CN106108850A - The recognition methods of the interference data of ecg database and device - Google Patents
The recognition methods of the interference data of ecg database and device Download PDFInfo
- Publication number
- CN106108850A CN106108850A CN201610492603.7A CN201610492603A CN106108850A CN 106108850 A CN106108850 A CN 106108850A CN 201610492603 A CN201610492603 A CN 201610492603A CN 106108850 A CN106108850 A CN 106108850A
- Authority
- CN
- China
- Prior art keywords
- data
- ecg
- value
- electrocardiogram
- identified
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7221—Determining signal validity, reliability or quality
Abstract
The present invention relates to recognition methods and the device of the interference data of ecg database.The method comprises the steps: S0, obtains electrocardiogram (ECG) data to be identified；S2, obtain the drift value of the baseline drift of described electrocardiogram (ECG) data to be identified and/or the power frequency value of Hz noise and/or myoelectricity interference myoelectricity value；S4, judge described drift value whether more than first threshold and/or power frequency value whether more than Second Threshold and/or myoelectricity value whether more than the 3rd threshold value；If the described drift value of S6 is more than three threshold values more than first threshold or power frequency value more than Second Threshold or myoelectricity value, the most described electrocardiogram (ECG) data to be identified is interference data.The recognition methods of the interference data of the ecg database of the embodiment of the present invention and device, by being estimated the background signal in electrocardiogram (ECG) data, power frequency component and electromyographic signal, identify interference data, have efficiently, advantage accurately and rapidly.
Description
Technical field
The present invention relates to the establishing techniques field of ecg database, particularly relate to a kind of ecg database disturbs data to know
Method for distinguishing and device.
Background technology
Along with progress and the development of science and technology of society, electrocardiograph has become the armarium of hospital's standard configuration.Electrocardiograph
Easy to use, result can quick and precisely reflect the pathological changes that heart exists, such as, ventricular hypertrophy, myocardial ischemia etc..Electrocardiographic
Reading is a professional skill, is to need constantly specially grind and learn, at present, is mostly ground by the ecg database of the U.S.
Study carefully and learn.The construction of China's ecg database is relatively slower, and the electrocardio studying compatriots to doctor brings the most very much not with heart disease
Profit, especially Study of China neonate or child's electrocardio.Everybody studies child's electrocardiogram, Shenzhen Children's Hospital for convenience
Develop jointly with Shenzhen Biocare Bio-Medical Equipment Co., Ltd. and set up Children in China ECG data storehouse, but, counting
According in the process of construction in storehouse, needing the child's electrocardiogram (ECG) data to interference signal is bigger to be identified, these work are by manually
Reading by example and identified, work efficiency is low, has a strong impact on the construction speed of data base, and artificial cognition unavoidably can simultaneously
Introduce unnecessary error and criterion differs.
Summary of the invention
The technical problem of the interference data identification that the present invention is to solve in prior art in ecg database, it is provided that a kind of
The recognition methods of the interference data of ecg database and device.Specific as follows:
The recognition methods of the interference data of a kind of ecg database, comprises the steps:
S0, obtain electrocardiogram (ECG) data to be identified；
S2, obtain the drift value of the baseline drift of described electrocardiogram (ECG) data to be identified and/or the power frequency value of Hz noise and/or myoelectricity
The myoelectricity value of interference；
S4, judge that whether whether described drift value more than first threshold and/or power frequency value more than Second Threshold and/or myoelectricity value be
No it is more than the 3rd threshold value；
If the described drift value of S6 is more than three threshold values more than first threshold or power frequency value more than Second Threshold or myoelectricity value, then
Described electrocardiogram (ECG) data to be identified is interference data.
Further preferably, also comprise the steps: between described step S0 and S2
S1, judge whether described electrocardiogram (ECG) data to be identified exists in continuous 100ms that magnitude of voltage is more than the 4th threshold value, if existing, then
Described electrocardiogram (ECG) data to be identified is interference data.
Further preferred:
The step of the drift value obtaining baseline drift is as follows:
S211, obtain the baseline magnitude in each heartbeat waveform in described electrocardiogram (ECG) data to be identified；
S213, the absolute value of the difference obtaining the baseline magnitude of adjacent two heartbeat waveforms are the first difference；
S215, obtain the standard deviation of the first difference as drift value；
Or, the step of the power frequency value obtaining Hz noise is as follows:
S221, use frequency filter carry out process to electrocardiogram (ECG) data to be identified and obtain the first electrocardiogram (ECG) data；
S223, electrocardiogram (ECG) data to be identified is deducted the first electrocardiogram (ECG) data obtain the second difference；
S225, the standard deviation obtaining the second difference are power frequency value；
Or, the step of the myoelectricity value obtaining myoelectricity interference is as follows:
S231, use myoelectricity wave filter carry out process to electrocardiogram (ECG) data to be identified and obtain the second electrocardiogram (ECG) data；
S233, electrocardiogram (ECG) data to be identified is deducted the second electrocardiogram (ECG) data obtain the 3rd difference；
S235, the standard deviation of acquisition the 3rd difference are myoelectricity value.
Further preferably, the baseline magnitude in a heartbeat waveform be last time waveform terminate the point between this P ripple
Amplitude meansigma methods or waveform last time terminate the amplitude of any point between this P ripple.
