CN113558632B - Cardio-cerebral electrical signal-based post cardiopulmonary resuscitation nerve function ending evaluation system - Google Patents
Cardio-cerebral electrical signal-based post cardiopulmonary resuscitation nerve function ending evaluation system Download PDFInfo
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Abstract
The application provides a cardio-cerebral electrical signal-based post-cardiopulmonary resuscitation nerve function ending evaluation system, which comprises: the information acquisition module is used for acquiring electrocardio and electroencephalogram signals; the signal processing module is used for performing signal processing on the acquired electrocardio-brain electrical signals; the characteristic calculation module is used for calculating heart rate variability characteristics and electroencephalogram characteristics according to the electrocardiosignals and the electroencephalogram signals after signal processing; the probability prediction module is used for calculating an electrocardiogram and electroencephalogram state index according to heart rate variability characteristics and electroencephalogram characteristics; the method is also used for calculating the probability of good nerve function according to the electrocardiogram and electroencephalogram state indexes; and the output result module is used for outputting and displaying the probability. The application can quantitatively evaluate the probability of recovering good nerve function prognosis after cardiopulmonary resuscitation of patients suffering from cardiac arrest, and can provide evaluation basis for clinicians to select reasonable treatment schemes.
Description
Technical Field
The application relates to the technical field of cardiopulmonary resuscitation, in particular to a post-cardiopulmonary resuscitation nerve function ending evaluation system based on an electrocardio-brain signal.
Background
Sudden cardiac arrest refers to sudden stop of the heart in unexpected situations and times caused by various reasons, thereby causing sudden suspension of the effective cardiac pump function and the effective circulation, causing serious ischemia, hypoxia and metabolic disturbance of the whole body tissue cells, and immediately losing life if not timely rescuing. After sudden cardiac arrest, the patient may be saved if correct and effective cardiopulmonary resuscitation is timely performed. Cardiopulmonary resuscitation, CPR for short, is a life-saving technique for sudden cardiac and respiratory arrest that helps patients recover spontaneous breathing and spontaneous circulation.
The goal of post cardiopulmonary resuscitation treatment is to restore the patient's physical condition to normal levels. However, many patients with sudden cardiac arrest are unable to fully recover brain function even if spontaneous circulation is restored, and about 80% of patients who have successful cardiopulmonary resuscitation have had coma for more than 1 hour. During hospitalization after a patient's coma, some patients may have their neurological function returned to good, and others may face death, permanent brain injury, bedridden or be in a persistent botanic state, which consumes significant medical resources. Thus, early prediction of neurological outcome is of great importance for diagnosis and treatment of patients after resuscitation. Poor prognosis of neurological outcome can be significantly improved by timely intervention, such as target temperature management treatment after resuscitation, inhalation of hydrogen. The system for objectively evaluating the prognosis of the nerve function after cardiopulmonary resuscitation can provide patient disease information for clinicians, and has important significance for rescuing the life of a patient and guiding the treatment of the patient.
Therefore, there is a need for a system that can assess the neurological outcome of cardiopulmonary resuscitation patients, providing an evaluation basis for the clinician to select a reasonable treatment regimen.
Disclosure of Invention
Aiming at the defects in the prior art, the application provides a cardiopulmonary resuscitation post-nerve function ending evaluation system based on electrocardio-brain signals, which aims at solving the technical problem that no system capable of evaluating the nerve function ending of a cardiopulmonary resuscitation patient exists in the prior art and a reasonable treatment scheme cannot be selected for a clinician to provide an evaluation basis.
