Monitoring method and system based on intrinsic kurtosis

Abstract

The invention provides a monitoring method and system based on intrinsic kurtosis. The monitoring method comprises the following steps: acquiring original data; preprocessing the data to obtain a data time sequence to be analyzed; a step of applying the EEMD to the data time sequence and decomposing the data time sequence into E-IMFs; calculating the iK to obtain an iK set; at least one part of the iK set is used as an output parameter of the monitoring method.

Description

Technical Field

[0001] The present application relates to the field of medical parameter monitoring, and particularly to a monitoring method and system based on intrinsic kurtosis. Background Art

[0002] Anesthesia is a unique medical procedure. It has no therapeutic value in itself, but is indispensable in medical interventions, from life-saving surgeries to routine invasive medical examinations. Methods for measuring the depth of anesthesia (DOA) are crucial in surgeries and play a good auxiliary role in the surgical process.

[0003] Existing methods for detecting the depth of anesthesia include continuous electrocardiogram (ECG) monitoring, continuous pulse oximetry monitoring (SpO2), blood pressure monitoring, anesthetic concentration monitoring, carbon dioxide monitoring, body temperature measurement, electroencephalogram (EEG) monitoring, etc.; among them, most devices are designed to monitor important physiological indicators mainly related to cardiovascular or pulmonary function, while electroencephalogram (EEG) is a device specifically used to monitor the depth of anesthesia through consciousness assessment and is the only device designed to monitor the dynamic function of the brain to determine the patient's level of consciousness; it is the basis for many objective measurements of the depth of anesthesia. Currently, methods for electroencephalogram (EEG) monitoring include compressed spectral array (CSA), bispectral index (BIS), index and entropy analysis, etc.; however, these existing methods generally have disadvantages such as complex analysis or lack of intuitiveness. In addition, there is also a great demand for effective monitoring methods in other directions in the medical field, such as muscle activity monitoring, etc. Summary of the Invention

[0004] In order to overcome some problems existing in the prior art, the present application provides a monitoring method and system based on intrinsic kurtosis.

[0005] The first aspect of the present application provides a monitoring method based on intrinsic kurtosis, including the following steps:

[0006] Step of obtaining raw data: Collect data as raw data;

[0007] Data preprocessing step: Preprocess the obtained raw data to obtain a data time series to be analyzed;

[0008] Step of applying EEMD to the data time series: Use the EEMD analysis method to process the data time series and adaptively decompose it into multiple ensemble empirical mode functions (E-IMFs); and

[0009] Step of calculating intrinsic kurtosis (iK):

[0010] The partial component sums of N ensemble empirical mode functions (E-IMFs) are obtained by using formula (1), resulting in N partial component sums;

[0011]

[0012] where c j (t) is the ensemble intrinsic mode function (E-IMF), t represents the number of data, N represents the total number of E-IMFs, n represents the nth E-IMF among them, and x n (t) represents the sum from the 1st E-IMF to the nth E-IMF;

[0013] The intrinsic kurtosis of each partial component sum is calculated by using formula (2), obtaining N intrinsic kurtoses (iK), which constitute the set of intrinsic kurtoses (iK set);

[0014]

[0015] where T represents the number of data time series corresponding thereto, μ is the mean of x n (t), and σ is the standard deviation of x n (t);

[0016] At least a part of the iK set (such as 2, 3, 5, or 8, etc. in 1K - 8K) is used as the output parameter of the monitoring method.

[0017] In one embodiment, the monitoring method further includes the calculation step of ensemble intrinsic kurtosis (eiK): setting multiple white noise sequences with different amplitude ratios, respectively applying them to the step of applying the EEMD to the data time series, and performing the calculation step of iK to obtain multiple rounds of iK sets; taking the ensemble average of the iK sets under multiple rounds as the result to obtain multiple eiKs, and multiple ensemble intrinsic kurtoses (eiKs) constitute the set of ensemble intrinsic kurtoses (eiK set); at least a part of the eiK set (such as 2, 3, 5, or 8, etc. in e1K - e8K) is used as the output parameter of the monitoring method, which can replace the iK set as the output parameter.

[0018] In one embodiment, in the step of acquiring the original data, data is collected by using a sliding window, and the data of each window is used as one of the original data; by using the monitoring method, multiple iKs and / or multiple eiKs of each window can be obtained.

[0019] In one embodiment, the monitoring method further includes the data output step, wherein at least a part of the multiple iKs and / or multiple eiKs of each window is sorted and output, and the result is displayed on a monitor and archived as a monitoring guide.

[0020] In one embodiment, in the step of acquiring the original data, the sampling frequency of the data is not lower than 100 Hz.

[0021] In one embodiment, in the data preprocessing step, the data preprocessing includes filtering, outlier removal, and electrical noise removal.

[0022] In one embodiment, in the step of applying EEMD to the data time series, specifically, it includes:

[0023] Adding white noise: Adding a white noise sequence to the data time series to be analyzed; the amplitude ratio of the white noise is any value in 0.05 - 0.2;

[0024] Using EMD to decompose IMFs: Decomposing the data time series added with the white noise sequence into multiple initial intrinsic mode functions (IMFs) adaptively by EMD;

[0025] Continuously repeating the step of adding white noise and the step of using EMD to decompose IMFs, but each time using a newly randomly generated white noise sequence with the same amplitude ratio; thereby obtaining multiple batches each having multiple IMFs;

[0026] Taking the ensemble average of the multiple IMFs of multiple batches as the decomposition result to obtain the multiple E - IMFs.

[0027] In one embodiment, in the step of applying EEMD to the data time series, the step of adding white noise and the step of using EMD to decompose IMFs are repeated 10 - 20 times to obtain 10 - 20 batches.

[0028] In one embodiment, in the calculation step of iK, at most 8 of the multiple E - IMFs are selected for calculating the partial component sum; that is, N is a positive integer less than or equal to 8, for example, it can be 8, 7, 6, etc.

