CN101099669A - Electrocardiogram data compression method and decoding method based on optimum time frequency space structure code - Google Patents

Abstract

The present invention belongs to the field of biomedical engineering technology, relates to a cardiac electric data compression method. Said method includes the following main steps: according to the time-domain autocorrelation characteristics of collected electrocardiosignal making multifragment optimum time-base decomposition of signal, then for every time base fragment respectively constructing low redundant mapping relationship under the optimum wavelet packet base, so that it can obtain optimum time frequency space structure code of whole electrocardiosignal, then according to said structure code making embedded encoding algorithm to implement high-effective compression of cardiac electric data.

Description

ECG Data Compression Method and coding/decoding method based on the optimum time frequency space structure sign indicating number

Technical field

The present invention relates to a kind of ECG Data Compression Method, belong to the biomedical engineering technology field.

Background technology

In recent years, cardiac monitoring develops into remote electrocardiogram monitor based on mobile communications network from the non-online monitoring of non real-time.Monitoring terminal not only can record data, also will have the ability of real-time transmission data.Brought two key technical problems that urgency is to be solved thus: the one, mass memory unit must be arranged to satisfy long data record requirement; The 2nd, answer tool high speed accurate data transmission performance to realize the online monitoring of the state of an illness.For this reason, be necessary electrocardiogram (ECG) data is efficiently compressed.

At present the electrocardiogram (ECG) data compression algorithm mainly is divided into two big classes, and a class is the time domain compression algorithm, and wherein using maximum is segmented fitting.This class algorithm advantage is that compression ratio is big, but bigger distorted signals is arranged behind the decompress(ion).Another kind of is the transform domain compression algorithm, mainly comprises KL (Karhunen-Loeve) conversion, discrete cosine transform and wavelet transformation.Wherein KL conversion and discrete cosine transform have good Static Compression Performance, early are introduced in the electrocardio compression; But because of it can not partly analyse and the layering processing in make-game, so can't transmit data progressively to appear mode in one's mind in real time.Wavelet transformation then because of its time, frequency domain has good localization property simultaneously, can adopt sub-band, level coding technology to realize that the progression transfer encoding to solve the real-time problem, has produced the embedded algorithm of wavelet field thus.This method is obtaining certain new development by means of the time frequency analysis advantage of wavelet transformation aspect the compress ecg data, but still exists compression result that big redundant bottleneck problem is arranged.

See that from information theory view data compression essence is to extract the order of signal, remove redundancy, that is to say the process that reduces comentropy.In theory of information, comentropy has reflected the unordered degree of signal, and comentropy is big more, and signal is unordered more.Because the embedded encoded employing fixed frequency band of traditional wavelet field is olation tends to cause the generation of high comentropy isolated zero in the encoding stream, this will inevitably make, and comentropy can't reduce fully, compression result still has big redundant.

On the other hand,, before compression, carry out time domain according to its correlation properties earlier and cut apart, and then segmentation is done data compression, the reduction of comentropy after should be able to more advantageously realizing compressing if can utilize the prioris such as relativity of time domain that are compressed signal.As everyone knows, electrocardiosignal has distinct cyclophysis, and promptly the time domain dependency utilizes this relativity of time domain and still reckon without in the embedded encoded process of wavelet field of electrocardiogram (ECG) data compression at present.

Shapiro finds, signal is being done in the process of wavelet transformation, extracts the order of striding frequency band, can realize the reduction of comentropy.Wavelet transformation to electrocardiosignal s (n) under the Mallat algorithm is

$s^{\left(j\right)}\left(n\right)=\underset{k}{\mathrm{\Σ}}h\left(k\right)\·s^{(j-1)}(n-2^{j-1}k)---\left(1\right)$

$d^{\left(j\right)}\left(n\right)=\underset{k}{\mathrm{\Σ}}g\left(k\right)\·s^{(j-1)}(n-2^{j-1}k)---\left(2\right)$

J is a wavelet transform dimension in the formula; H (k) is a low pass filter, and signal is level and smooth step by step by h (k), reflects the profile information of primary signal; And g (k) is a high pass filter, d ^(j)(n) be s ^(j-1)(n) and s ^(j)(n) difference between has reflected the detail section of signal.Fig. 1 (a) is the wavelet decomposition sketch map, and wherein node S represents primary signal; Node L _jBe illustrated in the profile information on the yardstick j, i.e. s ^(j)(n); Node H _jBe illustrated in the detailed information on the yardstick j, i.e. d ^(j)(n).

