CN105877739A - Clinical examination method of electrocardio intelligent analyzing system - Google Patents
Clinical examination method of electrocardio intelligent analyzing system Download PDFInfo
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- CN105877739A CN105877739A CN201610102096.1A CN201610102096A CN105877739A CN 105877739 A CN105877739 A CN 105877739A CN 201610102096 A CN201610102096 A CN 201610102096A CN 105877739 A CN105877739 A CN 105877739A
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- electrocardio
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
- A61B5/346—Analysis of electrocardiograms
- A61B5/349—Detecting specific parameters of the electrocardiograph cycle
- A61B5/352—Detecting R peaks, e.g. for synchronising diagnostic apparatus; Estimating R-R interval
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
- A61B5/346—Analysis of electrocardiograms
- A61B5/349—Detecting specific parameters of the electrocardiograph cycle
- A61B5/366—Detecting abnormal QRS complex, e.g. widening
-
- 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
- A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
Abstract
The invention discloses a clinical examination method of an electrocardio intelligent analyzing system. The method comprises the following steps that electrocardio signals are input and preprocessed, wherein after the electrocardio signals are input into the electrocardio intelligent analyzing system, morphology filtering and a self-adaptive threshold value are utilized for removing interference noise to the electrocardio signals; waveform detection and feature extraction are carried out, wherein a morphological analysis method is utilized for detecting and recognizing a QRS wave group, an ST wave band and a P wave band, and electrocardio parameters are output; diagnosis classification and result output are carried out, wherein a clinical database carries out diagnosis classification, and the clinical detection result and the clinical database reference result are compared to obtain the diagnosis result. The clinical examination method of the electrocardio intelligent analyzing system can effectively remove noise interference, achieve automatic diagnosing, and help people to find lesion from electrocardiogram examination in time.
Description
Technical field
The present invention relates to treating cardiovascular disease field, a kind of intelligent ECG analyzes the clinic of system
The method of inspection.
Background technology
Improving constantly the gradually aging with population along with our people's living standard, cardiovascular disease is just
Being increasingly becoming the big killer threatening human health, heart is sanguimotor power source, just as one
The most non-stop electromotor, along with all one's life of the mankind.The organic disease of heart or functional pathological changes,
Bring great misery will to patient and family members.As the Electrocardioscopy of the big routine examination of human body four,
The most easily survey with it, and the ability of functional pathological changes can be measured, accepted by a lot of people.But it is Electrocardiographic
Manual analysis, not only needs to carry out the work of big workload through the doctor of professional training, and subjectivity
Stronger.Therefore by means of the computer having powerful data-handling capacity, electrocardiogram is just automatically analyzed
Seem in the urgent need to, along with the fast development of digital processing technology and the continuous appearance of new theoretical algorithm,
New technique and theoretical being applied to are improved accuracy rate and the real-time that electrocardiograph automatically analyzes, has become as
Important medical skill developing direction.Electrocardiographic computer automatic analysis as a complete system,
Should include gathering original electrocardiographicdigital data, store a large amount of electrocardiogram (ECG) data, foundation comprises specialist annotation and examines
Disconnected explanation and high-resolution ECG data storehouse, set up the electronic health record of patient using as reference, then
Carry out computer automatic analysis, make with reference to functions such as prescriptions according to analysis result.Existing electrocardio is automatic
Analysis system and method have the disadvantage that
1, electrocardiosignal itself is unusual small-signal, inevitably by various during gathering
The impact of interference factor, uses any filtering method to be impossible to completely and filters these interference, to such an extent as to
The accuracy rate of analyzing and diagnosing declines.
2, due to complexity and the multiformity of ill-condition signal of ECG signal itself so that feature extraction
The most difficult.Thus the mistake 90% that electrocardiogram automatically analyzes is above owing to waveforms detection and identification cause
, the detection of especially P ripple the most really solves.
3, no matter the physiology signal that electrocardiosignal inherently individual difference is bigger, use which kind of
Detection and the method for analysis, be all difficult to ensure that it can be suitable for the electrocardiogram of all situations.
Electrocardiogram is automatically analyzed in all impacts and the accuracy that diagnoses rests on about 70%, Ke Yijian
Other pathology waveform is the most fairly simple, and kind is not a lot.Therefore, improve the most further existing
The accuracy of ecg analysis method becomes an important R&D direction.
