CN1850007A - Heart disease automatic classification system based on heart sound analysis and heart sound segmentation method - Google Patents
Heart disease automatic classification system based on heart sound analysis and heart sound segmentation method Download PDFInfo
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Abstract
The present invention relates to a heart disease automatic classification system based on heart sound analysis. It includes the following several portions: heart sound sensor, it is used for converting heart sound vibration signal of tested person into electric signal and outputting said electric signal; computer with multimedia function, it is connected with heart sound sensor by means of data acquisition card and can be used for recording the heart sound signal of tested person which is outputted by heart sound sensor and storing said signal in storage unit; and heart sound sectionalization program, heart sound characteristics extraction program and classification device which are mounted in the computer. Said invention also provides its working principle and its concrete operation method.
Description
Technical field
The present invention relates to cardiopathic automatic classification field, the heart disease automatic classification system of especially only analyzing based on hear sounds helps cardiophony to popularize in inferior grade hospital and community medicine.
Background technology
Clinically, the use of the new technologies such as echocardiogram, color ultrasound, CT, MRI is so that the doctor can obtain than the more direct and accurate diagnosis basis of cardiac auscultation. Yet, because these equipment manufacturing cost costlinesses, bulky and complicated operation are unfavorable for popularizing in inferior grade hospital and community medicine.
On the other hand, hear sounds can react especially movable, the blood flow situation of valve of heart effectively, such as, closing of atrioventricular valve is the principal element that produces first heart sound, produces the Main Ingredients and Appearance of second heart sound during closure of the semilunar valves. Many angiocardiopathies, especially valve class disease, hear sounds all are important reference diagnostic messages, therefore use clinically very extensive. But cardiac auscultation is easy to be subject to the impact of doctors experience, auscultation level.
The hear sounds segmentation is to set up the basis of decision system and prerequisite, its objective is the Main Ingredients and Appearance (first heart sound S1, systole phase, second heart sound S2 and diastole) of location hear sounds, for feature extraction and pattern-recognition provide positioning datum. Can say, segmentation accurately whether, directly have influence on the success or failure of whole decision system. In existing document, the following several segmentation algorithms of general employing:
1) manual segmentation is finished by veteran doctor. Its advantage is that segmentation is accurate, but seriously relies on doctor's experience.
2) with reference to the segmentation algorithm of electrocardiogram (ECG). There are matching relationship in time in S1, the S2 of cardiac electrical R ripple, T ripple and hear sounds; Increase the complexity of instrument but introduce electrocardiosignal, and faced the problem of accurate identification R, T ripple.
3) based on the segmentation algorithm of signal energy. This algorithm only carries out segmentation according to the hear sounds own characteristic, need not other and assists; Shortcoming is that the segmentation error rate is larger, and section boundaries is inaccurate.
Summary of the invention
Order of the present invention provides a kind of heart disease automatic classification system of analyzing based on hear sounds, and this categorizing system volume is little, cost is low, and classification accurately, and is convenient universal in inferior grade hospital and community medicine.
The heart disease automatic classification system that the present invention is based on the hear sounds analysis comprises:
Heart sound transducer, it is used for measured's hear sounds vibration signal is converted to the formal output of the signal of telecommunication;
Computer with multimedia function, it is connected with described heart sound transducer by data collecting card, records and be stored in the measured's of heart sound transducer output cardiechema signals in the memory cell; And, being installed in the described computer, hear sounds segmented program, the CPU of computer move this hear sounds segmented program, the cardiechema signals in the storage unit access,
The cardiechema signals of one cardiac cycle is divided into four periods of first heart sound, systole phase, second heart sound and diastoles;
The hear sounds feature extraction program, the CPU of computer moves the hear sounds feature that this hear sounds feature extraction program is extracted the cardiechema signals of a described cardiac cycle;
Grader, the CPU of computer move this grader hear sounds feature and the heart disease criteria for classification in the Computer Storage unit of extracting are compared, and classification results is outputed to printer or display screen.
