Target waveform parameter detection method, detection system, and electroencephalogram machine

Abstract

The present invention belongs to the technical field of physiological electrical signal detection, and specifically relates to a method, a detection system, and an electroencephalograph for detecting target waveform parameters. A trend wave is first obtained based on a target band containing a target waveform. Then, by determining whether the first trend wave corresponding to the low-frequency component is a complete wave, the frequency is calculated using the zero-crossing method for the complete wave. For an incomplete wave, the first trend wave is subtracted from the target band to obtain the second trend wave corresponding to the high-frequency component, and the frequency is then re-determined. This detection method measures the frequency of the target waveform in the physiological electrical signal based on the adaptive trend wave and zero-crossing method, effectively eliminating the influence of strong interference components in the physiological electrical signal on the measurement results and improving measurement accuracy.

Description

Technical Field

[0001] The present invention belongs to the technical field of physiological electrical signal detection, and in particular relates to a method for detecting target waveform parameters, a detection system, and an electroencephalogram (EEG) machine. Background Art

[0002] In clinical electroencephalogram (EEG) interpretation, physicians have a significant need for real-time waveform measurement, particularly for rapid assessment of the frequency, amplitude, and duration of specific EEG activity, such as epileptic discharges, rhythmic waveforms, or abnormal fluctuations. This demand requires waveform measurement methods that are both accurate and efficient, capable of capturing key features in short-term data. For example, patent CN105092966B discloses a method for detecting the frequency of a sampled signal using zero-crossings. The preset bandpass range indicates that the measured signal in this technical solution is a stable, high-amplitude, high-signal-to-noise ratio signal. Two detection methods are employed: "In the absence of interference or minimal interference, the zero-crossings are used to determine the frequency of the measured signal, enabling timely detection of signal anomalies and initiation of subsequent protective actions. In the presence of strong interference, the method automatically switches to filtering detection, accurately determining signal anomalies and initiating appropriate protective actions based on this accurate determination."

[0003] As we all know, the amplitude of physiological electrical signals generally ranges from microvolts to millivolts. For example, electroencephalogram (EEG) signals range from approximately 5μV to 100μV, typically only around 50μV; electrocardiogram (ECG) signals range from approximately 0.5 mV to 5 mV; electromyography (EMG) signals range from approximately 10μV to 1mV; electrooculography (EOG) signals range from approximately 10μV to 100μV; and evoked potentials (EP) signals range from approximately 1μV to 50μV. During the signal acquisition process, multiple types of physiological electrical signals typically coexist. Because their amplitudes overlap or are relatively close, the target signal being acquired is often subject to strong interference from other physiological electrical signals. For example, scalp EEG signals are often mixed with myocardial and oculoscopic signals, making it difficult to detect the frequency of physiological electrical signals using existing zero-crossing techniques. Summary of the Invention

[0004] The present invention provides a method, a detection system and an electroencephalograph for detecting target waveform parameters, which are used to detect the frequency of physiological electrical signals by using zero-crossing points under strong interference.

[0005] In order to solve the above technical problems, the present invention provides a method for detecting target waveform parameters, including: selecting a target band containing a target waveform in a waveform diagram of a physiological electrical signal; obtaining a trend wave of the target band, including a first trend wave corresponding to a low-frequency component and a second trend wave corresponding to a high-frequency component; judging whether the trend wave is a complete wave, including: making a first judgment, i.e., judging whether the first trend wave is a complete wave; if so, calculating the frequency using a cross-zero point method; if not, making a second judgment, i.e., judging whether the second trend wave is a complete wave; if so, calculating the frequency using a cross-zero point method; if not, reselecting the target band.

[0006] Furthermore, the acquisition method of the first trend wave includes any one of low-pass filtering, sliding average window, and downsampling; judging whether the first trend wave is a complete wave includes: removing the baseline of the first trend wave to obtain the residual component; calculating the number of zero-crossing points of the residual component; if the number of zero-crossing points is greater than or equal to two, the first trend wave is considered to be a complete wave, otherwise, the first trend wave is considered not to be a complete wave.

[0007] Furthermore, the method for acquiring the second trend wave is configured as the target band minus the first trend wave; judging whether the second trend wave is a complete wave includes: calculating the number of zero-crossing points of the second trend wave; if the number of zero-crossing points is greater than or equal to two, the second trend wave is considered to be a complete wave, otherwise, the second trend wave is considered not to be a complete wave.

