CN109818940B - Electrocardio waveform rapid compression algorithm suitable for realizing real-time transmission in embedded hardware - Google Patents
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Abstract
An electrocardiographic waveform fast compression algorithm suitable for realizing real-time transmission in embedded hardware comprises the following steps: s1: the electrocardiographic waveform has a certain change rule, and a large number of data points are based near a base line, so that data are subjected to one-time differential compression and two-time statistical coding compression; s2: before actual compression, grouping the acquired electrocardiographic waveform data according to a fixed time period, respectively calculating a recompressed compression value Ci and a recompressed compression value Ci of each group of data, and comparing the recompressed compression value C2 with a recompressed compression value C1; s3: if the compressed value of the double compression C2 is lower than a heavy compressed value C1, then the actual compression is performed using the double compression, otherwise the one-time compression is directly used; s4: performing electrocardiographic waveform data compression calculation according to the selected compression weight; s5: and the data receiving end decompresses and calculates the electrocardiographic waveform data through the inverse operation of the compression step according to the selected compression weight.
Description
Technical Field
The invention relates to an electrocardio waveform rapid compression algorithm suitable for realizing real-time transmission in embedded hardware, belonging to the technical field of electrocardio signal processing.
Background
Nowadays, monitoring instruments related to vital signs are popularized in clinical application, and can monitor vital signs of electrocardio, blood pressure, respiration and the like of bedridden patients, particularly postoperative patients or critical patients in real time, so that the patients can know the physical health conditions of the patients in time, and doctors can also know the illness conditions of the patients in time, and more effective treatment means can be adopted. With the improvement of the aging degree of the society, more and more old people need to carry out long-term medical monitoring at home, and especially some people suffering from chronic senile diseases or old people lying in bed for a long time need to frequently monitor some vital signs of the old people so as to know the physical conditions of the old people in time and seek medical advice in time. The portable wireless network physiological parameter monitor is developed by adopting an embedded system, has small volume and convenient wearing, and can meet the requirements. But the computing power is weaker and the available resources are less. In addition, with the continuous update of internet and internet of things technologies, the data transmission performance of the network is continuously improved, but the mobile network still has the disadvantages of small data transmission amount, high cost and the like due to the particularity of the mobile network.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provide a rapid electrocardiographic waveform compression algorithm which can carry out remote real-time monitoring on the most important electrocardiographic waveform in vital signs and timely discover and control the state of an illness and is suitable for realizing real-time transmission in embedded hardware.
The invention aims to complete the following technical scheme, and the electrocardio waveform rapid compression algorithm suitable for realizing real-time transmission in embedded hardware comprises the following specific steps:
s1: aiming at that the electrocardiographic waveform has a certain change rule and a large number of data points are based near a base line, the data are subjected to one-time differential compression and two-time statistical coding compression;
s2: before actual compression, grouping the acquired electrocardiographic waveform data according to a fixed time period, respectively calculating a recompressed compression value Ci and a recompressed compression value Ci of each group of data, and comparing the recompressed compression value C2 with a recompressed compression value C1;
s3: if the compressed value of double compression C2 is lower than the compressed value of single compression C1, then the double compression is used for actual compression, otherwise, the single compression is directly used;
s4: performing electrocardiographic waveform data compression calculation according to the selected compression weight;
s5: and the data receiving end decompresses and calculates the electrocardiographic waveform data through the inverse operation of the compression step according to the selected compression weight.