Wherein, above-mentioned hear sounds automatic segmentation program comprises:
Cardiechema signals envelope computing module, this module is utilized shannon energy E=-x2·logx
2Obtain the envelope of described cardiechema signals;
Determine the hear sounds root module, this module is sought the continuous hear sounds section of all more than certain amplitude of energy in the envelope of described cardiechema signals, and definite its coarse border, then according to first heart sound in the normal cardiac sound and the statistics in the second heart sound time interval, remove unnecessary hear sounds section, merge the hear sounds section of division;
Judge module, whether this module is close to criterion with the ceiling capacity of number, all first heart sounds or all second heart sounds of hear sounds section, check and determine whether the determined hear sounds section of hear sounds root module is real first heart sound or second heart sound, if then segmentation success, otherwise, adjust statistical parameter, rerun and determine the hear sounds root module; And,
Determine the exact boundary module, this module is distinguished cardiechema signals and heart murmur signal by the short-time zero-crossing rate of the signal at statistics first heart sound section and/or second heart sound segment boundary place, thereby determines the exact boundary of first heart sound section and second heart sound section.
Above-mentioned hear sounds feature extraction program comprises:
Temporal signatures extraction module, this module extract two maximum peak values in the duration of two maximum in the duration, first heart sound envelope of described first heart sound peak values, second heart sound and the second heart sound envelope, extract simultaneously the heart rate of described cardiechema signals;
Time-frequency characteristics extraction module, this module extract respectively first heart sound, systole phase, second heart sound and the diastole energy in every one deck with the cardiechema signals layering in described cardiac cycle;
Power spectrum characteristic extraction module, this module extract respectively the AR spectral coefficient of described first heart sound, systole phase, second heart sound and diastole; And,
The Cancers Fractional Dimension Feature extraction module, this module is extracted respectively the fractal dimension of described systole phase and diastole.
The hear sounds segmentation is to set up the basis of decision system and prerequisite, its objective is the Main Ingredients and Appearance (being first heart sound S1, systole phase, second heart sound S2 and diastole) of location hear sounds, for feature extraction and pattern-recognition provide positioning datum. The present invention also provides a kind of hear sounds segmentation method, and it may further comprise the steps:
1) computer calculates the envelope of cardiechema signals by cardiechema signals envelope computing module;
2) computer is determined the hear sounds section by determining the hear sounds root module in the envelope of described cardiechema signals;
3) computer checks to determine whether the determined hear sounds section of hear sounds root module is real first heart sound or second heart sound by judge module, if then segmentation success, otherwise, adjust parameter, rerun and determine the hear sounds root module;
4) computer is determined the exact boundary of first heart sound, systole phase, second heart sound and diastole in the described cardiechema signals by determining the exact boundary module.
This based on the heart disease automatic classification system of hear sounds analysis by heart sound transducer being placed on auscultation place of measured's body surface, change the hear sounds vibration signal into the signal of telecommunication, change digital signal record into the data collecting card of AD conversion and be stored in the computer by sound card or other, operation segmentation, feature extraction and pattern recognition program on computers finished the automatic classification to cardiechema signals. This system bulk is little, cost is low, and is convenient universal in inferior grade hospital and community medicine.
Its hear sounds segmented program adopts the special-purpose segmentation method based on signal energy and envelope, has the advantages that segmentation is accurate, antijamming capability is strong, and introduces short-time zero-crossing rate to locate more accurately section boundaries.
Its hear sounds feature extraction program comprises the extraction of temporal signatures, time-frequency characteristics, power spectrum characteristic and Cancers Fractional Dimension Feature, can represent more accurately the hear sounds feature, thereby more accurately heart disease be classified.
Description of drawings
Fig. 1 is that this is based on the structured flowchart of the heart disease automatic classification system of hear sounds analysis;
Fig. 2 is the flow chart of its assorting process;
Fig. 3 is its hear sounds segmentation method flow chart;
Fig. 4 extracts the time-frequency characteristics schematic diagram;
Fig. 5 is that box counting dimension calculates schematic diagram.
