Carbon dioxide waveform anomaly detection method in mechanical ventilation and anesthetic machine

Abstract

The present application belongs to the technical field of medical apparatus and instruments, and particularly relates to a method for detecting abnormal carbon dioxide waveform in mechanical ventilation and an anesthesia machine. The method comprises: preprocessing carbon dioxide waveforms of a plurality of continuous breathing cycles to be detected; performing Hilbert transform on the preprocessed signals and constructing analytic signals; calculating a phase sequence of the carbon dioxide waveforms to be detected according to the analytic signals; generating a standard carbon dioxide waveform from the phase sequence; calculating the similarity and difference between the standard carbon dioxide waveform and the carbon dioxide waveforms to be detected; and comparing the similarity and difference with preset threshold values, respectively, to determine whether the carbon dioxide waveforms to be detected are abnormal. The method of the present application is not restricted by ventilation modes and ventilation parameter settings, and can be used for various invasive mechanical ventilation. The method can accurately identify various abnormal carbon dioxide waveforms that may occur in clinical practice, and has low requirements for users.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of medical devices, and particularly relates to a method for detecting abnormality of carbon dioxide waveform in mechanical ventilation and an anesthesia machine. BACKGROUND

[0002] During the process of mechanical ventilation by using a breathing machine or an anesthesia machine, medical staff often pay attention to the monitored carbon dioxide waveform, because the carbon dioxide concentration in the breathing process is a very important monitoring index, which can not only reflect the ventilation function, circulation and pulmonary blood flow of the tester, but also provide information about the breathing rate and depth; in the case of patient spontaneous breathing, the carbon dioxide waveform is also helpful for estimating the depth of anesthesia; in controlled ventilation, the carbon dioxide waveform can prompt the tracheal tube mistakenly entering the esophagus, respiratory arrest, catheter obstruction, etc.; it is generally considered that carbon dioxide monitoring is the best way to determine the position of the tracheal tube.

[0003] The abnormal features of the carbon dioxide waveform can often reflect various adverse events occurring in the process of mechanical ventilation, such as repeated inhalation of gas, bronchial asthma, increased dead space of ventilation, changes in spontaneous breathing function, etc., which has high clinical guidance significance. Therefore, in the application scenarios of intensive care and anesthesia induction, a continuous and automatic carbon dioxide waveform abnormality identification means is necessary, which can not only make early warning for some adverse events in time, but also reduce the work burden of medical staff. However, the current breathing machine or anesthesia machine only focuses on the monitoring of two indicators of inhaled and end-tidal carbon dioxide concentrations, and does not make early warning for some adverse events from the aspect of waveform mode. SUMMARY

[0004] The present application aims to overcome the defects of the prior art and provides a method for detecting abnormality of carbon dioxide waveform in mechanical ventilation and an anesthesia machine.

[0005] In order to achieve the above technical purpose, the present application provides a method for detecting abnormality of carbon dioxide waveform in mechanical ventilation, which comprises the following steps:

[0006] Step 1) pre-processing the carbon dioxide waveform of a plurality of continuous breathing cycles to be detected;

[0007] Step 2) performing Hilbert transform on the pre-processed signal and constructing an analytical signal;

[0008] Step 3) calculating the phase sequence of the carbon dioxide waveform to be detected according to the analytical signal;

[0009] Step 4) generating a standard carbon dioxide waveform from the phase sequence;

[0010] Step 5) calculating the similarity and difference between the standard carbon dioxide waveform and the carbon dioxide waveform to be detected;

[0011] Step 6) comparing the result of step 5) with a set threshold respectively, realizing the judgment of whether the to-be-detected carbon dioxide waveform is abnormal.

[0012] Preferably, the step 1) comprises:

[0013] The obtained carbon dioxide waveform X(t) of several continuous breath cycles is normalized to obtain a preprocessed signal f(t) with amplitude limited in [-1, 1]:

[0014]

[0015] Wherein, max(X) and min(X) represent the maximum and minimum values of X respectively, and T is a set time length.

[0016] Preferably, the analytical signal z(t) of the step 2) is:

[0017]

[0018]

[0019] Wherein, is the signal after Hilbert transform, HT represents Hilbert transform, and i represents imaginary unit.

[0020] Preferably, the phase sequence θ(t) of the step 3) is:

[0021]

[0022] Preferably, the standard waveform Y(t) of the step 4) is:

[0023]

[0024] Wherein, a, b and c are three optimal coefficients obtained by fitting the known standard waveform and the phase sequence, and are all constant values.