Shown in Fig. 1 (b), under the Mallat algorithm, the wavelet coefficient that obtains in the sampling process is inverted pyramid structure, and its sum is constant.These wavelet coefficients have two characteristics: 1. for each detailed information coefficient H _{J, k}(k coefficient on the j yardstick) must find two corresponding with it under adjacent small scale correlation coefficient H _J-1,2kAnd H _J-1,2k+1And profile information with have coefficient relation one to one with the detailed information of yardstick.2. generally speaking, if wavelet coefficient H _{J, k}Meaningless under given coding threshold value, its pairing correlation coefficient H then _J-1,2kAnd H _J-1,2k+1Generally also meaningless under this threshold value.

More than 2 just Shapiro the embedded encoded foundation of wavelet field, the 2. pairing wavelet coefficient H of characteristics are proposed _{J, k}Be called zerotree root, H _{J, k}Constituted zero the tree with its all correlation coefficienies, and zero tree has very low comentropy, only need represent by considerably less bit stream, this is the primary condition of this method realization compression just.But sufficient entropy subtracts because general wavelet decomposition still is unrealized, and therefore can have redundancy, makes its wavelet coefficient some special circumstances may occur: as wavelet coefficient H _{J, k}Meaningless under given coding threshold value, and significant situation appears in its pairing correlation coefficient, this coefficient H _{J, k}Be called as isolated zero.The existence of isolated zero, making can't be according to H _{J, k}Directly judge the information of its correlation coefficient, H therefore can not only encode _{J, k}, also must examination H _{J, k}Pairing all correlation coefficienies, this will increase the coefficient of required coding, also will increase the computing workload simultaneously, and these all are unfavorable for the realization of efficient compression.

Summary of the invention

Purport of the present invention is to propose a kind of ECG Data Compression Method, with the embedded encoded compression ratio bottleneck of traditional wavelet field of seeking to achieve breakthrough, further reduce compression back comentropy and redundancy, solve the problem of mass data storage and transmission in the remote electrocardiogram monitor with this.

For this reason, the present invention adopts following technical scheme:

A kind of ECG Data Compression Method is characterized in that, follows these steps to order and carries out:

(1) electrocardiosignal is carried out normalized, i.e. Y _k=(X _k-Avr)/Max (X _k-Avr), wherein Avr is

Original electrocardiographicdigital signal sequence X _kMeansigma methods, Max represents to get maximum, obtains electrocardiosignal sequence (Y after the normalization with this _k);

(2) according to the signal relativity of time domain, will be through the electrocardiosignal sequence (Y of normalized _k) carry out optimum time domain and cut apart, obtain time domain fragment sequence S _i

(3) make i=1;

(4) according to the signal frequency domain dependency, to time domain fragment (S _i) carry out the decomposition of optimal wavelet bag, obtain optimum frequency-domain structure sign indicating number (β _i) and wavelet packet coefficient (C _j);

(5) structure refines sequence (RL), search sequence (SL), channel bit stream (D _m), with wavelet packet coefficient (C _j) give and refine sequence (RL _j), with search sequence (SL) and channel bit stream (D _m) be initialized as null sequence, establish m=0;

(6) calculate n=floor[log ₂(max|C _j|)], make k=0;

(7) each the element value assignment that will refine in the sequence (RL) is given corresponding element in the search sequence (SL); Make j=1, setting threshold T _k=2 ⁿ

