CN103892822A - Electrocardiogram signal processing method and device - Google Patents

Abstract

The invention discloses an electrocardiogram signal processing method and device, which are used for improving the accuracy of sleep analysis results. The method includes: obtaining ECG signal data within a preset time interval; sampling the obtained ECG signal data according to a preset sampling period, and obtaining ECG signal data corresponding to each sampling period, wherein, within each sampling period The ECG signal data includes the ECG signal data within the sampling period, before and/or after the sampling period, and within a preset time period; according to the ECG signal data corresponding to each sampling period, the HRV characteristics in each sampling period are respectively determined parameter. Further, according to a preset algorithm, the HRV feature vectors in each sampling period are respectively determined and processed to obtain a filtered HRV feature vector; and the HRV feature vector and the filtered HRV feature vector are combined.

Description

A kind of ECG signal processing method and device

Technical field

The present invention relates to signal processing technology field, relate in particular to a kind of ECG signal processing method and device

Background technology

Sleep study is the important component part of hypnosphy and electroencephalography, one of focus of scientific research in the Ye Shi world today.The structure of Accurate Analysis sleep, can, to abundant assessment sleep quality, analyze sleep disorder patient's health condition, provides the diagnostic recommendations holding water.

Early stage research thinks that sleep state is only associated with the synchronization slow wave of electroencephalogram (Electroencephalography, EEG) EEG, and sleep is a single process.By means of early stage polysomnogram (Polysomnographic, PSG), the Kleitman of the U.S. and Aserinsky(1957) find that the mankind's sleep is not a homogeneous state, but there are two different time phase cycles to replace: non-rapid eye movement sleep (non-rapid eye movement, and rapid eye movement sleep (rapid eye movement NREM), REM), whether the two is to have eyeball paroxysmal rapid movement and different brain wave features to distinguish.The physical sign parameters such as REM stage heart rate are unstable, the limb action such as stand up also relatively many, belong to the shallow stage of sleeping; The physical sign parameters such as NREM stage heart rate are stable, belong to the sound sleep stage.

Current clinical analysis of sleeping structure method is to analyze PSG, and analysis need to be carried out at the Sleep Monitoring Room of hospital, adopts PSG to record measured's brain electricity, and limb motion, breathes, heart rate, and the data such as eye movement are also analyzed, and obtain measured's Sleep architecture.In actual applications, there is following problem in PSG:

1. whole process operation complexity, consuming time, and professional technique requires high, and testing cost is high, can only carry out in hospital; 2. while monitoring, need on human body, press close to ten electrodes (as shown in Figure 1), very uncomfortable, wear rear patient and have difficulty in going to sleep; 3. patient may be not suitable with hospital environment, increases the weight of insomnia; 4. hospital's sleep monitor resource-constrained, cannot guarantee the medical of most patients, according to Hospital Statistics, does a sleep monitor need to shift to an earlier date reservation in 3 months in Beijing; 5. sleep disorder and other neurosis coincidences are high, be unwilling to go to a doctor, and such patient mostly are social elite stratum because compatriots' idea haunts more, focus on the protection of privacy, and existing medical model can not effectively be protected their right of privacy; 6. patient from far-off regions is medical inconvenient, and to sum up, current sleep monitor pattern cannot meet patient's demand far away, and the monitoring of monitoring rate hospital of family is more practical, and the sleep monitor system that the applicable family of research and development is used is significant.

At present, the sleep monitor system of monitoring for family is mainly by being attached to multiple electrodes of human body surface, can obtain electrocardiogram (Electrocardiography, ECG), these electrodes can be by the integrated volume that reduces, strengthen portability, for example Fig. 2 a is the EGC sensor of a pectoral girdle formula.Electrocardiogram is to describe the figure that the body surface potential that causes of heartbeat changes, its shape as shown in Figure 2 b, a cardiac electrical cycle mainly comprises Q, R, tri-Important Characteristic Points ripples of S, and the R wave spacing of two adjacent periods is exactly RR interval (R-R Interval), can analyze heart rate by RR interval.Heart rate variability rate (Heart Rate Variability, HRV), refers to the small variation that the human heart cycle of beating exists, and this variation difference can reflect the activity of human body autonomic nerve, can reflect monitored person's Depth of sleep.Because HRV has the feature such as high s/n ratio and easy acquisition, can stay at home and carry out record in common medical environment.