[0008] Furthermore, the frequency is calculated using the zero-crossing point method, which includes: taking the first-order difference of the time indexes of all zero-crossing points corresponding to the trend wave to obtain a set of time differences; eliminating outliers in the set of time differences; taking the integer multiples of the average value of the remaining time differences as the average period of the target waveform; and setting the frequency of the target waveform to the inverse of the average period.

[0009] Furthermore, selecting a target band containing a target waveform in the waveform diagram of the physiological electrical signal includes: providing a waveform display area on the operation interface of the human-computer interaction machine, and setting any selected area in the waveform display area; judging whether the boundary of the selected area crosses the baseline of the channel; if so, the target band of the channel is configured as the waveform in the selected area; if not, resetting the selected area; and setting the target band to , 、 are the times corresponding to the two boundaries of the selected area, is the sampling interval of the waveform time.

[0010] Furthermore, selecting a target band containing a target waveform in the waveform diagram of the physiological electrical signal includes: when a boundary of the selected area passes through baselines of multiple channels, the target band of each channel is configured as a waveform of the selected area within each channel.

[0011] Furthermore, it also includes: calculating the time domain parameters of the target waveform; the time domain parameters include: positive wave peak value, that is, the maximum value of the target waveform; negative wave peak value, that is, the minimum value of the target waveform; peak-to-peak value, that is, the difference between the positive wave peak value and the negative wave peak value; waveform time length, that is, the length of the selected area.

[0012] In a second aspect, the present invention provides a target waveform parameter detection system, comprising: a processor for running the detection method; a human-computer interaction machine, an operating interface of which is provided with a waveform display area for selecting a target band in a waveform diagram of a physiological electrical signal; and a display for displaying a target waveform and its waveform parameters.

[0013] In a third aspect, the present invention provides an electroencephalograph, comprising: a processor for running the detection method; a human-computer interaction machine, an operating interface of which is provided with a waveform display area for selecting a target band containing a target waveform in the waveform diagram of the electroencephalogram signal; and a display for displaying the target waveform and its waveform parameters.

[0014] Furthermore, the target waveform is configured as at least one of epileptic discharges, rhythmic waveforms, and abnormal fluctuations of brain electrical activity; and the waveform parameters of the target waveform are configured as frequency and time domain parameters.

[0015] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the detection method when executed by a processor.

[0016] In a fifth aspect, the present invention provides a computer program product, comprising a computer program, which implements the steps of the detection method when executed by a processor.

[0017] The beneficial effect of the present invention is that the detection method of the target waveform parameters of the present invention first obtains the trend wave based on the target band containing the target waveform, and then determines whether the first trend wave corresponding to the low-frequency component is a complete wave. For the complete wave, the frequency is calculated using the zero-crossing method; for an incomplete wave, the target band is subtracted from the first trend wave to obtain the second trend wave corresponding to the high-frequency component, and then the judgment is made again; the detection method realizes the frequency measurement of the target waveform in the physiological electrical signal based on the adaptive trend wave and the zero-crossing method, effectively solves the influence of the strong interference component in the physiological electrical signal on the measurement result, and improves the measurement accuracy.

[0018] Other features and advantages of the present invention will be described in the following description and, in part, will become apparent from the description or be learned through practice of the present invention. To make the above-mentioned objects, features, and advantages of the present invention more readily apparent, preferred embodiments are described below in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0020] Figure 1 It is an operational flow chart of the detection method.

[0021] Figure 2 This is a schematic diagram of the operation interface for selecting a single-channel target band in the waveform graph.

[0022] Figure 3 It is a schematic diagram of the operation interface for selecting a multi-channel target band in a waveform graph.

[0023] Figure 4 It is a waveform chart that obtains trend waves based on the target band.

[0024] Figure 5 It is a waveform diagram that obtains waveform parameters based on raw data.

[0025] Figure 6 This is a waveform diagram of the main frequency of the target waveform obtained under test condition 1.

[0026] Figure 7 This is a waveform diagram of the main frequency of the target waveform obtained under test condition 2.

[0027] Figure 8 This is a waveform diagram of the main frequency of the target waveform obtained under test condition 3.

