CN102323977A - Electrocardio data storage method based on electrocardio characteristic points - Google Patents
Electrocardio data storage method based on electrocardio characteristic points Download PDFInfo
- Publication number
- CN102323977A CN102323977A CN201110178695A CN201110178695A CN102323977A CN 102323977 A CN102323977 A CN 102323977A CN 201110178695 A CN201110178695 A CN 201110178695A CN 201110178695 A CN201110178695 A CN 201110178695A CN 102323977 A CN102323977 A CN 102323977A
- Authority
- CN
- China
- Prior art keywords
- point
- ripple
- value
- dictionary
- ecg
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Abstract
The invention relates to an electrocardio data compression storage method based on electrocardio characteristic points. Electrocardio waveforms are predicted by adopting a prediction function and errors of the electrocardio waveforms are modified and stored by adopting a dictionary. The electrocardio data compression storage method comprises the steps of: firstly, constructing the prediction function according to electrocardio physiological properties and constructing a dictionary base according to precision requirements; secondly, figuring out a coefficient in the prediction function according to specific characteristic point information of each hearth beat, predicting a voltage value of each sampling point through the function, storing an error between the prediction value and the actual value in a dictionary manner, i.e., searching an optimally-matched value from the dictionary for presenting the error, and using a serial number of the error in the dictionary as a storage value of the prediction error; and finally, coding and compressing the storage value, and storing other useable information. Because the processing in the process is limited in a smaller error range, the distortion rate of signals is lower, analysis is not influenced by restored signals, and coding storage is adopted by adopting the dictionary method so that compression efficiency is greatly increased.
Description
Technical field
The present invention relates to a kind of electrocardiogram (ECG) data storage means, belong to the medical information process field based on ecg characteristics point.
Background technology
Heart disease is just in the serious threat human beings'health; Cardiogram (ECG) is to analyze and the of paramount importance information source of diagnose heart disease; And cardiogram comprises huge data volume; For the remote transmission of the structure, systematic searching and the electrocardiogram (ECG) data that effectively carry out ecg database, the compress technique of electrocardiogram (ECG) data seems more and more important.
Present compression method is broadly divided on the time domain and directly compresses, adopts certain transform domain and make up three kinds of methods of characteristic of division template.Wherein to have made full use of electrocardiosignal be the characteristics of a quasi-periodic signal to the construction feature template method, and electrocardiosignal is analyzed back modeling classification, possesses preferable performance, become the emphasis of electrocardiogram (ECG) data Compression Study in the last few years.
Summary of the invention
The objective of the invention is in order to solve the compression memory problem of electrocardiogram (ECG) data, propose a kind ofly to predict electrocardiogram (ECG) data, to predicting the outcome and actual result compares, then with the electrocardiogram (ECG) data storage means of dictionary library correction and compression memory through the ecg characteristics point.
According to technical scheme provided by the invention; Said electrocardiogram (ECG) data storage means based on ecg characteristics point; Come the structure forecast function that ecg information is predicted through cardiac electrical unique point, construct a correction dictionary library and come the error of predicted value and actual value is revised and stored; Said anticipation function is following:
For a given cardiac cycle, its each sampled point is followed successively by [(t
1, v
1), (t
2, v
2) .. (t
n, v
n)]; t
iThe sampling time of representing this point, v
iThe sampled voltage of representing this point, n are the heartbeat sampling length, i=1, and 2 ..., n, the unique point that wherein comprises is called after respectively: P ripple starting point [T
0, V
0], P crest value point [T
1, V
1], P ripple terminal point [T
2, V
2], QRS ripple starting point [T
3, V
3], Q point [T
4, V
4], R point [T
5, V
5], S point [T
6, V
6], QRS ripple terminal point [T
7, V
7], T ripple starting point [T
8, V
8], T crest value point [T
9, V
9], T ripple terminal point [T
10, V
10], heartbeat terminal point [T
11, V
11];
Anticipation function P:P [t]=P then
Base[t]+P
Seg[t];--------formula A
Wherein, sampling time t=t
1, t
2..., t
n, P [t] is a t point prediction value, P
Base[t] is the baseline forecast function, P
Seg[t] is the signature waveform anticipation function;
P
base[t]＝A+k*t t∈[T
0，T
11]....
