Animal electrocardiogram monitoring device and monitoring method
By calculating the heartbeat interval difference and frequency range information noise pollution degree of electrocardiogram data, and combining it with the wavelet threshold algorithm, the problem of insufficient noise suppression in animal electrocardiogram detection in traditional methods is solved, and more accurate electrocardiogram data acquisition and diagnosis are achieved.
Patent Information
- Application Number
- CN202512029431.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-30
- Publication Date
- 2026-02-17
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
Traditional wavelet thresholding algorithms cannot adapt to the electrocardiogram (ECG) detection of different animals, leading to problems such as over-smoothing or failure to effectively suppress noise in animal ECG data in animal husbandry.
By calculating the heartbeat interval difference in ECG data, the ECG sequence is divided into multiple frequency intervals. Noise removal is then performed using the information noise level of the frequency intervals and wavelet thresholding algorithm to obtain accurate ECG data.
It improves the ability to identify different animal electrocardiogram data, enhances data denoising, and improves the accuracy of animal condition diagnosis and treatment efficacy.
Smart Images
Figure CN121533742A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of electrocardiogram data processing technology, specifically to an animal electrocardiogram monitoring device and monitoring method. Background Technology
[0002] With the development of animal husbandry, the demand for veterinary medicine is also expanding, as an animal's physical condition determines its subsequent growth. Electrocardiogram (ECG) testing when an animal is sick allows for targeted treatment based on the ECG reading, ensuring effective and healthy growth, providing higher-quality animal products, and thus improving the economic benefits for farmers.
[0003] Currently, animal electrocardiogram (ECG) monitoring typically involves collecting ECG data using an ECG monitor and then filtering the data using a wavelet thresholding algorithm to determine the animal's condition. However, in livestock farming, there are many different animal species, each with varying heart rate ranges. Furthermore, an animal's ECG data can change after it becomes ill. Therefore, traditional fixed-threshold wavelet thresholding algorithms are unsuitable for ECG monitoring in livestock farming, leading to problems such as over-smoothing or ineffective noise suppression of ECG data from different animals. Summary of the Invention
[0004] To address the aforementioned technical problems, this application provides an animal electrocardiogram monitoring device and monitoring method, the specific technical solution of which is as follows: In a first aspect, one embodiment of this application provides an animal electrocardiogram monitoring method, which includes the following steps: The electrocardiogram (ECG) sequence of the animal was collected using an ECG monitor, and the ECG sequence consisted of ECG data collected at multiple sampling times. Based on the distribution of maximum points in animal electrocardiogram (ECG) sequences, the heartbeat interval difference in ECG data is calculated. Based on the heartbeat interval difference, the animal electrocardiogram sequence is divided into multiple cardiac electronic sequences; the frequency of the cardiac electronic sequence in the frequency domain is divided into several frequency intervals; using the value and number of amplitude spectra of non-zero elements at the same frequency in the frequency domain of the cardiac electronic sequence, the information noise pollution degree of each frequency interval is calculated. Based on the aforementioned information noise level, a wavelet thresholding algorithm is used to denoise the animal electrocardiogram (ECG) sequence, resulting in a processed ECG sequence, which serves as reference ECG data for auxiliary judgment during animal ECG monitoring.
[0005] Preferably, the method for calculating the heart rate interval difference is as follows: In the formula, This represents the heart rate interval difference in electrocardiogram (ECG) data. This represents the number of elements in the heartbeat peak sequence. , Let i and j represent the i-th and 1-th elements in the heart rate peak sequence, respectively. The function represents the rounding function; wherein the heart rate peak sequence is determined using all the maximum values and their positions in the animal electrocardiogram sequence.
[0006] Preferably, the method for determining the heart rate peak sequence is as follows: Identify all maxima and their locations in an electrocardiogram (ECG) sampling sequence; Arrange the position values in descending order of the magnitude of the maximum points to obtain the peak position sequence; Based on the number of heartbeats S obtained from the animal's electrocardiogram sequence, the first S maxima in the peak position sequence are selected, and the S maxima are rearranged in ascending order of element value to obtain the heartbeat peak position sequence.
[0007] Preferably, the method for dividing the frequency range is as follows: The cardiac electron sequence is used as the input to the Fourier transform algorithm, and the output is the spectrum of each cardiac electron sequence. Obtain the maximum frequency values in the spectrum of all cardiac electron sequences; The frequency is evenly divided into N frequency intervals from 0 to the maximum value; where N is a preset value.