The specific embodiment
The present invention will be further described below in conjunction with accompanying drawing.
With reference to Fig. 1, this heart disease automatic classification system based on the hear sounds analysis comprises: heart sound transducer 1, and it is used for measured's hear sounds vibration signal is converted to the formal output of the signal of telecommunication, and heart sound transducer 1 can adopt microphone or piezoelectric transducer etc.; Computer 2 with multimedia function, it is connected with described heart sound transducer 1 by data collecting card 21, and the measured's that heart sound transducer 1 is exported cardiechema signals is recorded and is stored in the memory cell; And, be installed in the described computer, hear sounds segmented program, the CPU of computer move this hear sounds segmented program, cardiechema signals in the storage unit access is divided into four periods of first heart sound, systole phase, second heart sound and diastoles with the cardiechema signals of a cardiac cycle;
The hear sounds feature extraction program, the CPU of computer moves the hear sounds feature that this hear sounds feature extraction program is extracted the cardiechema signals of a described cardiac cycle;
Grader, the CPU of computer move this grader hear sounds feature and the heart disease criteria for classification in the Computer Storage unit of extracting are compared, and classification results is outputed to printer or display screen 22.
The characteristics of this system are: cost is low, because the cardiechema signals frequency is not high, uses computer sound card just can meet the demands, and can save the exclusive data capture card; System suitability is strong, can be according to the parameter of practical application situation adjustment algorithm.
The hear sounds segmentation is to set up the basis of decision system and prerequisite, its objective is the Main Ingredients and Appearance (first heart sound S1, systole phase, second heart sound S2 and diastole) of location hear sounds, for feature extraction and pattern-recognition provide positioning datum. In the native system, hear sounds automatic segmentation program comprises:
Cardiechema signals envelope computing module, this module is utilized shannon energy E=-x2·logx
2Obtain the envelope of described cardiechema signals, can give prominence to the signal of moderate strength, weaken low intensive noise. In order to improve the flatness of envelope, signal is divided into long 16 milliseconds frame simultaneously, frame moves 8 milliseconds, calculates the average shannon energy of each frame;
Determine the hear sounds root module, this module is sought the continuous hear sounds section of all more than certain amplitude of energy in the envelope of described cardiechema signals, and definite its coarse border, then according to first heart sound in the normal cardiac sound and the statistics in the second heart sound time interval, remove unnecessary hear sounds section, merge the hear sounds section of division;
Judge module, whether this module is close to criterion with the ceiling capacity of number, all first heart sounds or all second heart sounds of hear sounds section, check and determine whether the determined hear sounds section of hear sounds root module is real first heart sound or second heart sound, if then segmentation success, otherwise, adjust statistical parameter, rerun and determine the hear sounds root module; And,
Determine the exact boundary module, this module is by the short-time zero-crossing rate of the signal at statistics first heart sound section and/or second heart sound segment boundary place, distinguish cardiechema signals and heart murmur signal, thereby determine the exact boundary of first heart sound section and/or the exact boundary of second heart sound section. When adjacent two sampling point symbol of signal are different, be called " zero passage "; Add up frame by frame the zero passage number of times in each frame, be short-time zero-crossing rate, its computing formula is Wherein sgn is sign function, and N is frame length. To the analysis showed that of great amount of samples, the short-time zero-crossing rate of heart murmur will apparently higher than S1 or S2, can be distinguished heart murmur thus. From the angle of frequency spectrum, the main frequency composition of S1, S2 is below 100Hz, and the main frequency composition of heart murmur is often more than 200Hz even higher frequency range. Short-time zero-crossing rate can be made differentiation significantly as the rough description of signal spectrum characteristic.