The application adopts the technical scheme that the cardiac and cerebral electrical signal-based system for assessing the ending of nerve function after cardiopulmonary resuscitation comprises the following components:
the information acquisition module is used for acquiring electrocardiosignals and electroencephalogram signals;
the signal processing module is used for carrying out signal processing on the collected electrocardiosignals and the electroencephalogram signals, wherein the signal processing comprises filtering denoising, R wave identification, RRI extraction and spectrum analysis;
the characteristic calculation module is used for calculating heart rate variability characteristics according to the electrocardiosignals after signal processing and also used for calculating electroencephalogram characteristics according to the electroencephalogram signals after signal processing;
the probability prediction module is used for calculating an electrocardiogram state index according to heart rate variability characteristics and calculating an electroencephalogram state index according to electroencephalogram characteristics; the method is also used for calculating the probability of good nerve function according to the electrocardiogram state index and the electroencephalogram state index;
and the output result module is used for outputting and displaying the probability.
Further, in the process of acquiring electrocardiosignals, synchronously intercepting electrocardiosignals with the length of 5 minutes from the II leads in real time, and resampling to 250Hz to obtain a signal x; in the process of acquiring the electroencephalogram signals, the electroencephalogram data with the duration of 5 minutes is synchronously intercepted for the C3-P3 lead, and resampled to 250Hz to obtain a signal y.
Further, the heart rate variability features include root mean square, very low frequency energy, high frequency energy of adjacent RR interval differences.
Further, an electrocardiogram state index H 1 (t) is calculated as follows:
in the above formula, RMSSD represents root mean square of the interval difference between adjacent RRs, LVF represents very low frequency energy, VF represents low frequency energy, HF represents high frequency energy, a 1 、a 2 、a 3 、a 4 、a 5 Coefficients representing the characteristics of the individual heart rate variability,a trend function representing the variation of heart rate variability characteristics with time, e being a natural constant, b 1 And t is the time after cardiopulmonary resuscitation, and is a parameter in the trend function.
Further, the electroencephalogram features include burst suppression ratio, sample entropy, weighted ordering entropy, and energy E of delta, theta, alpha, beta, gamma bands δ 、E θ 、E α 、E β 、E γ 。
Further, electroencephalogram state index H 2 (t) is calculated as follows:
in the above formula, BSR represents burst suppression ratio, SE represents sample entropy, WPE represents weighted ordering entropy, E δ 、E θ 、E α 、E β 、E γ Represents energy in delta, theta, alpha, beta, gamma frequency bands, a 6 、a 7 、a 8 、a 9 、a 10 、a 11 、a 12 、a 13 、a 14 Is a coefficient of the characteristics of the electroencephalogram,e is a natural constant, b is a trend function of the electroencephalogram characteristics changing with time 2 And t is the time after cardiopulmonary resuscitation, and is a parameter in the trend function.
Further, the value of t is in the range of 0 to 72 hours.
Further, the probability of good neurological function P (t) is calculated as follows:
in the above formula, e is a natural constant, H 1 (t) is an electrocardiogram state index, H 2 (t) is an electroencephalogram state index，c 0 、c 1 、c 2 Is a coefficient of a logistic regression equation.
According to the technical scheme, the beneficial technical effects of the application are as follows:
the evaluation system can quantitatively evaluate the probability of recovering good nerve function prognosis after cardiopulmonary resuscitation of patients suffering from cardiac arrest, can provide evaluation basis for clinicians to select reasonable treatment schemes, and has important significance for rescuing lives of patients and guiding treatment of the patients; realizing the effective utilization of medical resources.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. Like elements or portions are generally identified by like reference numerals throughout the several figures. In the drawings, elements or portions thereof are not necessarily drawn to scale.
FIG. 1 is a diagram of an evaluation system architecture according to an embodiment of the present application;
FIG. 2 is a schematic diagram of an evaluation system workflow according to an embodiment of the application.
Detailed Description
Embodiments of the technical scheme of the present application will be described in detail below with reference to the accompanying drawings. The following examples are only for more clearly illustrating the technical aspects of the present application, and thus are merely examples, and are not intended to limit the scope of the present application.
It is noted that unless otherwise indicated, technical or scientific terms used herein should be given the ordinary meaning as understood by one of ordinary skill in the art to which this application belongs.
