CN103892822B - A kind of ECG signal processing method and device - Google Patents
A kind of ECG signal processing method and device Download PDFInfo
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Abstract
The invention discloses a kind of ECG signal processing method and device, in order to improve the precision of sleep analysis result.Described method, comprising: obtain the ECG signal data in prefixed time interval; According to the sampling period of presetting, the ECG signal data obtained are sampled, obtain ECG signal data corresponding to each sampling period, wherein, the ECG signal data in each sampling period comprise in this sampling period and before this sampling period and/or after this sampling period, in preset duration ECG signal data; According to the ECG signal data that each sampling period is corresponding, determine the HRV characteristic parameter in each sampling period respectively.Further, the HRV characteristic vector determined respectively in each sampling period according to preset algorithm is carried out process and is obtained filtering HRV characteristic vector; And merge HRV characteristic vector and filtering HRV characteristic vector.
Description
Technical field
The present invention relates to signal processing technology field, particularly relate 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, to fully assessing sleep quality, can analyze the health condition of sleep disorder patient, providing the diagnostic recommendations held 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), Kleitman and Aserinsky(1957 of the U.S.) find that the sleep of the mankind is not a homogeneous state, but have two different time phase cycle alternately: non-rapid eye movement sleep (non-rapideyemovement, and rapid eye movement sleep (rapideyemovement 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 is unstable, and it is also relatively many limb action such as to stand up, and belongs to the shallow stage of sleeping; The physical sign parameters such as NREM stage heart rate are stablized, and belong to deep sleep stages.
Current clinical analysis of sleeping structure method analyzes PSG, analyzes and need to carry out at the Sleep Monitoring Room of hospital, and adopt PSG to record the brain electricity of measured, the data such as limb motion, breathes, heart rate, and eye is dynamic are also analyzed, and obtain the Sleep architecture of measured.In actual applications, there is following problem in PSG:
1. whole process operation is complicated, consuming time, and professional technique requires high, and testing cost is high, can only carry out in hospital; 2. need during monitoring 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 ensure the medical of most patients, and according to Hospital Statistics, doing a sleep monitor in Beijing needs reservation in 3 months in advance; 5. sleep disorder and other neurosis coincidences high, be unwilling to go to a doctor because compatriots' idea haunts, and such patient mostly is social elite stratum more, focuses on the protection of privacy, existing medical model can not their right of privacy of available protecting; 6. patient assessment from far-off regions is inconvenient, and to sum up, current sleep monitor pattern cannot meet the demand of patient far away, and the monitoring of monitoring rate hospital of family is more practical, and the sleep monitor system that research and development are applicable to family's use is significant.
At present, for family monitoring sleep monitor system mainly through being attached to multiple electrodes of human body surface, electrocardiogram (Electrocardiography can be obtained, ECG), these electrodes can by integrated reduction volume, strengthen portability, such as Fig. 2 a is the EGC sensor of a pectoral girdle formula.Electrocardiogram is the figure describing the body surface potential change that heartbeat causes, its shape as shown in Figure 2 b, a cardiac electrical cycle mainly comprises Q, R, S tri-Important Characteristic Points ripples, and the R wave spacing of two adjacent periods is exactly RR interval (R-RInterval), can analyze heart rate by RR interval.Heart rate variability rate (HeartRateVariability, HRV), refer to the Minor variations that human heart beat cycles exists, this change difference can reflect the activity of human body autonomic nerve, can reflect the Depth of sleep of monitored person.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, groundwork at present for the sleep monitor system of family's monitoring is concentrated and the exploitation of sensor and design, to improve the accuracy in measurement of physical signs, and utilizing sensor acquisition to carry out analyzing to judge in the process of Depth of sleep to ECG signal, 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 the frequency domain character of HRV inadequate, directly reduce the precision of Depth of sleep analysis result like this, cannot practical application request be met.
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 prefixed time interval;
According to the sampling period of presetting, the ECG signal data obtained are sampled, obtain ECG signal data corresponding to each sampling period, wherein, the ECG signal data in each sampling period comprise in this sampling period and before this sampling period and/or after this sampling period, in preset duration ECG signal data;
According to the ECG signal data that each sampling period is corresponding, determine the heart rate variability rate characteristic vector in each sampling period respectively.