Further preferably, described step S6 also includes:
If described drift value is less than three threshold values less than first threshold and power frequency value less than Second Threshold and myoelectricity value, then judge
Whether meeting condition A, if meeting, the most described electrocardiogram (ECG) data to be identified is interference data；
Condition A: described drift value is positioned at the first interval and described power frequency value and is positioned at the second interval and described myoelectricity value and is positioned at the 3rd
Interval.
The present invention also provides for the identification device of the interference data of a kind of ecg database, and this device includes:
Acquisition module, is used for obtaining electrocardiogram (ECG) data to be identified；
Drift value acquisition module, for the drift value obtaining baseline drift of described electrocardiogram (ECG) data to be identified；
Power frequency value acquisition module, for the drift value obtaining baseline drift of described electrocardiogram (ECG) data to be identified；
Myoelectricity value acquisition module, for the drift value obtaining baseline drift of described electrocardiogram (ECG) data to be identified；
Judge module, be used for judging described drift value whether more than first threshold and/or power frequency value whether more than Second Threshold and/
Or whether myoelectricity value is more than the 3rd threshold value；And
Identification module, if described judge module judges that described drift value is more than Second Threshold or flesh more than first threshold or power frequency value
When electricity value is more than three threshold values, electrocardiogram (ECG) data to be identified described in described identification module identification is interference data.
Further preferably, also include that saturated judge module, described electrocardiogram (ECG) data to be identified exist magnitude of voltage in continuous 100ms
During more than four threshold values, electrocardiogram (ECG) data to be identified described in described saturated judge module is interference data.
Further preferred:
Described drift value acquisition module includes:
Baseline magnitude acquiring unit, for the baseline magnitude obtained in described electrocardiogram (ECG) data to be identified in each heartbeat waveform；
First difference acquiring unit, is first poor for obtaining the absolute value of difference of the baseline magnitude of adjacent two heartbeat waveforms
Value；And
Drift value acquiring unit, for obtaining the standard deviation of the first difference as drift value；
Or, described power frequency value acquisition module includes:
First electrocardiogram (ECG) data acquiring unit, is used for using frequency filter that electrocardiogram (ECG) data to be identified is carried out process and obtains first heart
Electricity data；
Second difference acquiring unit, obtains the second difference for electrocardiogram (ECG) data to be identified is deducted the first electrocardiogram (ECG) data；And
Power frequency value acquiring unit, is power frequency value for obtaining the standard deviation of the second difference；
Or, described myoelectricity value acquisition module includes:
Second electrocardiogram (ECG) data acquiring unit, is used for using myoelectricity wave filter that electrocardiogram (ECG) data to be identified is carried out process and obtains second heart
Electricity data；
3rd difference acquiring unit, obtains the 3rd difference for electrocardiogram (ECG) data to be identified is deducted the second electrocardiogram (ECG) data；And
Myoelectricity value acquiring unit, is myoelectricity value for obtaining the standard deviation of the 3rd difference.
Further preferably, the baseline magnitude in a heartbeat waveform be last time waveform terminate the point between this P ripple
Amplitude meansigma methods or waveform last time terminate the amplitude of any point between this P ripple.
Further preferably, described judge module judges that described drift value is less than the second threshold less than first threshold and power frequency value
When value and myoelectricity value are less than three threshold values, further determine whether to meet condition A；If described judge module judges to meet condition A, institute
State electrocardiogram (ECG) data to be identified described in identification module identification for interference data；
Condition A: described drift value is positioned at the first interval and described power frequency value and is positioned at the second interval and described myoelectricity value and is positioned at the 3rd
Interval.
Beneficial effect:
The recognition methods of the interference data of the ecg database of the embodiment of the present invention and device are by the baseline in electrocardiogram (ECG) data
Signal, power frequency component and electromyographic signal are estimated, and identify interference data, have efficiently, advantage accurately and rapidly.
Accompanying drawing explanation
Fig. 1 is the recognition methods flow chart of the interference data of the ecg database of the embodiment of the present invention.
Fig. 2 is that the drift value of the embodiment of the present invention obtains flow chart.
Fig. 3 is that the power frequency value of the embodiment of the present invention obtains flow chart.
Fig. 4 is that the myoelectricity value of the embodiment of the present invention obtains flow chart.
Fig. 5 is a typical heartbeat waveform schematic diagram.
Fig. 6 is the identification apparatus structure schematic diagram of the interference data of the ecg database of the embodiment of the present invention.
Fig. 7 is the drift value acquisition module structural representation of the embodiment of the present invention.
Fig. 8 is the power frequency value acquisition module structural representation of the embodiment of the present invention.
Fig. 9 is the myoelectricity value acquisition module structural representation of the embodiment of the present invention.
Detailed description of the invention
In order to make the purpose of the present invention, technical scheme and advantage clearer, below in conjunction with drawings and Examples, right
The present invention is further elaborated.Should be appreciated that specific embodiment described herein only in order to explain the present invention, and
It is not used in the restriction present invention.