[0029] In one embodiment, in the calculation step of eiK, the amplitude ratio of the white noise is set to 0.05 - 0.2, and 6 - 16 ensemble trials are taken within 0.05 - 0.2 as the multiple white noise sequences with different amplitude ratios.

[0030] In one embodiment, in the data output step, at least a part of the multiple iK values of each window are output respectively to obtain a curve of a single iK on the time axis, that is, the iK monitoring curve, as the monitoring guidance.

[0031] In one embodiment, in the data output step, at least a part of the multiple eiK values of each window are output respectively to obtain a curve of a single eiK on the time axis, that is, the eiK monitoring curve, as the monitoring guidance.

[0032] In one embodiment, in the step of outputting the data, the second one of the multiple eiK, that is, e2K, is selected as the main result, and the other eiK are used as supporting results.

[0033] The second aspect of the present application provides a monitoring system based on intrinsic kurtosis, which adopts the monitoring method described in any one of the previous embodiments. The monitoring system includes a data acquisition module and a data analysis module, wherein,

[0034] The data acquisition module is configured to collect data so as to obtain raw data;

[0035] The data analysis module includes a windowing module, a data preprocessing module, an EEMD decomposition module, an iK calculation module, and a result output module; wherein,

[0036] The windowing module is configured to window the collected data to obtain the raw data of each window;

[0037] The data preprocessing module is configured to filter, remove outliers, and remove electrical noise from the collected raw data to obtain a data time series to be analyzed;

[0038] The EEMD decomposition module is configured to adaptively decompose the data time series to be analyzed in the current window into multiple E-IMFs by using the EEMD analysis method;

[0039] The iK calculation module is configured to select the first N E-IMFs from the multiple E-IMFs, use formula (1) to find the partial component sum, obtain N partial component sums, and further use formula (2) to calculate the iK of each partial component sum to obtain the iK set of the current window; wherein, formula (1) and formula (2) are as follows:

[0040]

[0041] wherein, c j (t) is an E-IMF, t represents the number of data, N represents the total number of E-IMFs, n represents the nth E-IMF among them, and x n (t) represents the sum from the first E-IMF to the nth E-IMF;

[0042]

[0043] wherein, T represents the corresponding number of data time series, μ is the mean of x n (t), and σ is the standard deviation of x n (t); and

[0044] The result output module is configured to sort and output the multiple iKs of each window.

[0045] In one embodiment, the monitoring system further includes: an eiK calculation module configured to set white noise sequences with multiple different amplitude ratios, apply them to an EEMD decomposition module and an iK calculation module respectively to obtain multiple rounds of iK sets; take the set average of the iK sets in multiple rounds as the result to obtain multiple eiKs of the current window; and a result output module configured to sort and output the multiple eiKs of each window.

[0046] In one embodiment, the monitoring system further includes:

[0047] a data archiving module configured to store data for future reference;

[0048] a data display module configured to display the output result of the result output module on a monitor as a monitoring guide.

[0049] A third aspect of the present application provides an application of the monitoring method based on intrinsic kurtosis described in any of the foregoing embodiments. The monitoring method can be used for medical monitoring, including but not limited to anesthesia depth monitoring and muscle activity monitoring. The original data respectively correspond to electroencephalogram data and electromyogram data.

[0050] A fourth aspect of the present application provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements the monitoring method based on intrinsic kurtosis described in any of the foregoing embodiments.

[0051] A fifth aspect of the present application provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the monitoring method based on intrinsic kurtosis described in any of the foregoing embodiments.

[0052] The monitoring method provided by at least one embodiment of the present application is based on intrinsic kurtosis (iK) or ensemble intrinsic kurtosis (eiK), and has the following advantages: (1) Intrinsic kurtosis (iK) or ensemble intrinsic kurtosis (eiK) is easier to calculate, so it can provide a score closer to real time; (2) The calculation of intrinsic kurtosis (iK) or ensemble intrinsic kurtosis (eiK) depends less on the data length than entropy, and entropy depends on the threshold within the data range, which makes the calculation unstable; (3) The measurement of intrinsic kurtosis (iK) or ensemble intrinsic kurtosis (eiK) provides modulation or interaction of direct physiological and neural information and can be used as a guide for clinical anesthesia depth and muscle monitoring.

[0053] The monitoring method provided by at least one embodiment of the present application can be effectively applied to the medical field and other related fields to contribute to the monitoring of data. Description of the Drawings

[0054] Figure 1 is a flowchart of a monitoring method according to an embodiment of the present application;

[0055] Figure 2 is a schematic diagram of collecting electroencephalogram data;

[0056] Figure 3a is the original data of the current window;

[0057] Figure 3b is the data time series to be analyzed after preprocessing;

[0058] Figure 4 is an example of multiple E-IMFs obtained by EEMD decomposition;

[0059] Figure 5 is an example of an eiK set containing 8 eiKs obtained by calculation;

[0060] Figure 6 are 8 eiK monitoring curves obtained by the monitoring method of this embodiment during the entire anesthesia operation process;

[0061] Figure 7 is an enlarged view of the e1 K monitoring curve and the e2K monitoring curve;

[0062] Figure 8a is the BIS monitoring curve and the preprocessed electroencephalogram data;

[0063] Figure 8b is the e2K monitoring curve;

[0064] Figure 9a is the monitoring curve of intrinsic sample entropy;

[0065] Figure 9b is the monitoring curve of multi-scale sample entropy;

[0066] Figure 10 is the preprocessed electromyogram data of the tremor muscle monitored by Parkinson's patients;

[0067] Figure 11 is the e2K monitoring curve obtained by processing the electromyogram data;

[0068] Figure 12 are 8 eiK monitoring curves obtained by processing the electromyogram data;

[0069] Figure 13 is a schematic diagram of the connection of computer devices. Specific embodiments

[0070] In order to make the objectives, technical solutions and advantages of this application more clear and understandable, the following describes and explains this application in combination with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not used to limit this application. Based on the embodiments provided in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of this application.