But, exploitation and the design with sensor concentrated in the groundwork of the sleep monitor system of monitoring for family at present, to improve the accuracy in measurement of physical signs, and utilizing sensor acquisition to judge to ECG signal analysis in the process of Depth of sleep, just carry out simple feature extraction, be characterized as example to extract HRV, HRV feature is a kind of statistical nature, needs the observed data of long period accurately to calculate.And existing sleep monitor system adopts the ECG data of 30s to calculate HRV feature.On the one hand, data volume is less, and feature is stable not; On the other hand, because the sampling time is short, cause the resolution of frequency domain character of HRV inadequate, so directly reduced the precision of Depth of sleep analysis result, cannot meet practical application request.

Summary of the invention

The embodiment of the present invention provides a kind of ECG signal processing method and device, in order to improve the precision of sleep analysis result.

The embodiment of the present invention provides a kind of ECG signal processing method, comprising:

Obtain the electrocardiogram ECG signal data in Preset Time interval;

According to the default sampling period to obtain ECG signal data sample, obtain ECG signal data corresponding to each sampling period, wherein, the ECG signal data in each sampling period comprises the ECG signal data in duration in this sampling period and before this sampling period and/or after this sampling period, default;

According to ECG signal data corresponding to each sampling period, determine respectively the heart rate variability rate characteristic vector in each sampling period.

The embodiment of the present invention provides a kind of ECG signal analytical equipment, comprising:

Obtain unit, for obtaining the electrocardiogram ECG signal data in Preset Time interval;

Sampling unit, for the ECG signal data obtaining being sampled according to the default sampling period, obtain ECG signal data corresponding to each sampling period, wherein, the ECG signal data in each sampling period comprises the ECG signal data in duration in this sampling period and before this sampling period and/or after this sampling period, default;

The first determining unit, for according to ECG signal data corresponding to each sampling period, determines respectively the heart rate variability rate characteristic vector in each sampling period.

ECG signal processing method and device that the embodiment of the present invention provides, due within each sampling period, the ECG signal data obtaining not only comprises the ECG signal data in the current sampling period, ECG signal data in certain time length before also comprising the current sampling period and/or after the current sampling period, like this, can promote frequency resolution, calculate accurately the low frequency component of ECG signal data, thereby improve the precision that Depth of sleep is analyzed.

Other features and advantages of the present invention will be set forth in the following description, and, partly from description, become apparent, or understand by implementing the present invention.Object of the present invention and other advantages can be realized and be obtained by specifically noted structure in write description, claims and accompanying drawing.

Accompanying drawing explanation

Accompanying drawing described herein is used to provide a further understanding of the present invention, forms a part of the present invention, and schematic description and description of the present invention is used for explaining the present invention, does not form inappropriate limitation of the present invention.In the accompanying drawings:

Fig. 1 is in prior art, PSG sleep monitor scene schematic diagram;

Fig. 2 a is in prior art, the EGC sensor schematic diagram of pectoral girdle formula;

Fig. 2 b is in prior art, ECG signal schematic diagram data;

Fig. 3 is in prior art, based on Electrocardiographic analysis of sleeping structure schematic flow sheet;

Fig. 4 is in the embodiment of the present invention, HRV frequency domain character computational methods schematic diagram;

Fig. 5 is in the embodiment of the present invention, the implementing procedure schematic diagram of ECG signal processing method;

Fig. 6 is in the embodiment of the present invention, CS filtering implementation procedure schematic diagram;

Fig. 7 is in the embodiment of the present invention, the calculation process schematic diagram of CS filtering;

Fig. 8 is in the embodiment of the present invention, the structural representation of ECG signal blood processor.