[0028] Figure 9 This is a waveform diagram of the main frequency of the target waveform obtained under test condition 4.

[0029] Figure 10 This is a waveform diagram of the main frequency of the target waveform obtained under test condition 5.

[0030] In the figure: rectangular boxes 1, 2, 3, 4, 5; target bands 11, 21, 41, 42, 43, 51. DETAILED DESCRIPTION

[0031] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0032] Compared with digital or analog electrical signals in electronic components or integrated circuits, physiological electrical signals are weak signals with randomness, whose amplitude is at the microvolt level, signal-to-noise ratio is low and nonlinear, which can easily cause strong interference with each other during the acquisition process. Figure 1-Figure 5 This embodiment provides a method for detecting target waveform parameters in physiological electrical signals. Taking EEG signals as an example, the specific operation steps are as follows.

[0033] Step S1: selecting a target band containing a target waveform in a waveform diagram of a physiological electrical signal.

[0034] A waveform display area is provided on the operation interface of the human-computer interaction machine, and any selected area is set in the waveform display area; it is judged whether the waveform in the selected area is greater than the channel baseline; if so, the target band of the channel is set to the waveform in the selected area; if not, the selected area is reset.

[0035] Specifically, such as Figure 2 As shown in the figure, use tools (such as a mouse, etc.) to manually draw a rectangular box or a regular quadrilateral box in the waveform diagram on the computer screen as the selected area, and detect whether there is a target waveform object in the rectangular box. If so, define the target band as , The left border of the rectangle corresponds to the time. The right side of the rectangle corresponds to the time. It is the sampling interval of the waveform displayed on the interface. Specifically, first determine whether the starting position of the mouse selection is within the waveform display area; if not, directly return to the undefined waveform prompt or redraw the rectangular frame; if it is, continue to determine whether the rectangular frame crosses the baseline of a channel; if it crosses, it is determined to be the target waveform and continue to identify the channel parameters, such as Figure 2 As shown in , in the waveform of channel F3, you can select target band 11 and target band 21 through rectangle 1 and rectangle 2 respectively, and calculate the band duration through the time axis of the horizontal axis (in seconds); otherwise, it will return an undefined waveform prompt, such as Figure 2 In particular, if the rectangular box spans the baselines of multiple channels, they are used as identification channels in turn, such as Figure 3As shown, the target bands of channel F3, channel FZ, and channel F4, namely target band 41, target band 42, and target band 43, are selected in the fourth rectangular frame 4, and the band duration is calculated through the time axis of the horizontal axis (in seconds).

[0036] Step S2, determine whether the trend wave of the target band is a complete wave.

[0037] Only when a waveform is complete can its period be calculated based on its start and end times. Otherwise, without knowing the start or end times, the signal length can be arbitrary, making it impossible to accurately calculate the period. Therefore, before calculating the frequency, it is necessary to obtain the trend wave of the target band. For physiological electrical signals, low-frequency components often reflect the overall characteristics of the signal, while high-frequency components reflect local characteristics. Furthermore, the amplitude of low-frequency components is higher than that of high-frequency components, and low-frequency components dominate. Therefore, in practical applications, when determining the trend wave of the target band and using it to obtain waveform parameters, it is generally believed to first estimate the waveform parameter characteristics of the dominant component. When the dominant component is absent, the high-frequency component is considered the dominant component for waveform parameter estimation. If the task objective is to measure the waveform parameters of the high-frequency component, simply select the portion of the waveform that does not contain a complete low-frequency trend wave when selecting the frame. Therefore, determining whether the target band's trend wave is a complete wave requires at most two determinations. The first determination involves calculating the frequency of the low-frequency component. If no low-frequency component signal is present, the target band is subtracted from the trend wave to obtain a second trend wave corresponding to the high-frequency component, and a second determination is made to determine if the wave is complete. If neither the low-frequency nor the high-frequency signal contains a complete wave, no further determination is required. Specifically, the first trend wave corresponding to the low-frequency component and the second trend wave corresponding to the high-frequency component are used. A determination is then made to determine if the first trend wave is a complete wave. If so, the frequency is calculated using the zero-crossing method. If not, a second determination is made to determine if the second trend wave is a complete wave. If so, the frequency is calculated using the zero-crossing method. If not, the target band is reselected. Of course, if the target band's trend wave is not a complete wave after both determinations, an undefined prompt is returned, or the target band is reselected. Details are as follows.