[0071] Obviously, the accompanying drawings in the following description are only some examples or embodiments of this application. For those of ordinary skill in the art, without creative efforts, this application can also be applied to other similar scenarios based on these drawings. In addition, it can also be understood that although the efforts made in this development process may be complex and lengthy, for those of ordinary skill in the art related to the content disclosed in this application, some design, manufacturing or production changes based on the technical content disclosed in this application are only conventional technical means and should not be understood as the content disclosed in this application being insufficient.

[0072] When "embodiment" is mentioned in this application, it means that the specific features, structures or characteristics described in combination with the embodiment can be included in at least one embodiment of this application. The appearance of this phrase in various positions in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those of ordinary skill in the art explicitly and implicitly understand that the embodiments described in this application can be combined with other embodiments without conflict.

[0073] Unless otherwise defined, the technical terms or scientific terms involved in this application should be understood in the general sense by those of ordinary skill in the technical field to which this application belongs. The words "a", "one", "kind", "the" and other similar words involved in this application do not indicate a quantity limitation and can represent a singular or plural number. The terms "including", "comprising", "having" and any variations thereof involved in this application are intended to cover non-exclusive inclusion; for example, a process, method, system, product or device including a series of steps or modules (units) is not limited to the listed steps or units, but may also include unlisted steps or units, or may also include other steps or units inherent to these processes, methods, products or devices. The words "connected", "coupled" and other similar words involved in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The "multiple" involved in this application refers to two or more. "And / or" describes the association relationship of associated objects and indicates that three relationships can exist.

[0074] This application proposes a new concept of Intrinsic kurtosis (iK) and Ensemble intrinsic kurtosis (eiK), and applies them to fields such as medical monitoring, such as the monitoring of anesthetic depth, muscle activity monitoring, and so on. This embodiment mainly takes the monitoring of anesthetic depth as an example for illustration; however, the analysis and processing methods in other fields or directions are the same or similar. The medical community generally believes that the unconscious state is a state in which various parts of the brain can hardly communicate, connect, and interact with each other. Communication, connection, and interaction mean the modulation of brain waves, and the intrinsic kurtosis and ensemble intrinsic kurtosis provided by this application can quantify the intensity of this interaction, thereby monitoring the anesthetic depth.

[0075] The first embodiment of this application provides an anesthetic depth monitoring method based on intrinsic kurtosis (hereinafter referred to as the monitoring method for short). Figure 1 It is a flowchart of the monitoring method according to this embodiment. It should be noted that the steps shown in the process of the monitoring method or the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. The monitoring method includes the following steps:

[0076] S100 The step of obtaining the original data: As Figure 2 shown, a single electrode deployed on the patient's forehead and a reference electrode near the ear (mastoid process of the temporal bone) can be used to collect the patient's electroencephalogram data (EEG data, unit: μV), so as to obtain the original data. The sampling frequency of the electrode is generally not less than 100 Hz, for example, it can be 100 Hz, 256 Hz, 500 Hz, and so on.

[0077] In order to report the results close to real time, windowing can be implemented for the acquisition of EEG data. In this embodiment, a sliding window is adopted; after setting the window length and the sliding step size, enter the window sliding stage, so as to obtain the original data of each window.

[0078] Next, taking specific data as an example, a more detailed description will be given for better understanding; however, the protection scope of this application is not limited thereto. Set the sampling frequency of the electrode to 100 Hz, set the window length to 60 s, and the sliding step to 1 s; after turning on the system, start collecting data. 100 EEG data are collected every 1 second and transmitted to the computer; when the computer finishes receiving 60 s of EEG data, the original data of the current window is obtained, and this original data contains 6000 (60 * 100) data. During the window sliding stage, multiple original data can be obtained in sequence. For example, since entering the window sliding stage, after starting to collect EEG data, first, 6000 data from the 1st second to the 60th second are obtained, which is the first original data of the first window; after the window moves 1 s, 6000 data from the 2nd second to the 61st second can be obtained, which is the second original data of the second window; after the window moves 1 s again, 6000 data from the 3rd second to the 62nd second can be obtained, which is the third original data of the third window... And so on. During the window sliding stage, multiple original data of each window can be obtained in sequence. According to this method, numerous original data of each window from the start to the end of the operation can be obtained. The first 59 s of the subsequent window are the same as the last 59 s of the previous window, so that the result can be reported almost in real time.

[0079] For the acquisition of other monitored data, corresponding acquisition devices and means can be adopted. For example, for the acquisition of muscle activity data, an electromyography device can be used for acquisition, and the sampling frequency of electromyography data is generally above 1000 Hz.

[0080] S200 Data preprocessing step: Preprocess the original data of the obtained current window to obtain the data time series to be analyzed of the current window.

[0081] It should be noted that when analyzing data subsequently, only the data of the current window is taken as an example for analysis; the data of other windows are processed in the same way.

[0082] Since the amplitude of EEG data of a typical adult scalp is only 10 μV to 100 μV, which is relatively small, but the obtained original data is full of signal interferences with relatively large amplitudes, such as interferences from sources like blinking, teeth grinding, body movement, touching EEG electrodes and nearby power supply devices, and there is also drift. Therefore, the original data needs to be preprocessed.

[0083] The data preprocessing includes filtering, outlier removal, and electrical noise removal.

[0084] Taking specific data as an example, the sampling frequency of the original data is 100 Hz, and 6000 original data are obtained in the current window. The filter is set to high-pass 0.5 Hz and low-pass 48 Hz. Thus, the high-pass filter removes frequencies below 0.5 Hz, and the low-pass filter removes frequencies above 48 Hz, simultaneously achieving the removal of electrical noise (50 Hz power frequency). In addition, to eliminate the interference of other devices or actions in the operating room, all outliers are deleted. Specifically, any value greater than the mean of the absolute values of the filtered data of the original data in the current window plus 5 times the standard deviation is defined as an outlier. Specifically, the mean and standard deviation of the absolute values of the 6000 filtered data in the current window are calculated. When the absolute value of the filtered data is greater than the mean plus 5 times the standard deviation, it is defined as an outlier and removed; when removing the outlier, the corresponding outlier is filled in with the average of multiple surrounding values. Therefore, the total data length does not decrease and remains 6000, that is, the data time series to be analyzed obtained after processing still contains 6000 data. Figure 3a is an example of the original data of a certain window, Figure 3b is an example of the data to be analyzed after preprocessing of this window; their abscissas are all time (unit: s), and the ordinates are electroencephalogram data (unit: μV). When the data of a certain window is in the processing state, it is the current window mentioned above.

