JPS6111017A - Bio-signal processing system - Google Patents

Abstract

(57) [Summary] This bulletin contains application data before electronic filing, so abstract data is not recorded.

Description

DETAILED DESCRIPTION OF THE INVENTION [Industrial Application Field] The present invention relates to a biological signal processing method for recognizing and measuring biological signals such as electrocardiograms.

[Conventional technology]

In general, biological signals are composed of a combination of multiple types of waves with a circular shape, or their periodic repetition, but in order to understand the nature of the biological signals, the baseline (zero level) of their detection The shape of the bee wave is estimated by measuring the time of rise from the peak, the time of return to the baseline, the time of peak occurrence, or the time of inflection point, and find the wave width (time interval) and height (voltage value) to estimate the shape of the bee wave. The method is commonly used.

However, it has been considered difficult to accurately measure biological signals because their voltage levels are small and high-frequency noise such as hum is easily superimposed on them. For example, taking an electrocardiogram as an example, the electrocardiogram signal is P, Q, S, as shown in Figure 2.
It is composed of line waves called T and U waves, but when automatically analyzing this electrocardiogram signal, conventionally the QR8 wave, which has a high frequency component and a large amplitude value, is easy to detect. Regarding the recognition of P waves or T waves, accurate waveform recognition and measurement have been difficult due to the effects of noise and baseline fluctuations.

In particular, when analyzing arrhythmia, it is necessary to accurately recognize the presence or absence of P waves, their shape, etc., but since P waves have low frequency components and small amplitude values, electromyograms and hums may be mixed in. It was said that the signal components were buried in noise, making it difficult to distinguish them. Regarding T waves, when diagnosing myocardial abnormalities, it is necessary to recognize the tendency of the S-T component and the shape of the T wave (distinguishing between negative waves and biphasic waves).
This method has the disadvantage that noise tends to be superimposed on the components, and the recognition of negative (biphasic) T waves is susceptible to the mineral echoes of baseline oscillations, resulting in large measurement errors.

Conventionally, the following method has been used to detect this P wave (T wave). (1) A method in which the occurrence time, termination time, and peak occurrence time of P waves and T waves are determined by converting the electrocardiogram analog signal into analog-to-digital conversion and performing band-pass digital filtering processing on the digital signal. , (2) A method in which the electrocardiogram analog signal is converted into analog-to-digital, and the original digital signal is subjected to differential processing to obtain the first-order differential coefficient, and the time at which the change in slope is maximum is determined as the occurrence time of the P wave or T wave. It is. Method (1)
If the digital filters used in these applications are implemented in software, they require highly accurate decimal point data as coefficients, and require repeated high-precision multiplication/division processing, which takes processing time and requires a large amount of memory. There was a drawback that it required something. Furthermore, in order to discriminate between P waves and T waves, a digital filter with different frequency characteristics is required. Method (2) is susceptible to high-frequency noise such as electromyography and hum superimposed on the raw electrogram signal, and there is a high possibility of erroneous recognition.

[Problem that the invention seeks to solve]

The purpose of the present invention is to detect the waveform occurrence time without being affected by high frequency noise or baseline fluctuation compared to conventional biological signal processing methods.
It is an object of the present invention to provide a biological signal processing method that can accurately determine the termination time and the peak occurrence time with the same configuration, and can also recognize the shape of the waveform only from the pattern of the second-order differential output signal.

[Means for solving problems]

According to the present invention, an operation of applying second-order differential filtering processing to a signal to be processed, an operation of detecting a peak from a time-series signal group outputted from the second-order differential, and an occurrence pattern of the detected peak signal sequence are performed to obtain an original signal. By analogy with the pattern of
A biosignal processing method is obtained which is comprised of an operation of classifying, and an operation of determining the occurrence time, termination time, and peak occurrence time of the original signal from the second-order differential peak signal sequence, and further calculating the peak voltage value of the original signal by 1 Qtl in total. .

[Principle of the invention]

Considering the basic form of one cycle of the waveform of a signal to be processed such as a biological signal as shown in FIG. ) is (a)
can be thought of as two consecutive waveforms5.Furthermore, a general continuous waveform can be thought of as a combination of these waveforms that appears with a continuous period. Therefore, a second-order differential operation is applied to this signal to be processed. The formula of the second-order differential filter used in the present invention is shown in formula (1).

+0 ・(JC&+2+3'! 2)+O・(”&+3
+”k-3)+1 ・(”&+4+”&-4)+2
- ("*+5+"1-s) where (xk) indicates the sampled actor shown in FIG. Since the coefficients of this filter shown in Equation (1) are simple integers (0, 2, or 2), processing is possible using only addition, subtraction, and pit shift operations, and it is fast and has good reduced quadratic differentiation. It has the features that realize the characteristics.