Human heart is regular beating, and often beats once, and body surface may detect that a heartbeat waveform, also referred to as
It it is a heartbeat.Fig. 5 is a typical heartbeat waveform schematic diagram.The one typical electrocardiogram (ECG) data of example is that Fig. 5 signal repeats
Composition, including P ripple, QRS wave and T ripple etc..As it is shown in figure 5, the straight line before P ripple and after T ripple is baseline, one does not has
Noisy electrocardiogram (ECG) data, its baseline should be horizontal linear.Each number in ecg database is according to all comprising a lot of weight
Appear again existing typical electrocardiogram (ECG) data.
The core concept disturbing data identification method and device in the ecg database of the present invention is by automatic decision number
Whether there is interference according to the data in storehouse, can quickly be identified, be substantially reduced the workload of people, beneficially ecg database
Quickly set up.Specifically, the present invention judges the heart in data base by obtaining drift value and/or power frequency value and/or myoelectricity value
Whether electricity data are interference data.
Fig. 1 is the recognition methods flow chart of the interference data of the ecg database of the embodiment of the present invention.
Refer to Fig. 1, the recognition methods of the interference data of the ecg database of the embodiment of the present invention, comprise the steps:
S0, obtain electrocardiogram (ECG) data to be identified.
Data are obtained as electrocardiogram (ECG) data to be identified from ecg database or quasi-ecg database.This electrocardio to be identified
Data are the electrocardiogram (ECG) datas of human body (including normal person and the patient) body surface gathered through medical practitioner by electrocardiograph.Specialty
Doctor gathers authority and the effectiveness of the electrocardiogram (ECG) data that ensure that collection.Some informal data base, to electrocardiogram (ECG) data
Acquisition may require that it is not the highest, then electrocardiogram (ECG) data to be identified can be that general nurse operation electrocardiograph collection obtains
The data obtained.
S1, saturated judgement, i.e. judge whether described electrocardiogram (ECG) data to be identified exists in continuous 100ms magnitude of voltage more than the 4th
Threshold value, if existing, the most described electrocardiogram (ECG) data to be identified is interference data.
The span of the present embodiment the preferably the 4th threshold value is 6.3mv to 9.75mv.Present inventor is sent out by research
Existing, if the amplitude of all of data point is more than 6.78mv in there is continuous 100ms in such as a electrocardiogram (ECG) data, then this electrocardio
Data would be at saturation, and this electrocardiogram (ECG) data does not has reference significance medically, belongs to the object that needs identify, i.e. disturbs
Data.
Using this step to judge electrocardiogram (ECG) data to be identified, this is non-existent in prior art, improves identification
Accuracy and decrease interference data down run occupying system resources, improve efficiency.
S2, the drift value obtaining baseline drift and/or the power frequency value of Hz noise and/or the myoelectricity value of myoelectricity interference.
Fig. 2 is that the drift value of the embodiment of the present invention obtains flow chart.Refer to Fig. 2, obtain the drift value of baseline drift
Step is as follows:
S211, obtain the baseline magnitude in each heartbeat waveform in described electrocardiogram (ECG) data to be identified.
In the ideal case, the amplitude of each point on baseline should be identical, and the most no matter take in baseline is any
Point, its amplitude all should be equal to baseline magnitude.Therefore, in certain embodiments, choose waveform last time to terminate between this P ripple
Any point amplitude (concrete such as: choose any range value of the P wavefront in heartbeat waveform as the baseline of this heartbeat waveform
Amplitude).In further embodiments, higher to accuracy requirement, it is preferred to use to be to terminate waveform last time (i.e. heartbeat waveform) to arrive
The amplitude meansigma methods of the point between this P ripple is as baseline magnitude.
S213, the absolute value of the difference obtaining the baseline magnitude of adjacent two heartbeat waveforms are the first difference.
The absolute value using difference can reduce the positive and negative punching that offsets of difference as the first difference, causes distortion.Ensure that step
The reliability of standard deviation calculated in rapid S215.
S215, obtain the standard deviation of the first difference as drift value.
The standard deviation of this first difference can reflect the discreteness of the first difference, and standard deviation the biggest expression the first difference is more
Discrete, show that baseline magnitude difference is bigger, mean that the most to a certain extent and there is baseline drift.So, drift value can be very
Reflect well baseline drift situation, meanwhile, there is good anti-interference.
The calculating of standard deviation is knowledge well known to those skilled in the art, at this by the most reinflated description.
Fig. 3 is that the power frequency value of the embodiment of the present invention obtains flow chart.Refer to Fig. 3, obtain the power frequency value of Hz noise
Step is as follows:
S221, use frequency filter carry out process to electrocardiogram (ECG) data to be identified and obtain the first electrocardiogram (ECG) data.
Frequency filter is 50Hz or 60Hz wave trap, can remove the signal of 50Hz or 60Hz.This first electrocardio
Data are through the filtered data of power frequency.
S223, electrocardiogram (ECG) data to be identified is deducted the first electrocardiogram (ECG) data obtain the second difference.
The point of electrocardiogram (ECG) data to be identified and the first electrocardiogram (ECG) data identical time carries out difference in magnitude value computing, and to obtain second poor
Value.This second difference means that the Hz noise value of the point of this identical time.
S225, the standard deviation obtaining the second difference are power frequency value.
The standard deviation of the second difference can be reduced interference as power frequency value, increase the accuracy judged.
Fig. 4 is that the myoelectricity value of the embodiment of the present invention obtains flow chart.Refer to Fig. 4, obtain the myoelectricity value of myoelectricity interference
Step is as follows:
S231, use myoelectricity wave filter carry out process to electrocardiogram (ECG) data to be identified and obtain the second electrocardiogram (ECG) data.
The myoelectricity wave filter of the embodiment of the present invention is Chebyshev's wave digital lowpass filter, uses Chebyshev's digital lowpass
Wave filter filters the electromyographic signal of high frequency and obtains the second electrocardiogram (ECG) data.It is, of course, also possible to use other wave filter to carry out myoelectricity letter
Number filter process.
S233, electrocardiogram (ECG) data to be identified is deducted the second electrocardiogram (ECG) data obtain the 3rd difference.
The point of electrocardiogram (ECG) data to be identified and the second electrocardiogram (ECG) data identical time carries out difference in magnitude value computing, and to obtain the 3rd poor
Value.3rd difference means that the Hz noise value of the point of this identical time.
S235, the standard deviation of acquisition the 3rd difference are myoelectricity value.
The standard deviation of the 3rd difference can be reduced interference as myoelectricity value, increase the accuracy judged.
S4, judge described drift value whether more than first threshold and/or power frequency value whether more than Second Threshold and/or myoelectricity
Whether value is more than the 3rd threshold value.
The span of this first threshold is 0.23mv to 0.39mv, the span of this Second Threshold be 0.132mv extremely
0.163mv, the span of the 3rd threshold value is 0.065 to 0.096mv.
If the described drift value of S6 is more than the 3rd threshold value more than first threshold or power frequency value more than Second Threshold or myoelectricity value
Time, the most described electrocardiogram (ECG) data to be identified is interference data.
In some less demanding embodiment, step S6 also makes the following judgment: if described drift value is less than first
When threshold value and power frequency value are less than three threshold values less than Second Threshold and myoelectricity value, the most described electrocardiogram (ECG) data to be identified is non-interference number
According to.
In order to improve the identification accuracy of interference data further, the embodiment of the present invention preferably this step S6 also includes: if
When described drift value is less than three threshold values less than first threshold and power frequency value less than Second Threshold and myoelectricity value, then judge whether full
Foot condition A, if meeting, the most described electrocardiogram (ECG) data to be identified is interference data；If being unsatisfactory for, the most described electrocardiogram (ECG) data to be identified is
Non-interference data.Condition A: described drift value is positioned at the first interval and described power frequency value and is positioned at the second interval and described myoelectricity value position
Interval in the 3rd.This first interval is (0.17,0.21), and the second interval is (0.111,0.128), the second interval be (0.053,
0.061).Increase three elements in step s 6 to combine and judge to contribute to reducing the interference data problem that brings of accumulated interference, significantly
Improve the accuracy of interference data identification, accuracy rate can reach 99.2%.
The embodiment of the present invention uses and is estimated the background signal in electrocardiogram (ECG) data, power frequency component and electromyographic signal, knows
Do not go out to disturb data, have efficiently, advantage accurately and rapidly.
Electrocardiogram (ECG) data to be identified is judged as disturbing data, shows that this electrocardiogram (ECG) data does not has reference significance medically,
After identification completes, may be deleted or additionally formed an interference ecg database.
Present invention preferably employs the mode successively judged to process, thus can reduce operand, preferably judge
Flow process is as follows: first judge whether saturated, then judges whether Hz noise or myoelectricity interference, then judges whether to deposit
At myoelectricity interference or Hz noise, judge whether baseline drift afterwards, finally combine judgement three key elements.The most whole
Individual judgement flow process just can more accurately determine whether to disturb data after completing, and accuracy rate can reach 99.89%.
Fig. 6 is the identification apparatus structure schematic diagram of the interference data of the ecg database of the embodiment of the present invention.Fig. 7 is this
The drift value acquisition module structural representation of bright embodiment.Fig. 8 is the power frequency value acquisition module structural representation of the embodiment of the present invention
Figure.Fig. 9 is the myoelectricity value acquisition module structural representation of the embodiment of the present invention.
Refer to Fig. 6 to Fig. 9, the identification device of the interference data of the ecg database of the embodiment of the present invention includes obtaining mould
Block 10, saturated judge module 20, drift value acquisition module 30, power frequency value acquisition module 50 and myoelectricity value acquisition module 70, judgement
Module 80 and identification module 90.
This acquisition module 10 is used for obtaining electrocardiogram (ECG) data to be identified, can obtain the electrocardiogram (ECG) data in ECG data storehouse,
Can also obtain and will become the electrocardiogram (ECG) data of an one's share of expenses for a joint undertaking in data base.This electrocardiogram (ECG) data to be identified is to be passed through by electrocardiograph
The electrocardiogram (ECG) data of human body (including normal person and the patient) body surface that medical practitioner gathers.Medical practitioner collection ensure that the heart of collection
The authority of electricity data and effectiveness.Some informal data base, the acquisition to electrocardiogram (ECG) data may require that it is not so
High, then electrocardiogram (ECG) data to be identified can be the data that general nurse operation electrocardiograph collection obtains.
According to whether saturated, saturated judge module 20, for judging whether electrocardiogram (ECG) data to be identified is interference data.Saturated
Judge as follows: if magnitude of voltage is more than the 4th threshold value in electrocardiogram (ECG) data to be identified exists continuous 100ms, it is believed that the most saturated.This is saturated
Judge module 20 judges that in the case of the most saturated this electrocardiogram (ECG) data to be identified is as interference data.The present embodiment the preferably the 4th threshold
The span of value is 6.3mv to 9.75mv.
Present inventor is found by research, if all of number in there is continuous 100ms in such as a electrocardiogram (ECG) data
The amplitude at strong point is more than 7.8mv, then this electrocardiogram (ECG) data would be at saturation, and this electrocardiogram (ECG) data does not has reference medically
Meaning, belongs to the object that needs identify, i.e. disturbs data.
Using this saturated judge module 20 to judge electrocardiogram (ECG) data to be identified, this is non-existent in prior art,
The accuracy that improve identification down runs occupying system resources with decreasing interference data, improves efficiency.
Drift value acquisition module 30, for the drift value obtaining baseline drift of described electrocardiogram (ECG) data to be identified.Specifically, should
Drift value acquisition module 30 includes the baseline magnitude for obtaining in described electrocardiogram (ECG) data to be identified in each heartbeat waveform
Baseline magnitude acquiring unit 310, is first poor for obtaining the absolute value of difference of the baseline magnitude of adjacent two heartbeat waveforms
First difference acquiring unit 320 of value, and single as the drift value acquisition of drift value for obtaining the standard deviation of the first difference
Unit 330.
In the ideal case, the amplitude of each point on baseline should be identical, and the most no matter take in baseline is any
Point, its amplitude all should be equal to baseline magnitude.Therefore, in certain embodiments, choose waveform last time to terminate between this P ripple
Any point amplitude (concrete such as: choose any range value of the P wavefront in heartbeat waveform as the baseline of this heartbeat waveform
Amplitude).In further embodiments, higher to accuracy requirement, it is preferred to use to be to terminate waveform last time (i.e. heartbeat waveform) to arrive
The amplitude meansigma methods of the point between this P ripple is as baseline magnitude.
The absolute value using difference can reduce the positive and negative punching that offsets of difference as the first difference, causes distortion.Ensure that drift
The reliability of standard deviation that shifting value acquiring unit 330 calculates.
The standard deviation of this first difference can reflect the discreteness of the first difference, and standard deviation the biggest expression the first difference is more
Discrete, show that baseline magnitude difference is bigger, mean that the most to a certain extent and there is baseline drift.So, drift value can be very
Reflect well baseline drift situation, meanwhile, there is good anti-interference.
Power frequency value acquisition module 50, for the drift value obtaining baseline drift of described electrocardiogram (ECG) data to be identified.Specifically, should
Power frequency value acquisition module 50 includes obtaining the first electrocardio number for using frequency filter that electrocardiogram (ECG) data to be identified carries out process
According to the first electrocardiogram (ECG) data acquiring unit 510, obtain the second difference for electrocardiogram (ECG) data to be identified is deducted the first electrocardiogram (ECG) data
The second difference acquiring unit 520, and for obtain the second difference the power frequency value acquiring unit that standard deviation is power frequency value
530。
In the present embodiment, frequency filter is 50Hz or 60Hz wave trap, can remove the signal of 50Hz or 60Hz.
This first electrocardiogram (ECG) data is through the filtered data of power frequency.
The point of electrocardiogram (ECG) data to be identified and the first electrocardiogram (ECG) data identical time carries out difference in magnitude value computing, and to obtain second poor
Value.This second difference means that the Hz noise value of the point of this identical time.
The standard deviation of the second difference can be reduced interference as power frequency value, increase the accuracy judged.
Myoelectricity value acquisition module 70, for the drift value obtaining baseline drift of described electrocardiogram (ECG) data to be identified.Specifically, should
Myoelectricity value acquisition module 70 includes obtaining the second electrocardio number for using myoelectricity wave filter that electrocardiogram (ECG) data to be identified carries out process
According to the second electrocardiogram (ECG) data acquiring unit 710, obtain the 3rd difference for electrocardiogram (ECG) data to be identified is deducted the second electrocardiogram (ECG) data
The 3rd difference acquiring unit 720, and for obtain the 3rd difference the myoelectricity value acquiring unit that standard deviation is myoelectricity value
730。
The myoelectricity wave filter of the embodiment of the present invention is Chebyshev's wave digital lowpass filter, uses Chebyshev's digital lowpass
Wave filter filters the electromyographic signal of high frequency and obtains the second electrocardiogram (ECG) data.It is, of course, also possible to use other wave filter to carry out myoelectricity letter
Number filter process.
The point of electrocardiogram (ECG) data to be identified and the second electrocardiogram (ECG) data identical time carries out difference in magnitude value computing, and to obtain the 3rd poor
Value.3rd difference means that the Hz noise value of the point of this identical time.
The standard deviation of the 3rd difference can be reduced interference as myoelectricity value, increase the accuracy judged.
Judge module 80, is used for judging whether whether described drift value be more than second more than first threshold and/or power frequency value
Whether threshold value and/or myoelectricity value be more than the 3rd threshold value.
The span of this first threshold is 0.23mv to 0.39mv, the span of this Second Threshold be 0.132mv extremely
0.163mv, the span of the 3rd threshold value is 0.065 to 0.096mv.
Identification module 90, if described judge module judges that described drift value is more than the second threshold more than first threshold or power frequency value
When value or myoelectricity value are more than three threshold values, electrocardiogram (ECG) data to be identified described in described identification module identification is interference data.
In some less demanding embodiment, it is concluded that described drift value is less than first at judge module 80
Threshold value and power frequency value are less than the 3rd threshold value less than Second Threshold and myoelectricity value, then identification module 90 identifies described electrocardio number to be identified
According to for non-interference data.
In order to improve the identification accuracy of interference data further, the embodiment of the present invention preferably this step S6 also includes:
In the case of described drift value is less than the 3rd threshold value less than first threshold and power frequency value less than Second Threshold and myoelectricity value, it is judged that mould
Block 80 also carries out judging whether to meet condition A, if meeting, then identification module 90 identifies that described electrocardiogram (ECG) data to be identified is for interference number
According to；Otherwise, then identification module 90 identifies that described electrocardiogram (ECG) data to be identified is non-interference data.Condition A: described drift value is positioned at
One interval and described power frequency value is positioned at the second interval and described myoelectricity value and is positioned at the 3rd interval.This first interval be (0.17,
0.21), the second interval is (0.111,0.128), and the second interval is (0.053,0.061).Judge module 80 and identification module 90
Middle increase three elements are combined judgement and are contributed to reducing the interference data problem that accumulated interference is brought, and substantially increase interference data and know
Other accuracy, accuracy rate can reach 99.2%.
The embodiment of the present invention uses and is estimated the background signal in electrocardiogram (ECG) data, power frequency component and electromyographic signal, knows
Do not go out to disturb data, have efficiently, advantage accurately and rapidly.
Present invention preferably employs the mode successively judged to process, thus can reduce operand, preferably judge
Flow process is as follows: first judge whether saturated, then judges whether Hz noise or myoelectricity interference, then judges whether to deposit
At myoelectricity interference or Hz noise, judge whether baseline drift afterwards, finally combine judgement three key elements.The most whole
Individual judgement flow process just can more accurately determine whether to disturb data after completing, and accuracy rate can reach 99.89%.
Saturated judge module 20, drift value acquisition module 30, power frequency value acquisition module 50 and myoelectricity in the embodiment of the present invention
Value acquisition module 70, judge module 80 and identification module 90 can be made up of ECU or single-chip microcomputer.
Ecg database provided by the present invention disturb the recognition methods of data and device have carried out detailed Jie above
Continuing, principle and the embodiment of the present invention are set forth by specific case used herein, and the explanation of above example is only
It it is the core concept being adapted to assist in and understanding the present invention；Simultaneously for one of ordinary skill in the art, according to the think of of the present invention
Thinking, the most all will change, in sum, it is right that this specification content should not be construed as
The restriction of the present invention.
Claims (10)
1. the recognition methods of the interference data of an ecg database, it is characterised in that comprise the steps:
S0, obtain electrocardiogram (ECG) data to be identified；
S2, obtain the drift value of the baseline drift of described electrocardiogram (ECG) data to be identified and/or the power frequency value of Hz noise and/or myoelectricity
The myoelectricity value of interference；
S4, judge that whether whether described drift value more than first threshold and/or power frequency value more than Second Threshold and/or myoelectricity value be
No it is more than the 3rd threshold value；
If the described drift value of S6 is more than three threshold values more than first threshold or power frequency value more than Second Threshold or myoelectricity value, then
Described electrocardiogram (ECG) data to be identified is interference data.
2. the recognition methods of the interference data of ecg database as claimed in claim 1, it is characterised in that described step S0 with
Also comprise the steps: between S2
S1, judge whether described electrocardiogram (ECG) data to be identified exists in continuous 100ms that magnitude of voltage is more than the 4th threshold value, if existing, then
Described electrocardiogram (ECG) data to be identified is interference data.
3. the recognition methods of the interference data of ecg database as claimed in claim 1, it is characterised in that:
The step of the drift value obtaining baseline drift is as follows:
S211, obtain the baseline magnitude in each heartbeat waveform in described electrocardiogram (ECG) data to be identified；
S213, the absolute value of the difference obtaining the baseline magnitude of adjacent two heartbeat waveforms are the first difference；
S215, obtain the standard deviation of the first difference as drift value；
Or, the step of the power frequency value obtaining Hz noise is as follows:
S221, use frequency filter carry out process to electrocardiogram (ECG) data to be identified and obtain the first electrocardiogram (ECG) data；
S223, electrocardiogram (ECG) data to be identified is deducted the first electrocardiogram (ECG) data obtain the second difference；
S225, the standard deviation obtaining the second difference are power frequency value；
Or, the step of the myoelectricity value obtaining myoelectricity interference is as follows:
S231, use myoelectricity wave filter carry out process to electrocardiogram (ECG) data to be identified and obtain the second electrocardiogram (ECG) data；
S233, electrocardiogram (ECG) data to be identified is deducted the second electrocardiogram (ECG) data obtain the 3rd difference；
S235, the standard deviation of acquisition the 3rd difference are myoelectricity value.
4. the recognition methods of the interference data of ecg database as claimed in claim 3 a, it is characterised in that heartbeat waveform
In baseline magnitude be last time waveform terminate the amplitude meansigma methods of the point between this P ripple or waveform last time terminates to this
The amplitude of any point between P ripple.