[0085] For the preprocessing of other monitored data, corresponding methods can be adopted. For example, for electromyogram data, the sampling frequency is set to above 1000 Hz, such as 1000 Hz, 2000 Hz, etc. Here it is set to 1000 Hz, the window is set to 10 seconds (that is, the window length is 10 s), and the sliding step is 1 s, thus obtaining 10000 original data. For the preprocessing of 10000 original data, a high-pass filter of 0.5 Hz and a low-pass filter of 450 Hz can be performed; in addition, to remove the interference of power frequency 50 Hz and its harmonics, 50 Hz notch filtering and 100 Hz notch filtering can also be performed.

[0086] Steps of applying EEMD to the data time series: Use the EEMD analysis method to process the data time series to be analyzed in the current window, and adaptively decompose it into multiple ensemble empirical mode functions E-IMFs (s represents a complex number, multiple).

[0087] In the prior art, Empirical Mode Decomposition (EMD, Huang et al., 1998) is an adaptive method designed to analyze non-linear and non-stationary data. It can extract Intrinsic Mode Functions (IMFs) from any data, and has the following good characteristics: all are zero-mean, symmetric about zero, and binary narrow-band; therefore, IMFs provide a compact support for the distribution of data from the trend scale to the whole. To ensure the stability of the EMD method, an ensemble method adding different noises can also be used to assist the decomposition (Wu and Huang, 2009), called Ensemble Empirical Mode Decomposition (EEMD). EEMD can be achieved through the following steps: (1) adding a white noise sequence to the target data sequence to be analyzed; (2) decomposing the target data sequence with added white noise into IMFs using EMD; (3) continuously repeating steps (1) and (2), but using different white noise sequences each time; (4) taking the ensemble average of the IMFs obtained from multiple trials as the final decomposition result to obtain Ensemble Intrinsic Mode Functions E-IMFs.

[0088] In this embodiment, step S300 specifically includes:

[0089] S301 Adding white noise: Adding a white noise sequence to the data time series to be analyzed in the current window. This white noise sequence is randomly generated and has the same length as the data time series. The magnitude of the white noise is determined according to the magnitude of the data to be analyzed, generally taking any value in the range of 0.05 - 0.2 of the magnitude of the data time series, that is, the magnitude ratio of the white noise is selected from any value in the range of 0.05 - 0.2. If it is too small, the effect is not obvious; if it is too large, it will interfere with the original data.

[0090] S302 Using EMD to decompose IMFs: Decomposing the data time series with added white noise sequence into multiple initial Intrinsic Mode Functions IMFs using EMD adaptively, forming a set of initial Intrinsic Mode Functions (IMFs set).

[0091] S303 Continuously repeating S301 and S302, but using a newly randomly generated white noise sequence with the same magnitude ratio each time; thus obtaining multiple batches with IMFs sets respectively. Generally, repeat 10 - 20 times, so as to obtain 10 - 20 batches, and each batch has an IMFs set containing multiple IMFs.

[0092] S304 Taking the ensemble average of the IMFs sets of multiple batches as the decomposition result to obtain multiple Ensemble Intrinsic Mode Functions E-IMFs, forming a set of Ensemble Intrinsic Mode Functions E-IMFs (E-IMFs set).

[0093] Among them, in step S303, the data is processed by means of multiple loop decompositions with different white noises each time; such a processing method can not only make up for the deficiencies of EMD, but also reduce the influence of white noise on the final data.

[0094] The described set of IMFs contains multiple IMFs, which can be IMF1, IMF2, IMF3, etc. The described set of E-IMFs contains multiple E-IMFs, which can be E-IMF1, E-IMF2, E-IMF3, etc. What is well-known to those skilled in the art is that when calculating the ensemble average, it is the result obtained by averaging each element in each set separately; for example, in step S304, the ensemble average of multiple batches of sets of IMFs is to average IMF1 in each batch, average IMF2 in each batch, …, so as to obtain a set of E-IMFs containing these averages.

[0095] Both the set of IMFs and the set of E-IMFs can be used in the subsequent calculation steps of iK. Since the set of E-IMFs has been iterated multiple times relative to the set of IMFs, the set of E-IMFs is more preferably used. However, it should be understood that when describing the decomposition result of EEMD, in the absence of the set of E-IMFs, its result can also be considered as the set of IMFs, that is, regarding the set of IMFs as the described multiple set intrinsic mode functions. At this time, EEMD can also be regarded as EMD.

[0096] Continuing with a specific data example. The data time series to be analyzed has 6000 data. A white noise sequence containing 6000 data is randomly generated, and its amplitude is set to 10% of the amplitude of the data time series (amplitude ratio is 0.1). Then, the EEMD analysis method is used for decomposition, and 9 set intrinsic mode functions E-IMFs can be obtained, namely E-IMF1, E-IMF2, E-IMF3, E-IMF4, E-IMF5, E-IMF6, E-IMF7, E-IMF8, E-IMF9, which constitute a set of E-IMFs; among them, each set intrinsic mode function E-IMF contains 6000 data. As Figure 4 shows an example of 9 E-IMFs obtained by setting and adding a 10% white noise sequence and using EEMD decomposition; among them, E-IMF1 to E-IMF9 are arranged in order from high frequency to low frequency, which is the result of the adaptive decomposition of EEMD. Since the EMD and EEMD analysis methods are already very mature methods, no more detailed description will be given here.

[0097] Calculation steps of the intrinsic kurtosis (iK) of S400: Select up to 8 out of multiple E-IMFs in the current window for calculating the intrinsic kurtosis of the current window. Specifically:

[0098] S401 Select the first N E-IMFs in the current window, where N is a positive integer not greater than 8.