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 prefixed time interval;
Sampling unit, for sampling to the ECG signal data obtained according to the sampling period of presetting, obtain ECG signal data corresponding to each sampling period, wherein, the ECG signal data in each sampling period comprise in this sampling period and before this sampling period and/or after this sampling period, in preset duration ECG signal data;
First determining unit, for according to ECG signal data corresponding to each sampling period, determines the heart rate variability rate characteristic vector in each sampling period respectively.
The ECG signal processing method that the embodiment of the present invention provides and device, due within each sampling period, the ECG signal data obtained not only comprise the ECG signal data in current sample period, ECG signal data in certain time length before also comprising current sample period and/or after current sample period, like this, can frequency resolution be promoted, the accurate low frequency component calculating ECG signal data, thus improve the precision of Depth of sleep analysis.
Other features and advantages of the present invention will be set forth in the following description, and, partly become apparent from description, or understand by implementing the present invention.Object of the present invention and other advantages realize by structure specifically noted in write description, claims and accompanying drawing and obtain.
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, 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, electrocardiogram signal data schematic diagram;
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.
Detailed description of the invention
In order to improve the precision of sleep analysis result, embodiments provide 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 instruction and explanation of the present invention, be not intended to limit the present invention, and when not conflicting, the embodiment in the present invention and the feature in embodiment can combine mutually.
Embodiment one
Electrocardiogram is utilized to carry out the flow process of analysis of sleeping structure as shown in Figure 3, can comprise with lower part: first extract HRV characteristic vector from ECG, then the HRV characteristic vector extracted is inputted in grader, 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, improve for the HRV characteristic vector pickup flow process in above-mentioned flow process, to improve the accuracy of the HRV characteristic vector extracted, and then the sleep state result that grader is exported is more accurate.Concrete, according to the relevant criterion that Depth of sleep is analyzed, usually analyzed in units of 30 seconds, namely according to the sampling period of 30 seconds, electrocardiogram signal data is sampled, obtain 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), it can thus be appreciated that, 30 number of seconds are adopted to be 0.03Hz according to the frequency resolution obtained, and medical research shows, the information of 0.003Hz has more meaning for measurement autonomic nervous activity, that is at least needs the data of 5 minutes just can obtain low frequency component.For example is convenient to describe, be described with the data instance extracting 5 minutes in the embodiment of the present invention, when specifically implementing, extract the duration of data can more than 5 minutes, the present invention does not limit this.
Therefore, in the embodiment of the present invention, the extracting method of HRV frequency domain character is improved, when sampling to ECG signal data, sample according to the sampling period (such as can be, but not limited to be set to 30s) of presetting, but each sampling period participates in the ECG signal packet of statistics containing the ECG signal data in the front sampling period and before current sample period in certain time length, or the ECG signal data comprised in current sample period and after current sample period in certain time length, current sample period can also be comprised, ECG data before current sample period and after current sample period in certain time length.As shown in Figure 4, it is HRV frequency domain character computational methods schematic diagrams, and be that a unit extracts frequency domain character for the ECG signal data in 5 minutes, using the frequency domain character of its value as current sample period, next frequency domain character value is extracted in each translation for 30 seconds.By lengthening ECG signal data statistics window, can frequency resolution be promoted, calculate low frequency component accurately, describe sympathetic nerve and parasympathetic activity, thus embody Depth of sleep more accurately.
According to above-mentioned analysis, as shown in Figure 5, be the implementing procedure schematic diagram of the ECG signal processing method that the embodiment of the present invention provides, can comprise the following steps:
S501, the ECG signal data obtained in prefixed time interval;
During concrete enforcement, the time period can analyzing Depth of sleep as required determines to need the interval of the ECG signal data obtained, such as, to analyze the Depth of sleep situation between evening 22:00 to 22:05, can, from the ECG signal data in the certain hour section before and after the ECG signal extracting data 22:00 to 22:05 of record, suppose to extract the ECG signal data between 21:55 to 22:10.
S502, according to the sampling period of presetting, the ECG signal data obtained to be sampled, obtain ECG signal data corresponding to each sampling period;
Suppose that the sampling period arranged is 30 seconds, during 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, ECG signal data corresponding in 22 o'clock 4 minutes 30 seconds ~ 22 o'clock 05 minutes each time periods, wherein, ECG signal data in each time period above-mentioned comprise in this sampling period and before this sampling period and/or after this sampling period, ECG signal data in preset duration, with 22 o'clock sharp ~ the ECG signal data instance that comprises for 22: 30 seconds, it can comprise the ECG signal data between 21 o'clock 55 minutes 30 seconds ~ 22: 30 seconds, also can comprise 22 o'clock sharp ~ 22: 1 between ECG signal data, the ECG signal data between 21: 57: 30 ~ 22: 02: 30 can also be comprised, during concrete enforcement, if when the ECG data in a certain sampling period ESG data both comprised before this sampling period also comprise the ECG data after this sampling period, its comprise the sampling period before ECG data duration and after the sampling period duration of ECG data can distribute arbitrarily, the embodiment of the present invention does not limit this, as long as the total duration extracting data reaches preset duration (for 5 minutes in the embodiment of the present invention).
S503, according to ECG signal data corresponding to each sampling period, determine the HRV characteristic vector in each sampling period respectively.
Concrete, for the ECG signal data that each sampling period obtained is corresponding, R ripple position is detected (as a rule from ECG signal data, approximately every 800 milliseconds of R ripple occurs once), RR interval can be determined according to R ripple position, just can analyze heart rate according to RR interval, thus 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.
During concrete enforcement, in order to improve precision of analysis, it is longer that the interval obtaining ECG data can be arranged, and supposes the ECG signal data in 1000 cycles of acquisition, correspondingly, can obtain 1000 HRV characteristic vectors, respectively with HRV
1, HRV
2hRV
mrepresent (m be less than or equal to 1000 natural number) each HRV characteristic vector, because each HRV characteristic vector all comprises 12 characteristic parameters, then its arbitrary HRV characteristic parameter comprised can be expressed as HRV
(m, 1), HRV
(m, 2)... HRV
(m, n)(n be less than or equal to 12 natural number).Then can by 1000 the HRV characteristic vectors obtained in example in following matrix notation:
Due in said process, within each sampling period, the ECG signal data obtained not only comprise the ECG signal data in current sample period, ECG signal data in certain time length before also comprising current sample period and/or after current sample period, i.e. augmented data statistical window, like this, can promote frequency resolution, the low frequency component of accurate calculating ECG signal data, thus the precision that improve Depth of sleep analysis.
Embodiment two
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, preset algorithm can be utilized to carry out behavioral characteristics extraction to the HRV characteristic vector extracted in embodiment one, to improve the precision that Depth of sleep is analyzed further.
Based on this, the electrocardiogram signal data processing method that the embodiment of the present invention provides, can also comprise the following steps:
Step 1, after determining the HRV characteristic vector in each sampling period, the filtering HRV characteristic vector that the HRV characteristic vector determined respectively in each sampling period according to preset algorithm is corresponding;
Concrete, for the HRV characteristic vector in each sampling period, determine according to described preset algorithm the filtering HRV characteristic parameter that each HRV characteristic parameter that this HRV characteristic vector comprises is corresponding respectively; And determine the filtering HRV characteristic parameter composition filtering HRV characteristic vector that each HRV characteristic parameter that this HRV characteristic vector comprises is corresponding.
Step 2, merging HRV characteristic vector and filtering HRV characteristic vector.
For convenience of description, respectively with HRV
cs1, HRV
cs2hRV
csmrepresent (m be less than or equal to 1000 natural number) each filtering HRV characteristic vector, then its arbitrary filtering HRV characteristic parameter comprised can be expressed as HRV
cs (m, 1), HRV
cs (m, 2)... HRV
cs (m, n)(n be less than or equal to 12 natural number).HRV characteristic vector after then can merging by following matrix notation and filtering HRV characteristic vector:
During concrete enforcement, can be, but not limited to adopt central authorities-neighborhood (Center-Surround.CS) filtering carries out behavioral characteristics extraction to HRV characteristic vector.
