阅读说明：本技术 基于心脑电信号的心肺复苏后神经功能结局评估系统 (System for evaluating neurological outcome after cardio-pulmonary resuscitation based on cardiocerebral electrical signals ) 是由 李永勤 王建杰 戴晨曦 魏良 龚渝顺 李韵池 于 2021-07-26 设计创作，主要内容包括：本发明提供一种基于心脑电信号的心肺复苏后神经功能结局评估系统,包括：信息采集模块,用于采集心电、脑电信号；信号处理模块,用于对采集到的心电、脑电信号进行信号处理；特征计算模块,用于根据信号处理后的心电信号、脑电信号计算心率变异性特征、脑电图特征；概率预测模块,用于根据心率变异性特征、脑电图特征计算心电图、脑电图状态指数；还用于根据心电图、脑电图状态指数计算良好神经功能的概率；输出结果模块,用于输出并显示概率。本发明可以量化评估心脏骤停患者心肺复苏后,恢复良好神经功能预后的概率,能够为临床医生选择合理的治疗方案提供评价依据。(The invention provides a cardio-cerebral signal-based system for evaluating the neurological outcome after cardio-pulmonary resuscitation, which comprises: the information acquisition module is used for acquiring electrocardio signals and electroencephalogram signals; the signal processing module is used for carrying out signal processing on the acquired electrocardio-electroencephalogram 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 electrocardiogram and electroencephalogram state indexes according to the heart rate variability features and the electroencephalogram features; the probability of good nerve function is calculated according to the electrocardiogram and electroencephalogram state index; and the output result module is used for outputting and displaying the probability. The method can quantitatively evaluate the probability of good neural function prognosis recovery after cardiopulmonary resuscitation of the cardiac arrest patients, and can provide evaluation basis for clinicians to select reasonable treatment schemes.)

1. A system for assessing neurological outcome after cardiopulmonary resuscitation based on cardiac-cerebral electrical signals, comprising:

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 acquired electrocardiosignals and electroencephalogram signals, and the signal processing comprises filtering and 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 the signal processing and calculating electroencephalogram characteristics according to the electroencephalogram signals after the signal processing;

the probability prediction module is used for calculating an electrocardiogram state index according to the heart rate variability characteristics and calculating an electroencephalogram state index according to the electroencephalogram characteristics; the electroencephalogram state index 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.

2. The system for assessing the neurological outcome after the cardiopulmonary resuscitation based on the electrical cardio-cerebral signals according to claim 1, wherein during the process of collecting the electrical cardio signals, the electrical cardio data with a duration of 5 minutes is synchronously intercepted from the II lead in real time, and is resampled to 250Hz to obtain a signal x; in the process of collecting the electroencephalogram signals, electroencephalogram data with the duration of 5 minutes are synchronously intercepted from the electroencephalogram C3-P3 lead, and the electroencephalogram data are resampled to 250Hz to obtain signals y.

3. The system of claim 1, wherein the heart rate variability features comprise root mean square, very low frequency energy, high frequency energy of adjacent RR interval differences.

4. The system of claim 3, wherein the ECG status index H is₁(t) is calculated as follows:

in the above formula, RMSSD represents the root mean square of the difference between adjacent RR intervals, LVF represents the very low frequency energy, VF represents the low frequency energy, HF represents the high frequency energy, a₁、a₂、a₃、a₄、a₅The coefficients representing the individual heart rate variability characteristics,a trend function representing the variation of the heart rate variability characteristics with time, e is a natural constant, b₁As a parameter in the trend function, t is the time after cardiopulmonary resuscitation.

5. The system of claim 1, wherein the electroencephalographic features include burst suppression ratio, sample entropy, weighted ordering entropy, and energy E in delta, theta, alpha, beta, gamma bands_δ、E_θ、E_α、E_β、E_γ。

6. The system of claim 5, wherein the EEG state index H is an EEG status index₂(t) is calculated as follows:

in the above formula, BSR represents the burst suppression ratio, SE represents the sample entropy, WPE represents the weighted ordering entropy, E represents the weighted ordering entropy_δ、E_θ、E_α、E_β、E_γRepresents the energy of the delta, theta, alpha, beta, gamma bands, a₆、a₇、a₈、a₉、a₁₀、a₁₁、a₁₂、a₁₃、a₁₄Are the coefficients of the electroencephalogram feature,is a trend function of electroencephalogram features over time, e is a natural constant, b₂As a parameter in the trend function, t is the time after cardiopulmonary resuscitation.