Cardiechema signals is typical non-stationary signal, and its radio-frequency component changes over time. For sort signal, list all is difficult to represent all sidedly its feature from time-domain analysis or list from frequency-domain analysis. Wavelet transformation is the signal processing new tool that develops rapidly in recent ten years and be widely used, the title that " school microscop " arranged, have the function of automatically adjusting resolution ratio according to the characteristics of signal, namely for low frequency signal, time resolution height and the corresponding reduction of frequency domain resolution; For high-frequency signal, time resolution reduces and the corresponding rising of frequency domain resolution. By the time-frequency distributions feature that wavelet transformation extracts, cardiechema signals can be described more fully. The power spectrum of stationary random signal is defined as the Fourier transformation of its auto-correlation function, frequency structure that can the describing stable random signal. The power spectrum resolution ratio that classical power Spectral Estimation such as period map method, indirect method draw is low, and variance is large. Modern power Spectral Estimation is started with from setting up signal model (such as AR model, MA model, arma modeling), by prediction and the extrapolation to signal, can significantly improve resolution ratio and the variance of power Spectral Estimation. Fractally belong to nonlinear category, in order to describe the self-similarity of figure. Cardiechema signals is the envelope shape cardiopathic notable feature often of heart murmur signal especially. This feature of fractal dimension can be described the complexity of hear sounds envelope.
In order to represent cardiechema signals accurately, all sidedly, in the native system, the hear sounds feature extraction program has comprised temporal signatures extraction module, time-frequency characteristics extraction module, power spectrum characteristic extraction module and Cancers Fractional Dimension Feature extraction module. Wherein, the feature extracted of temporal signatures extraction module comprises: maximum two peak values in the duration of heart rate, first heart sound S1, the first heart sound envelope (if only have a peak then second be zero), maximum two peak values in the duration of second heart sound S2, second heart sound envelope (if only have a peak then second be zero). Wherein heart rate is defined as the time difference between the peak-peak of continuous two S1 or continuous two S2, and it is average to get algorithm. Peak value is defined as the maximum point in the hear sounds envelope.
The time-frequency characteristics extraction module utilizes wavelet arithmetic with the cardiechema signals layering in described cardiac cycle, extracts respectively first heart sound, systole phase, second heart sound and the diastole energy in every one deck; Institute's hierarchy number determines that according to hear sounds sample rate and noise level the hear sounds sample rate is higher, and noise is larger, and the number of plies after then decomposing the more. The process of utilizing wavelet transformation to extract time-frequency characteristics is: select certain small echo that the hear sounds of one-period is made n (value of n depends on hear sounds sample rate and noise level etc.) layer and decompose; The energy of each part of hear sounds (first heart sound, systole phase, second heart sound and diastole) in every one deck is arranged sequentially as characteristic vector. The calculating of energy is including but not limited to following three kinds. Make xiBe the projection of signal x (t) on a certain Orthogonal Subspaces base, then its definition is respectively:
1) Shannon (Shannon) entropy:
2) P norm entropy:
3) logarithm Energy-Entropy:
The power spectrum characteristic extraction module extracts the AR spectral coefficient of described first heart sound, systole phase, second heart sound and diastole. The AR spectrum is autoregressive spectrum, and it supposes that signal x to be studied (n) (being cardiechema signals) is the output by a list entries u (n) linear system H of excitation (z), and supposition H (z) has following form: When u (n) is that variance is σ2White noise signal the time, the power spectrum of x (n) α in the above-mentioned formula1，α
2，…，α
pBe the AR spectral coefficient, also the i.e. power spectrum characteristic that extracts of this algorithm.
The Cancers Fractional Dimension Feature extraction module extracts respectively the fractal dimension of hear sounds envelope systole phase, diastole. Fractals is the science of exploring its complexity take local and whole complexity with self similarity as research object, and the cardiechema signals envelope shape cardiopathic notable feature often of heart murmur signal especially, therefore can be with fractal scrambling and the complexity of describing cardiechema signals. Fractal dimension is the important indicator of the fractal complexity of tolerance, and the Cancers Fractional Dimension Feature that the present invention extracts comprises box counting dimension, information dimension, correlation dimension, similar dimension, spectrum dimension etc.
