Method for compressing electrocardiosignal

Abstract

The application provides an electrocardiosignal compression method, comprising: acquiring a network bandwidth at a current time, a data amount transmitted per second and a trauma grade corresponding to an electrocardiosignal; calculating a transmission compression ratio of the electrocardiosignal at a next time according to the network bandwidth at the current time, the data amount transmitted per second and the trauma grade; determining a quantization coefficient of the electrocardiosignal at the next time according to a quantization coefficient at the current time, a transmission compression ratio at the current time and a transmission compression ratio at the next time; quantizing the electrocardiosignal based on the quantization coefficient; and compressing the quantized electrocardiosignal to obtain a compressed electrocardiosignal. The application combines the trauma grade and the electrocardiosignal compression ratio, modifies the quantization coefficient and the compression ratio of the electrocardiosignal at the next time in real time, realizes effective distribution of limited bandwidth and meets different transmission requirements of electrocardiosignals of different trauma grades.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the field of medical devices and physiological signal processing, and in particular to a method for compressing electrocardiosignal. BACKGROUND

[0002] When performing outdoor medical emergency rescue, the rescuer needs to use the medical rescue gateway to upload the physiological parameters of the wounded in real time, so as to facilitate the decision-making of the background experts according to the injury condition of the wounded. Among the physiological parameters of the wounded, the electrocardiosignal is very important and the data overhead is relatively large. Since the satellite communication with low transmission bandwidth is usually used as the remote communication method in the process of emergency medical rescue, it is necessary to compress the electrocardiosignal before transmission, so as to transmit more data.

[0003] The existing electrocardiosignal compression method mainly changes the compression ratio of the electrocardiosignal according to the network state, lacks attention to the injury condition of the wounded, and cannot meet the electrocardiosignal transmission requirements of different injury conditions. SUMMARY

[0004] (I) Technical problems to be solved

[0005] The present disclosure provides a method for compressing electrocardiosignal, which is used to at least partially solve the above technical problems.

[0006] (II) Technical solutions

[0007] The present disclosure provides a method for compressing electrocardiosignal, comprising: acquiring the network bandwidth at the current time, the data amount transmitted per second, and the injury grade corresponding to the electrocardiosignal; calculating the transmission compression ratio of the electrocardiosignal at the next time according to the network bandwidth at the current time, the data amount transmitted per second, and the injury grade; determining the quantization coefficient of the electrocardiosignal at the next time according to the quantization coefficient at the current time, the transmission compression ratio at the current time, and the transmission compression ratio at the next time; quantizing the electrocardiosignal based on the quantization coefficient; and compressing the quantized electrocardiosignal to obtain the compressed electrocardiosignal.

[0008] Optionally, the compression of the quantized electrocardiosignal comprises: performing discrete wavelet decomposition on the quantized electrocardiosignal to obtain a plurality of groups of wavelet coefficients; performing dead zone quantization on the wavelet coefficients; and compressing the dead zone quantized wavelet coefficients to obtain the compressed electrocardiosignal.

[0009] Optionally, the calculation formula of the transmission compression ratio of the electrocardiosignal at the next time is:

[0010]

[0011] wherein i is the current time, CR i+1 is the transmission compression ratio of the electrocardiosignal at the next time, D is the data amount transmitted per second, x is the electrocardiosignal, L xiBW is the network bandwidth at the current moment, k i BW is the network bandwidth at the current moment, k i N is the total number of electrocardiogram signals at the current moment.

[0012] Optionally, the calculation formula of the next moment quantization coefficient of the electrocardiogram signal is:

[0013]

[0014] Wherein, i is the current moment, n i+l is the next moment quantization coefficient, n i is the current moment quantization coefficient, CR i is the current moment transmission compression ratio, CR i+1 is the next moment transmission compression ratio.

[0015] Optionally, the calculation formula for discrete wavelet decomposition of the quantized electrocardiogram signal is:

[0016] L = |log2F S -2.96|

[0017] Wherein, L is the decomposition layer, F S is the sampling frequency of the electrocardiogram signal.

[0018] Optionally, the wavelet coefficient quantized in the compression dead zone includes: calculating the proportion of the energy of each layer wavelet coefficient in the total energy, and dividing the wavelet coefficient into low energy wavelet coefficient and high energy wavelet coefficient based on the energy proportion; respectively compressing the low energy wavelet coefficient and the high energy wavelet coefficient.