Figure 3 (a) Mountain) The output results of applying the above filtering operation to the signal having the shape of (C) are respectively (a'Xb'
It is shown in X (!').

The peak occurrence time of the obtained second-order differential filtering output signal indicates the occurrence time, end time, and occurrence time or a time in the vicinity of the occurrence time in the original signal. Even when noise or the like is superimposed, it is easy to distinguish signal components from noise because it shows a noticeable peak without being affected by it. Further, even if there is baseline fluctuation in the original signal, it does not affect the peak occurrence time of the second-order differential output signal, so that the frozen ground waveform can be measured accurately. Furthermore, the peak of the second-order differential output signal component for the components shown in FIGS. 3(a), (b), and (C) of the original signal is more prominent than the other components.
) (bIt is possible to infer the shapes of the original waveforms (a), (b), and (e) by recognizing the occurrence pattern of the peak series shown in (C').Here, let us take an electrocardiogram as an example. Among the wave components that make up the electrocardiogram, P waves and T waves generally show one of the patterns shown in Figure 3 (a), (b), and (e). The biphasic pattern (C) is called a bimodal pattern. If the general form of the second-order differential output pattern of the P(T) wave in each signal (a), (b), and (e) is (C'), then (a') and (b') are the P of (C'), respectively.
Since I to L are considered to be special waveforms when P5 or Pl and P5 do not exist, the second-order differential filtering output signal of P wave (T wave) is calculated from its peak number, peak voltage value, peak time interval, etc. (a') (b') ((!'
), it is possible to measure the occurrence time, end time, and start time of each P(T) wave, which are called classifications necessary for electrocardiogram signal analysis. (T) It becomes possible to judge whether the wave shape is unimodal, biphasic, or bimodal.

For this reason, first, the component related to the QRSK wave is removed from the second-order differential output signal related to the electrocardiogram original signal, or the time of occurrence of QR8 is clarified to avoid the influence of the QR8R wave when detecting the P(T) fluid.

This is because the Bsn wave group exhibits a more prominent voltage value at its secondary filter output than other outputs. Next, the noise superimposed on the second-order differential output signal is measured, and its average voltage level is determined. If the voltage level of the graph or hum mixed in the original signal is high, the noise level will also be high in the second derivative output signal, so peak detection is performed to prevent this from affecting the detection accuracy of the P(T) wave signal component. The threshold value for this is determined by a dynamically settable voltage level.

A peak series that is above the noise level is selected using a noise threshold corresponding to this voltage level, and from among the peak series, a plurality of peaks that exist before and after the peak at the time when the absolute value of the voltage level is the maximum are selected. The selection criteria is to first select peaks that are within a certain time interval near the time that indicates the maximum voltage level, and if the time interval between peaks does not exist within the specified time interval range, it is considered to be noise or a notch. Sift it down. It is determined whether the pattern of the peak series remaining in this way matches the second-order differential output cover pattern shown in FIGS. 3(a), 3(b), and 3(c). If the peak series pattern matches any of the peak occurrence times, the second-order differential output signal peak occurrence time indicates the origination time, end time, and peak occurrence time of the original signal, or the vicinity thereof, respectively. Depending on the nature of the original signal, if the change is not abrupt, such as at the end of a T wave, the deviation of the peak occurrence time from the actual dividing point is corrected. In the 01-beak series pattern, the peaks show excess and deficiency in Figure 3 (a') (
b') (If it does not match any of the e, correct this and classify the pattern. For example, if a pattern indicating the peak and occurrence time of the original waveform is found on the second-order differential output signal as shown in Figure 5. If the peak corresponding to b' is obtained, lower the noise threshold and find b' in order to re-detect the peak corresponding to b'.If there are more peaks than the model pattern, change the time interval, level, etc. of the peak. Perform re-detection and reduce the number of peaks.

〔Example〕

Next, one embodiment of the present invention will be described with reference to the drawings. 1st
The figure shows an embodiment of the present invention, in which 1 is a second-order differential digital filter, which applies filtering operations to biological signals such as electrocardiograms. Reference numeral 2 denotes a noise detection device, which detects the average voltage value of the signal of the second-order differential output component, which is unnecessary for noise analysis, from the signal output by the digital filter.