(8) if j element (SL of search sequence _j) absolute value more than or equal to T _k, and should just be worth, then export P to channel bit stream (D _m), and will refine j element (RL of sequence _j) value deduct T _kIf j element (SL of search sequence _j) absolute value more than or equal to T _k, and this is worth for negative, then exports N to channel bit stream (D _m), and will refine j element (RL of sequence _j) value add T _kIf search sequence j train value (SL _j) absolute value less than T _k, and wavelet decomposition tree is zero tree, then exports Z to channel bit stream (D _m); If search sequence j train value (SL _j) absolute value less than T _k, and wavelet decomposition tree is not zero tree, then exports T to channel bit stream (D _m), remove the descendants's coefficient in the search sequence (SL); If this channel coding does not finish, then make j=j+1, repeat this step, finish until this channel coding;

(9) if n 〉=1 then makes n=n-1, k=k+1, m=m+1 returns step (7); Otherwise, arrangement channel code stream (D according to the order of sequence _m), form time domain fragment (S _i) PTNZ symbol code stream, and with itself and corresponding optimum frequency-domain structure sign indicating number (β _i) output together;

(10) judge whether whole time domain fragment codings finish, if do not finish, make i=i+1, return step (4), repeated execution of steps (4) to (10) finishes until whole time domain fragment codings.

The present invention provides the decompression method of a kind of confession corresponding to this kind ECG Data Compression Method simultaneously, and the coding that the compression method according to claim 1 that is used to decompress obtains, this decompression method follow these steps to order and carry out:

(1) makes i=1;

(2) read time domain fragment (S _i) compressed encoding PTNZ symbol code stream;

(3) carry out passage and decompose, obtain the channel bit stream (D of each passage _m);

(4) structure output sequence (OL), search sequence (SL) all is initialized as null sequence, makes m=1, n=n _Max=log ₂T ₀, j=1;

(5) if n 〉=1, then output channel code stream (D _m) in symbol S;

(6) as if S=P, then with 2 ⁿGive search sequence j element (SL _j); If S=N is then with-2 ⁿGive search sequence j element (SL _j);

(7) with j element (OL of output sequence _j) and j element (SL of search sequence _j) sum gives j element (OL of output sequence _j); Make j element (SL of search sequence _j) be 0;

(8) if channel bit stream (D _m) output finish, then calculate optimum frequency-domain structure sign indicating number β _iUnder the mapping relations, next element correspondence position, i.e. j=β _i(j+1), return step (5), circulation is carried out, until channel bit stream (D _m) output finish.

(9) judge whether whole time domain fragment decodings finish, if do not finish, make m=m+1, n=n-1, j=1, and return step (5), and circulation execution in step (5) is finished until whole decodings to step (9), and output output sequence (OL) finishes.

The present invention is according to the time domain self correlation characteristics of the electrocardiosignal that collects, carry out the multi-disc section of signal when optimum base decompose, the substrate section makes up the low redundant mapping relations under the best wavelet packet basis respectively during then to each, so just obtained the optimum time frequency space structure sign indicating number of whole electrocardiosignal, then, carry out the efficient compression that embedded encoded algorithm is realized electrocardiogram (ECG) data according to this constructive code.This method has not only realized the efficient compression of electrocardiosignal, solved the problem of mass data storage in the remote electrocardiogram monitor, and possess the characteristics of progression transfer encoding, can carry out the telescopic Code And Decode of information source reconstruction quality as required, further satisfied the purpose of electrocardio data in real time transmission in the remote electrocardiogram monitor, thereby accurately provide condition in real time alternately with the cardiac monitoring specialist system for the monitoring that realizes the round-the-clock electrocardiogram (ECG) data of cardiac and record and for realizing.