Summary of the invention
Technical scheme overcomes the deficiency of art methods, it is provided that one can effectively be removed
Noise jamming, it is achieved automatically diagnose, it is possible to help people to find the heart of pathological changes from Electrocardioscopy in time
The clinical test method of electricity intelligent analysis system.
For achieving the above object, it is proposed that following technical scheme:
A kind of intelligent ECG analyzes the clinical test method of system, comprises the following steps:
S1, the input of electrocardiosignal and pretreatment: electrocardiosignal is input to intelligent ECG analyze system it
After, utilize morphologic filtering and adaptive threshold that the interference noise of electrocardiosignal is removed；
S2, waveforms detection and feature extraction: utilize morphological analysis method detect respectively and identify QRS wave
Group, ST wave band and pattern-band, export EGC parameter；
S3, diagnostic classification and result output: carry out diagnostic classification as clinical database, by Clinical detection
Result compares with clinical database reference result, draws diagnostic result.
Optionally, described S2 includes following sub-step:
S21, QRS complex detect: extract narrow structure element morphology peak valley and wide structural element form respectively
Learn peak valley, QRS wave position based on adaptive threshold Preliminary detection, estimate R--R interval afterwards, assessment
QRS wave position；
S22, ST wave band detects: judge ST field offset level, distinguishes linear type and shaped form ST section, sentences
The slope direction of disconnected linear type ST section, it is judged that the concave and convex direction of shaped form ST section, identifies ST section form；
S23, detection pattern-band: estimate P ripple position according to phase length information between QRS complex position and RR,
Determine that P ripple detects region, then utilize the moving window integration of the electrocardiosignal in detection region to carry out P
Ripple detects, and determines candidate's P ripple；Finally, determine candidate's P ripple boundary point by local distance conversion, and
Analyze its amplitude and time width accordingly, thus identify real P ripple, complete the detection of P ripple.
Optionally, in described S3 diagnostic classification, for common arrhythmia, use fuzzy neural network pair
Ventricular premature contraction (PVC) is identified, and uses syntactic analysis method based on image steganalysis to the rhythm of the heart
Not normal classify.
The beneficial effect of this technical scheme:
(1) for the feature of electrocardiosignal noise, utilize morphologic filtering and adaptive threshold that electrocardio is believed
Number interference noise be removed, effectively eliminate the common three kinds of noises in electrocardiosignal.
(2) in QRS wave shape and the detection of ST wave band and location, use morphological analysis method, carry
The high accuracy of detection.
(3) adding the detection of pattern-band, the detection of P ripple, for analyzing arrhythmia, diagnoses atrial Lesions
There is important effect, be one of key link of automatically diagnosing of electrocardio.
(4) for ARR Computer Automatic Recognition, have employed fuzzy neural network technology and based on
The sorting technique of Syntactic Recognition, achieves preliminary result.
Accompanying drawing explanation
Fig. 1 is the clinical test method FB(flow block) of the present invention；
Fig. 2 is Denoising of ECG Signal FB(flow block)；
Fig. 3 is QRS complex overhaul flow chart；
Fig. 4 is ST wave band analysis process figure；
Fig. 5 is pattern-band overhaul flow chart.
Detailed description of the invention
Below in conjunction with the accompanying drawings the clinical test method of a kind of intelligent ECG analysis system of the present invention is made into one
Walk detailed description:
As shown in Figure 1, 2, a kind of intelligent ECG analyzes the clinical test method of system, including following step
Rapid:
S1, the input of electrocardiosignal and pretreatment: electrocardiosignal is input to intelligent ECG analyze system it
After, utilize morphologic filtering and adaptive threshold that the interference noise of electrocardiosignal is removed；
S2, waveforms detection and feature extraction: utilize morphological analysis method detect respectively and identify QRS wave
Group, ST wave band and pattern-band, export EGC parameter；
S3, diagnostic classification and result output: carry out diagnostic classification as clinical database, by Clinical detection
Result compares with clinical database reference result, draws diagnostic result.