7. The system for assessing neurological outcome after cardiopulmonary resuscitation based on electrical cardio-cerebral signals according to claim 4 or 6, wherein t is selected from the range of 0 to 72 hours.

8. The system for assessing neurological outcome after cardiopulmonary resuscitation based on electrical cardio-cerebral signals according to claim 1, wherein the probability of good neurological function p (t) is calculated as follows:

in the above formula, e is a natural constant, H₁(t) is an index of electrocardiographic state, H₂(t) is an electroencephalogram status index, c₀、c₁、c₂Are coefficients of a logistic regression equation.

Technical Field

The invention relates to the technical field of cardio-pulmonary resuscitation, in particular to a system for evaluating the neurological outcome after cardio-pulmonary resuscitation based on a cardio-cerebral-electrical signal.

Background

Sudden cardiac arrest refers to sudden cardiac arrest caused by various reasons under unpredictable conditions and within unpredictable time, which results in sudden interruption of effective cardiac pump function and effective circulation, and severe ischemia, anoxia and metabolic disorders of systemic histiocyte, and immediate loss of life if rescue is not performed in time. After sudden cardiac arrest, patients may be saved life if appropriate and effective cardiopulmonary resuscitation is taken in time. CPR (cardio pulmonary resuscitation), which is a life-saving technology adopted for sudden cardiac arrest and respiration, can help a patient to recover spontaneous respiration and spontaneous circulation.

The goal of treatment after cardiopulmonary resuscitation is to restore the patient's physical condition to normal levels. However, many patients with cardiac arrest do not have complete recovery of brain function even if spontaneous circulation is restored, and the coma time for about 80% of patients with successful cardiopulmonary resuscitation exceeds 1 hour. In the process of hospitalization after coma, part of patients can return to good neurological functions, and the other part of patients face death, permanent brain injury, bedridden or persistent vegetative state, which consumes a great amount of medical resources. Therefore, early prediction of neurological outcome is of great importance for the diagnosis and treatment of patients after resuscitation. Poor neurological outcome can be significantly improved by timely intervention measures, such as target temperature management therapy after resuscitation and hydrogen inhalation therapy. The system for objectively evaluating the neural function prognosis after cardiopulmonary resuscitation can provide the patient condition information for a clinician, 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 capable of assessing the neurological outcome of a cardiopulmonary resuscitation patient, which provides evaluation basis for a clinician to select a reasonable treatment regimen.

Disclosure of Invention

Aiming at the defects in the prior art, the invention provides a system for evaluating the neurological outcome after cardiopulmonary resuscitation based on a cardio-cerebral electrical signal, and aims to solve the technical problems that no system capable of evaluating the neurological outcome of a cardiopulmonary resuscitation patient exists in the prior art and evaluation basis cannot be provided for a clinician to select a reasonable treatment scheme.

The invention adopts the technical scheme that a system for evaluating the neurological outcome after cardio-pulmonary resuscitation based on a cardio-cerebral electric signal comprises:

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 acquired electrocardiosignals and electroencephalogram signals, and the signal processing comprises filtering and denoising, R wave identification, RRI extraction and spectral analysis;

the characteristic calculation module is used for calculating heart rate variability characteristics according to the electrocardiosignals after the signal processing and calculating electroencephalogram characteristics according to the electroencephalogram signals after the signal processing;

the probability prediction module is used for calculating an electrocardiogram state index according to the heart rate variability characteristics and calculating an electroencephalogram state index according to the electroencephalogram characteristics; the probability of good nerve function is calculated 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 the electrocardiosignals, synchronously intercepting the II leads for 5 minutes in real time, and resampling to 250Hz to obtain signals x; in the process of collecting the electroencephalogram signals, electroencephalogram data with the duration of 5 minutes are synchronously intercepted from the electroencephalogram C3-P3 lead, and the electroencephalogram data are resampled to 250Hz to obtain signals 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 H₁(t) is calculated as follows:

in the above formula, RMSSD represents the root mean square of the difference between adjacent RR intervals, LVF represents the very low frequency energy, VF represents the low frequency energy, HF represents the high frequency energy, a₁、a₂、a₃、a₄、a₅The coefficients representing the individual heart rate variability characteristics,a trend function representing the variation of the heart rate variability characteristics with time, e is a natural constant, b₁As a parameter in the trend function, t is the time after cardiopulmonary resuscitation.

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, an electroencephalogram state index H₂(t) is calculated as follows:

in the above formula, BSR represents the burst suppression ratio, SE represents the sample entropy, WPE represents the weighted ordering entropy, E represents the weighted ordering entropy_δ、E_θ、E_α、E_β、E_γRepresents the energy of the delta, theta, alpha, beta, gamma bands, a₆、a₇、a₈、a₉、a₁₀、a₁₁、a₁₂、a₁₃、a₁₄Are the coefficients of the electroencephalogram feature,is a trend function of electroencephalogram features over time, e is a natural constant, b₂As a parameter in the trend function, t is the time after cardiopulmonary resuscitation.

Furthermore, the value range of t is 0-72 hours.

Further, the probability of good neural function p (t) is calculated as follows:

in the above formula, e is a natural constant, H₁(t) is an index of electrocardiographic state, H₂(t) is an electroencephalogram status index, c₀、c₁、c₂Are coefficients of a logistic regression equation.

According to the technical scheme, the beneficial technical effects of the invention are as follows:

the evaluation system can quantitatively evaluate the probability of good neural function prognosis recovery after cardiopulmonary resuscitation of a cardiac arrest patient, can provide evaluation basis for a clinician to select a reasonable treatment scheme, and has important significance for rescuing the life of the patient and guiding the treatment of the patient; realizing the effective utilization of medical resources.

Drawings

In order to more clearly illustrate the embodiments of the present invention 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. Throughout the drawings, like elements or portions are generally identified by like reference numerals. In the drawings, elements or portions are not necessarily drawn to scale.

FIG. 1 is a diagram of an evaluation system architecture according to an embodiment of the present invention;

fig. 2 is a schematic diagram of a workflow of an evaluation system according to an embodiment of the present invention.

Detailed Description

Embodiments of the present invention will be described in detail below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and therefore are only examples, and the protection scope of the present invention is not limited thereby.

It is to be noted that, unless otherwise specified, technical or scientific terms used herein shall have the ordinary meaning as understood by those skilled in the art to which the invention pertains.

Example 1

The present embodiment provides a system for evaluating neurological outcome after cardiopulmonary resuscitation based on cardiac-cerebral electrical signals (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 carrying out signal processing on the acquired electrocardiosignals and electroencephalogram signals, and comprises filtering and 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 the signal processing and calculating electroencephalogram characteristics according to the electroencephalogram signals after the signal processing;

a probability prediction module for calculating the probability of good neural function;

and the output result module is used for outputting and displaying the probability.

The evaluation system firstly extracts an RR interval sequence from an electrocardiosignal at the time t for calculating 4 heart rate variability features, simultaneously extracts 8 features from an electroencephalogram signal at the time t, then calculates 2 parameters by using the heart rate variability features and the electroencephalogram features, and finally evaluates the probability of recovering good nerve functions of a patient by using a logistic regression equation.

The operation principle of the evaluation system is described in detail below, as shown in fig. 2, specifically as follows:

the information acquisition module acquires the electrocardiosignals and the electroencephalogram signals through the sensor. Preferably, the cardiac electrical signals are collected using a 12-lead electrocardiograph and the brain electrical signals are collected using an electroencephalograph. In the process of acquiring electrocardio signals, synchronously intercepting the II leads from the electrocardio data with a period of 5 minutes in real time, and resampling to 250Hz to obtain a signal x. In the process of collecting the electroencephalogram signals, electroencephalogram data with the duration of 5 minutes are synchronously intercepted from the electroencephalogram C3-P3 lead, and the electroencephalogram data are resampled to 250Hz to obtain signals y.

When the signal processing module is used for carrying out signal processing on the acquired electrocardiosignals and 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 QRS wave group in x through an R wave identification algorithm to obtain an R wave position; the R wave identification algorithm is preferably a front-back amplitude difference method. Then, an RR interval signal (RRI) is calculated, and the position of the former R wave is subtracted from the position of the latter R wave to obtain the 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 of adjacent RR interval differences (RMSSD), very low frequency energy (VLF), low frequency energy (LF), high frequency energy (HF). The RMSSD is a time domain feature, and the VLF, LF, HF are frequency domain features.

RMSSD is calculated as follows:

r₁(n)＝r(n+1)-r(n)

the calculation of the very low frequency energy (VLF), low frequency energy (LF), and high frequency energy (HF) is as follows: firstly, carrying out cubic spline interpolation on r (n), and then carrying out power spectrum analysis on the interpolated signal by using a Welch method, wherein VLF is the sum of all power spectrum densities within the range of 0.0033-0.04 Hz, LF is the sum of all power spectrum densities within the range of 0.04-0.15 Hz, and HF is the sum of all power spectrum densities within the range of 0.15-0.40 Hz, and the formulas are respectively as follows:

in the above formula, R₁(f) Is a power spectral density function of r (n).

The characteristic calculation module also calculates electroencephalogram characteristics according to the electroencephalogram signals after the signal processing. In the present embodiment, the electroencephalogram features include a Burst Suppression Ratio (BSR), a Sample Entropy (SE), a weighted ordering entropy (WPE), and an energy E of a δ, θ, α, β, γ band_δ、E_θ、E_α、E_β、E_γ(ii) a Wherein BSR is a time domain feature, E_δ、E_θ、E_α、E_β、E_γFor frequency domain features, SE and WPE are nonlinear features.

BSR calculation mode: and (3) judging whether the electroencephalogram signals with the voltage of more than 10 mu V appear or not by taking 0.5 second as a window, if so, the electroencephalogram signals in the window are burst waves, and if not, the electroencephalogram signals are inhibition waves. And then judging whether the subsequent electroencephalogram signals are burst waves or not, wherein BSR is the ratio of the burst waves.

The calculation method of SE comprises the following steps: is to estimate the amount of increase of the new pattern when the embedding dimension increases from m to the m +1 dimension with a certain tolerance epsilon. The calculation flow is as follows: the electroencephalogram signal Y (i), i is 1,2 … N, N is the length of the time sequence, the reconstruction of the phase space is carried out, the number of vectors in the phase space is N-m +1, and the vector of the phase space is Y_m(i)：Y_m(i) Y (i), y (i +1), …, y (i + m-1) }, i (1, 2, …, N-m + 1). Computing a 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, the above steps are repeated, and B is calculated^m:1(ε). And finally obtaining:

WPE calculation mode: reconstructing a phase space by using the brain waves Y (i), i-1, 2 … N to obtain Y_m(j) Y (j), y (j + τ), …, y (j + (m-1) τ) }, m being the embedding dimension, τ being the delay time. Each Y_m(i) Arranging according to the sequence from low to high to obtain an arrangement ordinal number modeSuch as [0.1,0.07,0.13,0.09]The ordinal number pattern of the array can be obtained as [3,1,4,2 ]]. Each Y_m(j) Total m! A possible ordinal ranking pattern. The weight factors are defined as follows:

whereinIs the arithmetic mean:

then the sequential pattern is arrangedIn the whole vector Y_m(i) The weight ratio of

Then, WPE can be derived

Performing 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_yIs the power spectral density of y.

The probability prediction module maps RMSSD, LVF, VF, HF, BSR,SE(0.1,2)、WPE(6,6)、E_δ、E_θ、E_α、E_β、E_γSubstituting the following formula, and respectively calculating the electrocardiogram state index H at the current time t₁(t) and electroencephalogram state index H₂(t), the calculation formula is as follows:

in the above formula, a₁、a₂、a₃、a₄、a₅For the coefficients of the individual heart rate variability features,is a function of the time-dependent trend of the heart rate variability characteristic, t is the time after cardiopulmonary resuscitation, a₆、a₇、a₈、a₉、a₁₀、a₁₁、a₁₂、a₁₃、a₁₄The coefficients that are characteristic of the electroencephalogram,is a trend function of electroencephalogram features over time, e is a natural constant, b₁、b₂Are parameters in the trend function. In a specific embodiment, a₁～a₁₄Has a value range of (0,10), b₁、b₂The value range of (1) is (0,0.5), and the value range of t is 0-72 hours.

The probability prediction module is used for predicting the electrocardiogram state index H according to the electrocardiogram state index₁(t) and electroencephalogram state index H₂(t) calculating the probability of good neural function p (t) by using a logistic regression method; the calculation formula is as follows:

in the above formula, c₀、c₁、c₂For coefficients of the logistic regression equation, in a particular embodiment, c₀、c₁、c₂The value range of (1) is (0, 100).

And finally, outputting and displaying the calculated probability P of good neural function through an output result module.

By using the evaluation system provided by the embodiment, the probability of good neural function prognosis recovery after cardiopulmonary resuscitation of a cardiac arrest patient 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 examples are only intended to illustrate the technical solution of the present invention, but not to limit it; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present invention, and they should be construed as being included in the following claims and description.