Description of drawings

Fig. 1: the wavelet decomposition sketch map, Fig. 1 (a) is the wavelet decomposition tree construction, Fig. 1 (b) coefficient of wavelet decomposition graph of a relation;

Fig. 2: WAVELET PACKET DECOMPOSITION structural representation;

Fig. 3: the cost curve synoptic diagram of father and son's node in the wavelet package transforms;

Fig. 4 adopts the compression result contrast sketch map of distinct methods;

Fig. 5 is based on the coding flow chart of optimum time frequency space structure sign indicating number;

Fig. 6 is based on the decoding process figure of optimum time frequency space structure sign indicating number.

The specific embodiment

The present invention is directed to the mass data storage that exists in the remote electrocardiogram monitor system and the problem of real-time Transmission, try hard to find out a kind of compression algorithm of electrocardio efficiently from the angle of theory analysis and practice demonstration.At first, determined to have the wavelet field deployment algorithm research of time frequency resolution advantage by comparing the characteristics of time domain compression algorithm and this two classes algorithm of transform domain compression algorithm.Then according to electrocardiosignal comentropy characteristics in time domain and frequency domain, find out the compression ratio bottleneck problem that traditional wavelet field compression method exists theoretically, proposition is carried out the efficient compression of embedded encoded realization electrocardiogram (ECG) data by structure optimum time frequency space structure sign indicating number, has realized the telescopic progression transfer encoding of information source reconstruction quality simultaneously.Through practical proof, the efficient compression algorithm that realizes electrocardiogram (ECG) data based on the optimum time frequency space structure sign indicating number greatly reduces the comentropy of electrocardiosignal by making full use of the time-frequency autocorrelation performance of electrocardiosignal, has realized the efficient compression of electrocardiosignal.

How to construct the optimum time frequency space structure sign indicating number below in conjunction with accompanying drawing and case introduction.And provide encryption algorithm and the decoding algorithm that the present invention proposes based on the optimum time frequency space structure sign indicating number.

In order to realize the structure of optimum time frequency space structure sign indicating number, must make full use of the time-frequency characteristics of electrocardiosignal, construct the frequency domain space structure sign indicating number under the optimum time domain space structure, therefore, the present invention is at first from the frequency domain angle, consider to adopt new method to replace the fixed frequency band is olation of traditional wavelet, subtract with the entropy that compresses the back signal of guaranteeing to encode with the appearance that reduces the embedded encoded middle isolated zero of traditional wavelet field.And wavelet package transforms just in time can address that need, because it can obtain corresponding optimal wavelet bag band decomposition structure according to the frequency domain autocorrelation of electrocardiosignal, the optimum frequency-domain structure sign indicating number that structure is corresponding, reach the further reduction of comentropy, so just be expected to solve the bottleneck of Signal Compression.

In order to obtain this optimum frequency-domain structure sign indicating number, need the high frequency details in the traditional wavelet (Fig. 1 (a)) is also taken in, carry out suitable decomposition.When all nodes all need to decompose, can obtain complete wavelet bag decomposition texture as shown in Figure 2.

Whether in actual applications, the process of obtaining optimum frequency-domain structure sign indicating number is the root node that decomposes from frequency domain, be worth decomposing by the node of weighing on each layer yardstick, and the complete wavelet pack arrangement is successively pruned to obtain optimum structure.Need in the pruning process by the foundation of suitable cost function, promptly will fully coordinate distortion rate and compress recently to make up cost function from judging the criterion of compression performance quality as the optimum frequency-domain structure of search.

Had after the cost function, just can be by weigh decomposing cost, the frequency-domain structure (as shown in Figure 3) that search is optimum, then structure is corresponding.If (D _C1+ λ ₂R _C1)+(D _C2+ λ ₃R _C2)≤D _P+ λ ₁R _P, then father node is decomposed into two child nodes.

Definition D is the data distortion rate, and R is the data volume after quantizing, and then compresses purpose and can be expressed as: satisfying (D≤D under the distortion requirement prerequisite _b, D _bBe maximum admissible distortion), reach maximum compression (min R).Take all factors into consideration distortion rate and data volume, can obtain corresponding Lagrange cost function

J＝D+λR (3)

In the formula: J is the compression cost, and λ is the Lagrangian factor (λ 〉=0), and expression is transformed into distortion rate with data bit-rate and explains spatial quality, and definition λ=Δ D/ Δ R can require to choose according to compression quality.Thus, above-mentioned compression purpose can be exchanged into: under the prerequisite that distortion meets the demands, find the pairing frequency domain decomposition texture of minimal compression cost J.