Described grader can adopt any grader of processing the multicategory classification problem, separately or the feature of combination said extracted, and with this hear sounds sample classification, and Output rusults. Spendable grader includes but not limited to linear classifier, SVMs (SVM), decision tree etc.
With reference to Fig. 2, the process that adopts native system that heart disease is classified is: the cardiechema signals that gathers is done certain preliminary treatment, such as adjusting sample rate f s=2000Hz, being truncated to N=4096 and order etc., then press above-mentioned hear sounds segmentation method with the segmentation of this section hear sounds. If owing to the reasons such as noise is too strong, hear sounds is too in disorder cause a minute segment fault, finish classification process after then reporting to the police. Move temporal signatures, time and frequency domain characteristics, power spectrum characteristic and the Cancers Fractional Dimension Feature that the hear sounds feature extraction program is extracted cardiechema signals if segmentation is successful. The grader that calls at last design and train is classified to this hear sounds sample, and the output category result. Show that at the classification results to normal cardiac sound, aortic stenosis, aorta valve regurgitation, mitral stenosis and mitral regurgitation total accuracy is more than 90%.
With reference to Fig. 3, adopt native system hear sounds segmentation module to the method that cardiechema signals carries out segmentation to be:
1) computer calculates the envelope of cardiechema signals by cardiechema signals envelope computing module;
2) computer is determined the hear sounds section by determining the hear sounds root module in the envelope of described cardiechema signals;
3) computer checks to determine whether the determined hear sounds section of hear sounds root module is real first heart sound or second heart sound by judge module, if then segmentation success, otherwise, adjust parameter, rerun and determine the hear sounds root module;
4) computer is determined the exact boundary of first heart sound, systole phase, second heart sound and diastole in the described cardiechema signals by determining the exact boundary module.
Wherein, determine that by definite exact boundary module the method for exact boundary is:
The signal at sampling first heart sound segment boundary place is added up the short-time zero-crossing rate of the signal at first heart sound segment boundary place frame by frame, calculates the frequency of each frame signal; If the frequency that calculates and cardiechema signals frequency approach, then divide this frame signal into the first heart sound signal, otherwise divide this frame signal into the heart murmur signal, process according to this each frame signal at first heart sound segment boundary place, thereby determine the exact boundary of first heart sound;
The signal at sampling second heart sound segment boundary place is added up the short-time zero-crossing rate of the signal at second heart sound segment boundary place frame by frame, calculates the frequency of each frame signal; If the frequency that calculates and cardiechema signals frequency approach, then divide this frame signal into second heart sounds signals, otherwise divide this frame signal into the heart murmur signal, process according to this each frame signal at second heart sound segment boundary place, thereby determine the exact boundary of second heart sound.
The hear sounds segmentation method that the present invention adopts is compared with algorithm in the past, has following advantages:
1) determine the hear sounds segment boundary with the double threshold method, anti-interference, algorithm stability are better.
2) use energy as the correct decision criteria of segmentation, higher with the section gap accuracy than simple.
3) introduce first short-time zero-crossing rate and analyze cardiechema signals, distinguishing successful aspect S1, S2 and the heart murmur, can determine exactly the border of S1, S2.
Result of the test by 6 routine normal cardiac sounds and the unusual hear sounds of 22 examples shows that the segmented general accuracy of the hear sounds segmentation method that the present invention adopts reaches 93.3%.