[0008] Preferably, the method for calculating the information noise pollution level includes: The amplitude spectra of each frequency range in the spectrograms of multiple cardiac electron sequences are arranged in ascending order of frequency to form multiple amplitude spectrum sequences for each frequency range. All amplitude spectra at the same frequency are grouped into a set of amplitude spectra with the same frequency. Determine the number of non-zero elements in the set of amplitude spectra at the same frequency to determine the standard amplitude spectrum and noise impact at the same frequency; Arrange the frequencies of each frequency range in ascending order to form a standard amplitude spectrum sequence for each frequency range. The frequency noise difference degree of each frequency interval is determined by using the differences between all amplitude spectrum sequences of each frequency interval and the standard amplitude spectrum sequence of each frequency interval; Based on the frequency noise difference and the number of non-zero amplitude spectra in different amplitude spectrum sequences of the frequency range, the information noise pollution level of each frequency range is calculated.
[0009] Preferably, determining the number of non-zero elements in the same-frequency amplitude spectrum set to determine the standard amplitude spectrum and noise impact at the same frequency includes: When the non-zero elements in the same frequency amplitude spectrum set are greater than or equal to the preset threshold, the mean of the non-zero elements in the same frequency amplitude spectrum set is calculated and recorded as the standard amplitude spectrum at the same frequency, and the noise impact degree at the same frequency is assigned to 0. When the number of non-zero elements in the amplitude spectrum set at the same frequency is less than a preset threshold, the standard amplitude spectrum at the same frequency is assigned a value of 0, and the noise impact at the same frequency is assigned the number of non-zero elements.
[0010] Preferably, the method for calculating the frequency noise difference is as follows: In the formula, This represents the frequency noise difference in each frequency range. This represents the mean of the noise impact of the set of amplitude spectra of all frequencies within each frequency interval. This represents the number of all amplitude spectrum sequences in each frequency range. This represents the j-th amplitude spectrum sequence in each frequency interval. This represents the standard amplitude spectrum sequence for each frequency range. Represents the Euclidean distance function. This indicates a zero-multiplication adjustment factor to prevent the factor from taking a value of 0.
[0011] Preferably, the information noise pollution level is determined by multiplying the standard deviation of the number of non-zero amplitude spectra in all amplitude spectrum sequences within each frequency interval by the frequency noise difference in each frequency interval.
[0012] Preferably, the step of denoising the animal electrocardiogram (ECG) sequence using a wavelet thresholding algorithm based on the information noise level to obtain the processed ECG sequence includes: Animal electrocardiogram (ECG) sequences are used as input to the wavelet thresholding algorithm, and the number of wavelet decomposition layers is the same as the number of frequency intervals divided in the ECG sequence. The soft threshold for each wavelet threshold is: In the formula, This represents the soft threshold of the J-th decomposition layer. This represents the normalized value of the information noise pollution level in the J-th frequency interval. This represents the root mean square error of noise in an animal electrocardiogram sequence. This indicates the sequence length of an animal's electrocardiogram (ECG) sequence.
[0013] Secondly, another embodiment of this application also provides an animal electrocardiogram monitoring device, comprising: An electrocardiogram (ECG) monitor is used to collect electrocardiogram (ECG) sequences from animals. An electrocardiogram (ECG) processor, which is communicatively connected to the ECG detector, is used to execute the method described above in order to output the processed ECG sequence. An electrocardiogram (ECG) display, which is communicatively connected to the ECG processor, is used to display the processed ECG sequence to assist in the assessment of the animal's physiological state.
[0014] This application has at least the following beneficial effects: 1. This application accurately identifies the Q and R waves in animal electrocardiogram data, effectively detecting the number of heartbeats during animal testing. It can adapt to the different heartbeat frequencies of different animals during animal testing, improving the identification of heartbeat data and enhancing the data noise reduction capability.
[0015] 2. By analyzing the differences in animal electrocardiogram data in different frequency ranges, this application can determine the frequency noise difference and information noise pollution level in different frequency ranges, quantify the degree of information pollution of animal electrocardiogram data by noise, and thus provide data support for accurate data denoising.