[0019] Optionally, the wavelet coefficient with a proportion less than 1% in the total energy is the low energy wavelet coefficient; the wavelet coefficient with a proportion greater than 95% in the total energy is the high energy wavelet coefficient.

[0020] Optionally, the low energy wavelet coefficient is compressed, including: counting the number of continuous occurrence of the same character in the original sequence of the low energy wavelet coefficient, replacing the string of the same character with the character and the number of occurrences to obtain a first run-length encoding sequence; counting the maximum value of the number of occurrences of 0 characters in the first run-length encoding sequence; converting the maximum value into binary, and the number of bits of the binary is the minimum bit number in the first run-length encoding sequence; count the number of occurrences of 0 string in the first run-length encoding, if the minimum bit number cannot represent the number of occurrences of the 0 string, replace the number of occurrences with the maximum value that can be represented by the minimum bit number to obtain a second run-length encoding sequence; record the index and interpolation value of the 0 character in the string to obtain a buffer sequence; each layer of low energy wavelet coefficient will obtain a second run-length encoding sequence and a buffer sequence after compression.

[0021] Optionally, the high-energy wavelet coefficients are compressed, including: calculating the difference between each value in the original sequence of high-energy wavelet coefficients and the previous data, replacing the original value with the difference, to obtain a first incremental encoding sequence; judging the sign of each value in the first incremental encoding sequence to obtain a sign sequence; taking the absolute value of each data in the first incremental encoding sequence to obtain a second incremental encoding sequence; converting each data in the second incremental encoding sequence into binary to obtain the binary data corresponding to each data, determining the bit number where the highest bit of the binary data is located, and the sequence formed by the bit number is a bit number sequence; converting the binary data into decimal data after removing the highest bit to obtain a third incremental encoding sequence; each layer of high-energy wavelet coefficients will obtain a third incremental encoding sequence, a sign sequence and a bit number sequence after compression.

[0022] Optionally, the injury level corresponding to the electrocardiosignal is obtained, including: obtaining a first injury level uploaded by a field rescuer; obtaining a second injury level uploaded by a background expert; comparing the first injury level and the second injury level, if the two are inconsistent, then taking the second injury level as the injury level corresponding to the electrocardiosignal.

[0023] (Three) beneficial effects

[0024] The electrocardiosignal compression method provided by the application at least has the following beneficial effects:

[0025] The injury level is combined with the electrocardiosignal compression ratio, the quantization coefficient is modified in real time, and the compression ratio of the electrocardiosignal at the next moment is modified, so that the effective allocation of limited bandwidth is realized. The transmission compression ratio of the electrocardiosignal is inversely proportional to the injury level, when the injury level is high, the transmission compression ratio is reduced, so as to transmit physiological parameters with higher quality by using more bandwidth, when the injury level is low, the compression ratio is increased to save bandwidth, and different transmission requirements of electrocardiosignals with different injury levels are met. BRIEF DESCRIPTION OF DRAWINGS

[0026] Figure 1 The flowchart of the electrocardiosignal compression method in the embodiment of the application is schematically shown;

[0027] Figure 2 The flowchart of compressing the quantized electrocardiosignal in the embodiment of the application is schematically shown;

[0028] Figure 3 The flowchart of compressing the wavelet coefficients quantized in the dead zone in the embodiment of the application is schematically shown;

[0029] Figure 4 The flowchart of compressing the low-energy wavelet coefficients in the embodiment of the application is schematically shown;

[0030] Figure 5A sequence diagram illustrating compression of low energy wavelet coefficients in an embodiment of the application is shown schematically;

[0031] Figure 6 A flow diagram illustrating compression of high energy wavelet coefficients in an embodiment of the application is shown schematically;

[0032] Figure 7 A sequence diagram illustrating compression of high energy wavelet coefficients in an embodiment of the application is shown schematically;

[0033] Figure 8 A block diagram of a medical rescue gateway in an embodiment of the application is shown schematically;

[0034] Figure 9 A block diagram of a medical rescue gateway in an embodiment of the application is shown schematically. DETAILED DESCRIPTION

[0035] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It is to be understood, however, the description is merely exemplary of the present disclosure, but not intended to limit the scope of the present disclosure. In the following detailed description of the embodiments of the present disclosure, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. However, it would be apparent to those skilled in the art that the embodiments of the present disclosure can be practiced without these specific details. In other instances, well-known structures and

[0036] The terms used herein are merely used to describe specific embodiments, and are not intended to limit the present disclosure. The terms "include" and "have" and the like used herein indicate the presence of the described features, steps, operations, and / or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.