0
Said correction dictionary library building method does
D [x]=D
0+ k*exp (x*x/2)-1-------formula B
Wherein x=[0,1,2 ..., n-1], the dictionary length of n for need making up, D
0Be the minimum prediction error that allows, if the maximum error that needs to revise is I
Max, then:
k＝(I
max+1-D
0)/(exp((n-1)*(n-1)/2))；
Can construct dictionary D this moment: [D
0, D
1..., D
N-1].
Said electrocardiogram (ECG) data storage means based on ecg characteristics point calculates the coefficient in the anticipation function according to the unique point of each heartbeat.Each sampled point through to a heartbeat carries out progressively iteration with anticipation function, obtains the predicted data value.
Calculate the error between predicted value and the actual sample value for each heartbeat successively through anticipation function; From dictionary library, seek the value of mating the most with error; Its position as storing value, and is numbered compression memory with these values together with anticipation function coefficient, unique point, heartbeat.
As in dictionary, do not find the value that is complementary, do not adopt the dictionary position to represent this moment, and adopt actual difference to store, and give mark, to guarantee the complete recovery to this abnormal data.
Be specially:
Coefficient A in the formula 0, k are by unique point [T
0, V
0], [T
2, V
2], [T
3, V
3], [T
10, V
10], [T
11, V
11] get through the least square line match;
Formula 1,2 is the prediction of P ripple, wherein
B
1＝V
0-P
base[T
0]
A
1＝V
1-V
0，A
2＝V
1-V
2
k
1＝0.5*π/(T
1-T
0)，k
2＝0.5*π/(T
2-T
1)；
When not having the P ripple, then make T
2=T
1=T
0, V
2=V
1=V
0, k
1=k
2=0;
Formula 4 is a Q ripple anticipation function, when not having the Q ripple, and T
3=T
4, V
3=V
4Its coefficient
B
3＝V
3-P
base[T
3]，k
3＝(V
4-V
3)/(T
4-T
3)；
When not having the Q ripple, T
3=T
4, V
3=V
4, k
3=0;
Formula 5,6 is a main ripple anticipation function in the QRS wave group, wherein
B
4＝V
4-P
base[T
4]
A
4＝V
5-V
4，A
5＝V
5-V
6
k
4＝0.784/(T
5-T
4)，k
5＝0.784/(T
6-T
5)；
Formula 7 is a S ripple anticipation function, when not having the S ripple, and T
6=T
7, V
6=V
7Its coefficient
B
6＝V
6-P
base[T
6]，k
6＝(V
7-V
6)/(T
7-T
6)；
When not having the S ripple, T
6=T
7, V
6=V
7, k
6=0;
Formula 8 is QRS terminal point to a T ripple starting point anticipation function, its coefficient
B
7＝V
7-P
base[T
7]，k
7＝(V
8-V
7)/(T
8-T
7)；
Formula 9 is a T ripple anticipation function, and this function is through three unique point [T of T ripple
8, V
8], [T
9, V
9], [T
10, V
10], [T wherein
9, V
9] constitute for its extreme point comes match, can try to achieve coefficient A
8, A
9, A
10, A
11,, make T if when not having the T ripple
8=T
9=T
10=T
11, V
8=V
9=V
10=V
11
Advantage of the present invention is: make up template according to each heartbeat unique point and store the influence that electrocardiogram (ECG) data can be eliminated different heartbeat differences; Cardiac electrical unique point can well reflect the electrocardio basic trend; Combine cardiac electrical physilogical characteristics can make predicted value and actual value difference very little again; Thereby making through the revised value of dictionary library is one to comprise the sparse signal of a large amount of 0 values, and the probability that more little value occurs is high more, adopts existing coding techniques can very carry out the compression of data effectively like this.On the fidelity of data, the at first complete of paramount importance data-unique point of electrocardiosignal of having preserved, next adopts certain limits of error periodical repair on the occasion of making that the ecg wave form distortion rate that recovers is very low, and not impact analysis is judged.
Description of drawings
Fig. 1 is implementing procedure figure of the present invention.
Embodiment
Below in conjunction with accompanying drawing and embodiment the present invention is described further.