5. the recognition methods of the interference data of ecg database as claimed in claim 1, it is characterised in that described step S6
Also include:
If described drift value is less than three threshold values less than first threshold and power frequency value less than Second Threshold and myoelectricity value, then judge
Whether meeting condition A, if meeting, the most described electrocardiogram (ECG) data to be identified is interference data；
Condition A: described drift value is positioned at the first interval and described power frequency value and is positioned at the second interval and described myoelectricity value and is positioned at the 3rd
Interval.
6. the identification device of the interference data of an ecg database, it is characterised in that including:
Acquisition module, is used for obtaining electrocardiogram (ECG) data to be identified；
Drift value acquisition module, for the drift value obtaining baseline drift of described electrocardiogram (ECG) data to be identified；
Power frequency value acquisition module, for the drift value obtaining baseline drift of described electrocardiogram (ECG) data to be identified；
Myoelectricity value acquisition module, for the drift value obtaining baseline drift of described electrocardiogram (ECG) data to be identified；
Judge module, be used for judging described drift value whether more than first threshold and/or power frequency value whether more than Second Threshold and/
Or whether myoelectricity value is more than the 3rd threshold value；And
Identification module, if described judge module judges that described drift value is more than Second Threshold or flesh more than first threshold or power frequency value
When electricity value is more than three threshold values, electrocardiogram (ECG) data to be identified described in described identification module identification is interference data.
7. the identification device of the interference data of ecg database as claimed in claim 6, it is characterised in that also include saturated sentencing
Disconnected module, when described electrocardiogram (ECG) data to be identified exists that magnitude of voltage is more than four threshold values in continuous 100ms, described saturated judge module
Described electrocardiogram (ECG) data to be identified is interference data.
8. the identification device of the interference data of ecg database as claimed in claim 6, it is characterised in that:
Described drift value acquisition module includes:
Baseline magnitude acquiring unit, for the baseline magnitude obtained in described electrocardiogram (ECG) data to be identified in each heartbeat waveform；
First difference acquiring unit, is first poor for obtaining the absolute value of difference of the baseline magnitude of adjacent two heartbeat waveforms
Value；And
Drift value acquiring unit, for obtaining the standard deviation of the first difference as drift value；
Or, described power frequency value acquisition module includes:
First electrocardiogram (ECG) data acquiring unit, is used for using frequency filter that electrocardiogram (ECG) data to be identified is carried out process and obtains first heart
Electricity data；
Second difference acquiring unit, obtains the second difference for electrocardiogram (ECG) data to be identified is deducted the first electrocardiogram (ECG) data；And
Power frequency value acquiring unit, is power frequency value for obtaining the standard deviation of the second difference；
Or, described myoelectricity value acquisition module includes:
Second electrocardiogram (ECG) data acquiring unit, is used for using myoelectricity wave filter that electrocardiogram (ECG) data to be identified is carried out process and obtains second heart
Electricity data；
3rd difference acquiring unit, obtains the 3rd difference for electrocardiogram (ECG) data to be identified is deducted the second electrocardiogram (ECG) data；And
Myoelectricity value acquiring unit, is myoelectricity value for obtaining the standard deviation of the 3rd difference.
9. the identification device of the interference data of ecg database as claimed in claim 8 a, it is characterised in that heartbeat waveform
In baseline magnitude be last time waveform terminate the amplitude meansigma methods of the point between this P ripple or waveform last time terminates to this
The amplitude of any point between P ripple.
10. the identification device of the interference data of ecg database as claimed in claim 6, it is characterised in that described judgement mould
Block is judged, when described drift value is less than three threshold values less than first threshold and power frequency value less than Second Threshold and myoelectricity value, also to sentence
Break and whether meet condition A；If described judge module judges to meet condition A, electrocardio to be identified described in described identification module identification
Data are interference data；
Condition A: described drift value is positioned at the first interval and described power frequency value and is positioned at the second interval and described myoelectricity value and is positioned at the 3rd
Interval.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610492603.7A CN106108850A (en) | 2016-06-30 | 2016-06-30 | The recognition methods of the interference data of ecg database and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610492603.7A CN106108850A (en) | 2016-06-30 | 2016-06-30 | The recognition methods of the interference data of ecg database and device |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN106108850A true CN106108850A (en) | 2016-11-16 |
Family
ID=57285234
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610492603.7A Pending CN106108850A (en) | 2016-06-30 | 2016-06-30 | The recognition methods of the interference data of ecg database and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106108850A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107550484A (en) * | 2017-09-28 | 2018-01-09 | 漫迪医疗仪器（上海）有限公司 | A kind of mcg-signalses quality evaluating method and system |
