JPS63270026A - System for comparing and classifying shapes of qrs groups of electrocardiograph - Google Patents

Abstract

PURPOSE:To compare and classify the shapes of QRS groups with high accuracy, by developing time series electrocardiographic data on a frequency region by FET processing. CONSTITUTION:The electrocardiographic signal amplified by an electrocardiograph amplifier 2 is digitalized by an A/D converter part 3 and the position of a QRS group is detected by a QRS group detection part 4. High speed Fourier transform processing is performed on the basis of time sequential electrocardiographic data and the QRS group detecting position in an FFT processing part 5. A statistical processing part 6 calculates the correlation coefficient and regression curve of the frequency/amplitude characteristic being the output of the processing part 5 and the amplitude characteristic of the template QRS group stored in a classification result storing part 8. A shape comparator 7 judges whether the shapes of the template QRS groups coincides with each other from the output of the statistical processing part 6. A result display part 9 displays the incidence number of QRS groups coinciding in shape and the time sequential incidence positions thereof. By this simple constitution, the shapes of the QRS groups can be compared and classified.

Description

DETAILED DESCRIPTION OF THE INVENTION [Field of Industrial Application] The present invention relates to electrocardiogram signal processing, particularly to an electrocardiogram QRS complex shape comparison and classification method for comparing and classifying the waveforms of QRS complexes in an electrocardiogram.

[Prior Art] Conventionally, as this type of electrocardiogram QRS complex shape comparison classification system, there is a system as shown in FIG.

This conventional electrocardiogram QRS complex shape comparison classification method performs second-order differential processing on the electrocardiogram waveform (a), and generates the second-order differential waveform (b).
By detecting the peak of the QRS complex, the starting point QR
A method was used in which Sb and the end point QRS e of the QRS complex were determined, and the time width QRSw of the QRS complex, the amplitude QRSh of the QRS complex, the area of the QRS complex QRS complex, etc. were calculated and compared.

[Problems to be solved] In the conventional electrocardiogram QRSJ7 shape comparison classification method, the electrocardiogram waveform (a) is subjected to second-order differential processing, and the peak detection of the second-order differential waveform (b) is performed. , find the starting point QRSb of the QRS complex and the ending point QRSe of the QRS complex, and calculate Q
RS group time width QRSw, QRSJT (7) amplitude QRS
h and the surface MQRSa of the QRS complex, etc. were calculated and compared, so it was necessary to accurately recognize the starting point of the QRS complex and the bonding point of the QRS complex, and a complex recognition logic that considered various waveform patterns was required. The disadvantage was that it required

In addition, since it is easily affected by 3tB such as hum mixed in the electrocardiogram signal, electroconcentration diagram, and drift, errors occur in recognizing the start and end points of the QRS complex, reducing the accuracy of measuring the duration, amplitude, and area of the QRS complex. However, it had the disadvantage of making incorrect comparisons.

[First step of solving the problem] The present invention was made to solve the above-mentioned conventional problems. As a first step of solving the problem, the present invention performs comparative classification of the shapes of QRS complexes in time-series electrocardiogram signals. electrocardiogram qrs
The R3-shaped shape comparison classification method includes a fast Fourier transform processing section that performs fast Fourier transform processing on an electrocardiogram signal, and a statistic that performs statistical processing on the frequency amplitude characteristics and frequency phase characteristics of the electrocardiogram signal that is the output of the fast Fourier transform processing section. The apparatus is configured to include a processing section, a shape comparison section that performs shape comparison processing of QRS complexes based on the results of the statistical processing section, and a classification result storage section that stores the results of the shape comparison section.

[Example] Next, an example of the present invention will be described with reference to the drawings.

FIG. 1 is a block diagram showing one embodiment of the present invention.
RS group shape comparison classification processor l, electrocardiogram amplifier 2, A
Consists of /D conversion section 3, QRS complex detection section 4, fast Fourier transform processing section (hereinafter referred to as FFT processing section) 5, statistical processing section 6, shape comparison section 7, classification result storage section 8, and result display section 9. be done.