[0038] Step S21, obtaining the first trend wave of the target band.

[0039] Generally, the first trend wave corresponding to the low-frequency component is obtained by, for example but not limited to, low-pass filtering, sliding average window, or downsampling.

[0040] Low-pass filtering method: can be used to filter the target waveform Perform low-pass filtering and use the filtering result as the trend waveform , Indicates low-pass filtering of the signal, Fc is the cutoff frequency of the low-pass filter. Generally, the cutoff frequency is 5HZ, and signals below 5Hz are considered low-frequency signals. Figure 4 , which shows the trend wave corresponding to the low-frequency component of the EEG signal in the original data.

[0041] Sliding average window method: the data With M point mean filter Perform convolution operation, expressed as , where the trend wave .

[0042] Downsampling method: Extract a point every M points , and then The smooth trend is obtained by cubic spline interpolation, and the trend wave is obtained. .

[0043] Step S22, following step S21, determines whether the first trend wave is a complete wave. If so, proceed to step 23; if not, proceed to step 25.

[0044] Step 23, using a zero crossing method to calculate the number of frequency zero crossing points.

[0045] 1) Setup , , ;

[0046] 2) Yes Perform symbol judgment, , the result is recorded as ;

[0047] 3) Yes Do first-order differences ;

[0048] 4) Find The positions of all points in the , is the time index of the nth zero crossing point.

[0049] Step S24, following step S23, calculates the frequency using the zero crossing method.

[0050] Time index of all zero crossing points corresponding to the trend wave Take the first difference , get a set of time differences; using box curve Figure 4 Elimination by quantile range method or 3σ principle outliers in , and then calculate the remaining time difference The N-fold value of the average value is taken as the average period of the target waveform, and the frequency of the target waveform is the inverse of the average period.

[0051] Box Line Figure 4 Quantile range method: first calculate The quarter point Q1 and the quarter point Q3, the interquartile range IQR = Q3-Q1, the upper boundary of the box plot is the quarter point plus 1.5 times the interquartile range, that is, , the lower boundary is the quarter quantile minus 1.5 times the interquartile range, that is, , the data beyond the boundary can be regarded as outliers, and the time difference after removing the outliers is recorded as Averaging period , then the frequency of the target waveform N is configured as the number of zero crossings in one signal cycle minus 1.

[0052] The 3σ principle: According to the normal distribution probability, approximately 68.27% of the data values ​​are within one standard deviation of the mean, 95.45% are within two standard deviations, and 99.73% are within three standard deviations. Based on this, data outside the range of three standard deviations can be considered outliers and deleted.

[0053] Step S25, following step S22, obtains the second trend wave of the target band.

[0054] Generally, the second trend wave corresponding to the high-frequency component is obtained by, for example but not limited to, subtracting the first trend wave from the target band.

[0055] Step S26, following step S25, determines whether the second trend wave is a complete wave. If so, proceed to steps S23 and S24 in sequence. If not, proceed to step 27.

[0056] Step 27, returns to the undefined prompt, or returns to step S1 to reselect the target band.

[0057] Step S3, after step S24, calculate the time domain parameters of the target waveform.

[0058] The time domain parameters include: positive wave peak value, i.e. the maximum value of the target waveform, ; Negative wave peak value, that is, the minimum value of the target waveform, ; Peak-to-peak value, that is, the difference between the positive wave peak value and the negative wave peak value, ; Waveform time length, that is, the length of the selected area, .like Figure 5As shown, the original data within the time range of 1.5s-1.7s is selected in the rectangular frame 5 as the target band 51, and the measurement results are: negative wave peak value NPeak = -13.54uV, positive wave peak value PPeak = 13.24uV, peak-to-peak value PNPeak = 26.78uV, frequency Freq = 9.25Hz, and length Time = 200ms.

[0059] In some embodiments, a target waveform parameter detection system is also provided, including: a processor for running the detection method; a human-computer interaction machine, whose operating interface is provided with a waveform display area for selecting a target band in the waveform diagram of the physiological electrical signal; and a display for displaying the target waveform and its waveform parameters.