Further, the step S2 further includes:
s21: calculating the actual compression value of each recompression, wherein the calculated actual compression value of each recompression is Ci, the original grouped data is D, and the number of the original grouped data is DL; the sampling precision is P bits;
s22: wherein, a recompression value C1 can be obtained quickly without actual compression:
reserving first data of the original data, and performing first-order difference operation on other grouped original data D to obtain a result of a sequence X;
X(0)＝D(0) {i＝0}
X(i)＝D(i)-D(i-1) {i>0}
recording the maximum value XMax and the minimum value XMin of the difference result while carrying out difference operation;
by the formula
Y＝|XMax-XMin|
Calculating to obtain the absolute value Y of the maximum difference of the data after the difference, and obtaining the data bit number b1 of each recompressed data according to the absolute value Y:
b1＝1；
while((1<<b1))<Y)
{b1++；}；
by the formula
C1＝(((DL-1)*b1)+P+8)/8；
Calculating to obtain a recompression value C1 byte;
s23: wherein, the double compression value C2 can be obtained quickly on the basis of calculation of a double compression value without actual compression:
counting the times of repeated numerical values in the sequence X obtained in the calculation of a recompression value, and sequencing the numerical values from large to small to obtain a mapping sequence Y1{ data, count }; the number of sequences is n;
coding the statistical result, replacing the numerical value with binary coding 0 with the most number of occurrences, occupying 1 bit, replacing with 10, occupying 2 bits, replacing with 110 again and occupying 3 bits, recursion in turn, quickly establishing a coding mapping table, and according to the formula:
Ni＝i*Y1(i).count；{i＝1,2,3,4…..n}
calculating the length Ni of the data bit required to be occupied by the final coding of each different data, and accumulating each item of data to obtain the total occupied bit number b 2;
because an encoding mapping table is needed during decompression, the encoding mapping table needs to be stored, and the total bit number occupied is b 3-n b 1;
recording compressed repeated number information and mapping table occupation information, wherein the total byte is 1; recording mapping table quantity information, wherein the mapping table quantity information is 1 byte in total;
by the formula
C2＝((b2+b3)+P+8+8)/8；
Calculating a double compression value C2 byte;
further, the step S4 further includes:
s41: extra recording of the compression weight is required for decompression;
s42: for a recompression, the first value of the original packet data is recorded additionally to decompress and restore the data
S43: for double compression, an encoding mapping table is additionally recorded so as to decompress and restore data.
The invention is based on the principle of calculating effective value priority, simultaneously considers that the high-speed operation can occupy smaller memory resources in an embedded hardware environment, and maximally reduces the complexity of a compression algorithm and reduces the CPU time and storage space occupied by waveform data compression on the premise of ensuring the compression value so as to improve the real-time property of data transmission in a network.
The invention has the characteristics of remote real-time monitoring of the most important electrocardiographic waveforms in vital signs, timely discovery and control of illness state and the like.
Drawings
FIG. 1 is a flow chart of the steps of the compression algorithm of the present invention;
FIG. 2 is a flowchart of the steps of calculating the differential weight compression according to the present invention
FIG. 3 is a flowchart of the steps of the double statistical coding compression calculation of the present invention
Detailed Description
The present invention will be described in detail with reference to specific embodiments below: an electrocardiographic waveform fast compression algorithm suitable for real-time transmission in embedded hardware, the fast compression algorithm comprising the steps of:
s1: the electrocardiographic waveform has a certain change rule, and a large number of data points are based near a base line, so that data are subjected to one-time differential compression and two-time statistical coding compression;
s2: before actual compression, grouping the acquired electrocardiographic waveform data according to a fixed time period, respectively calculating a recompressed compression value Ci and a recompressed compression value Ci of each group of data, and comparing the recompressed compression value C2 with a recompressed compression value C1;
s3: if the compressed value of the double compression C2 is lower than a heavy compressed value C1, then the actual compression is performed using the double compression, otherwise the one-time compression is directly used;
s4: performing electrocardiographic waveform data compression calculation according to the selected compression weight;
s5: and the data receiving end decompresses and calculates the electrocardiographic waveform data through the inverse operation of the compression step according to the selected compression weight.