Example 1
The embodiment provides a cardiac and cerebral electrical signal based post-cardiopulmonary resuscitation nerve function outcome evaluation system (hereinafter referred to as evaluation system), as shown in fig. 1, including:
the information acquisition module is used for acquiring electrocardiosignals and electroencephalogram signals;
the signal processing module is used for performing signal processing on the acquired electrocardiosignals and electroencephalogram signals, and comprises filtering denoising, R wave identification, RRI extraction and spectrum analysis;
the characteristic calculation module is used for calculating heart rate variability characteristics according to the electrocardiosignals after signal processing and also used for calculating electroencephalogram characteristics according to the electroencephalogram signals after signal processing;
the probability prediction module is used for calculating the probability of the good nerve function;
and the output result module is used for outputting and displaying the probability.
The evaluation system firstly extracts RR interval sequences from the electrocardiosignals at the t moment to be used for calculating 4 heart rate variability characteristics, meanwhile, 8 characteristics are extracted from the electrocardiosignals at the t moment, then 2 parameters are calculated by using the heart rate variability characteristics and the electroencephalogram characteristics, and finally, the probability of the patient to recover good nerve functions is evaluated by using a logistic regression equation.
The following describes the working principle of the evaluation system in detail, as shown in fig. 2, specifically as follows:
the information acquisition module acquires electrocardiosignals and electroencephalogram signals through the sensor. Preferably, the electrocardiograph with 12 leads is used for acquiring electrocardiosignals, and the electroencephalogram is used for acquiring the electroencephalogram. In the process of acquiring electrocardiosignals, synchronously intercepting electrocardiosignals with the length of 5 minutes from the II leads in real time, and resampling to 250Hz to obtain a signal x. In the process of acquiring the electroencephalogram signals, the electroencephalogram data with the duration of 5 minutes is synchronously intercepted for the C3-P3 lead, and resampled to 250Hz to obtain a signal y.
When the signal processing module processes the collected electrocardiosignals and the collected electroencephalogram signals, the electrocardiosignals and the electroencephalogram signals are filtered and denoised by a 50Hz wave trap and a high-pass filter with the cutoff frequency of 0.05 Hz. When R wave identification is carried out, extracting the highest point of the QRS wave group in x through an R wave identification algorithm to obtain the R wave position; the R-wave recognition algorithm is preferably a front-to-back amplitude difference method. Then, RR interval signals (RRI) are calculated, and the R wave position is subtracted from the R wave position to obtain RRI, namely the signal R (n).
The characteristic calculation module calculates heart rate variability characteristics according to the electrocardiosignals after signal processing. In this embodiment, there are 4 heart rate variability features, including: root mean square (RMSSD) of adjacent RR interval differences, very low frequency energy (VLF), low frequency energy (LF), high frequency energy (HF). Where RMSSD is a time domain feature and VLF, LF, HF is a frequency domain feature.
RMSSD is calculated as follows:
r 1 (n)＝r(n+1)-r(n)
the very low frequency energy (VLF), low frequency energy (LF), high frequency energy (HF) are calculated as follows: firstly, performing cubic spline interpolation on r (n), then performing power spectrum analysis on the interpolated signal by using a Welch method, wherein VLF is the sum of all power spectrum densities within a range of 0.0033-0.04 Hz, LF is the sum of all power spectrum densities within a range of 0.04-0.15 Hz, and HF is the sum of all power spectrum densities within a range of 0.15-0.40 Hz, and the formulas are as follows:
in the above formula, R 1 (f) Is a function of the power spectral density of r (n).
The characteristic calculation module also calculates the electroencephalogram characteristic according to the electroencephalogram signal after signal processing. In the present embodiment, the electroencephalogram features include Burst Suppression Ratio (BSR), sample Entropy (SE), weighted ordering entropy (WPE), and delta, theta, alpha,Energy E of beta, gamma band δ 、E θ 、E α 、E β 、E γ The method comprises the steps of carrying out a first treatment on the surface of the Wherein BSR is a time domain feature, E δ 、E θ 、E α 、E β 、E γ For the frequency domain features, SE and WPE are nonlinear features.