[0060] In some embodiments, an electroencephalogram (EEG) machine is provided, comprising: a processor for running the detection method; a human-computer interaction machine, an operating interface of which is provided with a waveform display area for selecting a target band in the waveform diagram of the EEG signal; and a display for displaying the target waveform and its waveform parameters.

[0061] Furthermore, the target waveform is configured as at least one of epileptic discharges, rhythmic waveforms, and abnormal fluctuations of brain electrical activity; and the waveform parameters of the target waveform are configured as frequency and time domain parameters.

[0062] The electroencephalograph is also provided with a memory, a computer program stored in the memory, and the processor executes the computer program to implement the steps of the detection method.

[0063] The processor may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention. In a specific implementation, if the memory and processor are implemented independently, the memory and processor may be connected to each other via a bus and communicate with each other. The bus may be an Industrial Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industrial Standard Architecture (EISA) bus. The bus may be categorized as an address bus, a data bus, a control bus, etc. If the memory and processor are integrated on a single chip, the memory and processor may communicate with each other via an internal interface.

[0064] In some embodiments, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the detection method are implemented.

[0065] The storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0066] In some embodiments, a computer program product is also provided, including a computer program, which implements the steps of the detection method when executed by a processor. The computer program may include program code, and the program code includes computer operation instructions, which can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or partly contributed to the prior art or part of the technical solution can be implemented in the form of a software product or sold or used as an independent product. The computer software product is stored in a storage medium and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to perform all or part of the steps of the method described in each embodiment of the present invention.

[0067] Test example 1.

[0068] Test object: Based on the sine signal with an amplitude of 10uV and a frequency of 2Hz, the mixed signal of the sine signal with an amplitude of 5uV and a frequency of 10Hz and the Gaussian signal as the test signal, , It is Gaussian noise with a mean of zero and a standard deviation of 1. A mixed signal with a length of 8 seconds is selected as the test object. The main frequency of this signal is a sine wave of 2 Hz. The frequency is detected.

[0069] Test condition 1: Use the detection method of this case to detect the test object, obtain the trend wave based on the target band, obtain the zero crossing point position based on the trend wave, and finally calculate the main frequency of the target waveform to be 2Hz. The results are as follows Figure 6 shown.

[0070] Test condition 2: Calculate the main frequency of the signal based on FFT (Fast Fourier Transform), that is, calculate the frequency of the maximum peak of the spectrum after FFT transformation of the signal, which is 2Hz. The result is as follows: Figure 7 shown.

[0071] Test results: Obviously, when detecting the frequency of the target signal over a longer period of time, the detection method in this case can achieve the same target waveform frequency recognition effect as FFT.

[0072] Test example 2.

[0073] Test object: Based on the sine signal with an amplitude of 10uV and a frequency of 2Hz, the mixed signal of the sine signal with an amplitude of 5uV and a frequency of 10Hz and the Gaussian signal as the test signal, , It is a Gaussian noise with a mean of zero and a standard deviation of 1. A mixed signal with a length of 200 milliseconds is selected as the test object. The main frequency of this signal is a sine wave of 10 Hz. The frequency is detected.

[0074] Test condition 3: Get the trend wave based on the target band, get the zero crossing point position based on the trend wave, and finally calculate the main frequency of the target waveform to be 9.52Hz. The results are as follows: Figure 8 shown.

[0075] Test condition 4: Calculate the main frequency of the signal based on FFT, that is, calculate the frequency of the maximum peak of the spectrum after the signal FFT transformation. The result is as follows Figure 9 The main frequency is the second peak in the FFT spectrum, which is the highest peak and corresponds to 12 Hz.

[0076] Test condition 5: Based on the original data of the target band, the zero crossing point position is obtained, and the main frequency of the target waveform is finally calculated to be 11.72Hz. The results are as follows Figure 10 shown.

[0077] Experimental results: Because the frequency calculated using the Fourier transform is affected by the number of sampling points and the sampling rate, the calculated frequency accuracy decreases when the number of sampling points is less than the sampling rate of a 1s time period. The fewer the points, the lower the accuracy. Therefore, when detecting the frequency of a target signal over a shorter time period, this detection method can obtain the true frequency of the target waveform that is closer to the FFT identification result. Furthermore, for weak signals with randomness, the waveform frequency obtained based on the trend wave is more accurate than the raw data.