Step S2 of the present invention further includes:
s21: calculating the actual compression value of each recompression, wherein the calculated actual compression value of each recompression is Ci, the original grouped data is D, and the number of the original grouped data is DL; the sampling precision is P bits;
s22: wherein, a recompression value C1 can be obtained quickly without actual compression:
reserving first data of the original data, and performing first-order difference operation on other grouped original data D to obtain a result of a sequence X;
X(0)＝D(0) {i＝0}；
X(i)＝D(i)-D(i-1) {i>0}；
recording the maximum value XMax and the minimum value XMin of the difference result while carrying out difference operation;
by the formula:
Y＝|XMax-XMin|；
calculating to obtain the absolute value Y of the maximum difference of the data after the difference, and obtaining the data bit number b1 of each recompressed data according to the absolute value Y:
b1＝1；
while((1<<b1))<Y)；
{b1++；}；
by the formula:
C1＝(((DL-1)*b1)+P+8)/8；
calculating to obtain a recompression value of C1 bytes;
s23: wherein, the double compression value C2 can be obtained quickly on the basis of calculation of a double compression value without actual compression:
counting the times of repeated numerical values in the sequence X obtained in the calculation of a recompression value, and sequencing the numerical values from large to small to obtain a mapping sequence Y1{ data, count }; the number of sequences is n;
coding the statistical result, replacing the numerical value with binary coding 0 with the most number of occurrences, occupying 1 bit, replacing with 10, occupying 2 bits, replacing with 110 again and occupying 3 bits, recursion in turn, quickly establishing a coding mapping table, and according to the formula:
Ni＝i*Y1(i).count；{i＝1,2,3,4…..n}；
calculating the length Ni of the data bit required to be occupied by the final coding of each different data, and accumulating each item of data to obtain the total occupied bit number b 2;
because the coding mapping table is needed during decompression, the coding table needs to be stored, and the total bit occupation is as follows:
b3＝n*b1；
recording compressed repeated number information and mapping table occupation information, wherein the total byte is 1; recording mapping table quantity information, wherein the mapping table quantity information is 1 byte in total;
by the formula:
C2＝((b2+b3)+P+8+8)/8；
the double compression value C2 bytes is calculated.
Step S4 of the present invention further includes:
s41: extra recording of the compression weight is required for decompression;
s42: for a recompression, the first value of the original packet data is recorded additionally to decompress and restore the data
S43: for double compression, an encoding mapping table is additionally recorded so as to decompress and restore data.
The invention will now be further described with reference to the accompanying figures 1-3 and a piece of exemplary data:
as shown in fig. 1, the present invention provides an electrocardiographic waveform fast compression algorithm suitable for real-time transmission in embedded hardware, which has a certain change rule for electrocardiographic waveforms and a large number of data points are based on the vicinity of a baseline, so that data is compressed by one-time difference compression and two-time statistical coding. The method comprises the following specific steps:
for example, there are the following segments of electrocardiographic waveform data:
512，515，517，518，519，521，519，517，514，512，510，513；
wherein, the number DL of original data packets is 12, and the sampling precision P is 16;
step 1: the data is differentiated once to obtain:
512，3，2，1，1，2，-2，-2，-3，-2，-2，3；
step 2: before actual compression, calculating the compression values Ci of one-time compression and two-time compression of each group of data,
specifically, a recompression value is calculated. According to the statistics of the difference result, the maximum value is 3 and the minimum value is-3 after the difference,
obtaining Y as 6 by the formula Y as XMax-XMin;
one recompression data bit number b1 is 3;
by the formula C1 (((DL-1) × b1) + P + 8)/8;
the compression values are given as:
c1 ═ ((12-1) × 3+16+8)/8 ═ 8 bytes;
specifically, the recompression value is recalculated. Converting all values into positive numbers according to the difference result to obtain a sequence:
515，6，5，4，4，5，1，1，0，1，1，6；
counting the difference data to obtain the occurrence frequency of each data and sequencing the data from large to small as follows:
1: 4 times;
4: 2 times;
5: 2 times;
6: 2 times;
0: 1 time;
according to the formula Ni i Y1(i). 1, { i ═ 1,2,3,4 ….. n };
obtaining that the compressed data occupies 27 bits and needs 4 bytes for storage;
plus the number of mapping tables, record 1 byte, the original header 2 bytes,
recording compressed repeated number information and mapping table occupation information, wherein the total byte is 1;
each mapping code requires 3 bits of storage (maximum 6), 15 bits, 2 bytes,
the compression values are:
c2 ═ 4+1+2+1+2 ═ 10 bytes;
and step 3: the compressed value of double compression C2 is compared with a compressed value of double compression C1.