The specific embodiment

In order to improve the precision of sleep analysis result, the embodiment of the present invention provides a kind of ECG signal processing method and device.

Below in conjunction with Figure of description, the preferred embodiments of the present invention are described, be to be understood that, preferred embodiment described herein is only for description and interpretation the present invention, be not intended to limit the present invention, and in the situation that not conflicting, the feature in embodiment and embodiment in the present invention can combine mutually.

Embodiment mono-

Utilize flow process that electrocardiogram carries out analysis of sleeping structure as shown in Figure 3, can comprise following part: first extract HRV characteristic vector from ECG, then by the HRV characteristic vector input grader extracting, wherein, grader is that training obtains in specific sample, grader export structure is exactly sleep state, comprises REM or NREM.

In the embodiment of the present invention, extract flow process for the HRV characteristic vector in above-mentioned flow process and improve, to improve the accuracy of the HRV characteristic vector of extracting, and then make the sleep state result of grader output more accurate.Concrete, the relevant criterion of analyzing according to Depth of sleep, analyzes take 30 seconds as unit conventionally, according to the sampling period of 30 seconds, ECG signal data is sampled, and obtains the data in one-period, by frequency resolution formula f ₀known (the f of=1/T ₀for frequency to be asked, T is the corresponding cycle), hence one can see that, adopting 30 number of seconds is 0.03Hz according to the frequency resolution obtaining, and medical research shows, the information of 0.003Hz has more meaning for weighing autonomic nervous activity, that is the data that at least need 5 minutes just can obtain low frequency component.Be convenient to describe for example, in the embodiment of the present invention, describe with the data instance that extracts 5 minutes, while specifically enforcement, the duration of the data of extracting can be more than 5 minutes, and the present invention does not limit this.

Therefore, in the embodiment of the present invention, extracting method to HRV frequency domain character improves, in the time that ECG signal data is sampled, for example, sample according to the default sampling period (can be, but not limited to be set to 30s), but each sampling period participates in the ECG signal data of statistics comprises the ECG signal data in certain time length in the front sampling period and before the current sampling period, or comprise the ECG signal data in certain time length in the current sampling period and after the current sampling period, can also comprise the current sampling period, ECG data in certain time length before the current sampling period and after the current sampling period.As shown in Figure 4, it is HRV frequency domain character computational methods schematic diagrams, extracts frequency domain character as example take the ECG signal data in 5 minutes as a unit, is worth the frequency domain character as the current sampling period, and next frequency domain character value is extracted in each translation for 30 seconds.By lengthening ECG signal data statistical window, can promote frequency resolution, calculate accurately low frequency component, describe sympathetic nerve and parasympathetic activity, thereby embody more accurately Depth of sleep.

According to above-mentioned analysis, as shown in Figure 5, the implementing procedure schematic diagram of the ECG signal processing method providing for the embodiment of the present invention, can comprise the following steps:

ECG signal data in S501, acquisition Preset Time interval;

When concrete enforcement, can analyze as required the time period of Depth of sleep and determine the interval that needs the ECG signal data obtaining, for example, so that the Depth of sleep situation between evening 22:00 to 22:05 is analyzed as example, can from the ECG signal data of record, extract the ECG signal data in the certain hour section before and after 22:00 to 22:05, suppose the ECG signal data between extraction 21:55 to 22:10.

S502, according to the default sampling period to obtain ECG signal data sample, obtain ECG signal data corresponding to each sampling period;