In the storage of electrocardiogram (ECG) data, adopt good template construction method can improve compression efficiency effectively.Along with the development of Computer signal detection technique, the feature point detection accuracy rate is very high.Consider the characteristic of electrocardiogram (ECG) data, also roughly the same on its form under the identical physiological condition, if adopt unique point to come each heartbeat is carried out template prediction, can eliminate the difference between different heartbeats, simultaneously again can be in small range with the error limitation of predicting.Through revising storage to predicting the outcome, utilize dictionary to carry out encoding compression, restore data preferably reaches the purpose of efficient storage.
The technical scheme that the present invention adopts comprises the steps:
(1) constructs according to cardiac electrical physilogical characteristics and can adapt to the anticipation function of various decentraction electrical features, and be used for a dictionary library that predicted value is revised;
The anticipation function building method is following: for a given cardiac cycle, its each sampled point is followed successively by: [(t
1, v
1), (t
2, v
2) .. (t
n, v
n)] (t
iThe sampling time of representing this point, v
iThe sampled voltage of representing this point, n are the heartbeat sampling length, i=1, and 2 ..., n), the unique point that wherein comprises is called after respectively: P ripple starting point [T
0, V
0] (also being pacemaker), P crest value point [T
1, V
1], P ripple terminal point [T
2, V
2], QRS ripple starting point [T
3, V
3], Q point [T
4, V
4], R point [T
5, V
5], S point [T
6, V
6], QRS ripple terminal point [T
7, V
7], T ripple starting point [T
8, V
8], T crest value point [T
9, V
9], T ripple terminal point [T
10, V
10], heartbeat terminal point [T
11, V
11].
Anticipation function P:P [t]=P then
Base[t]+P
Seg[t];--------formula A
Wherein, sampling time t=t
1, t
2..., t
n, P [t] is a t point prediction value, P
Base[t] is the baseline forecast function, P
Seg[t] is the signature waveform anticipation function;
P
base[t]＝A+k*t t∈[T
0，T
11]....
0
1. the coefficient A in the formula 0, k are by unique point [T
0, V
0], [T
2, V
2], [T
3, V
3], [T
10, V
10], [T
11, V
11] get through the least square line match.
2. formula 1,2 is the prediction of P ripple, wherein
B
1＝V
0-P
base[T
0]
A
1＝V
1-V
0，A
2＝V
1-V
2
k
1＝0.5*π/(T
1-T
0)，k
2＝0.5*π/(T
2-T
1)
When not having the P ripple, then make T
2=T
1=T
0, V
2=V
1=V
0, k
1=k
2=0;
3. formula 4 is a Q ripple anticipation function, its coefficient:
B
3＝V
3-P
base[T
3]，k
3＝(V
4-V
3)/(T
4-T
3)
When not having the Q ripple, T
3=T
4, V
3=V
4, k
3=0;
4. formula 5,6 is a main ripple anticipation function in the QRS wave group, wherein:
B
4＝V
4-P
base[T
4]
A
4＝V
5-V
4，A
5＝V
5-V
6
k
4＝0.784/(T
5-T
4)，k
5＝0.784/(T
6-T
5)
5. formula 7 is a S ripple anticipation function, its coefficient:
B
6＝V
6-P
base[T
6]，k
6＝(V
7-V
6)/(T
7-T
6)
When not having the S ripple, T
6=T
7, V
6=V
7, k
6=0;
6. formula 8 is QRS terminal point to a T ripple starting point anticipation function, its coefficient:
B
7＝V
7-P
base[T
7]，k
7＝(V
8-V
7)/(T
8-T
7)
7. formula 9 is a T ripple anticipation function, and this function is through three unique point [T of T ripple
8, V
8], [T
9, V
9], [T
10, V
10], [T wherein
9, V
9] constitute for its extreme point comes match, can try to achieve coefficient A
8, A
9, A
10, A
11,, then can make T if when not having the T ripple
8=T
9=T
10=T
11, V
8=V
9=V
10=V
11
Revising the dictionary library building method is:
D [x]=D
0+ k*exp (x*x/2)-1-------formula B
Wherein x=[0,1,2 ..., n-1], the dictionary length of n for need making up, D
0Be the minimum prediction error that allows, if the maximum error that needs to revise is I
Max, then:
k＝(I
max+1-D
0)/(exp((n-1)*(n-1)/2))
Can construct dictionary D this moment: [D
0, D
1..., D
N-1].