Specifically, the method comprises the following steps: in this example, since C2 is greater than C1, a recompression algorithm is directly employed for the actual compression.
And 4, step 4: performing electrocardiographic waveform data compression calculation according to the selected compression weight;
specifically, the method comprises the following steps: in this example, one recompression is employed;
recording the compressed repeated number information and the differential digit number information, 1 byte in total
② recording differential data head and differential value, total 7 bytes
In total: 8 bytes
The compression ratio is: 24/8 ═ 3;
in this example, the compressed data is represented as:
01 000011 0000001000000011 110 101 100 100 101 001 001 000 001 001 110；
additionally, assuming that the segment of data employs dual compression, the compression steps are as follows:
step 4':
specifically, the method comprises the following steps: in this example, dual compression is employed;
firstly, according to the difference statistical data obtained in the step 2, a mapping code (binary representation) can be rapidly obtained as follows:
1：0；
4：10；
5：110；
6：1110；
0：11110；
the compressed data is represented as:
1110 110 10 10 110 0 0 11110 0 0 1110 00000。
a total of 27 bits, 4 bytes;
recording the compressed repeated number information and mapping table occupation information, wherein the total length of the compressed repeated number information and the mapping table occupation information is 1 byte;
in this example, the maximum mapping table is 6, 3 bits are needed for storage, and the first byte binary is represented as:
10 000011；
recording mapping table quantity information, 1 byte in total, 5 in this example, and binary representation as:
00000101；
record mapping table information, 5 in this example, each 3 bits, 15 bits in total, 2 bytes, and binary representation is:
001 100 101 110 000 0；
recording the difference data head and the mapping coding value, wherein the total number of the data head and the mapping coding value is 6 bytes, and the binary expression is as follows:
0000001000000011 1110 110 10 10 110 0 0 11110 0 0 1110；
in total: 10 bytes
The compression ratio is: 24/10 ═ 2.4;
in this example, the compressed data is represented as:
10 000011 00000101 001 100 101 110 000 0 0000001000000011 1110 110 10 10 110 0 0 11110 0 0 1110 00000。
the invention provides an electrocardio waveform rapid compression algorithm suitable for realizing real-time transmission in embedded hardware, which is suitable for running in various embedded hardware environments due to low algorithm time and space complexity, high operation speed and less occupied resources, compressed data can be transmitted in various network transmissions, the real-time performance is higher, and the requirements of real-time electrocardio monitoring, observation and diagnosis are met.