Secondly, the present invention further investigates the time domain autocorrelation performance of electrocardiosignal, and base divides the optimum frequency-domain structure sign indicating number structure of taking off when realizing optimum.As mentioned above, by obtaining optimum frequency domain decomposition texture, realized the further reduction of comentropy, this has just been avoided because the redundancy in the signal causes dependency reduction between frequency band, thereby causes the isolated zero problem.And on the other hand, similar electrocardiosignal has very strong dependency on time domain, so because each subclass signal inside has very high order, subclass signal message entropy comprehensively will be far below the comentropy of original signal.Therefore, consider that above-mentioned time domain is cut apart (classification) thought to combine with the frequency domain subdivision method, structure optimum time frequency space structure sign indicating number is in the hope of reaching better compression effectiveness.Count the EGC waveform data storehouse with the MIT arrhythmia and be recorded as example No. 203, at s ₂Electrocardiosignal in the section all has is inverted R ripple and high T ripple, obviously is different from the front and rear part, this species diversity increased greatly should the period electrocardiosignal unordered degree (make its information Entropy Changes big, this deviates from mutually with requirement of compressing).Therefore, consider to adopt the method for adjacent time series electrocardiosignal classification, at first extract the characteristic point information (extreme point of P ripple, T ripple and R ripple, the starting point and the terminating point of P ripple, T ripple and QRS wave group) of each ecg wave form, the characteristic vector of then utilizing these information to constitute is carried out waveform separation.Finally Fig. 4 (a) can be divided into s as shown in the figure ₁, s ₂And s ₃, carry out optimum frequency-domain structure to these 3 sections respectively then and decompose, so just obtained the optimum time frequency space structure of whole section electrocardiosignal, the optimum time frequency space structure sign indicating number of structure correspondence then, with this as embedded encoded basis.Because the comentropy sum of this 3 segment signal obtains better compression effectiveness the most at last less than the overall information entropy of whole segment signal.Fig. 4 (a) is an optimum time frequency space structure sign indicating number organigram; Fig. 4 (b) is the embedded encoded result (λ of traditional wavelet field _CR=10.13, λ _PRD=5.15) Fig. 4 (c) is based on optimum time frequency space structure sign indicating number coding result (λ _CR=15.23, λ _PRD=8.39).

The embedded encoded final output result of tradition wavelet field is two code streams: main make code stream is PTNZ symbol stream, and inferior make code stream is 01 data flow, and needs to set up corresponding relation between the two.The encryption algorithm that the present invention proposes based on the optimum time frequency space structure sign indicating number, not only improved the isolated zero problem greatly, and only need a PTNZ symbol code stream and a small amount of coding of describing optimum frequency-domain structure (optimal wavelet bag decomposition texture), its Code And Decode algorithm flow respectively as shown in Figure 5 and Figure 6.

In addition, for the convenience that follow-up data is handled, convergence is accelerated in the time of the operation of assurance program, before electrocardiogram (ECG) data is encoded, at first will carry out normalized, i.e. Y to it _k=(X _k-Avr)/Max (X _k-Avr), wherein Avr is original electrocardiographicdigital signal sequence X _kMeansigma methods, Max represents to get maximum, obtains electrocardiosignal sequence (Y after the normalization with this _k).

Provide the experimental data that adopts in the invention process below and adopt the compression result that coded method provided by the invention obtained.