As shown in Figure 6, be CS filtering implementation procedure schematic diagram, each characteristic parameter comprised for each HRV characteristic vector carries out CS filtering respectively, obtains the filtered characteristic parameter that each characteristic vector is corresponding, 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, usual T
cbe less than T
s, filter 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 is merged and obtain new HRV characteristic vector.
Make full use of the time-varying characteristics that CS difference gives prominence to HRV feature self in embodiment two, thus the precision of sleep analysis result can be improved further.
MIT-BIH polysomnographic data storehouse is the data base that the sleep of authority is relevant, utilizes on this data base and tests, and in test, adopt the method that the embodiment of the present invention provides, grader adopts support vector machine (SVM).For ensureing test validity, training set is separated completely with the data of test set, and data are respectively from the different persons of being observed.Test result shows, and use traditional HRV data analysis, the sleep analysis accuracy obtained is 81.0%; After the method adopting the embodiment of the present invention to provide, sleep analysis accuracy is increased to 86.9%.
Based on same inventive concept, a kind of ECG signal blood processor is additionally provided in the embodiment of the present invention, the principle of dealing with problems due to said apparatus is similar to ECG signal processing method, and therefore the enforcement of said apparatus see the enforcement of method, can repeat part and repeat no more.
As shown in Figure 8, be the structural representation of the ECG signal blood processor that the embodiment of the present invention provides, comprise:
Obtain unit 801, for obtaining the ECG signal data in prefixed time interval;
Sampling unit 802, for sampling to the ECG signal data obtained according to the sampling period of presetting, obtain ECG signal data corresponding to each sampling period, wherein, the ECG signal data in each sampling period comprise in this sampling period and before this sampling period and/or after this sampling period, in preset duration ECG signal data;
First determining unit 803, for according to ECG signal data corresponding to each sampling period, determines the HRV characteristic vector in each sampling period respectively.
During concrete enforcement, the ECG signal blood processor that the embodiment of the present invention provides, can also comprise:
Second determining unit, for determine the HRV characteristic vector in each sampling period in the first determining unit 803 after, the filtering HRV characteristic vector that the HRV characteristic vector determined in each sampling period according to preset algorithm is corresponding;
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 for the HRV characteristic vector in each sampling period, determines according to described preset algorithm the filtering HRV characteristic parameter that each HRV characteristic parameter that this HRV characteristic vector comprises is corresponding respectively;
Second determines subelement, for determining the filtering HRV characteristic parameter composition filtering HRV characteristic vector that each HRV characteristic parameter that this HRV characteristic vector comprises is corresponding.
Wherein, can be, but not limited to the filtering HRV characteristic vector using the HRV characteristic vector in CS filtering algorithm fixed each sampling period corresponding during concrete enforcement.
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 the form of complete hardware embodiment, completely software implementation or the embodiment in conjunction with software and hardware aspect.And the present invention can adopt in one or more form wherein including the upper computer program implemented of computer-usable storage medium (including but not limited to disk memory, CD-ROM, optical memory etc.) of computer usable program code.
The present invention describes with reference to according to the flow chart of the method for the embodiment of the present invention, equipment (system) and computer program and/or block diagram.Should understand can by the combination of 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 square frame.These computer program instructions can being provided to the processor of general purpose computer, special-purpose computer, Embedded Processor or other programmable data processing device to produce a machine, making the instruction performed by the processor of computer or other programmable data processing device produce device for realizing the function of specifying in flow chart flow process or multiple flow process and/or block diagram square frame or multiple square frame.
These computer program instructions also can be stored in can in the computer-readable memory that works in a specific way of vectoring computer or other programmable data processing device, the instruction making to be stored in this computer-readable memory produces the manufacture comprising command device, and this command device realizes the function of specifying in flow chart flow process or multiple flow process and/or block diagram square frame or multiple square frame.