Suppose that the sampling period arranging is 30 seconds, when concrete enforcement, can determine respectively 22 o'clock sharp ~ 22 30 seconds, 22: 30 seconds ~ 22: 1, 22: 1 ~ 22: 1: 30, 22: 1 minute 30 seconds 1, 22: 1 ~ 22: 2: 30, 22: 2 minutes 30 seconds 1, 22: 1 ~ 22: 3: 30, 22: 3 minutes 30 seconds 1, 2: 1 ~ 22: 4: 30, the ECG signal data of correspondence in 22 o'clock 4 minutes 30 seconds ~ 22 o'clock 05 minutes each time periods, wherein, ECG signal data in above-mentioned each time period comprises in this sampling period and before this sampling period and/or after this sampling period, ECG signal data in default duration, take 22 o'clock sharp ~ 22, an ECG signal data comprising for 30 seconds is as example, it can comprise the ECG signal data between 21 o'clock 55 minutes 30 seconds ~ 22: 30 seconds, also can comprise the ECG signal data between 22 o'clock sharp ~ 22 o'clock 05 minutes, can also comprise 21: 57: 30 ~ ECG signal data between 22: 02: 30, when concrete enforcement, if the ECG data in a certain sampling period had both comprised ESG data before this sampling period while also comprising the ECG data after this sampling period, after the duration that it comprises the ECG data before the sampling period and sampling period, the duration of ECG data can distribute arbitrarily, the embodiment of the present invention does not limit this, reach default duration (in the embodiment of the present invention take 5 minutes as example) as long as extract total duration of data.

S503, according to ECG signal data corresponding to each sampling period, determine respectively the HRV characteristic vector in each sampling period.

Concrete, for ECG signal data corresponding to each sampling period obtaining, from ECG signal data, detect R ripple position (as a rule, the about every 800 milliseconds of appearance of R ripple once), can determine RR interval according to R ripple position, just can analyze heart rate according to RR interval, thereby obtain HRV characteristic vector.Wherein, HRV characteristic vector can comprise 12 characteristic vectors, and different characteristic vector can characterize Depth of sleep from different angles.

When concrete enforcement, in order to improve precision of analysis, obtain longer that the interval of ECG data can arrange, suppose the ECG signal data in 1000 cycles of acquisition, correspondingly, can obtain 1000 HRV characteristic vectors, respectively with HRV ₁, HRV ₂hRV _mrepresent (m is less than or equal to 1000 natural number) each HRV characteristic vector, because each HRV characteristic vector all comprises 12 characteristic parameters, its arbitrary HRV characteristic parameter comprising can be expressed as HRV _{(m, 1)}, HRV _{(m, 2)}... HRV _{(m, n)}(n is less than or equal to 12 natural number).Can be by 1000 HRV characteristic vectors that obtain in example in following matrix notation:

$\begin{array}{cccc}{\mathrm{HRV}}_{\left(\mathrm{1,1}\right)}& {\mathrm{HRV}}_{\left(\mathrm{1,2}\right)}& ...& {\mathrm{HRV}}_{\left(\mathrm{1,12}\right)}\\ {\mathrm{HRV}}_{\left(\mathrm{2,1}\right)}& {\mathrm{HRV}}_{\left(\mathrm{2,2}\right)}& ...& {\mathrm{HRV}}_{\left(\mathrm{2,12}\right)}\\ .& .& .& .\\ .& .& .& .\\ .& .& .& .\\ {\mathrm{HRV}}_{\left(\mathrm{1000,1}\right)}& {\mathrm{HRV}}_{\left(\mathrm{1000,2}\right)}& ...& {\mathrm{HRV}}_{\left(\mathrm{1000,12}\right)}\end{array}$

In said process, within each sampling period, the ECG signal data obtaining not only comprises the ECG signal data in the current sampling period, ECG signal data in certain time length before also comprising the current sampling period and/or after the current sampling period, lengthen data statistics window, like this, can promote frequency resolution, the accurate low frequency component that calculates ECG signal data, thus the precision that Depth of sleep is analyzed improved.

Embodiment bis-

Compared with NREM, the breathing of REM device and the relevant parameter of heart beating have significant change, according to this feature, in the embodiment of the present invention, can utilize preset algorithm to carry out behavioral characteristics extraction to the HRV characteristic vector of extracting in embodiment mono-, the precision of analyzing further to improve Depth of sleep.