[0016] 3. This application processes animal electrocardiogram (ECG) data at different scales using different wavelet soft thresholds, which effectively improves the filtering effect of ECG data, thereby increasing the accuracy of the diagnosis of animal conditions and enhancing the effectiveness of subsequent targeted treatments. Attached Figure Description
[0017] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0018] Figure 1 This is a flowchart of an animal electrocardiogram monitoring method provided in one embodiment of this application. Detailed Implementation
[0019] Example 1 This application provides an embodiment of an animal electrocardiogram monitoring method, which can be found in the following details. Figure 1 The method includes the following steps: Step 1: Collect the animal's electrocardiogram (ECG) sequence using an ECG monitor. The ECG sequence consists of ECG data collected at multiple sampling times.
[0020] The animal electrocardiogram (ECG) monitoring device includes an ECG detector, which is equipped with an intelligent sensor for collecting ECG data. In this embodiment, the acquisition frequency is 20Hz, and the acquisition duration is 1 minute. The collected ECG data are arranged in chronological order of acquisition time.
[0021] Since animals are usually tested and treated in farms, the environment in livestock farms is complex and may lead to missing data. In this embodiment, the mean interpolation method is used to complete the missing data, and the completed data is recorded as animal electrocardiogram sequence.
[0022] Step 2: Based on the distribution of the maximum points in the animal electrocardiogram (ECG) sequence, calculate the heart rate interval difference in the ECG data.
[0023] Animal electrocardiogram (ECG) data, like human ECG data, exhibits five waves: P, Q, R, S, and T. The R wave represents the peak region of the ECG data, indicating ventricular depolarization. Each heartbeat produces exactly one P, Q, R, S, and T wave.
[0024] Therefore, the peak value of the R-wave region represents the maximum value of the ECG data. The animal ECG sequence is used as input to the maximum point detection algorithm, and the algorithm outputs all the maximum points in the animal ECG sequence and their corresponding position values. All the position values of the maximum points are then arranged in descending order of their corresponding maximum values to obtain the peak position sequence.
[0025] Meanwhile, the built-in automatic analysis function of the electrocardiogram (ECG) monitor can analyze the animal's heart rate. Therefore, the number of heartbeats in the animal's ECG sequence can be obtained through the built-in function of the ECG monitor and its value is recorded as S.
[0026] Next, the first S elements of the peak position sequence are obtained and arranged in ascending order to obtain the heartbeat peak position sequence, which is used to characterize the position of the R-wave peak value in each heartbeat data. From this, the heartbeat interval difference of the ECG data is calculated.
[0027] In the formula, This represents the heart rate interval difference in electrocardiogram (ECG) data. This represents the number of elements in the heartbeat peak sequence. , Let i and j represent the i-th and 1-th elements in the heart rate peak sequence, respectively. This represents the rounding function.
[0028] The greater the difference between each element in the heartbeat peak sequence and the first element, the longer the average heartbeat time at the corresponding moment during the detection process. Conversely, the more elements in the heartbeat peak sequence, the more heartbeats are detected. This method can adapt to different animal species and types of ECG data, reducing the impact of ectopic peaks on ECG data. Therefore, the larger the average mean of the intervals between elements in the heartbeat peak sequence and the first element, the greater the difference in heartbeat intervals in the ECG data, accurately reflecting the state of different heartbeats in the animal.
[0029] Step 3: Divide the animal electrocardiogram sequence into multiple cardiac electronic sequences based on the heartbeat interval difference; divide the frequency of the cardiac electronic sequence in the frequency domain into several frequency intervals; calculate the information noise pollution degree of each frequency interval by using the amplitude spectrum values and numbers of non-zero elements at the same frequency in the frequency domain of the cardiac electronic sequence.
[0030] Then, the animal electrocardiogram (ECG) sequence is evenly divided according to the length of the heartbeat interval difference t, and the excess part is discarded to obtain multiple ECG sequences, which are used to characterize the ECG data of each heartbeat. The number of ECG sequences is denoted as L.
[0031] During electrocardiogram (ECG) monitoring in animals, the data is affected by the animal's respiration and electromyography (EMG), resulting in low-frequency noise interference and causing errors. However, this interference is a chaotic state and does not occur with every heartbeat. Therefore, there will be frequency differences between ECG data from different time periods. The greater the frequency difference, the stronger the external interference to the animal's ECG sequence, thus requiring stronger noise interference capabilities.
[0032] Therefore, the cardiac electron sequence is used as the input to the Fourier transform algorithm, and the output is the spectrum of each cardiac electron sequence. The spectrum shows the frequency and the corresponding amplitude spectrum, which is used to characterize the frequency change of the heartbeat data at each time period.
