the primary pathogenesis. The clinical base of critical care medicine is the intensification of Intensive Care Units (ICUs). The first ICU of modern size historically was established in the united states in 1985. By early detection and management of abnormal blood flow distribution in major vital organs, the consequences caused by abnormal inflammatory responses and multiple organ failure syndrome can be prevented or reduced.
Advanced technical devices are currently equipped, including various vital sign signal monitoring systems and vital support devices, which dynamically and quantitatively monitor the physiological functions of the patient, capture transient changes, and feed back to the treatment in a timely manner.
However, the following problems still exist in the current monitoring system: for one, since modern ICUs employ a large number of sophisticated monitoring devices to monitor the physiological and pathological states of critically ill patients, the data volume of the various vital sign data streams recorded by these monitoring devices is extremely large, the integration and interpretation of these data streams takes a lot of effort and time, and even experienced physicians sometimes have difficulty in efficiently and quickly utilizing this information, a phenomenon known as "information overload". Due to the overload of information, medical staff often can only passively respond to and deal with the change of the state of illness of critical patients by means of alarm signals sent by monitors. In the ICU, on the one hand, with the development of related scientific technologies such as sensing detection technology, analog and digital communication technology, large-scale integrated circuit technology, computer software and hardware technology, signal and image processing technology, display and recording technology, etc., the ability to acquire new monitored signals and parameters is continuously improved, and on the other hand, medical personnel face that more and more signals and parameters cannot be fully utilized, and prefer simple, clear and definite diagnostic information, thereby taking rapid and intermittent therapeutic measures instead of endless data streams and physical parameters. Secondly, the monitored signal is often interfered by noise, artifacts, data missing and the like, so that the estimated value of the monitored signal parameter is wrong, and the monitor alarms falsely. The abnormal alarm of the current monitor generally sets an abnormal alarm threshold value in advance, and triggers the monitor to alarm when the monitored signal parameter exceeds the threshold value. Due to the overrun alarm based on the single parameter, when the estimation of the monitoring parameter is wrong due to the influence of interference, the monitor can give an alarm by mistake. Research shows that the current monitor has high alarm sensitivity, but the false alarm rate is high. The alarm sound sent by the monitor can reach 80 decibels at most, which has serious influence on the rest and sleep of the patient, thereby influencing the normal recovery of the patient. The high false alarm rate deprives the patient of normal rest, increases the psychological pressure and burden of the patient and medical staff, delays the response time of the medical staff to real alarm, and more seriously causes the distrust and paralysis carelessness of the medical staff to alarm signals, thereby seriously influencing the timely and correct treatment of real critical alarm by clinicians and greatly reducing the intensive care effect. Because of the distrust of alarms, 70% of physicians have experience with turning off the monitor alarm signal; because of the false alarm of the monitor caused by the influence of the body motion disturbance of the patient, a patient with atrial fibrillation and sinus arrest is possibly misdiagnosed as ventricular fibrillation and is treated by electrical defibrillation.
In conclusion, it is very necessary to perform data fusion on multi-lead monitoring signals, effectively overcome the problem of information overload, evaluate the signal quality, correctly estimate the monitoring parameters, avoid the influence of interference, reduce the false alarm rate of the monitor, establish an intelligent and integrated patient monitoring and emergency diagnosis system, comprehensively analyze and process various acquired vital sign signals, provide simple, clear and real information for assisting a doctor in decision-making diagnosis, really play the role of intensive care, and improve the diagnosis quality and the emergency level.
Disclosure of Invention
In view of this, the embodiments of the present application provide a method, an apparatus, and a device for reducing false alarms in an ICU, so as to solve the problem in the prior art that a monitor is prone to false alarms due to information overload.
A first aspect of an embodiment of the present application provides a method for reducing an ICU false alarm, where the method for reducing an ICU false alarm includes:
filtering noise interference in the electrocardiosignal and the arterial blood pressure signal;
respectively determining the integral quality indexes of the electrocardiosignals and the arterial blood pressure signals according to the filtered electrocardiosignals and the filtered arterial blood pressure signals;
performing data fusion calculation according to the overall signal quality index to obtain fusion heart rate estimation;
and comparing the fusion heart rate estimation with a judgment threshold value, and determining whether to alarm or not according to a comparison result.