BSR calculation mode: and judging whether an electroencephalogram signal with the voltage of more than 10 mu V appears or not by taking 0.5 seconds as a window, if so, judging that the electroencephalogram signal in the window is an explosion wave, otherwise, judging that the electroencephalogram signal is a suppression wave. Then, whether the subsequent electroencephalogram signal is a burst wave or not is judged, and BSR is the ratio of the burst wave.
The calculation method of SE: the increase of the new pattern is estimated when the embedding dimension is increased from m to m +1 dimension with a certain margin epsilon. The calculation flow is as follows: the electroencephalogram signals Y (i), i=1, 2 … N, N are time sequence lengths, and are subjected to reconstruction of phase space, the number of vectors in the phase space is N-m+1, and the vectors in the phase space are Y m (i)：Y m (i) = { y (i), y (i+1), …, y (i+m-1) }, i=1, 2, …, N-m+1. Calculating vector Y in phase space m (i) And Y m (j) Is defined as:
then calculate
Where θ (·) is the Heaviside function. Defining an intermediate parameter B m (ε)：
Next, the embedding dimension is increased to m+1, and the above steps are repeated to calculate B m:1 (epsilon). Finally, the method comprises the following steps:
WPE calculation mode: reconstructing phase space of brain telecommunication Y (i), i=1, 2 … N to obtain Y m (j) = { y (j), y (j+τ), …, y (j+ (m-1) τ) }, m is the embedding dimension, τ is the delay time. Each Y m (i) Arranging in order from low to high to obtain an arrangement ordinal patternSuch as [0.1,0.07,0.13,0.09 ]]Can obtain the arrangement ordinal number mode of [3,1,4,2 ]]. Each Y m (j) Common m-! One possible permutation ordinal pattern. The weighting factors are defined as follows:
wherein the method comprises the steps ofIs the arithmetic mean:
then rank order modeIn the whole vector Y m (i) The weight ratio of the occurrence of (a) is
WPE can then be derived
Carrying out power spectrum analysis on the electroencephalogram signal y by using a Welch method to obtain energy of delta, theta, alpha, beta and gamma frequency bands:
wherein R is y Is the power spectral density of y.
The probability prediction module will RMSSD, LVF, VF, HF, BSR, SE (0.1,2), WPE (6, 6), E δ 、E θ 、E α 、E β 、E γ Carrying out the following formulas to respectively calculate the electrocardiogram state index H at the current t moment 1 (t) and electroencephalogram State index H 2 (t) the calculation formula is as follows:
in the above formula, a 1 、a 2 、a 3 、a 4 、a 5 For the coefficients of the individual heart rate variability characteristics,is the trend function of heart rate variability characteristics with time, t is the time after cardiopulmonary resuscitation, a 6 、a 7 、a 8 、a 9 、a 10 、a 11 、a 12 、a 13 、a 14 The coefficients of the electroencephalogram feature, +.>E is a natural constant, b is a trend function of the electroencephalogram characteristics with time 1 、b 2 Is a parameter in the trend function. In a specific embodiment, a 1 ～a 14 The value range of (2) is (0, 10), b 1 、b 2 The value range of t is 0 to 72 hours, and the value range of t is (0, 0.5).
The probability prediction module is further based on the electrocardiogram state index H 1 (t) and electroencephalogram State index H 2 (t) calculating probability P (t) of good nerve function by adopting a logistic regression method; the calculation formula is as follows:
in the above formula, c 0 、c 1 、c 2 As coefficients of logistic regression equations, in a particular embodiment c 0 、c 1 、c 2 The value range of (2) is (0, 100).
And finally, outputting and displaying the calculated probability P of the good nerve function through an output result module.