Claims (1)
1. An electrocardiographic waveform fast compression algorithm suitable for real-time transmission in embedded hardware, the fast compression algorithm comprising the steps of:
s1: the electrocardiographic waveform has a certain change rule, and a large number of data points are based near a base line, so that data are subjected to one-time differential compression and two-time statistical coding compression;
s2: before actual compression, grouping the acquired electrocardiographic waveform data according to a fixed time period, respectively calculating a recompressed compression value Ci and a recompressed compression value Ci of each group of data, and comparing the recompressed compression value C2 with a recompressed compression value C1;
s3: if the compressed value of the double compression C2 is lower than a heavy compressed value C1, then the actual compression is performed using the double compression, otherwise the one-time compression is directly used;
s4: performing electrocardiographic waveform data compression calculation according to the selected compression weight;
s5: the data receiving end decompresses and calculates the electrocardiographic waveform data through the inverse operation of the compression step according to the selected compression weight;
wherein step S2 further includes:
s21: calculating the actual compression value of each recompression, wherein the calculated compression value of each recompression is Ci, the original packet data is D, and the number of the original packet data is DL; the sampling precision is P bits;
s22: wherein, a recompression value C1 can be obtained quickly without actual compression:
reserving first data of the original data, and performing first-order difference operation on other grouped original data D to obtain a result of a sequence X;
X(0)＝D(0) {i＝0}
X(i)＝D(i)-D(i-1) {i>0}
recording the maximum value XMax and the minimum value XMin of the difference result while carrying out difference operation;
by the formula
Y＝|XMax-XMin|
Calculating to obtain an absolute value Y of the maximum difference of the data after the difference, and obtaining a data bit number b1 of each recompressed data according to the value Y:
b1＝1；
while((1<<b1))<Y)
{b1++；}；
recording compressed repeated number information and mapping table occupation information, wherein the total byte is 1;
by the formula
C1＝(((DL-1)*b1)+P+8)/8；
Calculating to obtain a recompression value of C1 bytes;
s23: wherein, the double compression value C2 can be obtained quickly on the basis of calculation of a double compression value without actual compression:
counting the times of repeated numerical values in the sequence X obtained in the calculation of a recompression value, and sequencing the numerical values from large to small to obtain a mapping sequence Y1{ data, count }; the number of sequences is n;
coding the statistical result, replacing the numerical value with binary coding 0 with the most number of occurrences, occupying 1 bit, replacing with 10, occupying 2 bits, replacing with 110 again and occupying 3 bits, recursion in turn, quickly establishing a coding mapping table, and according to the formula:
Ni＝i*Y1(i).count；{i＝1,2,3,4…..n}
calculating the length Ni of the data bit required to be occupied by the final coding of each different data, and accumulating each item of data to obtain the total occupied bit number b 2;
because the coding mapping table is needed during decompression, the coding mapping table needs to be stored, and the total bit occupation is
b3＝n*b1；
Recording compressed repeated number information and mapping table occupation information, wherein the total byte is 1; recording mapping table quantity information, wherein the mapping table quantity information is 1 byte in total;
by the formula
C2＝((b2+b3)+P+8+8)/8；
Calculating a double compression value C2 byte;
wherein step S4 further includes:
s41: extra recording of the compression weight is required for decompression;
s42: for a single compression, the compression is repeated,
1) recording compression repeated number information and difference digit number information;
2) recording the differential data head and the differential value so as to decompress and restore the data;
s43: for the purpose of the dual-compression,
1) recording compression weight information and mapping table occupation information;
2) recording the quantity information of the mapping table;
3) recording mapping table information;
4) and recording the differential data header and the mapping coding value so as to decompress and restore the data.
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Address after: 310012 Block A, 13/F, Building E, Paradise Software Park, No. 3, Xidoumen Road, Xihu District, Hangzhou, Zhejiang Patentee after: ZHEJIANG HELOWIN MEDICAL TECHNOLOGY CO.,LTD. Address before: 310012 block B, 5 / F, building e, Paradise Software Park, No.3 xidoumen Road, Xihu District, Hangzhou City, Zhejiang Province Patentee before: ZHEJIANG HELOWIN MEDICAL TECHNOLOGY CO.,LTD. |
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Address after: Building D, 14th Floor, Block C, Tian Tang Software Park, No. 3 Xidoumen Road, Gudang Street, Xihu District, Hangzhou City, Zhejiang Province 310012 Patentee after: ZHEJIANG HELOWIN MEDICAL TECHNOLOGY CO.,LTD. Country or region after: China Address before: 310012 Block A, 13/F, Building E, Paradise Software Park, No. 3, Xidoumen Road, Xihu District, Hangzhou, Zhejiang Patentee before: ZHEJIANG HELOWIN MEDICAL TECHNOLOGY CO.,LTD. Country or region before: China |
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