The electrocardiogram signal amplified by the electrocardiogram amplifier 2 is sent to the A/D
The conversion section 3 digitizes the data, and the QRS complex detection section 4 detects the position of the QRS complex. In the FFT processing section 5, based on the digitized time-series electrocardiogram data that is the output of the A/D conversion section 3 and the QRS complex detection position δ that is the output of the QRS complex detection section 4, On the other hand, fast Fourier transform processing (hereinafter referred to as FFT processing) is performed. FFT processing is set so that the range of electrocardiogram data subject to FFT processing sufficiently covers the QRS complex, and the number of points for FFT processing is determined by 0, for example, 4.
In the case of a sec sampling interval, F
When FT processing is performed, the number of points becomes 64. The statistical processing unit 6 calculates the frequency amplitude characteristic (
(hereinafter simply referred to as amplitude characteristic) A (n) (n=0.1.2
...k) and the amplitude characteristic At (n) of the QRS complex to be compared (hereinafter referred to as template QRS complex) stored in the classification result storage unit 8 (n=0.1.2...k )
The correlation coefficient γA and the coefficients α0 and α1 of the regression line y=α0+αIX are calculated. However, when calculating the correlation coefficient and the regression line, the calculation is performed for n of the frequency band of the QRS complex. )
'(n=0.1.2...k) and Tenblay) QRS
Group phase characteristic It (n) (n=0.1.2 ・k
) and the correlation coefficient γO of n corresponding to the frequency and the coefficients β0 and βI of the regression line y=β0+βIX are calculated. Here again, the n range of the frequency band of the QRS complex is targeted.

In the shape comparison section 7, the outputs of the statistical processing section 6 are γ^ and α.
0, α1, γ0. Based on β0 and β1, it is then determined whether the shapes match the template QRs group. FIG. 2 shows a flowchart of the shape comparing section 7.

The classification result storage unit 8 stores the input time-series electrocardiogram data,
QRS complex detection position l, Tenbrey) QRS complex waveform data and each Tenbrey) At corresponding to the QRS complex
(n), θ1(n), the number of occurrences, and the location of occurrence. When it is determined by the shape comparing section 7 that the shape matches the template QR8 group, the number of occurrences of the corresponding template QRS group is added up and the occurrence position is registered. If it is determined that the shape does not match the template QRS complex, the shape is compared with other template QRS complexes. If it is determined that the template does not match any of the QRS complexes, a new template QR will be created.
It is registered as group S and stored in the classification result storage section 8.

The result display section 9 displays the input time-series electrocardiogram data, the waveform data of the Tenbrey) QRS complexes, and the number and time-series occurrence positions of QRS complexes that match the shape of each Tenbley hQRS complex.

[Effects of the Invention] As explained below, the electrocardiogram QRS complex shape comparison and classification method of the present invention expands time-series electrocardiogram data into the frequency domain by FFT processing, so that the hum mixed in the electrocardiogram signal, This has the advantage that noise components such as power saving diagrams and drift can be easily separated, and QRS complex shape comparison and classification can be performed with high accuracy without being affected by noise. Further, there is an effect that QRS complex shape comparison and classification can be performed with a simple configuration without requiring complicated logic for recognizing the starting point and ending point of the QRS complex.

[Brief explanation of drawings]

FIG. 1 is a block diagram showing an embodiment of the electrocardiogram QRS complex shape comparison and classification method of the present invention, FIG. 2 is a flowchart showing the configuration of the shape comparison section in FIG. 1, and FIG. 3 is an electrocardiogram waveform of a conventional example. FIG. 3 is a schematic diagram of a second-order differential waveform. 4: QRS complex detection section 52 FFT processing section 6: Statistical processing section 7: Shape comparison section 8: Classification result storage section 9: Result display section

Claims (1)

[Claims] In an electrocardiogram QRS complex shape comparison classification method for comparing and classifying the shapes of QRS complexes in time-series electrocardiogram signals, a fast Fourier transform processing section that performs fast Fourier transform processing on the electrocardiogram signal, and an electrocardiogram that is the output of the fast Fourier transform processing section a statistical processing section that performs statistical processing on the frequency amplitude characteristics and frequency phase characteristics of the signal; and a QR processing section based on the results of the statistical processing section.
An electrocardiogram QRS complex shape comparison and classification method, comprising: a shape comparison section that performs shape comparison processing of the S group; and a classification result storage section that stores the results of the shape comparison section.