[0025] Preferably, the similarity of the standard carbon dioxide waveform and the to-be-detected carbon dioxide waveform of the step 5) adopts Pearson correlation index ρ, satisfying the following formula:

[0026]

[0027] Wherein, X i , Y i are the to-be-detected carbon dioxide waveform and the standard carbon dioxide waveform of the i th sampling point respectively, n is the number of sampling points, respectively represent the mean values of the to-be-detected carbon dioxide waveform and the standard carbon dioxide waveform;

[0028] The difference is calculated by using the Euclidean distance d, which satisfies the following formula:

[0029]

[0030] Preferably, the step 6) comprises:

[0031] When ρ is higher than the first threshold value and d is lower than the second threshold value, the detected carbon dioxide waveform is determined to be normal; otherwise, it is determined to be abnormal.

[0032] In a second aspect, the present application provides a carbon dioxide waveform abnormality detection module in mechanical ventilation, which implements the steps of the above method.

[0033] In another aspect, the present application provides an anesthesia machine, which uses the above carbon dioxide waveform abnormality detection module in mechanical ventilation.

[0034] Compared with the prior art, the present application has the following advantages:

[0035] 1. The method is based on real-time monitoring of carbon dioxide waveform, and can be embedded into a breathing machine or anesthesia machine with a carbon dioxide concentration sensor as software;

[0036] 2. The use of the method is not restricted by ventilation mode and ventilation parameter settings, and can be used in various invasive mechanical ventilation;

[0037] 3. The method can accurately identify various abnormal carbon dioxide waveforms that may occur in clinical practice, and has low requirements for users. BRIEF DESCRIPTION OF DRAWINGS

[0038] Figure 1 is a flow chart of the carbon dioxide waveform abnormality detection method;

[0039] Figure 2 is a phase sequence of the carbon dioxide waveform obtained by the method, which varies from -π to π;

[0040] Figure 3 is a comparison between the abnormal carbon dioxide waveform of the tester in the airway obstruction state and the standard waveform obtained by the method;

[0041] Figure 4 is a comparison between the abnormal carbon dioxide waveform caused by cardiogenic oscillation of the tester and the standard waveform obtained by the method. DETAILED DESCRIPTION

[0042] The present application provides a carbon dioxide waveform abnormality detection method for a breathing machine and an anesthesia machine, which can calculate phase information of carbon dioxide waveforms in multiple ventilation periods, give a standard carbon dioxide waveform conforming to current breathing depth and breathing frequency through the phase information, and calculate correlation or difference between the actual waveform and the standard waveform to evaluate abnormal waveforms.

[0043] The technical solutions of the present application will be described in detail below in combination with the drawings and embodiments.

[0044] Embodiment 1

[0045] As shown in Figure 1 Embodiment 1 of the present application provides a carbon dioxide waveform abnormality detection method in mechanical ventilation, which includes the following steps:

[0046] Step 1) preprocessing carbon dioxide waveforms of several continuous breathing periods to be detected;

[0047] Step 2) performing Hilbert transform on the preprocessed signal and constructing an analytic signal;

[0048] Step 3) calculating a phase sequence of the carbon dioxide waveform to be detected according to the analytic signal;

[0049] Step 4) generating a standard carbon dioxide waveform from the phase sequence;

[0050] Step 5) calculating similarity and difference between the standard carbon dioxide waveform and the carbon dioxide waveform to be detected;

[0051] Step 6) comparing the results of step 5) with the set threshold value respectively to determine whether the carbon dioxide waveform to be detected is abnormal.

[0052] The specific discussion is as follows:

[0053] 1. Preprocessing the obtained monitoring CO2 waveform data. For a CO2 waveform data containing several breathing periods, it can be regarded as a time sequence X(t), t∈[0, T], and normalized as follows:

[0054]

[0055] Where max(X) and min(X) represent the maximum and minimum values of X respectively, and f(t) is the normalized result of the signal, with amplitude limited to [-1, 1].

[0056] 2. Hilbert Transform (HT). HT is a common tool in signal processing system and communication system. Since the respiratory related physiological signal has good periodicity, the phase change of f(t) can be calculated by this way. Here, the analytic form of the original sequence f(t) is solved by HT, and the analytic signal is constructed, so that the time-varying phase sequence is solved. The HT and the analytic signal form are as follows:

[0057]

[0058]

[0059] Where P.V. represents the Cauchy principal value of the integral.

[0060] 3. Calculate the phase sequence of the waveform according to the analytic signal z(t). Z is a complex signal composed of orthogonal real and imaginary parts, so the phase is the inverse tangent function of the ratio of the imaginary component to the real component:

[0061]

[0062] The obtained phase sequence is in the range [-π, π]. As shown in Figure 2 .

[0063] 4. Establish the mapping relationship between the standard carbon dioxide waveform and the phase. After obtaining the above phase, it is hoped that the mapping relationship between the normal respiratory carbon dioxide waveform and the phase can be established. Through this relationship, a set of standard and ideal CO2 waveforms can be generated, which can be used for comparison with the monitored waveforms, so as to facilitate the finding of abnormal features. This mapping relationship can be described by a function with phase θ(t) as the independent variable, and the expression of the standard waveform Y(t) is as follows:

[0064]

[0065] Where a, b and c are the optimal coefficients obtained by fitting the known standard waveform and the phase sequence, and are all constant values. As shown in Figure 3 , which is the comparison between the abnormal carbon dioxide waveform of the testee in the airway obstruction state and the standard waveform obtained by the method. In the figure, the red color is the standard waveform, and the blue color is the abnormal carbon dioxide waveform in the airway obstruction state; Figure 4 is the comparison between the abnormal carbon dioxide waveform of the testee caused by cardiogenic oscillation and the standard waveform obtained by the method. The red color is the standard waveform, and the blue color is the abnormal carbon dioxide waveform caused by cardiogenic oscillation.

[0066] 5. Calculate the similarity between X(t) and Y(t). Here, the Pearson correlation index is adopted:

[0067]

[0068] wherein, X i , Y i are the i-th sampling point of the waveform to be detected and the standard waveform of carbon dioxide respectively, n is the number of sampling points, respectively represent the mean of the waveform to be detected and the standard waveform of carbon dioxide;

[0069] When the index is below a certain level, it indicates that the current monitoring waveform has low correlation with the standard waveform of carbon dioxide, and the probability of abnormal characteristics is very high.

[0070] 6. Calculate the difference between X(t) and Y(t), here the Euclidean distance is used:

[0071]

[0072] When the index is above a certain level, it indicates that the current monitoring waveform has large difference with the standard waveform of carbon dioxide, and the probability of abnormal characteristics is very high.

[0073] 7. Compare the size relationship of the correlation index and the distance index with the corresponding threshold value, and give the judgment result of whether the current monitoring carbon dioxide is abnormal.

[0074] Embodiment 2

[0075] Embodiment 2 of the present application provides a carbon dioxide waveform anomaly detection module in mechanical ventilation, which realizes the method steps of embodiment 1.

[0076] Embodiment 3

[0077] Embodiment 3 of the present application provides an anesthesia machine, which adopts the above-mentioned carbon dioxide waveform anomaly detection module in mechanical ventilation.

[0078] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and are not limiting. Although the present application has been described in detail with reference to the embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the present application, and they should be covered in the scope of the claims of the present application.

Claims

1. A method for detecting abnormality of carbon dioxide waveform in mechanical ventilation, comprising: Step 1) preprocessing carbon dioxide waveforms of several continuous breath cycles to be detected; Step 2) performing Hilbert transform on the preprocessed signal and constructing an analytic signal; Step 3) calculating a phase sequence of the carbon dioxide waveform to be detected according to the analytic signal; Step 4) generating a standard carbon dioxide waveform from the phase sequence; Step 5) calculating similarity and difference between the standard carbon dioxide waveform and the carbon dioxide waveform to be detected; Step 6) comparing the results of Step 5) with set thresholds respectively to determine whether the carbon dioxide waveform to be detected is abnormal; The standard waveform Y(t) of Step 4) is: wherein a, b and c are three optimal coefficients obtained by fitting the known standard waveform with the phase sequence, and are all constant values; t ∈ [0, T], and T is a set time length; The similarity between the standard carbon dioxide waveform and the carbon dioxide waveform to be detected of Step 5) adopts Pearson correlation index ρ, which satisfies the following formula: wherein X i , Y i are the i-th sampling point of the carbon dioxide waveform to be detected and the standard carbon dioxide waveform, respectively, n is the number of sampling points, respectively represent the mean values of the carbon dioxide waveform to be detected and the standard carbon dioxide waveform. The difference adopts Euclidean distance d, which satisfies the following formula: Step 6) comprises: When ρ is higher than a first threshold and d is lower than a second threshold, the carbon dioxide waveform to be detected is determined to be normal; otherwise, it is determined to be abnormal.

2. The method of carbon dioxide waveform abnormality detection in mechanical ventilation according to claim 1, characterized by, Step 1) comprises: Normalizing the obtained carbon dioxide waveforms X(t) of several continuous breath cycles to obtain the preprocessed signal f(t) with amplitude limited in [-1, 1]: wherein max(X) and min(X) represent the maximum and minimum values of X respectively, and T is a set time length.

3. The method of carbon dioxide waveform abnormality detection in mechanical ventilation according to claim 2, characterized in that, The analytic signal z(t) of Step 2) is: wherein is the signal after the Hilbert transform, HT denotes the Hilbert transform and i denotes the imaginary unit.

4. The method of carbon dioxide waveform abnormality detection in mechanical ventilation according to claim 3, characterized by, The phase sequence θ(t) of Step 3) is:

5. A carbon dioxide waveform anomaly detection module in mechanical ventilation, characterized by, The module realizes the steps of the method of any one of claims 1-4.

6. An anaesthesia machine characterised in that, The anesthesia machine adopts the carbon dioxide waveform abnormality detection module of claim 5.

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