With reference to the first aspect, in a first possible implementation manner of the first aspect, the step of filtering noise interference in the cardiac electrical signal and the arterial blood pressure signal includes:
carrying out 8-layer decomposition on the original electrocardiosignal and the arterial blood pressure signal by using wavelets;
and filtering the 8 th layer average component and the 1 st, 2 nd and 3 rd layer detail components in the decomposed signal, setting the average components to 0, reconstructing waveforms of the remaining 1-7 layers of average components and 4-8 th layer detail components, and finishing filtering.
With reference to the first aspect, in a second possible implementation manner of the first aspect, the step of determining the overall quality index of the electrocardiograph signal according to the filtered electrocardiograph signal includes:
detecting the number of R peaks of the same filtered electrocardiosignal through a first algorithm and a second algorithm;
generating a first electrocardiosignal quality index according to the number of the R peaks with small quantity and the number of the R peaks with large quantity detected in the two algorithms;
generating a second electrocardiosignal quality index according to the number of the R peaks matched by the two algorithms and the total number of the R peaks detected by the two algorithms;
and determining the overall quality index of the electrocardiosignals by combining the first electrocardiosignal quality index and the second electrocardiosignal quality index according to a preset coefficient.
With reference to the first aspect, in a third possible implementation manner of the first aspect, the step of determining an overall quality index of the arterial blood pressure signal according to the filtered arterial blood pressure signal includes:
obtaining a first blood pressure signal quality index by a beat-to-beat ABP signal quality evaluation method based on fuzzy logic;
obtaining a second blood pressure signal quality index based on a heuristic constrained signal quality evaluation algorithm;
and determining the value relationship between the whole arterial blood pressure signal quality index and the first blood pressure signal quality index according to the value of the second blood pressure signal quality index.
With reference to the first aspect, in a fourth possible implementation manner of the first aspect, the step of performing data fusion calculation according to the overall signal quality index to obtain a fusion heart rate estimate includes:
performing Kalman filtering on a heart rate obtained based on an Electrocardiogram (ECG) signal to obtain a heart rate recorded as HR1, and simultaneously obtaining a filtered residual signal recorded as r 1;
performing Kalman filtering on a heart rate obtained based on an arterial blood pressure ABP signal to obtain a heart rate recorded as HR2, and simultaneously obtaining a filtered residual signal recorded as r 2;
according to the formula
Performing fusion calculation to obtain a fusion heart rate estimate, wherein: sigma1 2＝(r1/SQI1)2，σ2 2＝(r2/SQI2)2。
A second aspect of the embodiments of the present application provides an apparatus for reducing an intra-ICU false alarm, where the apparatus for reducing an intra-ICU false alarm includes:
the filtering unit is used for filtering noise interference in the electrocardiosignal and the arterial blood pressure signal;
the integral quality index determining unit is used for respectively determining the integral quality indexes of the electrocardiosignals and the arterial blood pressure signals according to the filtered electrocardiosignals and the filtered arterial blood pressure signals;
the heart rate fusion estimation unit is used for carrying out data fusion calculation according to the overall signal quality index to obtain fusion heart rate estimation;
and the comparison unit is used for comparing the fusion heart rate estimation with a judgment threshold value and determining whether to alarm or not according to a comparison result.
With reference to the second aspect, in a first possible implementation manner of the second aspect, the overall quality index determining unit includes:
the decomposition subunit is used for performing 8-layer decomposition on the original electrocardiosignals and the arterial blood pressure signals by utilizing wavelets;
and the reconstruction subunit is used for filtering out the 8 th layer average component and the 1 st, 2 nd and 3 rd layer detail components in the decomposed signal, setting the average components to 0, reconstructing waveforms of the remaining 1-7 layers of average components and 4-8 th layer detail components, and finishing filtering.