These computer program instructions also can be loaded in computer or other programmable data processing device, make on computer or other programmable devices, to perform sequence of operations step to produce computer implemented process, thus the instruction performed on computer or other programmable devices is provided for the step realizing the function of specifying in flow chart flow process or multiple flow process and/or block diagram square frame or multiple square frame.
Although describe the preferred embodiments of the present invention, those skilled in the art once obtain the basic creative concept of cicada, then can make other change and amendment to these embodiments.So claims are intended to be interpreted as comprising preferred embodiment and falling into all changes and the amendment of the scope of the invention.
Obviously, those skilled in the art can carry out various change and modification to the present invention and not depart from the spirit and scope of the present invention.Like this, if these amendments of the present invention and modification belong within the scope of the claims in the present invention and equivalent technologies thereof, then the present invention is also intended to comprise these change and modification.
Claims (6)
1. an ECG signal processing method, comprising: obtain the electrocardiogram ECG signal data in prefixed time interval; It is characterized in that, also comprise:
According to the sampling period of presetting, the ECG signal data obtained are sampled, obtain ECG signal data corresponding to each sampling period, wherein, the ECG signal data in each sampling period comprise in this sampling period and before this sampling period and/or after this sampling period, in preset duration ECG signal data;
According to the ECG signal data that each sampling period is corresponding, determine the heart rate variability rate HRV characteristic vector in each sampling period respectively.
2. the method for claim 1, is characterized in that, also comprises:
After determining the HRV characteristic vector in each sampling period, the filtering HRV characteristic vector that the HRV characteristic vector determined respectively in each sampling period according to preset algorithm is corresponding, described preset algorithm comprises central authorities-neighborhood CS filtering algorithm; And
Merge HRV characteristic vector and filtering HRV characteristic vector.
3. method as claimed in claim 2, it is characterized in that, described HRV characteristic vector comprises at least one HRV characteristic parameter; And
The filtering HRV characteristic vector that the HRV characteristic vector determined in each sampling period according to preset algorithm is corresponding, specifically comprises:
For the HRV characteristic vector in each sampling period, determine according to described preset algorithm the filtering HRV characteristic parameter that each HRV characteristic parameter that this HRV characteristic vector comprises is corresponding respectively; And
Determine the filtering HRV characteristic parameter composition filtering HRV characteristic parameter that each HRV characteristic parameter that this HRV characteristic vector comprises is corresponding.
4. an ECG signal blood processor, comprises acquisition unit, for obtaining the electrocardiogram ECG signal data in prefixed time interval; It is characterized in that, also comprise:
Sampling unit, for sampling to the ECG signal data obtained according to the sampling period of presetting, obtain ECG signal data corresponding to each sampling period, wherein, the ECG signal data in each sampling period comprise in this sampling period and before this sampling period and/or after this sampling period, in preset duration ECG signal data;
First determining unit, for according to ECG signal data corresponding to each sampling period, determines the heart rate variability rate HRV characteristic vector in each sampling period respectively.
5. device as claimed in claim 4, is characterized in that, also comprise:
Second determining unit, for determine the HRV characteristic vector in each sampling period in described first determining unit after, the filtering HRV characteristic vector that the HRV characteristic vector determined in each sampling period according to preset algorithm is corresponding, described preset algorithm comprises central authorities-neighborhood CS filtering algorithm;
Merge cells, for merging HRV characteristic vector and filtering HRV characteristic vector.
6. device as claimed in claim 5, it is characterized in that, described HRV characteristic vector comprises at least one HRV characteristic parameter; And
Described second determining unit, comprising:
First determines subelement, for for the HRV characteristic vector in each sampling period, determines according to described preset algorithm the filtering HRV characteristic parameter that each HRV characteristic parameter that this HRV characteristic vector comprises is corresponding respectively;
Second determines subelement, for determining the filtering HRV characteristic parameter composition filtering HRV characteristic vector that each HRV characteristic parameter that this HRV characteristic vector comprises is corresponding.
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| AU2016353346B2 (en) * | 2015-11-11 | 2021-09-09 | Inspire Medical Systems, Inc. | Cardiac and sleep monitoring |
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