[0037] All terms used herein, including technical and scientific terms, have the same meanings as those generally understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein should be interpreted as having meanings consistent with the context of the present description, and should not be interpreted in an idealized or excessively formal manner.

[0038] In the case of using expressions similar to "at least one of A, B, and C, etc.", it should generally be interpreted to include at least one of each item enumerated, but not limited to the combinations of these items. In other words, the expression "at least one of A, B, and C, etc." should be interpreted to mean that A or B or C or any combination of the items are included, but not limited to only the combinations of the items.

[0039] In the technical solutions of the present disclosure, the collection, storage, use, processing, transmission, provision, disclosure and application of user personal information comply with relevant laws and regulations, necessary security measures are taken, and the public order and good customs are not violated.

[0040] In the technical solutions of the present disclosure, the authorization or consent of the user is obtained before the user personal information is acquired or collected.

[0041] The embodiment of the present application provides a kind of electrocardiosignal compression method, applied to medical rescue gateway scene, which combines injury grade with electrocardiosignal compression ratio, modifies quantization coefficient and the compression ratio of next moment electrocardiosignal in real time, realizes the effective distribution of limited bandwidth.

[0042] Figure 1 The flow chart of the electrocardiosignal compression method provided by the embodiment of the present application is schematically shown.

[0043] As Figure 1 The embodiment of the present application provides a kind of electrocardiosignal compression method, including operation S100-S500.

[0044] Operation S100, the network bandwidth of current moment, the data amount of transmission per second and the injury grade corresponding to electrocardiosignal are acquired.

[0045] In the embodiment of the present application, the injury grade corresponding to electrocardiosignal is acquired as follows:

[0046] The first injury grade uploaded by the field rescuer is acquired.The field rescuer judges the injury of the wounded person, and obtains the first injury grade

[0047] The second injury grade uploaded by the background expert is acquired.The background expert judges the injury of the wounded person according to the physiological parameters uploaded to the medical gateway, and obtains the second injury grade

[0048] The first injury grade and the second injury grade are compared, if they are inconsistent, the second injury grade is taken as the injury grade corresponding to electrocardiosignal.

[0049] Wherein, the injury grade is divided by pre-hospital index scoring method, and each physiological parameter such as heart rate, respiratory rate, systolic pressure used for scoring is acquired from sensor end by medical rescue gateway, and the scoring range is 0-24.Compared with the field rescuer, the rescue experience of the background expert is more abundant, and the injury grade given is more authoritative.

[0050] In the embodiment of the present application, the network bandwidth at the current time is obtained from a network bandwidth calculation unit in the medical rescue gateway, and the network bandwidth calculation unit calculates the current network bandwidth by acquiring the time stamps at the time of sending and receiving, wherein the network bandwidth of satellite communication ranges from about 1 to 3 kbps.

[0051] In operation S200, the transmission compression ratio of the electrocardiosignal at the next time is calculated according to the network bandwidth at the current time, the amount of data transmitted per second, and the injury level.

[0052] The calculation formula of the transmission compression ratio of the electrocardiosignal at the next time is as follows:

[0053]

[0054] i is the current time, CR i+1 is the transmission compression ratio of the electrocardiosignal at the next time, D is the amount of data transmitted per second, x is the electrocardiosignal, L xi is the injury level of the electrocardiosignal at the current time, BW i is the network bandwidth at the current time, k i is the total number of electrocardiosignals at the current time.

[0055] In the rescue process, the sampling frequency of the electrocardiosignal is basically unchanged, so the amount of data transmitted per second is constant as D when calculating the compression ratio of the electrocardiosignal. After the background end analyzes the physiological parameters, the injury level L xi of the xth injured person at the current time and the network bandwidth BW i at the current time are fed back to the medical rescue gateway to calculate the transmission compression ratio CR i+l of the electrocardiosignal at the next time.