[0078] With the above-mentioned ideal embodiment of the present invention as inspiration, through the above description, relevant personnel can make various changes and modifications without departing from the scope of the technical idea of ​​the present invention. That is, the technical scope of the present invention is not limited to the contents of the specification.

Claims

1. A method for detecting target waveform parameters, characterized in that: include: Selecting a target band containing a target waveform in a waveform diagram of a physiological electrical signal; Obtain the trend wave of the target band, including the first trend wave corresponding to the low-frequency component and the second trend wave corresponding to the high-frequency component; Determining whether a trend wave is a complete wave includes: Make a judgment, that is, determine whether the first trend wave is a complete wave; If so, the frequency is calculated using the zero-crossing method; if not, a secondary judgment is made, that is, whether the second trend wave is a complete wave; if so, the frequency is calculated using the zero-crossing method; if not, the target band is reselected.

2. The detection method according to claim 1, wherein The first trend wave is obtained by any one of low-pass filtering, sliding average window, and point-to-point downsampling; Determining whether the first trend wave is a complete wave includes: Remove the baseline from the first trend wave to obtain the residual component; Calculate the number of zero crossing points of the remaining components; If the number of zero crossing points is greater than or equal to two, the first trend wave is considered to be a complete wave; otherwise, the first trend wave is considered to be incomplete.

3. The detection method according to claim 1, wherein The second trend wave is obtained by subtracting the first trend wave from the target wave band; Determining whether the second trend wave is a complete wave includes: Count the number of zero crossings of the second trend wave; If the number of zero crossing points is greater than or equal to two, the second trend wave is considered to be a complete wave; otherwise, the second trend wave is considered to be not a complete wave.

4. The detection method according to claim 1, wherein Calculating frequency using the zero crossing method includes: Take the first-order difference of the time indexes of all zero-crossing points corresponding to the trend wave to obtain a set of time differences; Eliminate outliers from a set of time differences; The integer multiples of the average value of the remaining time difference are taken as the average period of the target waveform; Set the frequency of the target waveform to the inverse of the averaging period.

5. The detection method according to claim 1, wherein The target bands containing the target waveforms selected in the waveform diagram of the physiological electrical signal include: A waveform display area is provided on the operation interface of the human-computer interaction machine, and any selected area is provided in the waveform display area; Determine whether the boundary of the selected area crosses the baseline of the channel; if so, the target band of the channel is configured to be the waveform within the selected area; if not, reset the selected area; and Set the target band to , 、 are the times corresponding to the two boundaries of the selected area, is the sampling interval of the waveform time.

6. The detection method according to claim 5, characterized in that The target band containing the target waveform selected in the waveform diagram of the physiological electrical signal also includes: When the boundary of the selected area crosses the baselines of multiple channels, the target band of each channel is configured as a waveform in which the selected area is located within each channel.

7. The detection method according to claim 5, characterized in that Also includes: Calculate the time domain parameters of the target waveform; The time domain parameters include: Positive wave peak, i.e. the maximum value of the target waveform; Negative wave peak, i.e. the minimum value of the target waveform; Peak-to-peak value, which is the difference between the peak value of the positive wave and the peak value of the negative wave; The time length of the waveform, that is, the length of the selected area.

8. A target waveform parameter detection system, characterized in that: include: A processor, configured to execute the detection method according to any one of claims 1 to 7; A human-computer interaction machine, wherein a waveform display area is provided on its operation interface for selecting a target band in the waveform diagram of the physiological electrical signal; The display is used to display the target waveform and its waveform parameters.

9. An electroencephalograph, characterized in that: include: A processor, configured to execute the detection method according to any one of claims 1 to 7; A human-computer interaction machine, wherein a waveform display area is provided on the operation interface thereof for selecting a target band in the waveform diagram of the EEG signal; The display is used to display the target waveform and its waveform parameters.

10. The electroencephalograph according to claim 9, characterized in that The target waveform is configured as at least one of an epileptic discharge, a rhythmic waveform, and an abnormal fluctuation of brain electrical activity; The waveform parameters of the target waveform are configured as frequency and time domain parameters.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the detection method according to any one of claims 1 to 7 are implemented.

12. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the detection method according to any one of claims 1 to 7 are implemented.

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