Based on this, the ECG signal data processing method that the embodiment of the present invention provides, can also comprise the following steps:

Step 1, after the HRV characteristic vector of determining in each sampling period, determine respectively filtering HRV characteristic vector corresponding to HRV characteristic vector in each sampling period according to preset algorithm;

Concrete, for the HRV characteristic vector in each sampling period, determine respectively according to described preset algorithm filtering HRV characteristic parameter corresponding to each HRV characteristic parameter that this HRV characteristic vector comprises; And determine that filtering HRV characteristic parameter corresponding to each HRV characteristic parameter that this HRV characteristic vector comprises forms filtering HRV characteristic vector.

Step 2, merging HRV characteristic vector and filtering HRV characteristic vector.

For convenience of description, respectively with HRV _cs1, HRV _cs2hRV _csmrepresent (m is less than or equal to 1000 natural number) each filtering HRV characteristic vector, its arbitrary filtering HRV characteristic parameter comprising can be expressed as HRV _{cs (m, 1)}, HRV _{cs (m, 2)}... HRV _{cs (m, n)}(n is less than or equal to 12 natural number).HRV characteristic vector and filtering HRV characteristic vector after can merging by following matrix notation:

$\begin{array}{cccccccc}{\mathrm{HRV}}_{\left(\mathrm{1,1}\right)}& {\mathrm{HRV}}_{\left(\mathrm{1,2}\right)}& ...& {\mathrm{HRV}}_{\left(\mathrm{1,12}\right)}& {\mathrm{HRV}}_{\mathrm{cs}\left(\mathrm{1,1}\right)}& {\mathrm{HRV}}_{\mathrm{cs}\left(\mathrm{1,2}\right)}& ...& {\mathrm{HRV}}_{\mathrm{cs}\left(\mathrm{1,12}\right)}\\ {\mathrm{HRV}}_{\left(\mathrm{2,1}\right)}& {\mathrm{HRV}}_{\left(\mathrm{2,2}\right)}& ...& {\mathrm{<figure-callout\; id="2"\; label="HRV"\; filenames="HDA00002657492100031.png"\; state="\{\{state\}\}">HRV</figure-callout>}}_{\mathrm{2,12}}& {\mathrm{HRV}}_{\mathrm{cs}\left(\mathrm{2,1}\right)}& {\mathrm{HRV}}_{\mathrm{cs}\left(\mathrm{2,2}\right)}& ...& {\mathrm{HRV}}_{\mathrm{cs}\left(\mathrm{2,12}\right)}\\ .& .& .& .& .& .& .& .\\ .& .& .& .& .& .& .& .\\ .& .& .& .& .& .& .& .\\ {\mathrm{HRV}}_{\left(\mathrm{1000,1}\right)}& {\mathrm{HRV}}_{\left(\mathrm{1000,2}\right)}& ...& {\mathrm{HRV}}_{\left(\mathrm{1000,12}\right)}& {\mathrm{HRV}}_{\mathrm{cs}\left(\mathrm{1000,1}\right)}& {\mathrm{HRV}}_{\mathrm{cs}\left(\mathrm{1000,2}\right)}& ...& {\mathrm{HRV}}_{\mathrm{cs}\left(\mathrm{1000,12}\right)}\end{array}$

When concrete enforcement, can be, but not limited to adopt central authorities-neighborhood (Center-Surround.CS) filtering is carried out behavioral characteristics extraction to HRV characteristic vector.

As shown in Figure 6, be CS filtering implementation procedure schematic diagram, the each characteristic parameter comprising for each HRV characteristic vector carries out respectively CS filtering, obtains filtered characteristic parameter corresponding to each characteristic vector, filtered each characteristic parameter composition filtering HRV characteristic vector.

As shown in Figure 7, be the calculation process schematic diagram of CS filtering, wherein T _ccentered by interval, T _sfor between peripheral region, T conventionally _cbe less than T _s, filtering result is the difference of average in two intervals, obtains filtering HRV characteristic vector.For convenience of description, represent HRV characteristic vector with HRV, represent filtering HRV characteristic vector with HRVcs, finally HRVcs and HRV merging are obtained to new HRV characteristic vector.