[0056] The medical rescue gateway sends the compressed data to the background end at the ith time to obtain the quantization parameter n i at this time and the total number of injured persons k i at this time, and calculates the compression ratio CR i at the current time according to the amount of data before and after compression. After the background end analyzes the physiological parameters, the injury level (i.e., 6 in Lxi) of the xth injured person at this time and the transmission bandwidth (i.e., 5 in BWi) at this time are fed back to the gateway end. Figure 1 Figure 1

[0057] In operation S300, the quantization coefficient of the electrocardiosignal at the next time is determined according to the quantization coefficient at the current time, the transmission compression ratio at the current time, and the transmission compression ratio at the next time.

[0058] The calculation formula of the quantization coefficient of the electrocardiosignal at the next time is as follows:

[0059] ​​

[0060] i represents the current time, n i+1 n represents the quantization coefficient for the next time step. i CR is the quantization coefficient at the current time. i CR represents the current transmission compression ratio. i+1 The compression ratio for transmission at the next moment.

[0061] The medical rescue gateway sends the compressed electrocardiogram signal to the backend at time i, obtaining the quantization parameter n at that time. i and the current total number of injured k i The compression ratio CR at the current moment is calculated based on the amount of data before and after compression. i Based on the quantization coefficient n at the current time i Current transmission compression ratio CR i And the transmission compression ratio CR at the next moment i+1 Determine the quantization coefficient n of the ECG signal at the next moment. i+1 .

[0062] Operation S400 quantizes the electrocardiogram signal based on the quantization coefficient.

[0063] The formula for quantizing the electrocardiogram signal is as follows:

[0064]

[0065] x * Let x be the quantized ECG signal, i.e., the amount of data transmitted at the next moment, and let x be the mean-valued ECG signal. min x is the minimum value in the electrocardiogram signal. max is the maximum value of the electrocardiogram signal. n is the quantization coefficient.

[0066] The quantization coefficient is directly proportional to the amount of data transmitted in the quantized ECG signal. In other words, as the quantization coefficient increases, the amount of data transmitted in the ECG signal at the next moment also increases, allowing more bits to represent each data point, resulting in a better fit to the ECG signal. However, this also increases the amount of compressed data. By changing the actual amount of data transmitted in the ECG signal, the quantization coefficient indirectly controls the compression ratio at the next moment.

[0067] Operate S500 to compress the quantized ECG signal to obtain a compressed ECG signal.

[0068] Figure 2 The flowchart illustrating the compression of quantized electrocardiogram signals in an embodiment of the present invention is shown schematically.

[0069] like Figure 2As shown, the embodiment of the present application compresses the quantized electrocardio signal and includes operations S510-S530.

[0070] Operation S510, the quantized electrocardio signal is decomposed by discrete wavelet to obtain a plurality of groups of wavelet coefficients.

[0071] The calculation formula for decomposing the quantized electrocardio signal by discrete wavelet is:

[0072] L = |log2F S -2.96|

[0073] Wherein, L is the decomposition layer number, F S is the sampling frequency of the electrocardio signal.

[0074] In the embodiment of the present application, the sampling frequency of the electrocardio signal is 256Hz, and the calculated decomposition layer number L should be 5 layers, that is, 6 groups of wavelet coefficients are obtained after decomposition. Among them, most of the energy is concentrated in the low-frequency wavelet coefficients of the fifth layer, and the energy in the wavelet coefficients accounts for about 99% of the total energy.

[0075] Operation S520, the wavelet coefficients are dead-zone quantized.

[0076] After discrete wavelet decomposition, the energy proportion in each layer of wavelet coefficients is different.

[0077] For the wavelet coefficients with small energy proportion, dead-zone quantization can be used to set them to zero to reduce data redundancy.

[0078] The specific calculation formula for dead-zone quantization of the wavelet coefficients is as follows:

[0079]

[0080]

[0081] Wherein, D k is the input wavelet coefficient, R k is the wavelet coefficient after dead-zone quantization, δ is the step size of dead-zone quantization, T h is the threshold value of dead-zone quantization, and k is the dead-zone quantization step number.