[0099] S402 Use formula (1) to calculate the partial component sums of the selected E-IMFs, obtaining N partial component sums:

[0100]

[0101] Among them, c j (t) is the ensemble empirical mode function (E-IMF), t represents the number of data, N represents the total number of E-IMFs, n represents the nth E-IMF among them, and x n (t) represents the sum from the 1st E-IMF to the nth E-IMF.

[0102] S403 Use formula (2) to calculate the intrinsic kurtosis of each partial component sum, thereby obtaining N intrinsic kurtoses (iK) of the current window, forming the set of intrinsic kurtoses (iK set) of the current window;

[0103]

[0104] Among them, T represents the corresponding number of data time series, μ is the mean of x n (t), and σ is the standard deviation of x n (t).

[0105] Continuing with specific data as an example. In step S300, a white noise sequence with an added amplitude ratio of 10% is set, and 9 E-IMFs obtained by EEMD are as Figure 4 shown. Then select E-IMF1, E-IMF2, E-IMF3, E-IMF4, E-IMF5, E-IMF6, E-IMF7, E-IMF8 among them. It should be understood that in some cases, if only 7 E-IMFs are obtained, then all can be selected, not necessarily 8.

[0106] Use formula (1) to calculate the partial component sums of these 8 E-IMFs, respectively obtaining 8 partial component sums. In formula (1), c j (t) is the ensemble empirical mode function (E-IMF), t represents 6000 data, N = 8, representing the total number of E-IMFs, n represents the nth E-IMF, which is 1, 2,..., 8, and x n (t) represents the sum from the 1st E-IMF to the nth E-IMF. Each partial component sum obtained through formula (1) still contains 6000 data.

[0107] Then, the intrinsic kurtosis of the sum of each partial component is calculated respectively using formula (2). For the sum of 8 partial components, 8 intrinsic kurtoses (iK) are obtained, namely 1K, 2K, 3K, 4K, 5K, 6K, 7K, and 8K respectively; each intrinsic kurtosis is a specific numerical value; these 8 numerical values form the iK set of the current window.

[0108] Calculation steps for the ensemble intrinsic kurtosis (eiK) of the S500 set: Set multiple white noise sequences with different amplitude ratios, apply them respectively to step S300 (wherein, when step S303 is repeated, the same amplitude ratio is used), and perform step S400 to obtain multiple rounds of iK sets. Take the ensemble average of the iK sets under multiple rounds as the result to obtain multiple ensemble intrinsic kurtoses (eiK) of the current window, constituting the set of ensemble intrinsic kurtoses (eiK set) of the current window.

[0109] In one embodiment, set the amplitude ratio of the white noise to be 0.05 - 0.2, and take 6 - 16 ensemble trials within 0.05 - 0.2. For each ensemble trial, perform steps S300 and S400 respectively, so as to obtain the iK sets of each round respectively. Each iK set contains multiple iKs. For white noise with different amplitudes, there will be some differences in the E-IMFs obtained by EEMD, and the corresponding iK sets will also have some differences; the iK sets under multiple trials are averaged to obtain an eiK set containing multiple eiKs. Its advantage is that this is an ensemble average, which is the most likely feature in terms of probability and is also a more stable result.

[0110] Continuing with specific data as an example. Take 7 amplitude ratios of white noise within 0.05 - 0.2, so that 7 ensemble trials can be obtained, which can be used for 7 rounds of calculations. In the first round, set the amplitude of the white noise sequence to be 0.1, enter step S300, and obtain the first-round E-IMF set (including multiple cyclic iterations of 0.1 white noise) through steps S301 - S304, then enter step S400, and obtain the first-round iK set through steps S401 - S403. Assume it contains 8 iKs (i.e., 1K, 2K, 3K, 4K, 5K, 6K, 7K, 8K)… and so on… to obtain the iK sets of all 7 rounds.

[0111] Average the 1K in the iK sets of the 1st - 7th rounds to obtain the first ensemble intrinsic kurtosis e1K, average the 2K in the iK sets of each round to obtain the second ensemble intrinsic kurtosis e2K, …, average the 8K in the iK sets of each round to obtain the eighth ensemble intrinsic kurtosis e8K, so as to obtain eight ensemble intrinsic kurtoses (eiK), which constitute the eiK set. Figure 5 Shows an example of the eiK set calculated when taking 7 amplitude ratios of white noise within 0.05 - 0.2, which contains 8 eiKs.

[0112] Although the above steps describe how to obtain multiple set intrinsic kurtoses (eiK) and their sets for the current window; it is obvious that by using the above method, the set intrinsic kurtoses (eiK) and their sets for any one window can be obtained separately, and each eiK set includes multiple eiKs, for example, 8.

[0113] Output step of S600 data: Sort and output multiple intrinsic kurtoses (iK) and / or multiple set intrinsic kurtoses (eiK) of each window, and the results are displayed on the monitor and archived, which can be used as a guide for the depth of clinical anesthesia.

[0114] In a specific embodiment, each iK in the iK set of each window is output separately to obtain a curve of a single iK on the time axis (i.e., the iK monitoring curve), and the number of curves corresponds to the number of iKs. These curves can be used as a monitoring guide for clinical anesthesia.

[0115] In a specific embodiment, each eiK in the eiK set of each window is output separately to obtain a curve of a single eiK on the time axis (i.e., the eiK monitoring curve), and the number of curves corresponds to the number of eiKs. These curves can be used as a monitoring guide for clinical anesthesia.