Experimental data is taken from MIT arrhythmia data base, and every electrocardiographic recording contains 1500～3000 heartbeats.Use dominant frequency to carry out data compression process as the Pentium 4 type computer of 2.8GHZ.Adopt evaluation index commonly used: compression ratio (contraction ratio) and MSER (percent of root-mean-square difference) come the compression performance of assessment algorithm

Compression ratio is defined as

${\mathrm{\λ}}_{\mathrm{CR}}=\frac{N_x}{N_s+N_v}---\left(4\right)$

N in the formula _xFor importing the data volume of signal to be compressed, N _sData volume after input signal is encoded in representative, N _vFor describing the data that coding structure needs.

MSER (λ _PRD) characterize the contrast of signal and primary signal behind the decompress(ion).If x _iBe primary signal,

For rebuilding the signal that obtains behind the decompress(ion), n is signal length, then λ _PRDBe defined as:

${\mathrm{\&lambda;}}_{\mathrm{PRD}}=\sqrt{\frac{\underset{1}{\overset{n}{\mathrm{\&Sigma;}}}{(x_l-{\stackrel{\&OverBar;}{x}}_i)}^2}{\underset{1}{\overset{\mathrm{<figure-callout\; id="2"\; label="n\; x\; i"\; filenames="A20071005802000131.png"\; state="\{\{state\}\}">n</figure-callout>}}{\mathrm{\&Sigma;}}}{x_i}^{}{{}_{}}^2}}\&times;100---\left(5\right)$

Still to be recorded as example No. 203 among the above-mentioned MIT data base, contrast traditional wavelet field embedded encoded (Fig. 5 (a)) and based on the result (Fig. 5 (b)) of optimum time frequency space structure sign indicating number encryption algorithm, as can be seen, adopt the inventive method can obtain higher compression ratio to electrocardiosignal, and corresponding λ _PRDMay increase, but this can't cause losing of characteristic information, in fact, λ _PRDIncrease, be because the noise in the processed signal causes: because consider the requirement of practicality, the electrocardiographic recording that algorithm adopts does not pass through the de-noising pretreatment, the present invention will take from MIT data base's original electrocardiographicdigital signal and directly handle by optimum time frequency STRUCTURE DECOMPOSITION algorithm, and under the effect of decomposing through more careful band structure, noise signal is well separated, for these noise bands, only need give less transmitted bit number or do not transmit, and so, must cause λ _PRDIncrease.

Table 1 be depicted as use respectively traditional wavelet field embedded encoded with the experimental result contrast of MIT arrhythmia data base's electrocardiographic recording being compressed based on optimum time frequency space structure sign indicating number encryption algorithm.

The compression experiment result contrast of two kinds of encryption algorithms of table 1

Electrocardiographic recording	The tradition wavelet field is embedded encoded			Encode based on the optimum time frequency space structure sign indicating number
							λ CR	λ PRD	t/s	λ CR	λ PRD	t/s
	100.DAT	12.31	4.78	34	14.56	4.96							28
107.DAT							10.28	5.13	37	14.69	7.37	42
	116.DAT	10.63	4.58	37	15.96	7.63							42
203.DAT							10.13	5.15	43	17.23	8.39	50
	215.DAT	10.53	4.76	39	15.96	7.63							49
231.DAT							12.31	4.21	30	14.69	4.98	33

By table 1 as seen, can further reduce comentropy because of it and realize that the monadic symbols code stream obtained the bigger compression ratio of the embedded encoded algorithm of more traditional wavelet field based on optimum time frequency space structure sign indicating number encryption algorithm, meanwhile, the present invention has realized that better the noise sub-band separates, therefore can reduce the bit number of coding noise, this is to cause λ in the table 1 _PRDCause of increased also can be seen the signal that noise is big, λ simultaneously _PRDAlso can be bigger.And on the other hand, employing is more relatively based on the meeting consuming time of optimum time frequency space structure sign indicating number encryption algorithm, this is owing to relativity of time domain in the algorithm calculates complicated causing, yet because of it has the progression transmission characteristic, and data quantity transmitted reduced, so its signal real-time Transmission performance is not affected.