[0082] The size of the threshold value T h of dead-zone quantization will affect the compression effect of the algorithm. By changing the size of T h , the energy value of the remaining wavelet coefficients after dead-zone quantization is calculated to ensure that the energy value of the remaining wavelet coefficients is not less than 95% of the original. In (-T h , T hWavelet coefficients within a certain range will be set to zero, while those in other ranges will be set to the critical value. Wavelet coefficients obtained after dead-zone quantization will be rounded to positive numbers for easier subsequent steps.

[0083] Among them, T h The value of is determined by calculating the ratio of the ECG signal compression ratio to the distortion rate. This ratio is directly proportional to the compression quality of the ECG signal; the higher the ratio, the higher the compression quality. In this embodiment of the invention, when T... h The value is 0.8 (y max -y min When y is at its highest compression quality, the ECG signal compression quality is highest. max The maximum value among all wavelet coefficients, y min It is the minimum value among all wavelet coefficients.

[0084] The step size δ for dead-zone quantization is typically set to 0.8*T. h .

[0085] k is the number of dead zone quantization steps, used to represent the numerical relationship between the input wavelet coefficients and the output wavelet coefficients. The value of the number of dead zone quantization steps k is the ratio of the wavelet coefficient with the highest absolute value to the dead zone quantization step size δ.

[0086] By operating the S530, the wavelet coefficients after dead-zone quantization are compressed to obtain the compressed electrocardiogram signal.

[0087] Figure 3 The flowchart illustrating the wavelet coefficients after compressed dead-zone quantization in an embodiment of the present invention is shown.

[0088] like Figure 3 As shown, the wavelet coefficients after compressed dead-zone quantization in this embodiment of the invention include operations S531 to S532.

[0089] Operation S531 calculates the proportion of energy of each layer of wavelet coefficients in the total energy, and classifies the wavelet coefficients into low-energy wavelet coefficients and high-energy wavelet coefficients based on the energy proportion.

[0090] Among them, wavelet coefficients accounting for less than 1% of the total energy are low-energy wavelet coefficients, and wavelet coefficients accounting for more than 95% of the total energy are high-energy wavelet coefficients.

[0091] Operation S532 compresses the low-energy wavelet coefficients and the high-energy wavelet coefficients respectively.

[0092] Figure 4 The flowchart illustrating the compression of low-energy wavelet coefficients in an embodiment of the present invention is shown schematically.

[0093] like Figure 4As shown, the compressed low-energy wavelet coefficients include operations S5321-S5325.

[0094] In operation S5321, the number of times that the same character continuously appears in the original sequence of low-energy wavelet coefficients is counted, the string of the same character is replaced by the character and the number of times, and a first run-length encoding sequence is obtained.

[0095] In operation S5322, the maximum value other than the number of times that the 0 character appears in the first run-length encoding sequence is counted.

[0096] In operation S5323, the maximum value is converted into a binary number, and the number of bits of the binary number is the minimum number of bits in the first run-length encoding sequence.

[0097] In operation S5324, the number of times that the 0 string appears in the first run-length encoding is counted, and if the minimum number of bits cannot represent the number of times that the 0 string appears, the number of times is replaced by the maximum value that can be represented by the minimum number of bits, and a second run-length encoding sequence is obtained.

[0098] In operation S5325, the index of the 0 character in the string and the interpolation value are recorded, and a buffer sequence is obtained.

[0099] The compressed low-energy wavelet coefficients are composed of a second run-length encoding sequence and a buffer sequence.

[0100] The conventional run-length encoding usually converts the wavelet coefficients into a sequence of "character-number", wherein the "number" represents the number of times that the character continuously appears before it. Based on the previous dead-zone quantization and other operations, there are many 0s in the sequence of low-energy wavelet coefficients, and the number of times that the 0 continuously appears is much more than the number of times that other characters continuously appear. In order to reduce the data overhead, the present application improves the conventional run-length encoding, and counts the minimum number of bits that can represent all characters and numbers before the conversion, so as to facilitate the subsequent decoding operation.

[0101] The embodiment of the present application takes the original sequence of low-energy wavelet coefficients 00000000000000000661200000000511 as an example to further demonstrate the specific process of compressing the low-energy wavelet coefficients according to the present application.