[0033] To analyze the variation characteristics of heartbeat in different frequency ranges, the maximum frequency in all cardiac electron sequences is obtained. Then, the frequency is uniformly divided into N frequency ranges from 0 to the maximum value, where N is 6 in this embodiment. The calculation of the Fourier transform is a well-known technique, and the specific calculation steps will not be described in detail here.
[0034] Take the amplitude spectrum of any frequency range as an example: The greater the difference between the same frequencies, the more noise interference the frequency is subject to. At the same time, the more times the amplitude spectrum of the frequency is 0, the less external interference the frequency is subject to.
[0035] Therefore, for multiple amplitude spectrum sequences within a frequency range, i.e., L sequences, the amplitude spectra at the same frequency are extracted, i.e., the elements at the same position in the amplitude spectrum sequences, forming a set of amplitude spectra with the same frequency. When the non-zero elements in the set of amplitude spectra with the same frequency are greater than or equal to a preset threshold, the threshold in this embodiment is set to a value of [value missing]. c takes the value 0.85. Calculate the mean of the non-zero elements in the same frequency amplitude spectrum set, denoted as the standard amplitude spectrum, and assign the noise impact degree to 0. When the non-zero elements in the same frequency amplitude spectrum set are less than the preset threshold, the standard amplitude spectrum value is 0, and the noise impact degree is assigned to the number of non-zero elements.
[0036] Therefore, the standard amplitude spectrum is calculated for each frequency, and then arranged in ascending order according to their corresponding frequencies to obtain the standard amplitude spectrum sequence. Similarly, the standard amplitude spectrum sequence for each frequency interval can be obtained.
[0037] Therefore, the frequency noise difference in each frequency range is calculated: In the formula, This represents the frequency noise difference in each frequency range. This represents the mean of the noise impact of the set of amplitude spectra of all frequencies within each frequency interval. This represents the number of all amplitude spectrum sequences in each frequency range. This represents the j-th amplitude spectrum sequence in each frequency interval. This represents the standard amplitude spectrum sequence for each frequency range. Represents the Euclidean distance function. This represents the zero-multiplication adjustment factor, which prevents the factor from taking a value of 0. In this embodiment, since the noise impact degree is an integer, the zero-multiplication adjustment factor is set to 1 in this embodiment.
[0038] Similarly, calculate the frequency noise difference for each frequency range.
[0039] The greater the noise interference in electrocardiogram (ECG) data, the greater the difference in amplitude spectra at the same frequency across different heartbeats. This increases the number of non-zero elements in the frequency spectrum, resulting in a greater degree of noise influence within the set of amplitude spectra at the same frequency. Furthermore, a greater difference between different amplitude spectrum sequences and a standard amplitude spectrum sequence—that is, a larger Euclidean distance between the two sequences—indicates a greater difference between each heartbeat, indicating a stronger noise interference and a greater frequency noise variation within that frequency range. Therefore, it is crucial to suppress noise in this range to improve the accuracy of animal ECG data.
[0040] Regarding the number of non-zero amplitude frequencies in an amplitude spectrum sequence, the greater the difference in the number of frequencies between different amplitude spectrum sequences, the stronger the noise interference in the electrocardiogram (ECG) data. This is because noise causes changes in ECG data, thus causing normally zero amplitude frequencies to be non-zero. Therefore, the smaller the difference in the number of non-zero frequencies between different amplitude spectrum sequences within the same frequency range, the weaker the noise impact on the data, and the higher the purity of the animal ECG data.
[0041] Therefore, the information noise pollution level for each frequency range is calculated: In the formula, This indicates the level of information noise pollution in each frequency range. This represents the standard deviation of the number of frequencies with non-zero amplitude spectra in all amplitude spectrum sequences within each frequency interval. This indicates the frequency noise difference in each frequency range.
[0042] The smaller the difference between non-zero elements in the amplitude spectrum sequence within a frequency range, the less noise interference the data experiences, and the higher the purity of the data. This results in a smaller standard deviation of non-zero elements in all amplitude spectrum sequences within the range, and also a smaller value of frequency noise difference within the frequency range. Consequently, the information noise pollution level within the frequency range is lower, and the noise suppression capability should be lower to prevent the data from becoming too smooth, which could lead to a decrease in the accuracy of data calibration and denoising.