Optionally, S2 as described in Fig. 3,4,5 includes following sub-step:
S21, QRS complex detect: extract narrow structure element morphology peak valley and wide structural element form respectively
Learn peak valley, QRS wave position based on adaptive threshold Preliminary detection, estimate R--R interval afterwards, assessment
QRS wave position；
S22, ST wave band detects: judge ST field offset level, distinguishes linear type and shaped form ST section, sentences
The slope direction of disconnected linear type ST section, it is judged that the concave and convex direction of shaped form ST section, identifies ST section form；
S23, detection pattern-band: estimate P ripple position according to phase length information between QRS complex position and RR,
Determine that P ripple detects region, then utilize the moving window integration of the electrocardiosignal in detection region to carry out P
Ripple detects, and determines candidate's P ripple；Finally, determine candidate's P ripple boundary point by local distance conversion, and
Analyze its amplitude and time width accordingly, thus identify real P ripple.Complete the detection of P ripple；
Preferably, in described S3 diagnostic classification, for common arrhythmia, use fuzzy neural network pair
Ventricular premature contraction (PVC) is identified, and uses syntactic analysis method based on image steganalysis to the rhythm of the heart
Not normal classify.
Claims (5)
1. the clinical test method of an intelligent ECG analysis system, it is characterised in that comprise the following steps:
S1, the input of electrocardiosignal and pretreatment: after electrocardiosignal is input to intelligent ECG analysis system, utilize morphologic filtering and adaptive threshold to be removed the interference noise of electrocardiosignal；
S2, waveforms detection and feature extraction: utilize morphological analysis method to detect and identify QRS complex, ST wave band and pattern-band respectively, export EGC parameter；
S3, diagnostic classification and result output: carry out diagnostic classification as clinical database, Clinical detection result is compared with clinical database reference result, draws diagnostic result.
A kind of intelligent ECG the most according to claim 1 analyzes the clinical test method of system, it is characterised in that described S2 includes sub-step:
S21, QRS complex detect: extract narrow structure element morphology peak valley and wide structural element morphology peak valley, QRS wave position based on adaptive threshold Preliminary detection respectively, estimate R--R interval afterwards, assess QRS wave position.
A kind of intelligent ECG the most according to claim 1 analyzes the clinical test method of system, it is characterised in that described S2 includes sub-step:
S22, ST wave band detects: judge ST field offset level, distinguishes linear type and shaped form ST section, it is judged that the slope direction of linear type ST section, it is judged that the concave and convex direction of shaped form ST section, identifies ST section form.
A kind of intelligent ECG the most according to claim 1 analyzes the clinical test method of system, it is characterised in that described S2 includes sub-step:
S23, detection pattern-band: estimate P ripple position according to phase length information between QRS complex position and RR, determine that P ripple detects region, then utilize the moving window integration of the electrocardiosignal in detection region to carry out P ripple detection, determine candidate's P ripple；Finally, determine candidate's P ripple boundary point by local distance conversion, and analyze its amplitude and time width accordingly, thus identify real P ripple, complete the detection of P ripple.
A kind of intelligent ECG the most according to claim 1 analyzes the clinical test method of system, it is characterized in that, in described S3 diagnostic classification, for common arrhythmia, use fuzzy neural network that ventricular premature contraction (PVC) is identified, and use syntactic analysis method based on image steganalysis that arrhythmia is classified.
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Cited By (6)
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|---|---|---|---|---|
| CN107174236A (en) * | 2017-06-21 | 2017-09-19 | 广东工业大学 | A kind of Denoising of ECG Signal and device based on optimum theory |
| CN107451417A (en) * | 2017-09-08 | 2017-12-08 | 北京蓬阳丰业医疗设备有限公司 | Dynamic ECG analysis intelligent diagnosis system and method |
| WO2019100563A1 (en) * | 2017-11-27 | 2019-05-31 | 乐普（北京）医疗器械股份有限公司 | Method for assessing electrocardiogram signal quality |
| CN110327032A (en) * | 2019-03-27 | 2019-10-15 | 苏州平稳芯跳医疗科技有限公司 | It is a kind of singly to lead the accurate recognizer of electrocardiosignal PQRST wave joint |
| CN110742599A (en) * | 2019-11-01 | 2020-02-04 | 广东工业大学 | Electrocardiosignal feature extraction and classification method and system |
| CN113288169A (en) * | 2021-05-26 | 2021-08-24 | 东软集团股份有限公司 | Method, device and equipment for identifying waveform of electrocardiographic waveform signal |
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