(2) unique point according to each heartbeat calculates the coefficient in the anticipation function;
(3) carry out progressively iteration through each sampled point with anticipation function, obtain the predicted data value a heartbeat;
(4) calculate the difference of predicted data and real data, in revising dictionary, look for a value of mating most, and write down the position of this value in dictionary, be used as predicting the correction storing value with this position and symbol with this absolute difference;
(5) repeating step (3), (4) be up to the prediction of accomplishing a heartbeat, and carry out compression memory to revising storing value, preserves heartbeat numbering, unique point, anticipation function and coefficient thereof simultaneously;
(6) storage of whole electrocardiogram (ECG) data is accomplished in repeating step (2)～(5).
In the above-mentioned steps (4) as in dictionary, do not find the value that is complementary; Be to run into situation such as bigger interference of appearance or pulse pacing signal, do not adopt the dictionary position to represent this moment, and adopt actual difference to store; And give mark, to guarantee complete recovery to this abnormal data.
As shown in Figure 1, a specific embodiment of ecg information storage means of the present invention is following.
1. step 100: first structure forecast function and dictionary library;
(1). selecting formula A for use is anticipation function;
(2). use formula B to construct dictionary library, define the length of dictionary according to actual needs, the maximum modified error, minimum permissible error also constructs dictionary D: [D
0, D
1..., D
N-1];
2. step 110: gather one section continuous electrocardiosignal and carry out the identification and the extraction of characteristic point; Define heartbeat based on each QRS wave group; P ripple starting point with first appearance before the QRS wave group is a pacemaker; A bit be before first P ripple starting point after the QRS wave group heartbeat terminal point (preceding 0.2s is P ripple position if no P ripple is then looked the QRS starting point), on this basis electrocardiosignal be divided into the continuous heartbeat line number of going forward side by side;
3. step 120: select a heartbeat to begin to carry out processed compressed;
4. step 130: type and its unique point according to the heartbeat of selecting calculate the coefficient in the anticipation function;
5. step 140: to each sampled point (t in this heartbeat
i, v
i) carry out prediction and calculation successively;
(1). step 150: calculate predicated error S according to anticipation function
i=v
i-P [t
i];
(2). according to S
iCalculate storing value I
i:
| S
i|＜D
0: then remember I
i=0.
| S
i|＞=D
0: from dictionary D, look for one with | the value D that Si| matees most
x, x is D
xPosition number in dictionary, note I
i=x; If in dictionary, do not find the value of coupling, expression runs into situation such as high frequency interference or pacemaker impulse, uses I this moment
i=S
iExpression is to guarantee the complete recovery of this abnormal signal.
Step 160: with I
iAdd symbol and classification mark, as the storing value of i sampled point;
(3). handle next sampled point i+1 until the prediction and calculation of accomplishing this heartbeat;
6. step 170: preserve the numbering of this heartbeat, unique point, anticipation function with revise storing value, and carry out encoding compression to revising storing value;
7. repeat above-mentioned steps 120～step 170, accomplish the prediction and the storage of next heartbeat, accomplish until whole signal Processing.
Claims (6)
1. the electrocardiogram (ECG) data storage means based on ecg characteristics point is characterized in that, comes the structure forecast function that ecg information is predicted through cardiac electrical unique point, constructs a correction dictionary library and comes the error of predicted value and actual value is revised and stored; Said anticipation function is following:
For a given cardiac cycle, its each sampled point is followed successively by [(t
1, v
1), (t
2, v
2) .. (t
n, v
n)]; t
iThe sampling time of representing this point, v
iThe sampled voltage of representing this point, n are the heartbeat sampling length, i=1, and 2 ..., n, the unique point that wherein comprises is called after respectively: P ripple starting point [T
0, V
0], P crest value point [T
1, V
1], P ripple terminal point [T
2, V
2], QRS ripple starting point [T
3, V
3], Q point [T
4, V
4], R point [T
5, V
5], S point [T
6, V
6], QRS ripple terminal point [T
7, V
7], T ripple starting point [T
8, V
8], T crest value point [T
9, V
9], T ripple terminal point [T
10, V
10], heartbeat terminal point [T
11, V
11];
Anticipation function P:P [t]=P then
Base[t]+P
Seg[t];--------formula A
Wherein, sampling time t=t
1, t
2..., t
n, P [t] is a t point prediction value, P
Base[t] is the baseline forecast function, P
Seg[t] is the signature waveform anticipation function;
P
base[t]＝A+k*t t∈[T
0，T
11]....