With reference to the second aspect, in a second possible implementation manner of the second aspect, the overall quality index determining unit includes:
the R peak quantity detection subunit is used for detecting the R peak quantity of the same filtered electrocardiosignal through a first algorithm and a second algorithm;
the first generation subunit is used for generating a first electrocardiosignal quality index according to the number of the R peaks with less number and the number of the R peaks with more number, which are detected in the two algorithms;
the second generation subunit is used for generating a second electrocardiosignal quality index according to the number of the R peaks matched by the two algorithms and the total number of the R peaks detected by the two algorithms;
and the electrocardiosignal overall quality index determining subunit is used for determining the electrocardiosignal overall quality index according to a preset coefficient and by combining the first electrocardiosignal quality index and the second electrocardiosignal quality index.
With reference to the second aspect, in a third possible implementation manner of the second aspect, the overall quality index determining unit includes:
the first calculating subunit is used for obtaining a first blood pressure signal quality index by a beat-to-beat ABP signal quality evaluation method based on fuzzy logic;
the second calculating subunit is used for obtaining a second blood pressure signal quality index based on a heuristic constraint signal quality evaluation algorithm;
and the arterial blood pressure overall signal quality index determining subunit is used for determining the value relationship between the arterial blood pressure overall signal quality index and the first blood pressure signal quality index according to the value of the second blood pressure signal quality index.
With reference to the second aspect, in a fourth possible implementation manner of the second aspect, the heart rate fusion estimation unit includes:
performing Kalman filtering on a heart rate obtained based on an Electrocardiogram (ECG) signal to obtain a heart rate recorded as HR1, and simultaneously obtaining a filtered residual signal recorded as r 1;
performing Kalman filtering on a heart rate obtained based on an arterial blood pressure ABP signal to obtain a heart rate recorded as HR2, and simultaneously obtaining a filtered residual signal recorded as r 2;
according to the formula
Performing fusion calculation to obtain a fusion heart rate estimate, wherein: sigma1 2＝(r1/SQI1)2，σ2 2＝(r2/SQI2)2，SQI1Is the first quality index of the electrocardiosignal, SQI2The second cardiac signal quality index, HR is the fusion heart rate estimate.
A third aspect of the embodiments of the present application provides an apparatus for reducing an intra-ICU false alarm, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the method for reducing an intra-ICU false alarm according to any one of the first aspect.
A fourth aspect of embodiments of the present application provides a computer-readable storage medium, which stores a computer program, wherein the computer program, when executed by a processor, implements the steps of the method for reducing false alarms in an ICU according to any of the first aspects.
Compared with the prior art, the embodiment of the application has the advantages that: this application carries out the noise filtration back through electrocardiosignal and arterial blood pressure signal, acquires electrocardiosignal and arterial blood pressure signal's whole quality index, then according to whole quality index fuses the calculation and obtains fusing rhythm of the heart estimation, when electrocardiosignal or arterial blood pressure signal include serious interference, can carry out effectual suppression to serious interference through fusing rhythm of the heart estimation to effectual improvement reduces the false alarm rate.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
Fig. 1 is a schematic flow chart illustrating an implementation of a method for reducing false alarm in an ICU according to an embodiment of the present application;
FIG. 2 is a schematic diagram of an 8-layer exploded view of wavelet transform of an electrocardiosignal provided by an embodiment of the present application;
fig. 3 is a diagram of detecting an R peak of a QRS waveform of an electrocardiograph signal according to an embodiment of the present application;
FIG. 4 is a diagram illustrating the detection of the starting point of an arterial blood pressure signal according to an embodiment of the present application;
FIG. 5 is a schematic diagram of an apparatus for reducing false alarms within an ICU according to an embodiment of the present application;
fig. 6 is a schematic diagram of an apparatus for reducing false alarms in an ICU according to an embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
In order to explain the technical solution described in the present application, the following description will be given by way of specific examples.