In embodiment bis-, make full use of the time-varying characteristics of the outstanding HRV feature self of CS difference, thereby can further improve the precision of sleep analysis result.

MIT-BIH polysomnogram data base is the authoritative relevant data base of sleep, utilizes on this data base and tests, and in test, the method that adopts the embodiment of the present invention to provide, grader adopts support vector machine (SVM).For guaranteeing test validity, the data of training set and test set separate completely, and data are respectively from the different persons of being observed.Test result demonstration, is used traditional HRV data analysis, and the sleep analysis accuracy obtaining is 81.0%; After the method that the employing embodiment of the present invention provides, sleep analysis accuracy is increased to 86.9%.

Based on same inventive concept, a kind of ECG signal blood processor is also provided in the embodiment of the present invention, because the principle that said apparatus is dealt with problems is similar to ECG signal processing method, therefore the enforcement of said apparatus can be referring to the enforcement of method, repeats part and repeat no more.

As shown in Figure 8, the structural representation of the ECG signal blood processor providing for the embodiment of the present invention, comprising:

Obtain unit 801, for obtaining the ECG signal data in Preset Time interval;

Sampling unit 802, for the ECG signal data obtaining being sampled according to the default sampling period, obtain ECG signal data corresponding to each sampling period, wherein, the ECG signal data in each sampling period comprises the ECG signal data in duration in this sampling period and before this sampling period and/or after this sampling period, default;

The first determining unit 803, for according to ECG signal data corresponding to each sampling period, determines respectively the HRV characteristic vector in each sampling period.

When concrete enforcement, the ECG signal blood processor that the embodiment of the present invention provides, can also comprise:

The second determining unit, for after the first determining unit 803 is determined the HRV characteristic vector in each sampling period, determines filtering HRV characteristic vector corresponding to HRV characteristic vector in each sampling period according to preset algorithm;

Merge cells, for merging HRV characteristic vector and filtering HRV characteristic vector.

Wherein, HRV characteristic vector comprises at least one HRV characteristic vector, and the second determining unit, can comprise:

First determines subelement, for the HRV characteristic vector in each sampling period, determines respectively according to described preset algorithm filtering HRV characteristic parameter corresponding to each HRV characteristic parameter that this HRV characteristic vector comprises;

Second determines subelement, for filtering HRV characteristic parameter composition filtering HRV characteristic vector corresponding to each HRV characteristic parameter of determining that this HRV characteristic vector comprises.

Wherein, while specifically enforcement, can be, but not limited to use filtering HRV characteristic vector corresponding to HRV characteristic vector in fixed each sampling period of CS filtering algorithm.

Those skilled in the art should understand, embodiments of the invention can be provided as method, system or computer program.Therefore, the present invention can adopt complete hardware implementation example, completely software implementation example or the form in conjunction with the embodiment of software and hardware aspect.And the present invention can adopt the form at one or more upper computer programs of implementing of computer-usable storage medium (including but not limited to disk memory, CD-ROM, optical memory etc.) that wherein include computer usable program code.

The present invention is with reference to describing according to flow chart and/or the block diagram of the method for the embodiment of the present invention, equipment (system) and computer program.Should understand can be by the flow process in each flow process in computer program instructions realization flow figure and/or block diagram and/or square frame and flow chart and/or block diagram and/or the combination of square frame.Can provide these computer program instructions to the processor of general purpose computer, special-purpose computer, Embedded Processor or other programmable data processing device to produce a machine, the instruction that makes to carry out by the processor of computer or other programmable data processing device produces the device for realizing the function of specifying at flow process of flow chart or multiple flow process and/or square frame of block diagram or multiple square frame.

These computer program instructions also can be stored in energy vectoring computer or the computer-readable memory of other programmable data processing device with ad hoc fashion work, the instruction that makes to be stored in this computer-readable memory produces the manufacture that comprises command device, and this command device is realized the function of specifying in flow process of flow chart or multiple flow process and/or square frame of block diagram or multiple square frame.