Claims (2)

1. an ECG Data Compression Method is characterized in that, follows these steps to order and carries out:

(1) electrocardiosignal is carried out normalized, i.e. Y _k=(X _k-Avr)/Max (X _k-Avr), wherein Avr is original electrocardiographicdigital signal sequence X _kMeansigma methods, Max represents to get maximum, obtains electrocardiosignal sequence (Y after the normalization with this _k);

(2) according to the signal relativity of time domain, will be through the electrocardiosignal sequence (Y of normalized _k) carry out optimum time domain and cut apart, obtain time domain fragment sequence S _i

(3) make i=1;

(4) according to the signal frequency domain dependency, to time domain fragment (S _i) carry out the decomposition of optimal wavelet bag, obtain optimum frequency-domain structure sign indicating number (β _i) and wavelet packet coefficient (C _j);

(5) structure refines sequence (RL), search sequence (SL), channel bit stream (D _m), with wavelet packet coefficient (C _j) give and refine sequence (RL _j), with search sequence (SL) and channel bit stream (D _m) be initialized as null sequence, establish m=0;

(6) calculate n=floor[log ₂(max|C _j|], make k=0;

(7) each the element value assignment that will refine in the sequence (RL) is given corresponding element in the search sequence (SL); Make j=1, setting threshold T _k=2 ⁿ

(8) if j element (SL of search sequence _j) absolute value more than or equal to T _k, and should just be worth, then export P to channel bit stream (D _m), and will refine j element (RL of sequence _j) value deduct T _kIf j element (SL of search sequence _j) absolute value more than or equal to T _k, and this is worth for negative, then exports N to channel bit stream (D _m), and will refine j element (RL of sequence _j) value add T _kIf search sequence j train value (SL _j) absolute value less than T _k, and wavelet decomposition tree is zero tree, then exports Z to channel bit stream (D _m); If search sequence j train value (SL _j) absolute value less than T _k, and wavelet decomposition tree is not zero tree, then exports T to channel bit stream (D _m), remove the descendants's coefficient in the search sequence (SL); If this channel coding does not finish, then make j=j+1, repeat this step, finish until this channel coding;

(9) if n 〉=1 then makes n=n-1, k=k+1, m=m+1 returns step (7); Otherwise, arrangement channel code stream (D according to the order of sequence _m), form time domain fragment (S _i) PTNZ symbol code stream, and with itself and corresponding optimum frequency-domain structure sign indicating number (β _i) output together;

(10) judge whether whole time domain fragment codings finish, if do not finish, make i=i+1, return step (4), repeated execution of steps (4) to (10) finishes until whole time domain fragment codings.

2. electrocardiogram (ECG) data coding/decoding method, the coding that the compression method according to claim 1 that is used to decompress obtains is characterized in that, follows these steps to order and carries out:

(1) makes i=1;

(2) read time domain fragment (S _i) compressed encoding PTNZ symbol code stream;

(3) carry out passage and decompose, obtain the channel bit stream (D of each passage _m);

(4) structure output sequence (OL), search sequence (SL) all is initialized as null sequence, makes m=1, n=n _Max=log ₂T ₀, j=1;

(5) if n 〉=1, then output channel code stream (D _m) in symbol S;

(6) as if S=P, then with 2 ⁿGive search sequence j element (SL _j); If S=N is then with-2 ⁿGive search sequence j element (SL _j);

(7) with j element (OL of output sequence _j) and j element (SL of search sequence _j) sum gives j element (OL of output sequence _j); Make j element (SL of search sequence _j) be 0;

(8) if channel bit stream (D _m) output finish, then calculate optimum frequency-domain structure sign indicating number β _iUnder the mapping relations, next element correspondence position, i.e. j=β _i(j+1), return step (5), circulation is carried out, until channel bit stream (D _m) output finish.

(9) judge whether whole time domain fragment decodings finish, if do not finish, make m=m+1, n=n-1, j=1, and return step (5), and circulation execution in step (5) is finished until whole decodings to step (9), and output output sequence (OL) finishes.

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