As shown in fig. 1, an implementation flow diagram of a method for reducing false alarms in an ICU is provided in the embodiments of the present application, which is detailed as follows:
in step S101, noise interference in the cardiac electrical signal and the arterial blood pressure signal is filtered;
the filtering of the original electrocardiographic signal and the arterial blood pressure signal may specifically include, as shown in fig. 2:
1011, performing 8-layer decomposition on the original electrocardiosignal and the arterial blood pressure signal by using a wavelet, as shown in fig. 2, performing decomposition by using a coif4 wavelet to obtain an 8-layer signal image;
as can be seen from fig. 2, the average component of each layer in the 8-layer decomposition, and the signal form of the 8 th layer is substantially consistent with the baseline of the original signal, which indicates that the energy of the layer is mainly from the low-frequency baseline drift noise. The energy intensity of the detail components of the 1 st to 3 rd layers is basically consistent with the high-frequency interference in the original signal, which shows that the high-frequency noise in a large amount of electrocardiosignals, such as power frequency interference, myoelectric noise and the like, is concentrated in the 1 st to 3 rd layers.
And 1012, filtering out the 8 th layer average component and the 1 st, 2 nd and 3 rd layer detail components in the decomposed signal, setting the average components to be 0, and finally reconstructing waveforms of the remaining 1-7 layers of average components and 4-8 th layer detail components, thereby finishing filtering.
In step S102, respectively determining the overall quality indexes of the electrocardiosignals and the arterial blood pressure signals according to the filtered electrocardiosignals and the filtered arterial blood pressure signals;
the step comprises the steps of determining the integral quality index of the electrocardiosignals according to the filtered electrocardiosignals and determining the integral quality index of the arterial blood pressure signals according to the filtered arterial blood pressure signals, wherein the following steps are respectively discussed:
the electrocardiosignal quality index calculation steps are as follows:
(1) in order to evaluate and calculate the quality index of the electrocardiosignal, the R peak of the QRS waveform can be detected first, a first algorithm and a second algorithm can be used in the invention, for example, two R peak detection algorithms, namely a K mean algorithm and a thompkins Tompkins difference algorithm, can be used for performing R peak detection on the same filtered lead electrocardiosignal, and fig. 3 shows an R peak detection schematic diagram for detecting the QRS waveform of the electrocardiosignal through the algorithm;
(2) using two algorithms to divide the number of the R peaks with less number in the detection result by the number of the R peaks with more number in the detection result to obtain the first electrocardiosignal quality index SQI1；
(3) Calculating the number N of matched R peaks of two R peak detection algorithmsmatchedDividing the total number of the R peaks detected by the two R peak detection algorithms to obtain a second electrocardiosignal quality index SQI2；
(4) The quality index of the whole electrocardiosignal and the local electrocardiosignal with the alarm mark interval is calculated as SQI1And SQI2Summing to obtain the total quality index SQI (Global, Local) of the whole electrocardiosignal and the Local electrocardiosignal with an alarm interval;
SQI(Global,Local)＝SQI1+SQI2
(5) and finally, the quality indexes of the overall electrocardiosignals and the local electrocardiosignals with the alarm mark interval are proportionally distributed and combined to obtain the final quality index of the electrocardiosignals.
ECGSQI＝SQI(Global)·η2+SQI(Local)·(1-η2)
Wherein eta is1,η2Is an empirical coefficient adjusted by experiment, where we can take eta1＝0.5,η2＝0.4。
For the quality evaluation of the arterial blood pressure signal, we can combine two analysis algorithms, such as a beat-to-beat state blood pressure ABP signal quality evaluation algorithm based on fuzzy logic, and another arterial blood pressure ABP signal quality evaluation algorithm based on heuristic constraint, so the calculation steps of the arterial blood pressure signal quality index are as follows:
(1) obtaining a first blood pressure signal quality index wSQI based on a beat-to-beat arterial blood pressure ABP signal quality evaluation method of fuzzy logic, wherein wSQI is a continuous value between 0 and 1, the value is close to or equal to 0 to represent that the signal quality is poor, the value is close to or equal to 1 to represent that the signal quality is good, and wSQI is 0.5 to be used as a boundary for distinguishing the signal quality from the bad;
(2) the signal quality evaluation algorithm based on heuristic constraint comprises ABP beat starting point detection, waveform characteristic detection and a heuristic algorithm based on waveform characteristics, so that the ABP signal quality jSQI of the second arterial blood pressure is obtained, and as shown in FIG. 4, a schematic diagram of the starting point detection of the arterial blood pressure signal is shown;
(3) based on the above two signal quality indices, an evaluation arterial blood pressure ABP overall signal quality index (ABPSQI) is generated as follows.