0
Said correction dictionary library building method does
D [x]=D
0+ k*exp (x*x/2)-1-------formula B
Wherein x=[0,1,2 ..., n-1], the dictionary length of n for need making up, D
0Be the minimum prediction error that allows, if the maximum error that needs to revise is I
Max, then:
k＝(I
max+1-D
0)/(exp((n-1)*(n-1)/2))；
Can construct dictionary D this moment: [D
0, D
1..., D
N-1].
2. according to claim 1 based on the electrocardiogram (ECG) data storage means of ecg characteristics point, it is characterized in that calculating the coefficient in the anticipation function according to the unique point of each heartbeat.
3. like the said electrocardiogram (ECG) data storage means of claim 2, it is characterized in that carrying out progressively iteration with anticipation function, obtain the predicted data value through each sampled point to a heartbeat based on ecg characteristics point.
4. like the said electrocardiogram (ECG) data storage means of claim 3 based on ecg characteristics point; It is characterized in that calculating the error between predicted value and the actual sample value successively through anticipation function for each heartbeat; From dictionary library, seek the value of mating the most with error; Its position as storing value, and is numbered compression memory with these values together with anticipation function coefficient, unique point, heartbeat.
5. like the said electrocardiogram (ECG) data storage means of claim 4 based on ecg characteristics point; It is characterized in that do not adopt the dictionary position to represent this moment as in dictionary, not finding the value that is complementary, and adopt actual difference to store; And give mark, to guarantee complete recovery to this abnormal data.
6. according to claim 1 based on the electrocardiogram (ECG) data storage means of ecg characteristics point, it is characterized in that: the coefficient A in the formula 0, k are by unique point [T
0, V
0], [T
2, V
2], [T
3, V
3], [T
10, V
10], [T
11, V
11] get through the least square line match;
Formula 1,2 is the prediction of P ripple, wherein
B
1＝V
0-P
base[T
0]
A
1＝V
1-V
0，A
2＝V
1-V
2
k
1＝0.5*π/(T
1-T
0)，k
2＝0.5*π/(T
2-T
1)；
When not having the P ripple, then make T
2=T
1=T
0, V
2=V
1=V
0, k
1=k
2=0;
Formula 4 is a Q ripple anticipation function, when not having the Q ripple, and T
3=T
4, V
3=V
4Its coefficient
B
3＝V
3-P
base[T
3]，k
3＝(V
4-V
3)/(T
4-T
3)；
When not having the Q ripple, T
3=T
4, V
3=V
4, k
3=0;
Formula 5,6 is a main ripple anticipation function in the QRS wave group, wherein
B
4＝V
4-P
base[T
4]
A
4＝V
5-V
4，A
5＝V
5-V
6
k
4＝0.784/(T
5-T
4)，k
5＝0.784/(T
6-T
5)；
Formula 7 is a S ripple anticipation function, when not having the S ripple, and T
6=T
7, V
6=V
7Its coefficient
B
6＝V
6-P
base[T
6]，k
6＝(V
7-V
6)/(T
7-T
6)；
When not having the S ripple, T
6=T
7, V
6=V
7, k
6=0;
Formula 8 is QRS terminal point to a T ripple starting point anticipation function, its coefficient
B
7＝V
7-P
base[T
7]，k
7＝(V
8-V
7)/(T
8-T
7)；
Formula 9 is a T ripple anticipation function, and this function is through three unique point [T of T ripple
8, V
8], [T
9, V
9], [T
10, V
10], [T wherein
9, V
9] constitute for its extreme point comes match, can try to achieve coefficient A
8, A
9, A
10, A
11,, make T if when not having the T ripple
8=T
9=T
10=T
11, V
8=V
9=V
10=V
11
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201110178695.9A CN102323977B (en) | 2011-06-29 | 2011-06-29 | Electrocardio data storage method based on electrocardio characteristic points |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201110178695.9A CN102323977B (en) | 2011-06-29 | 2011-06-29 | Electrocardio data storage method based on electrocardio characteristic points |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN102323977A true CN102323977A (en) | 2012-01-18 |
| CN102323977B CN102323977B (en) | 2014-04-30 |
Family
ID=45451719