| CN108324271A (en) * | 2017-12-25 | 2018-07-27 | 中国科学院深圳先进技术研究院 | Electrocardiosignal recognition methods, system and cardioelectric monitor equipment |
| CN110074775A (en) * | 2019-04-30 | 2019-08-02 | 郑州大学 | Acute myocardial infarction AMI localization method based on GA-ELM mixed model |
| CN110772248A (en) * | 2019-10-17 | 2020-02-11 | 深圳邦健生物医疗设备股份有限公司 | Wearable device electrocardio real-time monitoring system and method |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090143693A1 (en) * | 2007-12-04 | 2009-06-04 | Shenzhen Mindray Bio-Medical Electronics Co., Ltd. | Method and apparatus for generating determination indexes for identifying ecg interfering signals |
| CN102697492A (en) * | 2012-05-25 | 2012-10-03 | 长春华讯信息科技有限公司 | Real-time electrocardio information analysis system and analysis method thereof |
| CN103735259A (en) * | 2013-12-27 | 2014-04-23 | 深圳市理邦精密仪器股份有限公司 | Electrocardio signal quality determination indication method and system |
| CN104434310A (en) * | 2013-09-13 | 2015-03-25 | 深圳迈瑞生物医疗电子股份有限公司 | Monitoring equipment and parameter performance improvement method and system thereof |
| CN104523265A (en) * | 2014-12-31 | 2015-04-22 | 广州视源电子科技股份有限公司 | Judging method and device for abnormal electrocardio data |
-
2016
- 2016-06-30 CN CN201610492603.7A patent/CN106108850A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090143693A1 (en) * | 2007-12-04 | 2009-06-04 | Shenzhen Mindray Bio-Medical Electronics Co., Ltd. | Method and apparatus for generating determination indexes for identifying ecg interfering signals |
| CN102697492A (en) * | 2012-05-25 | 2012-10-03 | 长春华讯信息科技有限公司 | Real-time electrocardio information analysis system and analysis method thereof |
| CN104434310A (en) * | 2013-09-13 | 2015-03-25 | 深圳迈瑞生物医疗电子股份有限公司 | Monitoring equipment and parameter performance improvement method and system thereof |
| CN103735259A (en) * | 2013-12-27 | 2014-04-23 | 深圳市理邦精密仪器股份有限公司 | Electrocardio signal quality determination indication method and system |
| CN104523265A (en) * | 2014-12-31 | 2015-04-22 | 广州视源电子科技股份有限公司 | Judging method and device for abnormal electrocardio data |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107550484A (en) * | 2017-09-28 | 2018-01-09 | 漫迪医疗仪器（上海）有限公司 | A kind of mcg-signalses quality evaluating method and system |
| CN108324271A (en) * | 2017-12-25 | 2018-07-27 | 中国科学院深圳先进技术研究院 | Electrocardiosignal recognition methods, system and cardioelectric monitor equipment |
| CN110074775A (en) * | 2019-04-30 | 2019-08-02 | 郑州大学 | Acute myocardial infarction AMI localization method based on GA-ELM mixed model |
| CN110772248A (en) * | 2019-10-17 | 2020-02-11 | 深圳邦健生物医疗设备股份有限公司 | Wearable device electrocardio real-time monitoring system and method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN101766484B (en) | Method and equipment for identification and classification of electrocardiogram | |
| CN104173043B (en) | Electrocardiogram (ECG) data analysis method suitable for mobile platform | |
| CN106108850A (en) | The recognition methods of the interference data of ecg database and device | |
| CN102835954A (en) | Method for generating heart beating waveform template and treating ventricular premature beat detection and device | |
| CN103083013A (en) | Electrocardio signal QRS complex wave detection method based on morphology and wavelet transform | |
| CN106473700A (en) | Detecting system based on the OSAS of HRV frequency-domain analysis | |
| CN101926642B (en) | Physiological signal interval series-based cardiac function noninvasive detection device | |
| CN105326477B (en) | A kind of breathing and heartbeat synchronization method for extracting signal and device for plurality of medical signal acquisition | |
| CN111449622A (en) | Atrial fibrillation recognition system based on BCG detection | |
| CN114648040A (en) | Method for extracting and fusing multiple physiological signals of vital signs | |
| Fleming et al. | Non-invasive measurement of respiratory rate in children using the photoplethysmogram | |
| Pirbhulal et al. | Analysis of efficient biometric index using heart rate variability for remote monitoring of obstructive sleep apnea | |
| CN111603170A (en) | Human body position detection method based on vector cardiogram | |
| RU182802U1 (en) | PULSE WAVE CONTOUR ANALYSIS DEVICE | |
| Rosli et al. | Electrocardiographic (ECG) and Electromyographic (EMG) signals fusion for physiological device in rehab application | |
| Khavas et al. | Robust heartbeat detection using multimodal recordings and ECG quality assessment with signal amplitudes dispersion | |
| Chen et al. | A fast ECG diagnosis using frequency-based compressive neural network | |
| Zhao et al. | Evaluation of lower-limb rehabilitation based on muscle fatigue | |
| RU168518U1 (en) | Acceleration photoplethysmography device | |
| Wang et al. | AN EFFICIENT ALGORITHM FOR R PEAKS DETECTION OF ELECTROCARDIOGRAM SIGNALS | |
| Li et al. | A Particle Swarm Algorithm-Guided Psychological Stress Analysis to ECG Signal Collecting | |
| CN115422976B (en) | Cardiopulmonary coupling relation analysis method and monitoring system based on artificial network | |
| Jaureguibeitia et al. | Impedance based automatic detection of ventilations during mechanical Cardiopulmonary resuscitation | |
| Sangketkit et al. | Robustness evaluation of a dual-threshold QRS detection method for wearable ECG recorders | |
| Shama et al. | Hamming filter design for ECG signal detection and processing using co-simulation |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WD01 | Invention patent application deemed withdrawn after publication | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20161116 |