These computer program instructions also can be loaded in computer or other programmable data processing device, make to carry out sequence of operations step to produce computer implemented processing on computer or other programmable devices, thereby the instruction of carrying out is provided for realizing the step of the function of specifying in flow process of flow chart or multiple flow process and/or square frame of block diagram or multiple square frame on computer or other programmable devices.

Although described the preferred embodiments of the present invention, once those skilled in the art obtain the basic creative concept of cicada, can make other change and modification to these embodiment.So claims are intended to be interpreted as comprising preferred embodiment and fall into all changes and the modification of the scope of the invention.

Obviously, those skilled in the art can carry out various changes and modification and not depart from the spirit and scope of the present invention the present invention.Like this, if within of the present invention these are revised and modification belongs to the scope of the claims in the present invention and equivalent technologies thereof, the present invention is also intended to comprise these changes and modification interior.

Claims (8)

1. an ECG signal processing method, is characterized in that, comprising:

Obtain the electrocardiogram ECG signal data in Preset Time interval;

According to the default sampling period to obtain ECG signal data sample, obtain ECG signal data corresponding to each sampling period, wherein, the ECG signal data in each sampling period comprises the ECG signal data in duration in this sampling period and before this sampling period and/or after this sampling period, default;

According to ECG signal data corresponding to each sampling period, determine respectively the heart rate variability rate HRV characteristic vector in each sampling period.

2. the method for claim 1, is characterized in that, also comprises:

After the HRV characteristic vector of determining in each sampling period, determine respectively filtering HRV characteristic vector corresponding to HRV characteristic vector in each sampling period according to preset algorithm; And

Merge HRV characteristic vector and filtering HRV characteristic vector.

3. method as claimed in claim 2, is characterized in that, described HRV characteristic vector comprises at least one HRV characteristic parameter; And

Determine filtering HRV characteristic vector corresponding to HRV characteristic vector in each sampling period according to preset algorithm, specifically comprise:

For the HRV characteristic vector in each sampling period, determine respectively according to described preset algorithm filtering HRV characteristic parameter corresponding to each HRV characteristic parameter that this HRV characteristic vector comprises; And

Determine filtering HRV characteristic parameter composition filtering HRV characteristic parameter corresponding to each HRV characteristic parameter that this HRV characteristic vector comprises.

4. method as claimed in claim 2 or claim 3, is characterized in that, described preset algorithm comprises central authorities-neighborhood CS filtering algorithm.

5. an ECG signal blood processor, is characterized in that, comprising:

Obtain unit, for obtaining the electrocardiogram ECG signal data in Preset Time interval;

Sampling unit, for the ECG signal data obtaining being sampled according to the default sampling period, obtain ECG signal data corresponding to each sampling period, wherein, the ECG signal data in each sampling period comprises the ECG signal data in duration in this sampling period and before this sampling period and/or after this sampling period, default;

The first determining unit, for according to ECG signal data corresponding to each sampling period, determines respectively the heart rate variability rate HRV characteristic vector in each sampling period.

6. device as claimed in claim 5, is characterized in that, also comprises:

The second determining unit, for after described the first determining unit is determined the HRV characteristic vector in each sampling period, determines filtering HRV characteristic vector corresponding to HRV characteristic vector in each sampling period according to preset algorithm;

Merge cells, for merging HRV characteristic vector and filtering HRV characteristic vector.

7. device as claimed in claim 6, is characterized in that, described HRV characteristic vector comprises at least one HRV characteristic parameter; And

Described the second determining unit, comprising:

First determines subelement, for the HRV characteristic vector in each sampling period, determines respectively according to described preset algorithm filtering HRV characteristic parameter corresponding to each HRV characteristic parameter that this HRV characteristic vector comprises;

Second determines subelement, for filtering HRV characteristic parameter composition filtering HRV characteristic vector corresponding to each HRV characteristic parameter of determining that this HRV characteristic vector comprises.

8. the device as described in claim 6 or 7, is characterized in that, described preset algorithm comprises central authorities-neighborhood CS filtering algorithm.

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