[0102] The number of times that the same character continuously appears in the original sequence is counted, the string of the same character is replaced by the character and the number of times, and a first run-length encoding sequence is obtained. At this time, the first run-length encoding sequence is 01762121085112.

[0103] The maximum value other than the number of times that the 0 character appears in the first run-length encoding sequence is counted. Since 17 is the number of times that the 0 character appears, it is excluded, and the maximum value 12 is obtained.

[0104] The maximum value 12 is converted into binary 1100, and the number of bits of the binary is 4, i.e. the minimum number of bits M=4 in the first run-length encoding sequence.

[0105] The first run-length encoding sequence is further encoded according to the minimum number of bits to obtain a second run-length encoding sequence. In the first run-length encoding sequence, the number of occurrences of the 0 string is 17, and the maximum value that can be represented by 4 bits is 15, so 17 is replaced by 15 to obtain the second run-length encoding sequence 01562121085112.

[0106] The index of the 0 value in the string is recorded as 0, the difference value is 2, and the buffer sequence is stored to obtain the buffer sequence 02.

[0107] As shown in Figure 5 , the low-energy wavelet coefficient after compression will obtain a second run-length encoding sequence and a buffer sequence.

[0108] Figure 6 The flowchart of compressing the high-energy wavelet coefficient in the embodiment of the application is schematically shown;

[0109] As shown in Figure 6 , the compression of the low-energy wavelet coefficient includes operations S5321' to S5325'.

[0110] In operation S5321', the difference between each value in the original sequence of high-energy wavelet coefficients and the previous data is calculated, and the original value is replaced by the difference value to obtain a first incremental encoding sequence.

[0111] In operation S5322', the sign of each value in the first incremental encoding sequence is determined to obtain a sign sequence.

[0112] In operation S5323', the absolute value of each data in the first incremental encoding sequence is taken to obtain a second incremental encoding sequence.

[0113] In operation S5324', each data in the second incremental encoding sequence is converted into binary to obtain the corresponding binary data of each data, and the number of bits at which the highest bit is located in the binary data is determined, and the sequence formed by the number of bits is a bit number sequence.

[0114] In operation S5325', the highest bit in the binary data is removed and converted into decimal data to obtain a third incremental encoding sequence.

[0115] The compressed high-energy wavelet coefficient is composed of a third incremental encoding sequence, a sign sequence and a bit number sequence.

[0116] The embodiment of the present application takes the high-energy wavelet coefficient original sequence of 120 120 140 177 140 140 189 120 120 105 105 73 90 as an example to further show the specific process of the present application for compressing high-energy wavelet coefficients.

[0117] The difference between each value in the high-energy wavelet coefficient original sequence and the previous value is calculated, and the difference is used to replace the original value. The first value in the original sequence remains unchanged, that is, the position of the first value 120 remains unchanged. The difference between the second value and 120 is 0, so 0 is used to replace the second value. The difference between the third value and the original second value is 20, so 20 is used to replace the third value. In this way, the first incremental encoding sequence 120 0 20 37 -37 0 49 -69 0 -15 0 -32 17 is obtained.

[0118] The sign of each value in the first incremental encoding sequence is determined. 0 represents a positive number, and 1 represents a negative number. The sign of each value is represented by 1 bit of data, and the symbol sequence 0 0 0 0 1 0 0 1 0 1 0 1 0 is obtained.

[0119] The absolute value of each data in the first incremental encoding sequence is taken, and the second incremental encoding sequence 120 0 20 37 37 0 49 69 0 15 0 32 17 is obtained.

[0120] Each data in the second incremental encoding sequence is converted into binary, and the binary data corresponding to each data is obtained. The bit number where the highest bit of the binary data is located is determined, and the sequence composed of the bit numbers is the bit number sequence. For example, 120 is converted into binary as 1111000, and the bit number where the 1 of the highest bit is located is 7. Therefore, the corresponding number in the bit number sequence is 7. In this way, the bit number sequence 7 0 5 6 6 0 6 7 0 4 0 6 5 is obtained.

[0121] After removing the highest bit in the binary data, the decimal data is converted, and the third incremental encoding sequence is obtained. For example, 120 is converted into binary as 1111000, and after removing the 1 of the highest bit, it becomes 111000. Converting 111000 into decimal data obtains 56. In this way, the third incremental encoding sequence 56 0 4 5 5 0 17 5 0 7 0 0 1 is obtained.