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201110178695.9A Active CN102323977B (en) | 2011-06-29 | 2011-06-29 | Electrocardio data storage method based on electrocardio characteristic points |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN102323977B (en) |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104114086A (en) * | 2012-02-08 | 2014-10-22 | 国立大学法人九州工业大学 | Biological information processing device, biological information processing system, biological information compression method, and biological information compression processing program |
| CN105989266A (en) * | 2015-02-11 | 2016-10-05 | 北京三星通信技术研究有限公司 | Electrocardiosignal-based authentication method, apparatus and system |
| CN107847174A (en) * | 2015-10-08 | 2018-03-27 | 深圳迈瑞生物医疗电子股份有限公司 | Pacing signal processing method, system and ECG monitor |
| US10089451B2 (en) | 2015-02-11 | 2018-10-02 | Samsung Electronics Co., Ltd. | Electrocardiogram (ECG)-based authentication apparatus and method thereof, and training apparatus and method thereof for ECG-based authentication |
| CN109414170A (en) * | 2015-12-07 | 2019-03-01 | 三星电子株式会社 | Electronic equipment and its control method |
| CN110420022A (en) * | 2019-07-29 | 2019-11-08 | 浙江大学 | A kind of P wave detecting method based on Double Density Wavelet Transform |
| CN110784288A (en) * | 2019-11-04 | 2020-02-11 | 浙江大学 | Real-time lossless compression method and system for electrocardiosignals |
| CN113143284A (en) * | 2021-04-13 | 2021-07-23 | 浙江大学 | Electrocardiosignal compression method based on wavelet transformation and dual-mode prediction |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1251449A (en) * | 1998-10-18 | 2000-04-26 | 华强 | Combined use with reference of two category dictionary compress algorithm in data compaction |
| CN1612252A (en) * | 2003-10-31 | 2005-05-04 | 浙江中控技术股份有限公司 | Real-time data on-line compression and decompression method |
| CN1786939A (en) * | 2005-11-10 | 2006-06-14 | 浙江中控技术有限公司 | Real-time data compression method |
| CN101669819A (en) * | 2009-09-25 | 2010-03-17 | 西安电子科技大学 | Electrocardiogram signal lossless compression method based on PT conversion and linear prediction combination |
-
2011
- 2011-06-29 CN CN201110178695.9A patent/CN102323977B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1251449A (en) * | 1998-10-18 | 2000-04-26 | 华强 | Combined use with reference of two category dictionary compress algorithm in data compaction |
| CN1612252A (en) * | 2003-10-31 | 2005-05-04 | 浙江中控技术股份有限公司 | Real-time data on-line compression and decompression method |
| CN1786939A (en) * | 2005-11-10 | 2006-06-14 | 浙江中控技术有限公司 | Real-time data compression method |
| CN101669819A (en) * | 2009-09-25 | 2010-03-17 | 西安电子科技大学 | Electrocardiogram signal lossless compression method based on PT conversion and linear prediction combination |
Non-Patent Citations (5)
| Title |
|---|
| 彭松: "EEG无损压缩技术的研究", 《上海生物医学工程》 * |
| 游晓明，等: "数据压缩算法分析与改进", 《小型微型计算机系统》 * |
| 王文成，等: "图像无损压缩的预测编码及量化误差处理", 《光电子·激光》 * |
| 阴玺: "心电远程监护系统的数据库系统设计与数据压缩算法研究", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
| 高永丽 等: "心电图(ECG)数据压缩方法综述", 《楚雄师范学院学报》 * |
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104114086A (en) * | 2012-02-08 | 2014-10-22 | 国立大学法人九州工业大学 | Biological information processing device, biological information processing system, biological information compression method, and biological information compression processing program |
| CN104114086B (en) * | 2012-02-08 | 2015-11-25 | 国立大学法人九州工业大学 | The compression method of biological information processing unit, Biont information processing system and Biont information |
| CN105989266A (en) * | 2015-02-11 | 2016-10-05 | 北京三星通信技术研究有限公司 | Electrocardiosignal-based authentication method, apparatus and system |
| US10089451B2 (en) | 2015-02-11 | 2018-10-02 | Samsung Electronics Co., Ltd. | Electrocardiogram (ECG)-based authentication apparatus and method thereof, and training apparatus and method thereof for ECG-based authentication |
| CN105989266B (en) * | 2015-02-11 | 2020-04-03 | 北京三星通信技术研究有限公司 | Authentication method, device and system based on electrocardiosignals |