Wherein 1 ≧ η ≧ 0 is an empirical coefficient adjusted by experiment, and we can take 0.7 here. When jSQI determines that the current beat is a normal waveform, we trust the value of wSQI; when the jSQI is judged to be an abnormal waveform, the confidence level of the wSQI value is reduced by multiplying the coefficient eta, and therefore the integral quality index of the arterial blood pressure with better confidence level is obtained.
In step S103, performing data fusion calculation according to the overall signal quality index to obtain a fusion heart rate estimate;
the heart rate information can be obtained by pulse detection of a cardiac signal waveform, an arterial blood pressure waveform, a pulse blood oxygen saturation signal waveform, and the like. When the physiological signal sources are applied to heart rate estimation, data among channels are redundant and approximately independent, meanwhile, interference among the channels does not have correlation, and accurate estimation of the heart rate can be obtained through a fusion method. The steps of estimating the electrocardiosignal and the arterial blood pressure signal through Kalman filtering and fusing data are as follows:
(1) performing Kalman filtering on the heart rate obtained based on electrocardiogram ECG signals, and recording the obtained heart rate as HR1And simultaneously obtaining a filtered residual signal which is recorded as r1；
(2) Performing Kalman filtering on the heart rate obtained based on the arterial blood pressure ABP signal, and recording the obtained heart rate as HR2And simultaneously obtaining a filtered residual signal which is recorded as r2；
(3) Calculating data fusion based on Kalman filtering residual errors, performing heart rate data fusion by using the Kalman filtering residual errors and signal quality indexes as weight coefficients, calculating fusion heart rate HR, and performing data fusion on the electrocardiosignals and the arterial blood pressure signals according to the following formula:
wherein σ1 2＝(r1/SQI1)2，σ2 2＝(r2/SQI2)2，SQI1Is the first quality index of the electrocardiosignal, SQI2The second cardiac signal quality index, HR is the fusion heart rate estimate.
HR results when one of the channel signals (e.g., ECG) is severely disturbed1Estimation errors when Signal Quality Index (SQI) due to the presence of interference1) Low, sudden heart rate changes produce a large residual r1Thereby making σ1 2Becomes significantly larger. If the ABP signal is stationary at this time, then σ2 2Smaller, HR1Weight of (2)
Becomes significantly smaller, HR2Weight of (2)
Larger, fusion heart rate HR will depend more on HR2Rather than HR1Thereby significantly reducing the impact of interference on the fused heart rate estimate.
In step S104, the fusion heart rate estimation is compared with a judgment threshold, and whether to alarm is determined according to the comparison result.
Because the influence of the interference on the fusion heart rate estimation is reduced, when the judgment and comparison are carried out, the preset judgment threshold value is compared with the fusion heart rate estimation, whether the alarm is needed or not can be more accurately determined, namely, the alarm is sent out when the alarm exceeds the preset judgment threshold value range, and the alarm is not sent out when the alarm does not exceed the judgment threshold value. The method can accurately judge 5 serious arrhythmia alarm signals generated by the monitor in real time, and the five arrhythmia diseases comprise asystole, serious bradycardia, serious tachycardia, ventricular tachycardia and ventricular fibrillation. We finally use RR intervals, heart rate and signal quality index SQI to discriminate the truth of an alarm.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.
Fig. 5 is a schematic structural diagram of an apparatus for reducing false alarm in an ICU according to an embodiment of the present application, which is detailed as follows:
the device for reducing the false alarm in the ICU comprises:
a filtering unit 501 for filtering noise interference in the cardiac electrical signal and the arterial blood pressure signal;
the overall quality index determining unit 502 is configured to determine overall quality indexes of the electrocardiographic signal and the arterial blood pressure signal according to the filtered electrocardiographic signal and the filtered arterial blood pressure signal;
a heart rate fusion estimation unit 503, configured to perform data fusion calculation according to the overall signal quality index to obtain a fusion heart rate estimation;
and the comparing unit 504 is configured to compare the fusion heart rate estimation with a judgment threshold value, and determine whether to alarm according to a comparison result.