[0116] In specific operations, taking the eiK set as the final result for output as an example, the output of the iK set (before set averaging) is similar. In step S100, the first raw data obtained from the first window is processed successively using steps S200, S300, S400, and S500 to obtain the eiK set of the first window, which includes multiple eiKs, assumed to be 8; the second raw data obtained from the second window is processed successively using steps S200, S300, S400, and S500 to obtain the eiK set of the second window, which also includes multiple eiKs, assumed to be 8; the third raw data obtained from the third window is processed successively using steps S200, S300, S400, and S500 to obtain the eiK set of the third window, which also includes multiple eiKs, assumed to be 8..... According to such a calculation method, during the data acquisition process, the newly acquired window data can be continuously calculated to obtain their respective eiK sets. The respective eiKs (e1K, e2K, e3K,... e8K) in the eiK sets of each window are arranged in the order of data acquisition time, thereby obtaining the sequentially output eiK monitoring curves; specifically, they are displayed as 8 curves, namely, the e1K monitoring curve output in chronological order for all windows (the entire anesthesia surgery process), the e2K monitoring curve output in chronological order for all windows, the e3K monitoring curve output in chronological order for all windows, the e4K monitoring curve output in chronological order for all windows, the e5K monitoring curve output in chronological order for all windows, the e6K monitoring curve output in chronological order for all windows, the e7K monitoring curve output in chronological order for all windows, and the e8K monitoring curve output in chronological order for all windows.

[0117] The following describes a monitoring curve of an anesthesia surgery process to more clearly describe and understand the present application. Figure 6The original data collected within the time axis of 0 - 8700 s during the entire anesthesia operation is shown. There are 8 monitoring curves obtained through the monitoring method described in this embodiment, corresponding to e1K - e8K in the eiK set of each window respectively. Among them, the abscissa of each monitoring curve is the time axis, and the ordinate is eiK. That is, each eiK corresponding to each second in 8700 s is the result obtained by calculating 6000 pieces of original data through the steps described above. Taking the second figure as an example, among them, the abscissa correspondingly is the operation time of 0 - 8700 s, from the start to the end of the operation; the ordinate correspondingly is the e2K value of each time window; each e2K value is obtained by using the original data of the current time window through steps S100 - S500. It can be seen from the figure that the e2K value at the front section of the monitoring curve is relatively high and fluctuates severely, indicating that it is in the surgical preparation and induction stage; then the e2K value in the middle section is relatively stable, indicating that it is in the middle stage of the operation; the final section is the later stage of the operation, and the patient's consciousness gradually wakes up, so the e2K value increases again and fluctuates severely.

[0118] Each collective intrinsic kurtosis in the concentration of collective intrinsic kurtosis can be used as a monitoring parameter. When output on the monitor, all can be output, for example, output Figure 6 the 8 monitoring curves shown in. In some embodiments, since the collective intrinsic kurtosis depends on the scale of the intrinsic mode function, and according to experience and data comparison, it is found that the second collective intrinsic kurtosis e2K in the concentration of collective intrinsic kurtosis has better effects. Therefore, the second collective intrinsic kurtosis can be selected as the main result, and other collective intrinsic kurtoses can be used as supporting results. At any time, a collective intrinsic kurtosis value roughly less than 4 will be marked as an unconscious state; roughly higher than 6 will be marked as a conscious state.

[0119] Figure 7 are EEG signals with the same source. The e1K monitoring curve corresponding to the first collective intrinsic kurtosis of the eiK set and the e2K monitoring curve corresponding to the second collective intrinsic kurtosis obtained through the monitoring method of this application are Figure 6 the enlarged views of the e1K monitoring curve and the e2K monitoring curve in, and the characteristics they show are close to the trend of the corresponding BIS curve, indicating that the monitoring method of this application is reliable.

[0120] Comparative analysis 1:

[0121] This content compares the anesthesia depth monitoring method based on collective intrinsic kurtosis provided by this application with the BIS anesthesia monitoring method in the prior art.

[0122] Both monitoring methods use the same original data. Among them, Figure 8a are the monitoring curves obtained by using the BIS monitoring method (hereinafter referred to as BIS monitoring curves) and the corresponding pre - processed EEG data,Figure 8a In it, the abscissa is time (unit: s), and the ordinate is EEG data (unit: μV), which is used to represent the value of EEG data. Only the trend of the BIS monitoring curve over time is shown in the figure. Figure 8b This is the e2K monitoring curve obtained in this application.

[0123] The overall trends of the two monitoring curves are similar; however, the e2K monitoring curve presents the anesthesia curve profile from the induction stage to the recovery stage. In addition, through comparison, it can be found that the BIS monitoring curve has an obvious sharp change in the induction stage (600 - 900 seconds); the data was disturbed to some extent during the operation, which led to a great interference in the BIS score; the BIS monitoring curve also shows that in the later stage of the operation, the recovery stage gradually extends (changes slowly). For the e2K monitoring curve of this application, it is more obvious when entering the operation from the induction stage; the key difference is that there are no large disturbances in the e2K monitoring curve during the operation because the patient does not wake up; in addition, small disturbances are possible. Moreover, the most significant difference lies in the recovery stage. The recovery of most operations is as sudden as the induction, but the BIS monitoring curve fails to show this feature; while the e2K monitoring curve has a large fluctuation amplitude in the later stage of the operation, which obviously shows the change of the patient's consciousness. Therefore, it can provide better monitoring guidance for doctors.

[0124] Comparative analysis 2:

[0125] This content also compares the anesthesia depth monitoring method based on ensemble intrinsic kurtosis provided in this application with the entropy analysis monitoring method in the prior art.

[0126] The entropy analysis monitoring method uses the same original data as in Comparative analysis 1. Among them, Figure 9a This is the monitoring curve of intrinsic sample entropy, Figure 9b This is the monitoring curve of multi-scale sample entropy. Figure 8b This is the e2K monitoring curve obtained in this application. It can be seen from the figure that although the monitoring curves of the entropy analysis method are relatively stable during the operation, there are problems in the induction stage and the recovery stage; they cannot directly show the trend of the change of the patient's consciousness and need further fitting to show the anesthesia depth, which is not intuitive and quantitative.

[0127] This embodiment provides a new tool for quantitatively studying anesthesia; since this method measures consciousness, it is independent of the anesthetic used, the age and gender of the patient; therefore, it can be used as a general anesthesia depth measurement method.