By using the evaluation system provided by the embodiment, the probability of recovering a good nerve function prognosis after cardiopulmonary resuscitation of a patient suffering from cardiac arrest can be quantitatively evaluated, an evaluation basis can be provided for a clinician to select a reasonable treatment scheme, and the evaluation system has important significance for rescuing the life of the patient and guiding the treatment of the patient; realizing the effective utilization of medical resources.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and are not limiting; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the application, and are intended to be included within the scope of the appended claims and description.
Claims (3)
1. A cardiopulmonary resuscitation post-neurological outcome assessment system based on an electroencephalogram signal, comprising:
the information acquisition module is used for acquiring electrocardiosignals and electroencephalogram signals;
the signal processing module is used for performing signal processing on the acquired electrocardiosignals and the acquired electroencephalogram signals, wherein the signal processing comprises filtering denoising, R wave identification, RRI extraction and spectrum analysis;
the characteristic calculation module is used for calculating heart rate variability characteristics according to the electrocardiosignals after signal processing, wherein the heart rate variability characteristics comprise root mean square, extremely low frequency energy, low frequency energy and high frequency energy of adjacent RR interval differences; and is also used for calculating the electroencephalogram characteristics according to the electroencephalogram signals after signal processing, wherein the electroencephalogram characteristics comprise explosion suppression ratio, sample entropy, weighted ordering entropy and energy E of delta, theta, alpha, beta and gamma frequency bands δ 、E θ 、E α 、E β 、E γ ；
A probability prediction module for calculating an electrocardiogram state index according to heart rate variability characteristics, wherein the electrocardiogram state index H 1 (t) is calculated as follows:
in the above formula, RMSSD represents root mean square of the interval difference between adjacent RRs, LVF represents very low frequency energy, VF represents low frequency energy, HF represents high frequency energy, a 1 、a 2 、a 3 、a 4 、a 5 Coefficients representing the characteristics of the individual heart rate variability,representing heart rate variability characteristicsA time-varying trend function, e being a natural constant, b 1 The parameter in the trend function, t is the time after cardiopulmonary resuscitation;
for calculating an electroencephalogram state index from electroencephalogram features, electroencephalogram state index H 2 (t) is calculated as follows:
in the above formula, BSR represents burst suppression ratio, SE represents sample entropy, WPE represents weighted ordering entropy, E δ 、E θ 、E α 、E β 、E γ Represents energy in delta, theta, alpha, beta, gamma frequency bands, a 6 、a 7 、a 8 、a 9 、a 10 、a 11 、a 12 、a 13 、a 14 Is a coefficient of the characteristics of the electroencephalogram,e is a natural constant, b is a trend function of the electroencephalogram characteristics changing with time 2 The parameter in the trend function, t is the time after cardiopulmonary resuscitation;
the method is also used for calculating the probability of good nerve function according to the electrocardiogram state index and the electroencephalogram state index; the probability of good neurological function P (t) is calculated as follows:
in the above formula, e is a natural constant, H 1 (t) is an electrocardiogram state index, H 2 (t) is an electroencephalogram state index, c 0 、c 1 、c 2 Coefficients for a logistic regression equation;
and the output result module is used for outputting and displaying the probability.
2. The post-cardiopulmonary resuscitation nerve function ending evaluation system based on the electrocardiosignals according to claim 1, wherein in the process of collecting the electrocardiosignals, electrocardio data with the length of 5 minutes are synchronously intercepted on a lead II in real time, and resampling is carried out to 250Hz to obtain a signal x; in the process of acquiring the electroencephalogram signals, the electroencephalogram data with the duration of 5 minutes is synchronously intercepted for the C3-P3 lead, and resampled to 250Hz to obtain a signal y.
3. The post cardiopulmonary resuscitation nerve function outcome assessment system based on an electrocardiographic signal according to claim 1, wherein the value range of t is 0 to 72 hours.
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