Preferably, the overall quality index determining unit includes:
the decomposition subunit is used for performing 8-layer decomposition on the original electrocardiosignals and the arterial blood pressure signals by utilizing wavelets;
and the reconstruction subunit is used for filtering out the 8 th layer average component and the 1 st, 2 nd and 3 rd layer detail components in the decomposed signal, setting the average components to 0, reconstructing waveforms of the remaining 1-7 layers of average components and 4-8 th layer detail components, and finishing filtering.
Preferably, the overall quality index determining unit includes:
the R peak quantity detection subunit is used for detecting the R peak quantity of the same filtered electrocardiosignal through a first algorithm and a second algorithm;
the first generation subunit is used for generating a first electrocardiosignal quality index according to the number of the R peaks with less number and the number of the R peaks with more number, which are detected in the two algorithms;
the second generation subunit is used for generating a second electrocardiosignal quality index according to the number of the R peaks matched by the two algorithms and the total number of the R peaks detected by the two algorithms;
and the electrocardiosignal overall quality index determining subunit is used for determining the electrocardiosignal overall quality index according to a preset coefficient and by combining the first electrocardiosignal quality index and the second electrocardiosignal quality index.
The apparatus for reducing the false alarm in the ICU shown in fig. 5 corresponds to the method for reducing the false alarm in the ICU shown in fig. 1.
Fig. 6 is a schematic diagram of an apparatus for reducing false alarms in an ICU according to an embodiment of the present application. As shown in fig. 6, the apparatus 6 for reducing the false alarm in the ICU of this embodiment includes: a processor 60, a memory 61 and a computer program 62 stored in said memory 61 and executable on said processor 60, such as a program for reducing false alarms within an ICU. The processor 60, when executing the computer program 62, implements the steps in the various method embodiments described above for reducing false alarms within an ICU, such as the steps 101-104 shown in fig. 1. Alternatively, the processor 60, when executing the computer program 62, implements the functions of each module/unit in the above-mentioned device embodiments, for example, the functions of the modules 501 to 503 shown in fig. 5.
Illustratively, the computer program 62 may be partitioned into one or more modules/units that are stored in the memory 61 and executed by the processor 60 to accomplish the present application. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions for describing the execution of the computer program 62 in the apparatus for reducing false alarms within an ICU 6. For example, the computer program 62 may be divided into a filtering unit, an overall quality index determination unit, a heart rate fusion estimation unit, and a comparison unit, each unit functioning specifically as follows:
the filtering unit is used for filtering noise interference in the electrocardiosignal and the arterial blood pressure signal;
the integral quality index determining unit is used for respectively determining the integral quality indexes of the electrocardiosignals and the arterial blood pressure signals according to the filtered electrocardiosignals and the filtered arterial blood pressure signals;
the heart rate fusion estimation unit is used for carrying out data fusion calculation according to the overall signal quality index to obtain fusion heart rate estimation;
and the comparison unit is used for comparing the fusion heart rate estimation with a judgment threshold value and determining whether to alarm or not according to a comparison result.
The means for reducing false alarms within the ICU may include, but is not limited to, a processor 60, a memory 61. It will be appreciated by those skilled in the art that fig. 6 is merely an example of the apparatus 6 for reducing intra-ICU false alarms and does not constitute a limitation of the apparatus 6 for reducing intra-ICU false alarms and may include more or fewer components than shown or some components may be combined or different components, e.g. the apparatus for reducing intra-ICU false alarms may also include input output devices, network access devices, buses, etc.
The Processor 60 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 61 may be an internal memory unit of the device 6 for reducing false alarms in the ICU, such as a hard disk or a memory of the device 6 for reducing false alarms in the ICU. The memory 61 may also be an external storage device of the device 6 for reducing false alarm in the ICU, for example, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are equipped on the device 6 for reducing false alarm in the ICU. Further, the memory 61 may also comprise both an internal memory unit and an external memory device of the device 6 for reducing false alarms within an ICU. The memory 61 is used to store the computer program and other programs and data required by the apparatus for reducing false alarms within an ICU. The memory 61 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other ways. For example, the above-described embodiments of the apparatus/terminal device are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow in the method of the embodiments described above can be realized by a computer program, which can be stored in a computer-readable storage medium and can realize the steps of the embodiments of the methods described above when the computer program is executed by a processor. . Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain other components which may be suitably increased or decreased as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media which may not include electrical carrier signals and telecommunications signals in accordance with legislation and patent practice.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.