[0122] As shown in Figure 7 , after compression, the high-energy wavelet coefficient will obtain a third incremental encoding sequence, a symbol sequence, and a bit number sequence.

[0123] The wavelet coefficient compression method provided by the application can save data overhead while ensuring high compression quality, and meets the data compression requirement in the medical rescue gateway application scenario.

[0124] In addition, the application also provides a medical rescue gateway for executing the electrocardiosignal compression method.

[0125] Figure 8 The block diagram of the medical rescue gateway in the embodiment of the application is schematically shown.

[0126] As shown in the figure, Figure 8 The medical rescue gateway comprises:

[0127] a power supply unit for providing power support for the gateway;

[0128] a signal receiving unit for receiving the physiological parameters of the wounded person sent by the collection end; wherein the signal receiving mode is wired or wireless;

[0129] a data storage unit for storing the received physiological parameters of the wounded person; wherein the storage mode is database storage;

[0130] a data display unit for realizing the visualization of the physiological parameters of the wounded person through a user interactive interface;

[0131] a wounded condition grade acquisition unit for acquiring the wounded condition grade; wherein the wounded condition grade of the wounded person can be filled by the on-site rescue personnel or fed back by the background experts;

[0132] a network bandwidth calculation unit for calculating the network transmission bandwidth through the data volume and the transmission time;

[0133] an electrocardiosignal compression unit for executing the electrocardiosignal compression method of the application and performing the dynamic adjustment of the electrocardiosignal;

[0134] a data uploading unit for realizing the remote communication of data by using the remote transmission mode such as 5G or satellite communication.

[0135] Figure 9 The block diagram of the electrocardiosignal compression unit in the embodiment of the application is schematically shown.

[0136] As shown in the figure, Figure 9 The subunit of the electrocardiosignal compression unit comprises:

[0137] a next time transmission compression ratio calculation subunit for calculating the compression ratio required for the transmission of the electrocardiosignal at the next time in combination with the current network bandwidth and the wounded condition grade;

[0138] a next time quantization coefficient calculation subunit for calculating the quantization coefficient of the electrocardiosignal at the next time through the compression ratio and the signal quantization coefficient of the existing electrocardiosignal.

[0139] quantizing subunit configured to quantize the electrocardio signal based on the quantization coefficient

[0140] wavelet decomposition subunit configured to perform discrete wavelet decomposition on the quantized electrocardio signal to obtain a plurality of groups of wavelet coefficients

[0141] wavelet coefficient dead-zone quantization subunit configured to perform dead-zone quantization on the wavelet-decomposed data

[0142] lossless compression method calculation subunit configured to compress the dead-zone quantized data to improve compression quality

[0143] It is understood by those skilled in the art that the features recited in various embodiments and / or claims of the present disclosure can be combined or / and integrated in various combinations, even if such combinations or integrations are not explicitly recited in the present disclosure. In particular, the features recited in various embodiments and / or claims of the present disclosure can be combined and / or integrated in various combinations without departing from the spirit and teachings of the present disclosure. All such combinations and / or integrations fall within the scope of the present disclosure.

[0144] The embodiments of the present disclosure are described above. However, these embodiments are merely for illustrative purposes, and are not intended to limit the scope of the present disclosure. Although each embodiment is described above separately, this does not mean that the measures in each embodiment cannot be used advantageously in combination. The scope of the present disclosure is defined by the appended claims and their equivalents. Without departing from the scope of the present disclosure, those skilled in the art can make various substitutions and modifications, which all fall within the scope of the present disclosure.