[0128] As mentioned above, the monitoring method provided in this application can also be used for the monitoring of muscle activity. Figure 10 This is the preprocessed electromyogram data of the tremor muscle monitored in Parkinson's patients, Figure 11The e2K monitoring curve is obtained by processing the EMG data of each window using the monitoring method provided in this application. Figure 12 The 8 eiK monitoring curves are obtained by processing the EMG data of each window using the monitoring method provided in this application. The sampling frequency of the EMG data is 1000 Hz, the window length is set to 10 seconds, and the sliding step is 1 s. After completing the preprocessing steps for the EMG data within the window, it enters the same or similar steps as the anesthesia depth monitoring to obtain Figure 11 and Figure 12 the eiK monitoring curves shown. The muscle tremor activity of Parkinson's patients is the action potential with a fixed rhythm sent from the upper motor neurons. The eiK monitoring curve reflects the modulation intensity of the upper neurons on the muscles during the tremor of the Parkinson's patient. Through monitoring for a period of time, the tremor occurrence structure (such as the percentage of no tremor, the percentage of tremor, etc.) of Parkinson's patients within this time period can be quantified. It can be seen that this monitoring method based on intrinsic kurtosis is also particularly applicable in muscle activity monitoring.

[0129] The second embodiment of this application provides an anesthesia depth monitoring system based on intrinsic kurtosis (which can be simply referred to as the monitoring system), and this system is used to implement the monitoring method described in any of the previous embodiments. The monitoring system includes: a data acquisition module and a data analysis module, where

[0130] The data acquisition module is configured to collect EEG data using electrodes to obtain the original data;

[0131] The data analysis module includes a windowing module, a data preprocessing module, an EEMD decomposition module, an iK calculation module, an eiK calculation module, and a result output module; where

[0132] The windowing module is configured to window the collected EEG data to obtain the original data of each window;

[0133] The data preprocessing module is configured to filter, remove outliers, and remove electrical noise from the collected original data to obtain the data time series to be analyzed;

[0134] The EEMD decomposition module is configured to adaptively decompose the data time series to be analyzed in the current window into multiple E-IMFs using the EEMD analysis method;

[0135] The iK calculation module is configured to select the first N E-IMFs from multiple E-IMFs, use formula (1) to find the partial component sums, obtain N partial component sums, and further calculate the intrinsic kurtosis (iK) of each partial component sum using formula (2), so as to obtain the N iKs of the current window and form an iK set; Formula (1) and formula (2) refer to the first embodiment;

[0136] The eiK calculation module is configured to set multiple white noise sequences with different amplitude ratios, apply them to the EEMD decomposition module and the iK calculation module respectively to obtain multiple rounds of iK sets; take the set average value of the iK sets in multiple rounds as the result to obtain multiple set intrinsic kurtoses (eiK) of the current window, and form an eiK set; and

[0137] The result output module is configured to sort and output multiple intrinsic kurtoses (iK) and / or multiple set intrinsic kurtoses (eiK) of each window.

[0138] In some embodiments, the monitoring system may further include:

[0139] The data archiving module is configured to store data for future reference;

[0140] The data display module is configured to display the output result of the result output module on a monitor, which can be used as a guide for the depth of clinical anesthesia.

[0141] It should be understood that the technical features described in the monitoring method can also be reasonably applied to the monitoring system of this embodiment. Those that have been described will not be repeated. It should be noted that the above-mentioned each module can be a functional module or a program module, and can be implemented by software or by hardware. For the modules implemented by hardware, the above-mentioned each module can be located in the same processor, or the above-mentioned each module can also be located in different processors in any combination form.

[0142] The third embodiment of the present application provides an application of the monitoring method described in any of the previous embodiments, which can be used for medical monitoring. The medical monitoring includes but is not limited to anesthesia depth monitoring and muscle activity monitoring.

[0143] The fourth embodiment of the present application provides a computer device, which may include a processor 101 and a memory 102 storing computer program instructions, as Figure 13 shown.

[0144] Specifically, the processor 101 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be one or more integrated circuits configured to implement the embodiments of the present application. The memory 102 may include a mass storage for data or instructions. By way of example and not limitation, the memory 102 may include a hard disk drive (HDD), a floppy disk drive, a solid state drive (SSD), a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory 102 may include removable or non-removable (or fixed) media. Where appropriate, the memory 102 may be internal or external to the data processing device.

[0145] The memory 102 can be used to store or cache various data files required for processing and / or communication, as well as possible computer program instructions executed by the processor 101.

[0146] The processor 101 reads and executes the computer program instructions stored in the memory 102 to implement the monitoring method described in any of the foregoing embodiments.

[0147] In some embodiments, the computer device may further include a communication interface 103 and a bus 104. As shown, the processor 101, the memory 102, and the communication interface 103 are connected via the bus 104 and complete communication with each other. The communication interface 103 is used to implement communication between the modules, devices, units, and / or devices in the embodiments of the present application. The communication interface 103 can also implement data communication with other components. Figure 13 As shown, the processor 101, the memory 102, and the communication interface 103 are connected via the bus 104 and complete communication with each other. The communication interface 103 is used to implement communication between the modules, devices, units, and / or devices in the embodiments of the present application. The communication interface 103 can also implement data communication with other components.

[0148] A fifth embodiment of the present application provides a computer-readable storage medium, on which computer program instructions are stored; when the computer program instructions are executed by a processor, the monitoring method described in any of the foregoing embodiments is implemented.

[0149] The technical features of the above-described embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0150] The above-described embodiments merely represent several embodiments of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the scope of the application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the appended claims.