Claims (6)
1. A method for reducing false alarms in an ICU, the method comprising:
filtering noise interference in the electrocardiosignal and the arterial blood pressure signal;
respectively determining the integral quality indexes of the electrocardiosignals and the arterial blood pressure signals according to the filtered electrocardiosignals and the filtered arterial blood pressure signals;
performing data fusion calculation according to the overall quality index to obtain fusion heart rate estimation;
comparing the fusion heart rate estimation with a judgment threshold value, and determining whether to alarm or not according to a comparison result;
the step of determining the integral quality index of the electrocardiosignals according to the filtered electrocardiosignals comprises the following steps:
detecting the number of R peaks of the electrocardiosignals after the same filtration through a first algorithm and a second algorithm;
generating a first electrocardiosignal quality index according to the number of the R peaks with small quantity and the number of the R peaks with large quantity detected in the two algorithms; the first electrocardiosignal quality index calculation mode is as follows:
SQI1is the first electrocardiosignal quality index, min (N)1,N2) Indicates the number of R peaks, max (N), corresponding to the detection result with the smaller number of R peaks in the two detection results1,N2) Indicates the number of R peaks, eta corresponding to the detection result with a large number of R peaks in the two detection results1Is an empirical coefficient;
generating a second electrocardiosignal quality index according to the number of the R peaks matched by the two algorithms and the total number of the R peaks detected by the two algorithms; the second electrocardiosignal quality index calculation mode is as follows:
SQI2is the second quality index of the electrocardiosignal, NmatchedThe number of R peaks matched for the two R peak detection algorithms;
according to a preset coefficient, combining the first electrocardiosignal quality index, the second electrocardiosignal quality index and the total electrocardiosignal quality index of the local electrocardiosignals with the alarm mark interval according to proportion distribution and combination to determine the integral quality index of the electrocardiosignals; the overall quality index calculation mode of the electrocardiosignals is as follows: computing SQI1And SQI2Summing to obtain the total quality index SQI (Global, Local) of the whole electrocardiosignal and the Local electrocardiosignal with alarm interval1+SQI2The SQI (Global, Local) is proportionally distributed and combined to obtain the final quality index ECGSQI ═ SQI (Global) · η ═2+SQI(Local)·(1-η2) ECGSQI is the overall quality index, eta, of the electrocardiosignal2The SQI (Global, Local) is the quality index of the whole electrocardiosignal and the overall electrocardiosignal of the Local electrocardiosignal with an alarm interval;
the step of filtering noise interference in the electrocardiosignal and the arterial blood pressure signal comprises:
performing 8-layer decomposition on the original electrocardiosignal and the arterial blood pressure signal by using a coif4 wavelet;
and filtering the 8 th layer average component and the 1 st, 2 nd and 3 rd layer detail components in the decomposed signal, setting the average components to 0, reconstructing waveforms of the remaining 1-7 layers of average components and 4-8 th layer detail components, and finishing filtering.
2. A method for reducing false alarms within an ICU as set forth in claim 1 wherein the step of determining an overall quality index of the arterial blood pressure signal from the filtered arterial blood pressure signal comprises:
obtaining a first blood pressure signal quality index by a beat-to-beat ABP signal quality evaluation method based on fuzzy logic;
obtaining a second blood pressure signal quality index based on a heuristic constrained signal quality evaluation algorithm;
and determining the value relationship between the whole arterial blood pressure quality index and the first blood pressure signal quality index according to the value of the second blood pressure signal quality index.