Claims

1. A method of electrocardiogram signal compression, characterized by, The method comprises the following steps: acquiring the current network bandwidth, the data transmission amount per second, and the injury level corresponding to the electrocardiosignal; calculating the transmission compression ratio of the electrocardiosignal at the next moment according to the current network bandwidth, the data transmission amount per second, and the injury level; determining the quantization coefficient of the electrocardiosignal at the next moment according to the current quantization coefficient, the current transmission compression ratio, and the transmission compression ratio at the next moment; quantizing the electrocardiosignal based on the quantization coefficient; compressing the quantized electrocardiosignal to obtain the compressed electrocardiosignal; the compression of the quantized electrocardiosignal comprises the following steps: performing discrete wavelet decomposition on the quantized electrocardiosignal to obtain a plurality of groups of wavelet coefficients; performing dead-zone quantization on the wavelet coefficients; and compressing the dead-zone quantized wavelet coefficients to obtain the compressed electrocardiosignal; the calculation formula of the transmission compression ratio of the electrocardiosignal at the next moment is as follows: Wherein, i is the current time, CR i+1 is the transmission compression ratio of the next time electrocardio signal, D is the data amount transmitted per second, x is the electrocardio signal, L xi is the injury grade of the current time electrocardio signal, BW i is the network bandwidth of the current time, k i is the total number of the current time electrocardio signal; the calculation formula of the quantization coefficient of the electrocardiosignal at the next moment is as follows: where i is the current time, n i+1 is the quantized coefficient at the next time, n i is the quantized coefficient at the current time, CR i is the transmission compression ratio at the current time, CR i+1 is the transmission compression ratio at the next time.

2. The electrocardiosignal compression method according to claim 1, characterized in that, the calculation formula of the discrete wavelet decomposition of the quantized electrocardiosignal is as follows: where L is the number of decomposition layers, F S is the sampling frequency of the ECG signal.

3. The electrocardiosignal compression method according to claim 1, characterized in that, the compression of the dead-zone quantized wavelet coefficients comprises the following steps: calculating the proportion of the energy of each layer of wavelet coefficients in the total energy, and dividing the wavelet coefficients into low-energy wavelet coefficients and high-energy wavelet coefficients based on the energy proportion; respectively compressing the low-energy wavelet coefficients and the high-energy wavelet coefficients.

4. The electrocardiosignal compression method according to claim 3, characterized in that, The wavelet coefficient with a proportion less than 1% in the total energy is a low-energy wavelet coefficient. The wavelet coefficient with a proportion greater than 95% in the total energy is a high-energy wavelet coefficient.

5. The electrocardiosignal compression method according to claim 4, characterized in that, The compression of the low-energy wavelet coefficients comprises the following steps: counting the number of continuous same characters in the original sequence of the low-energy wavelet coefficients, replacing the string of same characters with the character and the number of occurrences to obtain a first run-length encoding sequence; counting the maximum value of the number of occurrences of characters other than 0 in the first run-length encoding sequence; converting the maximum value into binary, and the number of bits of the binary is the minimum bit number in the first run-length encoding sequence; counting the number of occurrences of 0 strings in the first run-length encoding, and if the minimum bit number cannot represent the number of occurrences of the 0 string, replacing the number of occurrences with the maximum value that can be represented by the minimum bit number to obtain a second run-length encoding sequence; recording the index and the interpolation value of the 0 character in the string to obtain a buffer sequence; each layer of low-energy wavelet coefficients will obtain a second run-length encoding sequence and a buffer sequence after compression.

6. The electrocardiosignal compression method according to claim 4, characterized in that, The compression of the high-energy wavelet coefficients comprises the following steps: calculating the difference between each value and the previous value in the original sequence of the high-energy wavelet coefficients, replacing the original value with the difference to obtain a first incremental encoding sequence; judging the sign of each value in the first incremental encoding sequence to obtain a sign sequence; taking the absolute value of each data in the first incremental encoding sequence to obtain a second incremental encoding sequence; converting each data in the second incremental encoding sequence into binary to obtain the binary data corresponding to each data, determining the bit number where the highest bit of the binary data is located, and the sequence composed of the bit number is a bit number sequence; converting the binary data into decimal data after removing the highest bit to obtain a third incremental encoding sequence; Each layer of high energy wavelet coefficient will get a third incremental coding sequence, a symbol sequence and a bit number sequence after compression.

7. The electrocardiosignal compression method according to claim 1, characterized in that, Obtain the injury level corresponding to the electrocardiosignal, including: Obtain the first injury level uploaded by the on-site rescuer; Obtain the second injury level uploaded by the background expert; Compare the first injury level and the second injury level, if they are inconsistent, the second injury level is taken as the injury level corresponding to the electrocardiosignal.

Images