[0043] Step 4: Based on the noise level of the information, the wavelet thresholding algorithm is used to denoise the animal electrocardiogram (ECG) sequence to obtain the processed ECG sequence, which is used as reference ECG data to assist in the judgment during animal ECG monitoring.
[0044] The information noise contamination level for all frequency intervals mentioned above is normalized using summation normalization, which involves dividing each element by the sum of all elements. When the animal electrocardiogram sequence is not affected by noise, the information noise contamination level for all frequency intervals of the data is 0, and the normalized value is also 0.
[0045] Therefore, the animal electrocardiogram (ECG) sequence is used as input to the wavelet thresholding algorithm. The number of wavelet decomposition levels is the same as the number of frequency intervals divided in the ECG sequence, both being N. The soft threshold for each wavelet threshold level is: In the formula, This represents the soft threshold of the J-th decomposition layer. This represents the normalized value of the information noise pollution level in the J-th frequency interval. This represents the root mean square error of noise in an animal electrocardiogram sequence. This represents the sequence length of the animal electrocardiogram (ECG) sequence. The calculation of the root mean square error of noise is a well-known technique, and the specific calculation steps will not be elaborated here.
[0046] By analyzing the differences in electrocardiogram (ECG) data from different heartbeats of animals across different frequency ranges, calculating the differences within each frequency range, determining the noise intensity and intensity distribution range of the animal's ECG data, and calculating the information noise pollution level of the ECG data in different frequency ranges, the information noise pollution level can be used to accurately suppress noise in different frequency ranges of ECG data, prevent over-processing of data, improve the accuracy of ECG data, and provide veterinarians with a more accurate data basis for judging the animal's condition.
[0047] Next, the output of the wavelet thresholding algorithm is used as a more accurate animal electrocardiogram sequence, which is used to assist veterinarians in diagnosing the animal's health status. The calculation process of the wavelet thresholding algorithm is a well-known technique, and the specific calculation steps will not be elaborated here.
[0048] Example 2 Another embodiment of this application also provides an animal electrocardiogram monitoring device, comprising: An electrocardiogram (ECG) monitor is used to collect electrocardiogram (ECG) sequences from animals. An electrocardiogram (ECG) processor, which is communicatively connected to the ECG detector, is used to execute the method described above in order to output the processed ECG sequence. An electrocardiogram (ECG) display, which is communicatively connected to the ECG processor, is used to display the processed ECG sequence to assist in the assessment of the animal's physiological state.
[0049] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not invented in this application.
[0050] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope.
Claims
1. A method for monitoring the electrocardiogram of animals, characterized in that, The method includes the following steps: The electrocardiogram (ECG) sequence of the animal was collected using an ECG monitor, and the ECG sequence consisted of ECG data collected at multiple sampling times. Based on the distribution of maximum points in animal electrocardiogram (ECG) sequences, the heartbeat interval difference in ECG data is calculated. Based on the heartbeat interval difference, the animal electrocardiogram sequence is divided into multiple cardiac electronic sequences; the frequency of the cardiac electronic sequence in the frequency domain is divided into several frequency intervals; using the value and number of amplitude spectra of non-zero elements at the same frequency in the frequency domain of the cardiac electronic sequence, the information noise pollution degree of each frequency interval is calculated. Based on the aforementioned information noise level, a wavelet thresholding algorithm is used to denoise the animal electrocardiogram (ECG) sequence, resulting in a processed ECG sequence, which serves as reference ECG data for auxiliary judgment during animal ECG monitoring.
2. The method for monitoring animal electrocardiogram as described in claim 1, characterized in that, The method for calculating the heart rate interval difference is as follows: In the formula, This represents the heart rate interval difference in electrocardiogram (ECG) data. This represents the number of elements in the heartbeat peak sequence. , Let i and j represent the i-th and 1-th elements in the heart rate peak sequence, respectively. The function represents the rounding function; wherein the heart rate peak sequence is determined using all the maximum values and their positions in the animal electrocardiogram sequence.
3. The method for monitoring animal electrocardiogram as described in claim 2, characterized in that, The method for determining the heart rate peak sequence is as follows: Identify all maxima and their locations in an electrocardiogram (ECG) sampling sequence; Arrange the position values in descending order of the magnitude of the maximum points to obtain the peak position sequence; Based on the number of heartbeats S obtained from the animal's electrocardiogram sequence, the first S maxima in the peak position sequence are selected, and the S maxima are rearranged in ascending order of element value to obtain the heartbeat peak position sequence.