3. A method for reducing false alarms within an ICU as set forth in claim 1, wherein said step of performing a data fusion calculation based on said overall quality index to obtain a fused heart rate estimate comprises:
performing Kalman filtering on a heart rate obtained based on an Electrocardiogram (ECG) signal to obtain a heart rate recorded as HR1, and simultaneously obtaining a filtered residual signal recorded as r 1;
performing Kalman filtering on a heart rate obtained based on an arterial blood pressure ABP signal to obtain a heart rate recorded as HR2, and simultaneously obtaining a filtered residual signal recorded as r 2;
according to the formula
Performing fusion calculation to obtain a fusion heart rate estimate, wherein: sigma1 2＝(r1/SQI1)2，σ2 2＝(r2/SQI2)2，SQI1Is the first quality index of the electrocardiosignal, SQI2The second cardiac signal quality index, HR is the fusion heart rate estimate.
4. An apparatus for reducing false alarms within an ICU, the apparatus comprising:
the filtering unit is used for filtering noise interference in the electrocardiosignal and the arterial blood pressure signal;
the integral quality index determining unit is used for respectively determining the integral quality indexes of the electrocardiosignals and the arterial blood pressure signals according to the filtered electrocardiosignals and the filtered arterial blood pressure signals;
the heart rate fusion estimation unit is used for carrying out data fusion calculation according to the overall quality index to obtain fusion heart rate estimation;
the comparison unit is used for comparing the fusion heart rate estimation with a judgment threshold value and determining whether to alarm or not according to a comparison result;
the overall quality index determining unit includes:
the R peak quantity detection subunit is used for detecting the quantity of the R peaks of the electrocardiosignals after the same filtering through a first algorithm and a second algorithm;
the first generation subunit is used for generating a first electrocardiosignal quality index according to the number of the R peaks with less number and the number of the R peaks with more number, which are detected in the two algorithms; the first electrocardiosignal quality index calculation mode is as follows:
SQI1is the first electrocardiosignal quality index, min (N)1,N2) Indicates the number of R peaks, max (N), corresponding to the detection result with the smaller number of R peaks in the two detection results1,N2) Indicates the number of R peaks, eta corresponding to the detection result with a large number of R peaks in the two detection results1Is an empirical coefficient;
the second generation subunit is used for generating a second electrocardiosignal quality index according to the number of the R peaks matched by the two algorithms and the total number of the R peaks detected by the two algorithms; the second electrocardiosignal quality index calculation mode is as follows:
SQI2is the second quality index of the electrocardiosignal, NmatchedThe number of R peaks matched for the two R peak detection algorithms;
the electrocardiosignal overall quality index determining subunit is used for determining the electrocardiosignal overall quality index according to a preset coefficient by combining the first electrocardiosignal quality index, the second electrocardiosignal quality index and the overall electrocardiosignal quality index of the local electrocardiosignals with the alarm mark interval according to proportion distribution; the overall quality index calculation mode of the electrocardiosignals is as follows: computing SQI1And SQI2Summing to obtain the whole electrocardiosignal and the signalGlobal cardiac signal quality index SQI (Global, Local) of Local cardiac signal in alarm interval1+SQI2The SQI (Global, Local) is proportionally distributed and combined to obtain the final quality index ECGSQI ═ SQI (Global) · η ═2+SQI(Local)·(1-η2) ECGSQI is the overall quality index, eta, of the electrocardiosignal2The SQI (Global, Local) is the quality index of the whole electrocardiosignal and the overall electrocardiosignal of the Local electrocardiosignal with an alarm interval;
the overall quality index determining unit includes:
the decomposition subunit is used for performing 8-layer decomposition on the original electrocardiosignal and the arterial blood pressure signal by using a coif4 wavelet;
and the reconstruction subunit is used for filtering out the 8 th layer average component and the 1 st, 2 nd and 3 rd layer detail components in the decomposed signal, setting the average components to 0, reconstructing waveforms of the remaining 1-7 layers of average components and 4-8 th layer detail components, and finishing filtering.
5. An apparatus for reducing intra-ICU false alarms, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor when executing the computer program performs the steps of the method for reducing intra-ICU false alarms according to any of claims 1 to 3.
6. A computer-readable storage medium, having a computer program stored thereon, wherein the computer program, when being executed by a processor, is adapted to carry out the steps of the method of reducing false alarms in an ICU according to any one of the claims 1 to 3.
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