| CN107847174A (en) * | 2015-10-08 | 2018-03-27 | 深圳迈瑞生物医疗电子股份有限公司 | Pacing signal processing method, system and ECG monitor |
| CN107847174B (en) * | 2015-10-08 | 2022-06-24 | 深圳迈瑞生物医疗电子股份有限公司 | Pace-making signal processing method and system and electrocardiogram monitor |
| CN109414170A (en) * | 2015-12-07 | 2019-03-01 | 三星电子株式会社 | Electronic equipment and its control method |
| CN109414170B (en) * | 2015-12-07 | 2021-09-21 | 三星电子株式会社 | Electronic device and control method thereof |
| CN110420022A (en) * | 2019-07-29 | 2019-11-08 | 浙江大学 | A kind of P wave detecting method based on Double Density Wavelet Transform |
| CN110784288A (en) * | 2019-11-04 | 2020-02-11 | 浙江大学 | Real-time lossless compression method and system for electrocardiosignals |
| CN113143284A (en) * | 2021-04-13 | 2021-07-23 | 浙江大学 | Electrocardiosignal compression method based on wavelet transformation and dual-mode prediction |
Also Published As
| Publication number | Publication date |
|---|---|
| CN102323977B (en) | 2014-04-30 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102323977B (en) | Electrocardio data storage method based on electrocardio characteristic points | |
| Antoniol et al. | EEG data compression techniques | |
| CN104127194A (en) | Depression evaluating system and method based on heart rate variability analytical method | |
| CN104367317A (en) | Electrocardiogram electrocardiosignal classification method with multi-scale characteristics combined | |
| CN1935084A (en) | Pulse signal processing method based on lifting ripple | |
| CN110313894A (en) | Arrhythmia cordis sorting algorithm based on convolutional neural networks | |
| Zifan et al. | Automated ECG segmentation using piecewise derivative dynamic time warping | |
| CN111407243A (en) | Pulse signal pressure identification method based on deep learning | |
| Abo-Zahhad | ECG signal compression using discrete wavelet transform | |
| CN103560795A (en) | Compression method, transmission method, reconstruction method and device for electrocardiograph signals | |
| CN106137185A (en) | A kind of epileptic chracter wave detecting method based on structure of transvers plate small echo | |
| CN114582503A (en) | Blood pressure monitoring method based on multiple linear regression and correlation weighting selection algorithm | |
| Barman et al. | Rough set based segmentation and classification model for ECG | |
| CN112472101A (en) | Deep learning electrocardiogram data classification method and device based on conversion technology | |
| Kundu et al. | Electrocardiogram synthesis using Gaussian and fourier models | |
| Feng et al. | Dcmn: Double core memory network for patient outcome prediction with multimodal data | |
| Pathok et al. | Data compression of ECG signals using error back propagation (EBP) algorithm | |
| Sathawane et al. | Prediction and analysis of ECG signal behaviour using soft computing | |
| Wang et al. | Automatic diagnosis of ECG disease based on intelligent simulation modeling | |
| CN111528900A (en) | Heart sound segmentation method and device based on Butterworth filter and Shannon entropy method | |
| Ebrahimzadeh et al. | An efficient technique for classification of electrocardiogram signals | |
| Nibhanupudi | Signal denoising using wavelets | |
| Marasović et al. | Cardiac arrhythmia detection using DCT based compressive sensing and random forest algorithm | |
| CN115363594A (en) | Real-time heart disease screening method based on recurrent neural network | |
| CN115153581A (en) | Electrocardiosignal characteristic waveform detection method and device and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| CP03 | Change of name, title or address | ||
| CP03 | Change of name, title or address |
Address after: 214072 west side of three floor four, 100 Chui Cui Road (creative garden), Liyuan Development Zone, Binhu District, Wuxi, Jiangsu, China Patentee after: Jiangsu lucky Technology Co., Ltd. Address before: 214072 3 floor, 4 building, 100 Chui Cui Road (creative garden), Binhu District, Wuxi, Jiangsu. Patentee before: Wuxi Jitian Communication Technology Co., Ltd. |