4. The method for monitoring animal electrocardiogram as described in claim 1, characterized in that, The method for dividing the frequency range is as follows: The cardiac electron sequence is used as the input to the Fourier transform algorithm, and the output is the spectrum of each cardiac electron sequence. Obtain the maximum frequency values in the spectrum of all cardiac electron sequences; The frequency is evenly divided into N frequency intervals from 0 to the maximum value; where N is a preset value.
5. The method for monitoring an animal's electrocardiogram as described in claim 1, characterized in that, The method for calculating the information noise pollution level includes: The amplitude spectra of each frequency range in the spectrograms of multiple cardiac electron sequences are arranged in ascending order of frequency to form multiple amplitude spectrum sequences for each frequency range. All amplitude spectra at the same frequency are grouped into a set of amplitude spectra with the same frequency. Determine the number of non-zero elements in the set of amplitude spectra at the same frequency to determine the standard amplitude spectrum and noise impact at the same frequency; Arrange the frequencies of each frequency range in ascending order to form a standard amplitude spectrum sequence for each frequency range. The frequency noise difference degree of each frequency interval is determined by using the differences between all amplitude spectrum sequences of each frequency interval and the standard amplitude spectrum sequence of each frequency interval; Based on the frequency noise difference and the number of non-zero amplitude spectra in different amplitude spectrum sequences of the frequency range, the information noise pollution level of each frequency range is calculated.
6. The method for monitoring animal electrocardiogram as described in claim 5, characterized in that, The determination of the number of non-zero elements in the set of amplitude spectra at the same frequency to determine the standard amplitude spectrum and noise impact at the same frequency includes: When the non-zero elements in the same frequency amplitude spectrum set are greater than or equal to the preset threshold, the mean of the non-zero elements in the same frequency amplitude spectrum set is calculated and recorded as the standard amplitude spectrum at the same frequency, and the noise impact degree at the same frequency is assigned to 0. When the number of non-zero elements in the amplitude spectrum set at the same frequency is less than a preset threshold, the standard amplitude spectrum at the same frequency is assigned a value of 0, and the noise impact at the same frequency is assigned the number of non-zero elements.
7. The method for monitoring animal electrocardiogram as described in claim 5, characterized in that, The method for calculating the frequency noise difference is as follows: In the formula, This represents the frequency noise difference in each frequency range. This represents the mean of the noise impact of the set of amplitude spectra of all frequencies within each frequency interval. This represents the number of all amplitude spectrum sequences in each frequency range. This represents the j-th amplitude spectrum sequence in each frequency interval. This represents the standard amplitude spectrum sequence for each frequency range. Represents the Euclidean distance function. This indicates a zero-multiplication adjustment factor to prevent the factor from taking a value of 0.
8. The method for monitoring animal electrocardiogram as described in claim 5, characterized in that, The information noise pollution level is determined by multiplying the standard deviation of the number of frequencies of non-zero amplitude spectra in all amplitude spectrum sequences within each frequency interval by the frequency noise difference level of each frequency interval.
9. The method for monitoring an animal's electrocardiogram as described in claim 1, characterized in that, Based on the aforementioned information noise level, a wavelet thresholding algorithm is used to denoise the animal electrocardiogram (ECG) sequence, resulting in a processed ECG sequence, including: Animal electrocardiogram (ECG) sequences are used as input to the wavelet thresholding algorithm, and the number of wavelet decomposition layers is the same as the number of frequency intervals divided in the ECG sequence. The soft threshold for each wavelet threshold is: In the formula, This represents the soft threshold of the J-th decomposition layer. This represents the normalized value of the information noise pollution level in the J-th frequency interval. This represents the root mean square error of noise in an animal electrocardiogram sequence. This indicates the sequence length of an animal's electrocardiogram (ECG) sequence.
10. An animal electrocardiogram monitoring device, characterized in that, include: An electrocardiogram (ECG) monitor is used to collect electrocardiogram (ECG) sequences from animals. An electrocardiogram (ECG) processor, communicatively connected to the ECG detector, is used to execute the method described in any one of claims 1 to 9 to output a processed ECG sequence; An electrocardiogram (ECG) display, which is communicatively connected to the ECG processor, is used to display the processed ECG sequence to assist in the assessment of the animal's physiological state.