Fig. 4 is the time-frequency characteristics extraction module extraction time-frequency characteristics schematic diagram with native system. Use biorthogonal wavelet (bior2.2) that cardiechema signals is done 4 layers of decomposition among the figure, obtain wavelet coefficient a4, d4, d3, d2 and d1. The energy of each part of hear sounds (first heart sound, systole phase, second heart sound and diastole) in every one deck is arranged sequentially as characteristic vector. Select the energy computing formula The result who obtains is as follows:
| First heart sound | Systole phase | Second heart sound | Diastole | |
| a4 | 7.6042 | 0.69296 | 4.0252 | 0.2409 |
| d4 | 4.3068 | 0.69824 | 2.6792 | 0.083894 |
| d3 | 4.0218 | 3.22 | 2.19 | 0.062122 |
| d2 | 2.9651 | 3.3052 | 1.3101 | 0.044961 |
| d1 | 1.313 | 1.4157 | 0.51723 | 0.025518 |
Fig. 5 is that box counting dimension calculates schematic diagram. Box counting dimension is a kind of practical approach of calculating fractal graph minute dimension commonly used. Get the etui that the length of side is r, cover fractal. Because there are various levels of cavity and slit in fractal inside, so some etui is empty, some etui has covered a fractal part. How many etuis of number number are not empty, and the non-empty packet number scale of gained is N (r). Then dwindle the size r of box, gained N (r) nature will increase. When r → 0, obtain the box counting dimension that calculates with number box method Can only get limited r in actual applications, the practice is to ask a series of r and N (r) usually, then asks the straight slope D of lgN-lgr at log-log coordinate0。
Claims (10)
1, a kind of heart disease automatic classification system of analyzing based on hear sounds is characterized in that comprising:
Heart sound transducer, it is used for measured's hear sounds vibration signal is converted to the formal output of the signal of telecommunication;
Computer with multimedia function, it is connected with described heart sound transducer by data collecting card, records and be stored in the measured's of heart sound transducer output cardiechema signals in the memory cell; And, be installed in the described computer,
Hear sounds segmented program, the CPU of computer move this hear sounds segmented program, and the cardiechema signals in the storage unit access is divided into four periods of first heart sound, systole phase, second heart sound and diastoles with the cardiechema signals of a cardiac cycle;
The hear sounds feature extraction program, the CPU of computer moves the hear sounds feature that this hear sounds feature extraction program is extracted the cardiechema signals of a described cardiac cycle;
Grader, the CPU of computer move this grader hear sounds feature and the heart disease criteria for classification in the Computer Storage unit of extracting are compared, and classification results is outputed to printer or display screen.
2, the heart disease automatic classification system of analyzing based on hear sounds according to claim 1 is characterized in that: described hear sounds feature extraction program comprises,
Temporal signatures extraction module, this module extract two maximum peak values in the duration of two maximum in the duration, first heart sound envelope of described first heart sound peak values, second heart sound and the second heart sound envelope, extract simultaneously the heart rate of described cardiechema signals;
Time-frequency characteristics extraction module, this module extract respectively first heart sound, systole phase, second heart sound and the diastole energy in every one deck with the cardiechema signals layering in described cardiac cycle;
Power spectrum characteristic extraction module, this module extract respectively the AR spectral coefficient of described first heart sound, systole phase, second heart sound and diastole; And,
The Cancers Fractional Dimension Feature extraction module, this module is extracted respectively the fractal dimension of described systole phase and diastole.
3, the heart disease automatic classification system of analyzing based on hear sounds according to claim 2, it is characterized in that: described time-frequency characteristics extraction module utilizes wavelet arithmetic to the cardiechema signals layering, wherein, institute's hierarchy number determines according to hear sounds sample rate and noise level, the hear sounds sample rate is higher, noise is larger, and the number of plies after then decomposing the more.
4, the heart disease automatic classification system of analyzing based on hear sounds according to claim 1 is characterized in that: described hear sounds automatic segmentation program comprises,
Cardiechema signals envelope computing module, this module utilize shannon energy to obtain the envelope of described cardiechema signals;
Determine the hear sounds root module, this module is sought the continuous hear sounds section of all more than certain amplitude of energy in the envelope of described cardiechema signals, and definite its coarse border, then according to first heart sound in the normal cardiac sound and the statistics in the second heart sound time interval, remove unnecessary hear sounds section, merge the hear sounds section of division;
Judge module, whether this module is close to criterion with the ceiling capacity of number, all first heart sounds or all second heart sounds of hear sounds section, check and determine whether the determined hear sounds section of hear sounds root module is real first heart sound or second heart sound, if then segmentation success, otherwise, adjust statistical parameter, rerun and determine the hear sounds root module; And,
Determine the exact boundary module, this module is by the short-time zero-crossing rate of the signal at statistics first heart sound section and/or second heart sound segment boundary place, distinguish cardiechema signals and heart murmur signal, thereby determine the exact boundary of first heart sound section and/or the exact boundary of second heart sound section.
5, the heart disease automatic classification method of analyzing based on hear sounds according to claim 1 is characterized in that: the heart disease criteria for classification in the described Computer Storage unit is to formulate according to the auscultation experience of medical practitioner.
6, the heart disease automatic classification method of analyzing based on hear sounds according to claim 1, it is characterized in that: described heart sound transducer is microphone or piezoelectric transducer; Described grader is linear classifier or SVMs or decision tree.
7, a kind of hear sounds segmentation method is characterized in that may further comprise the steps:
1) computer calculates the envelope of cardiechema signals by cardiechema signals envelope computing module;
2) computer is determined the hear sounds section by determining the hear sounds root module in the envelope of described cardiechema signals;
3) computer checks to determine whether the determined hear sounds section of hear sounds root module is real first heart sound or second heart sound by judge module, if then segmentation success, otherwise, adjust parameter, rerun and determine the hear sounds root module;
4) computer is determined the exact boundary of first heart sound, systole phase, second heart sound and diastole in the described cardiechema signals by determining the exact boundary module.
8, hear sounds segmentation method according to claim 7 is characterized in that: described cardiechema signals envelope computing module utilizes shannon energy to obtain the envelope of described cardiechema signals.
9, hear sounds segmentation method according to claim 7, it is characterized in that, described definite hear sounds root module determines that the method for hear sounds section is: at first seek the continuous hear sounds section of all more than certain amplitude of energy in the envelope of cardiechema signals, and definite its coarse border, then according to first heart sound in the normal cardiac sound and the statistics in the second heart sound time interval, remove unnecessary hear sounds section, merge the hear sounds section of division.
10, hear sounds segmentation method according to claim 7 is characterized in that, and is described by determining that the exact boundary module determines that the method for exact boundary between the cardiechema signals each several part is:
The signal at sampling first heart sound segment boundary place is added up the short-time zero-crossing rate of the signal at first heart sound segment boundary place frame by frame, calculates the frequency of signal in each frame; If the frequency that calculates and cardiechema signals frequency approach, then divide this frame signal into the first heart sound signal, otherwise divide this frame signal into the heart murmur signal, process according to this each frame signal at first heart sound segment boundary place, thereby determine the exact boundary of first heart sound;
The signal at sampling second heart sound segment boundary place is added up the short-time zero-crossing rate of the signal at second heart sound segment boundary place frame by frame, calculates the frequency of signal in each frame; If the frequency that calculates and cardiechema signals frequency approach, then divide this frame signal into second heart sounds signals, otherwise divide this frame signal into the heart murmur signal, process according to this each frame signal at second heart sound segment boundary place, thereby determine the exact boundary of second heart sound.
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| DE202022103576U1 (en) | 2022-06-28 | 2022-10-17 | Sanjay Ramkrishna Bhoyar | A heart sound analysis system to detect heart disease |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR100387201B1 (en) * | 2000-11-16 | 2003-06-12 | 이병훈 | Diaortic apparatus |
| JP4256683B2 (en) * | 2001-05-28 | 2009-04-22 | エイチディー メディカル インコーポレイテッド | Cardiac diagnosis system |
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| US20050222515A1 (en) * | 2004-02-23 | 2005-10-06 | Biosignetics Corporation | Cardiovascular sound signature: method, process and format |
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