Claims

1. A monitoring method based on intrinsic kurtosis, characterized in that: The following steps are involved: Steps to obtain raw data: collect data as raw data; Data preprocessing step: preprocess the acquired raw data to obtain the data time series to be analyzed; The steps of applying EEMD to the data time series are as follows: using the EEMD analysis method to process the data time series and adaptively decomposing it into a plurality of ensemble intrinsic mode functions (E-IMFs); and The calculation steps of intrinsic kurtosis (iK) are: Formula (1) is used to calculate the partial component sum of N set intrinsic mode functions to obtain N partial component sums; Among them, c j (t) is the collective intrinsic mode function (E-IMF), t represents the number of data, N represents the total number of E-IMFs, which is a positive integer, n represents the nth E-IMF, and x n (t) represents the sum from the first E-IMF to the nth E-IMF; Formula (2) is used to calculate the intrinsic kurtosis of the sum of each partial component, and N intrinsic kurtosis (iK) are obtained, which constitute a set of intrinsic kurtosis (iK set); Among them, T represents the corresponding data time series number, μ is x n (t), σ is the mean of x n (t) standard deviation; At least a part of the iK set is used as an output parameter of the monitoring method.

2. The monitoring method according to claim 1, characterized in that: The method further comprises a step of calculating the set intrinsic kurtosis (eiK): setting a plurality of white noise sequences with different amplitude ratios, applying them to the step of applying the EEMD to the data time series respectively, and performing the step of calculating iK to obtain multiple rounds of iK sets; taking the set average of the iK sets under multiple rounds as the result, and obtaining multiple set intrinsic kurtosis (eiK); Wherein, at least a part of the plurality of set intrinsic kurtosis is used as an output parameter of the monitoring method.

3. The monitoring method according to claim 1 or 2, characterized in that: In the step of obtaining the raw data, a sliding window is used to collect data, and the data of each window is used as the raw data; the output parameters of each window can be obtained by using the monitoring method; The monitoring method also includes a data output step, wherein the output parameters of each window are displayed as results on a monitor and archived as a monitoring guide.

4. The monitoring method according to claim 1, characterized in that: In the step of acquiring raw data, the sampling frequency of the data is not less than 100 Hz; in the step of preprocessing the data, the preprocessing includes filtering, outlier removal and electrical noise removal.

5. The monitoring method according to any one of claims 1 to 4, characterized in that: The step of applying EEMD to the data time series specifically includes: Add white noise: add a white noise sequence to the data time series to be analyzed; the amplitude ratio of the white noise is any value between 0.05 and 0.2; Decompose IMFs using EMD: Adaptively decompose the data time series with white noise into multiple initial intrinsic mode functions (IMFs) using EMD; Repeat the steps of adding white noise and decomposing IMFs using EMD, but use a newly randomly generated white noise sequence with the same amplitude ratio each time; thereby obtaining multiple batches with multiple IMFs respectively; The ensemble average of multiple IMFs of multiple batches is taken as the decomposition result to obtain the multiple E-IMFs.

6. The monitoring method according to claim 5, characterized in that: The steps of adding white noise and decomposing IMFs using EMD were repeated 10-20 times to obtain 10-20 batches.

7. The monitoring method according to any one of claims 2 to 4, characterized in that: In the calculation step of iK, a maximum of 8 of the multiple E-IMFs are selected for calculating the sum of partial components; that is, N is a positive integer less than or equal to 8; in the calculation step of eiK, the amplitude ratio of the white noise is set to 0.05-0.2, and 6-16 sets of trials are taken in 0.05-0.2 as the white noise sequences with multiple different amplitude ratios.

8. The monitoring method according to claim 3, characterized in that: In the data output step, at least a part of the multiple eiKs of each window are output respectively to obtain a curve about a single eiK on the time axis, namely, an eiK monitoring curve, which serves as a monitoring guide.

9. The monitoring method according to claim 3 or 8, characterized in that: In the data output step, the second one of the multiple eiKs, namely e2K, is selected as the main result, and the other eiKs are used as supporting results.

10. A monitoring system based on intrinsic kurtosis, characterized in that: It includes data acquisition module and data analysis module, among which, The data acquisition module is configured to collect data, thereby obtaining raw data; The data analysis module includes a windowing module, a data preprocessing module, an EEMD decomposition module, an iK calculation module and a result output module; wherein, The windowing module is configured to collect data in a windowed manner to obtain raw data of each window; The data preprocessing module is configured to filter, remove outliers and remove electrical noise from the collected raw data to obtain a data time series to be analyzed; The EEMD decomposition module is configured to adaptively decompose the data time series to be analyzed in the current window into multiple E-IMFs using the EEMD analysis method; The iK calculation module is configured to select the first N E-IMFs from the plurality of E-IMFs, calculate the partial component sums using formula (1), obtain N partial component sums, and further calculate the iK of each partial component sum using formula (2) to obtain the iK set of the current window; wherein formula (1) and formula (2) are respectively as follows: Among them, c j (t) is the E-IMF, t represents the number of data, N represents the total number of E-IMFs, which is a positive integer, n represents the nth E-IMF, x n (t) represents the sum from the first E-IMF to the nth E-IMF; Among them, T represents the corresponding data time series number, μ is x n (t), σ is the mean of x n (t) standard deviation; The result output module is configured to output the multiple iKs of each window in sorted order.

11. The monitoring system according to claim 10, characterized in that: Further including: The eiK calculation module is configured to set multiple white noise sequences with different amplitude ratios, apply them to the EEMD decomposition module and the iK calculation module respectively, and obtain multiple rounds of iK sets; take the set average of the iK sets under multiple rounds as the result to obtain multiple eiKs of the current window; and the result output module is configured to sort and output the multiple eiKs of each window to replace the output of the iK.

12. The monitoring system according to claim 10 or 11, characterized in that: Also includes: a data archiving module configured to store data for future reference; The data display module is configured to display the output result of the result output module on a monitor as a monitoring guide.

13. An application of the monitoring method based on intrinsic kurtosis according to any one of claims 1 to 9, wherein the monitoring method can be used for medical monitoring, including anesthesia depth monitoring and muscle activity monitoring.

14. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the intrinsic kurtosis-based monitoring method according to any one of claims 1 to 9 when executing the computer program.

15. A computer-readable storage medium having a computer program stored thereon, wherein when the program is executed by a processor, the monitoring method based on intrinsic kurtosis as claimed in any one of claims 1 to 9 is implemented.

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