3 is a QR 8 wave detection device, which detects QR from the second-order differential output signal.
Detect components corresponding to 8 waves. 4 is a subtraction device,
Performs the difference between two input signals. 5 is a noise threshold level generator, which determines a threshold for peak detection. Reference numeral 6 denotes a peak detection device, which detects a peak that is above a set threshold value. Reference numeral 7 denotes a voltage level comparison device, which compares the levels of the peak voltages detected by the peak detection device 6.

8 is a maximum level detection device which selects the maximum peak from the input peaks. Reference numeral 9 denotes a peak pattern generation device which generates a pattern based on the peak sequence detected by the peak detection device tt and 6. A pattern comparison device 10 compares the pattern obtained by the peak pattern generation device 9 with a preset model pattern. Reference numeral 11 denotes a peak correction device, which corrects the peak occurrence time and outputs the peak time and peak voltage value. Reference numeral 12 denotes a threshold value correction device which changes the threshold level based on the result of pattern comparison by the Shibataturn comparison device 10 if the pattern differs from the model pattern. 13 is the time of occurrence of the original waveform,
The terminal time correction device outputs the accurate division point time. 14 is a signal delay device, which delays one input word for a certain period of time.

Next, the operation of this embodiment will be explained. First, the electrocardiogram signal shown in FIG. 6(a) is subjected to a filtering operation using a second-order differential digital filter 1. In this filtering operation, an output signal as shown in FIG. Set the threshold voltage for noise removal necessary for this purpose. Q
In the R8R8 quantity detection device 3 calculation device 4, when detecting the P(T) wave, unnecessary QR8R2O third-order differential output component Section t2 in Figure 6
remove. In the peak detection device #6, a peak that is equal to or higher than a set noise threshold value tb+ is detected from the signal from the t2 calculation device 4. This is shown in FIG. Further, a voltage level comparison device 7 and a maximum level detection device 8 select the peak series having the maximum voltage level. (7th
(Figure P) The pattern comparison device 10 compares whether the peak generation pattern shown in FIG. 8 obtained by the peak pattern generation device 9 matches any of the patterns shown in FIG. 3. If the patterns match, a trigger is generated to the peak correction device 11 and the time correction device 13. In the peak correction device 11, based on the time of the second-order differential output maximum peak level obtained by the maximum level detection device 8, the time is corrected if necessary depending on the nature of the original waveform.
Refers to the original waveform and outputs the peak occurrence time of the original waveform and its voltage value.

On the other hand, the time correction device 13 detects the peak pattern from the peak detection device 6 and the peak butter/generation device 9, the origin time of the original waveform, and the end time.
Correct the time and output. If the pattern comparison device does not match the model pattern, the threshold correction device 12 is triggered to change the noise threshold for peak detection, and the process returns to the noise threshold level generation device 5.

〔Effect of the invention〕

By adopting the above-mentioned method, there are the following effects compared to the pu-mei method. (1) 2 adopted in this method
The order differential digital filter is a simple lr with only an adder/subtractor 45 and a delay circuit,! It is possible to detect waveforms at high speed compared to the conventional P-wave peak detection method using a digital filter. (2) Since the second-order differential guidital filter employed in this method has good low-pass characteristics, it exhibits high detection accuracy even for signals containing high-frequency noise such as hum and myoelectric signals. (3) Since the occurrence time, peak occurrence time, termination time, and their vicinity of the original signal can be detected from only the second-order differential filtering output signal, there is no need for means for retaining the original signal or processing noise in the original signal. This simplifies the configuration. (4) Since P waves and T waves can be detected with the same configuration by simply changing the settings of externally applied parameters, the overall configuration of the signal processing device is simplified.

[Brief explanation of the drawing]

Figure 1 is a diagram showing an embodiment of the present invention, Figure 2 is a diagram showing an electrocardiogram signal as an example of a biological signal, Figure 3 is a diagram showing the principle of processing biological signals, and Figure 4 is a time series diagram. Figure 51'J is a diagram showing the corrected signal waveform, Figure 6 is a diagram showing an electrocardiogram signal and a signal subjected to second-order differential filtering processing, and Figure 7 is a diagram showing the signal data shown in . FIG. 8 is a diagram showing the generated beak pattern. 4 zu tea 5 wards

Claims (1)

[Claims] filter means for receiving an input biosignal and performing second-order differential filtering; peak detection means for receiving the output of the filter means and detecting a peak; The method includes a classification means that makes an analogy to a predetermined classification, and a measurement means that measures a peak occurrence time and a peak voltage value in the input biological signal based on a signal that has been subjected to a second-order differential filtering process after receiving the output of the classification means. A biosignal processing method featuring: