A method for real-time determination of signal quality in brain impedance measurement

Abstract

The invention belongs to the field of signal quality analysis, and particularly relates to quality analysis of human non-invasive brain impedance measurement signals. Specifically, a method for real-time signal quality determination in brain impedance measurement is disclosed. The method comprises: performing signal stability, signal strength and noise interference analysis on brain wave signals formed by head excitation, and judging the signal quality based on the analysis results, thereby realizing multi-dimensional judgment of the quality of brain wave signals. Then, in the process of brain wave signal acquisition, the contact impedance and contact movement information between the current electrode and the scalp are obtained by impedance monitoring equipment and displacement sensors while recording the brain wave signals by voltage electrodes, thereby expanding the measurement parameter range of the contact impedance. On this basis, by analyzing the collected contact impedance and contact movement data, more comprehensive data support can be provided for the identification of abnormal brain wave signal quality, thereby improving the accuracy and reliability of abnormal classification.

Description

Technical Field

[0001] The invention belongs to the field of signal quality analysis, and in particular relates to quality analysis of non-invasive brain impedance measurement signals of human body, and specifically discloses a method for real-time determination of signal quality in brain impedance measurement. Background Art

[0002] EEG measurement is a technology that records the brain's electrical signals. It has a wide range of application needs in many fields, from medical diagnosis to scientific research, to human-computer interaction and education. EEG measurement provides a valuable source of information to help us better understand the function of the brain and its manifestations in disease states.

[0003] In the process of EEG acquisition, current electrodes are usually used to contact the scalp and apply high-frequency AC excitation signals, while voltage electrodes are responsible for detecting and recording the resulting output signals to form brain waves. However, due to non-ideal factors such as electromagnetic interference in the environment and skin relaxation, the quality of brain wave signals may be affected, especially the signal quality problems caused by skin relaxation. Therefore, it is very necessary to conduct a detailed evaluation of the quality of brain wave signals, and this evaluation should be based on the measurement of the contact impedance formed by the electrode and the skin to ensure the reliability and accuracy of the signal.

[0004] The existing Chinese invention patent with publication number CN109730675A provides a system and method for detecting the quality of EEG signals. The system uses a signal processing module to access EEG signals and outputs EEG signal quality parameters, wherein the EEG signal quality parameters include signal quality ratio and lead wire impedance. Thus, the signal quality ratio and lead wire impedance can be used to detect in real time whether there is a problem of collection environment influence in the process of EEG signals being accessed to the signal processing module. However, the signal quality ratio defined in the scheme is mainly used to evaluate the stability of EEG signals during transmission to a low-noise amplifier through lead wires. However, judging signal quality based solely on the signal quality ratio has limitations because it cannot fully reflect the overall quality of EEG signals.

[0005] In addition, the lead wire impedance defined in this scheme only reflects the impedance change caused by the contact pressure between the electrode and the scalp. However, in the actual measurement process, the relative movement between the electrode and the scalp will also have a significant impact on the signal quality. Therefore, the measurement of contact impedance in this scheme appears to be too fixed, resulting in a lack of comprehensive data support for the evaluation of the quality of EEG signals based on contact impedance measurement. When abnormal EEG signals are found but the lead wire impedance is normal, the signal abnormality is easily mistakenly attributed to other factors, resulting in misjudgment of signal abnormality. Summary of the invention

[0006] In order to overcome the above-mentioned shortcomings, the present invention has established a method for real-time determination of signal quality in brain impedance measurement, which can effectively solve the corresponding technical problems.

[0007] The purpose of the present invention can be achieved through the following technical solutions: A method for real-time determination of signal quality in brain impedance measurement, comprising the following steps: placing current electrodes and voltage electrodes at designated positions on the head, and setting impedance monitoring equipment and displacement sensors at corresponding designated positions.

[0008] A high-frequency AC signal is used to apply an excitation signal to the head through the current electrode, and the voltage electrode collects the voltage signal to form an EEG signal. The impedance monitoring device and the displacement sensor collect the contact impedance and contact movement between the current electrode and the head to form a contact impedance timing set and a contact movement timing set.

[0009] The formed brain wave signal is filtered and then transformed into a spectrum, thereby identifying the periodic component and period length by analyzing the peak value in the spectrum diagram, and then selecting the collection time period according to the period length.

[0010] The measurement signal within the acquisition period is intercepted from the continuous brain wave signal and divided into several cycle windows.

[0011] The signal stability is analyzed by calculating the standard deviation of the signal in each cycle window and comparing the standard deviation of the signals in adjacent cycle windows.

[0012] The signal amplitude range of each period window is extracted to analyze the signal strength.

[0013] The signal in each period window is subjected to noise detection to analyze the noise interference degree.

[0014] The signal stability, signal strength and noise interference during the acquisition period are imported into the signal quality evaluation model to obtain the signal quality score of the acquisition period, and compared with the preset threshold. When the signal quality score is lower than the preset threshold, an abnormal prompt is given.

[0015] When an abnormal prompt appears, the contact impedance time series set and the contact movement time series set within the acquisition period of the abnormal prompt time are intercepted to identify the abnormal direction.

[0016] In combination with all the above-mentioned technical solutions, the positive effects of the present invention are as follows: 1. The present invention analyzes the signal stability, signal strength and noise interference of the brain wave signal formed by head excitation, and judges the signal quality according to the analysis results, thereby realizing multi-dimensional evaluation of the quality of brain wave signals, which can comprehensively cover various key attributes of signal quality, and ensure the comprehensiveness and comprehensiveness of signal quality evaluation by providing different perspectives in each dimension.

[0017] 2. In the process of EEG signal acquisition, the present invention uses voltage electrodes to record EEG signals, and obtains contact impedance and contact movement information between the current electrode and the scalp through impedance monitoring equipment and displacement sensors, thereby expanding the measurement parameter range of contact impedance. On this basis, by analyzing the collected contact impedance and contact movement data, more comprehensive data support can be provided for the identification of abnormal EEG signal quality, thereby improving the accuracy and reliability of abnormal classification. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0019] Figure 1 It is a diagram of the steps for implementing the method of the present invention.

[0020] Figure 2 It is a schematic diagram of the whole process of collecting and using brain wave signals, contact impedance and contact movement displacement in the present invention.

[0021] Figure 3 This is a flowchart for implementing abnormal pointing identification in the present invention. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] See also Figure 1 As shown, the present invention proposes a method for real-time determination of signal quality in brain impedance measurement, comprising the following steps: placing current electrodes and voltage electrodes at designated positions on the head, and setting impedance monitoring equipment and displacement sensors at corresponding designated positions.

[0024] In the operation of the above scheme, the current electrode is placed at a designated position on the head for applying a high-frequency AC signal. The voltage electrode is placed adjacent to or symmetrical to the current electrode for collecting the voltage signal. The electrode layout is usually selected to obtain the maximum signal-to-noise ratio.

[0025] The impedance monitoring device and the displacement sensor are placed under or close to the current electrode to monitor the contact impedance and contact movement displacement between the electrode and the scalp in real time.

[0026] A high-frequency AC signal is used to apply an excitation signal to the head through the current electrode, and the voltage electrode collects the voltage signal to form an EEG signal. The impedance monitoring device and the displacement sensor collect the contact impedance and contact movement displacement between the current electrode and the head to form a contact impedance timing set and a contact movement timing set.

[0027] It is important to know that contact impedance refers to the resistance encountered when current flows between the electrode and the skin surface. Generally speaking, contact impedance is related to the contact state between the electrode and the skin, such as contact pressure. Appropriate contact pressure can improve the contact between the electrode and the skin and reduce contact impedance. Excessive pressure may cause skin damage, while too little pressure may cause poor contact. Contact impedance is also related to skin conditions, such as changes in skin tension. Changes in skin tension (such as muscle contraction, sweating, etc.) will affect contact impedance. High contact impedance will cause more noise to be mixed into the signal. This is because high impedance increases the resistance in the signal transmission path, causing more random noise to be introduced into the signal, which may cause signal quality to deteriorate and affect signal reliability.

[0028] Once again, it is important to know that contact displacement refers to the displacement between the electrode and the designated position on the head, which is usually directly related to the relative movement between the electrode and the skin. Specifically, this movement can be due to the movement of the head, the movement of the electrode itself, or a combination of both. When the contact position between the electrode and the skin changes, it may cause poor contact, thereby increasing the contact impedance and affecting the signal quality.

[0029] The formed brain wave signal is filtered and then transformed into a spectrum, thereby identifying the periodic component and period length by analyzing the peak value in the spectrum diagram, and then selecting the collection time period according to the period length.

[0030] It should be understood that after the brain wave signal is formed, it needs to be filtered to remove unnecessary frequency components and retain useful signals. The filtered signal is clearer and easier to visualize and analyze, providing clear data support for signal quality judgment.

[0031] It is necessary to further understand that the spectrum transformation of the filtered EEG signal is to use Fourier transform to convert the time domain signal into a frequency domain spectrum, so that the energy distribution of the signal at different frequencies can be obtained.

[0032] In the operations that can be implemented by the above scheme, the process of identifying the periodic component and the periodic length is as follows: a peak detection algorithm is used to identify the peak in the spectrum graph, where the maximum peak corresponds to the periodic component of the signal. This is because the periodic component will produce a significant energy concentration in the spectrum, that is, the peak in the spectrum. Therefore, by analyzing the peak position in the spectrum graph, the periodic component present in the signal can be determined, and the frequency corresponding to the peak is extracted from the spectrum graph as the periodic frequency, and then the inverse of the periodic frequency is taken to obtain the periodic length.

[0033] It should be pointed out that since the neuronal activity in the brain presents a regular synchronous discharge pattern, the brain wave signals formed are periodic.

[0034] In the operations that can be further implemented by the above scheme, the collection time period can be selected according to the cycle length. The collection time period can be selected for a period of several cycle lengths, for example, 5 cycle lengths. Selecting a certain number of cycle lengths can ensure that the collected data is representative, that is, it can cover a complete periodic pattern. At the same time, by collecting data from multiple cycles, the bias caused by anomalies in a single cycle can be reduced, making the data more comprehensive and stable, and reducing the impact of accidental factors.

[0035] The brain wave signal within the acquisition period is intercepted from the continuous brain wave signal and divided into several cycle windows, wherein each cycle window corresponds to a signal of one cycle length. In the example where the acquisition period is 5 cycles in length, there are 5 cycle windows obtained by division.

[0036] The present invention identifies the periodic components of the formed brain wave signal and divides the signal into periodic windows based on the period length. The signal in each periodic window has a high internal standardization, which makes the data more comparable between different periods and facilitates the subsequent signal quality evaluation. At the same time, the periodic window division makes it easier to identify abnormal signals because abnormal signals may break the periodic pattern. In addition, dividing the signal into multiple periodic windows for processing can reduce the computational burden of processing a large amount of data at one time and improve processing efficiency.

[0037] The signal stability is analyzed by calculating the standard deviation of the signal in each cycle window and comparing the standard deviation of the signal in the adjacent cycle window. The specific analysis is as follows: the standard deviation of the signal in the subsequent cycle window in the adjacent cycle window is taken as the absolute value of the difference between the standard deviation of the signal in the previous cycle window and the standard deviation of the signal in the previous cycle window, and then divided by the cycle length to obtain the rate of change of the signal standard deviation in the adjacent cycle window.

[0038] It should be noted that the standard deviation of the signal reflects the fluctuation of the signal in each window.

[0039] The number of adjacent cycle windows that can be divided in the statistical collection period is 4, which are 1 and 2, 2 and 3, 3 and 4, and 4 and 5, respectively. Then, the signal standard deviation change rate of each adjacent cycle window is taken as the maximum value and the minimum value, and the difference between the maximum value and the maximum signal standard deviation change rate is taken as the subtrahend, and the value 1 is taken as the minuend, thereby constructing a subtraction operation to obtain the signal stability.

[0040] The present invention realizes the signal fluctuation analysis of a single period window by calculating the standard deviation of the signal in each period window within the acquisition period, and analyzes the signal standard deviation change rate with the help of the signal standard deviation of adjacent period windows. The signal stability thus obtained ensures comprehensive coverage of the signal changes in the entire acquisition period, and can capture the subtle fluctuations of the signal in each period window, making the signal stability analysis more scientific and reliable.

[0041] The signal amplitude range of each period window is extracted to analyze the signal strength. The specific analysis is as follows: the signal amplitude range of each period window within the acquisition period is formed into a signal amplitude change curve in a two-dimensional coordinate system with the period window as the horizontal axis and the signal amplitude range as the vertical axis.

[0042] It should be added that in order to ensure that the signal amplitude change curve is drawn reliably and truly reflects the actual situation, it is recommended to select an acquisition period with a larger cycle length to provide sufficient sample data for the signal amplitude range in each cycle window, thereby enhancing the representativeness and credibility of the curve.

[0043] It should be pointed out that the signal amplitude range refers to the difference between the maximum and minimum values ​​in each period window signal. Generally speaking, the larger the signal amplitude range, the greater the amplitude fluctuation of the signal. Therefore, within the same duration, the signal energy will be greater and the signal strength will be greater.

[0044] The inflection point is marked in the signal amplitude variation curve, and the horizontal segment is extracted from the signal amplitude variation curve based on the marked inflection point, and then the average signal amplitude range of the horizontal segment is calculated as the main signal amplitude range.

[0045] It should be noted that although the formed brain wave signal has a certain periodicity, there are certain differences in the signal amplitude range of adjacent periods. This is due to the inherent randomness and noise of the signal itself. This causes the signal amplitude change curve to be not a completely horizontal straight line, but a fluctuating state. By identifying the inflection point (the inflection point is usually the location of the local maximum or minimum in the data curve), the signal amplitude change curve can be divided into different sections according to the location of the inflection point. The horizontal section refers to the part where the slope of the curve is close to zero. The horizontal section refers to the state in which the signal amplitude range remains relatively stable for a long time. This stability usually means that the signal is in a relatively ideal working state, that is, the signal is less disturbed. The horizontal section represents the typical performance of the signal under normal working conditions and can be used as a basis for evaluating signal strength.

[0046] The signal strength is calculated by comparing the subject signal amplitude range with the reference signal amplitude range.

[0047] It should be understood that since the signal amplitude range is dimensional, the main signal amplitude range needs to be compared with the reference signal amplitude range when performing signal strength analysis to eliminate the dimension. For example, the main signal amplitude range can be divided by the reference signal amplitude range to obtain the signal strength.

[0048] In particular, the reference signal amplitude range can be selected based on historical brain wave measurement signals. Specifically, the historical brain wave measurement signals formed under the same high-frequency AC signal excitation can be used, and then the signal amplitude range average of the historical brain wave measurement signals is taken as the reference signal amplitude range.

[0049] The signal in each period window is subjected to noise detection to analyze the noise interference degree.

[0050] As an improved operation of the above scheme, noise detection is implemented as follows: extract the signal envelope of each period window, and then compare the signal envelopes of adjacent period windows for overlap, mark the non-overlapping areas, and obtain the envelope length and time interval corresponding to the non-overlapping areas.

[0051] It should be added that the envelope of a signal is the trend line of the signal amplitude changing over time, specifically the upper and lower limit curves of the signal, which are usually used to represent the maximum and minimum value trajectories of the signal.

[0052] It is further necessary to add that by comparing the envelopes of adjacent period windows, we can find areas that should be consistent under normal circumstances but appear significantly different in certain time periods. These non-overlapping areas are often caused by noise, because noise can cause abnormal changes in signal amplitude. Obtaining the envelope length and time interval corresponding to the non-overlapping area can further quantify the scope of noise influence.

[0053] The envelope lengths and time intervals corresponding to the non-overlapping areas formed by all adjacent periodic windows are calculated for difference, respectively, to obtain the non-overlapping envelope length difference and the non-overlapping time interval difference, and then the two differences are added together to obtain the envelope overlap difference of noise detection.

[0054] In the optional operation of the above scheme, the difference of the length of the non-overlapping envelope line is calculated as follows: Substitute the envelope line length corresponding to each non-overlapping area into the calculation formula Calculate the difference in length DD of the non-coinciding envelope lines long , where l i represents the envelope length corresponding to the i-th non-overlapping area, i represents the non-overlapping area number, i=1,2,......,n, and n represents the number of non-overlapping areas. It can be seen from this formula that the greater the difference in the envelope lengths corresponding to the non-overlapping areas, the greater the difference in the non-overlapping envelope lengths.

[0055] In another optional operation of the above scheme, the difference degree of non-overlapping time intervals is calculated as follows: the start time and the end time are extracted from the time intervals corresponding to each non-overlapping area, and the duration is obtained.

[0056] The start time corresponding to each non-overlapping area is compared to obtain the start interval duration.

[0057] Compare the end times corresponding to the non-overlapping areas to obtain the end interval duration.

[0058] Substitute the duration of each non-overlapping area into the calculation formula, combined with the start interval duration and the end interval duration. Get the non-overlapping time interval difference DD time , where Δt start , Δt end Respectively represent the start interval length, end interval length, DN i represents the duration of the i-th non-overlapping area, and T represents the cycle length. From this formula, it can be seen that the longer the start interval, the end interval, and the greater the difference between the durations of each non-overlapping area, the greater the difference in the non-overlapping time interval.

[0059] It needs to be explained that the non-overlapping area formed by the envelope lines of adjacent period windows is caused by noise, wherein the defined difference in the length of the non-overlapping envelope lines indicates the difference in the range of signal amplitude changes caused by noise, and the difference in the non-overlapping time intervals indicates the difference in the time span of signal amplitude changes caused by noise. When these two differences are large, it indicates that the influence range and time span of the noise are unstable, and the degree of noise influence is large. Therefore, the degree of noise influence can be quantified with the help of the difference in the length of the non-overlapping envelope lines and the difference in the non-overlapping time intervals.

[0060] The signal in each period window is transformed into a spectrum, and the signal-to-noise ratio of each period window is calculated from the obtained spectrum diagram, and the minimum signal-to-noise ratio is taken as the signal-to-noise ratio of noise detection.

[0061] It is important to know that the signal-to-noise ratio is a measure of the ratio of signal energy to noise energy in the signal.

[0062] It is important to understand that the minimum signal-to-noise ratio usually represents the worst signal quality condition in all cycle windows. In signal processing, we are concerned with the performance of the signal under the most unfavorable conditions, because this is the limit of the signal processing system's performance.

[0063] The noise interference is further analyzed as follows: Substitute the envelope overlap difference and signal-to-noise ratio of noise detection into the analytical formula The noise interference degree NL is obtained, where ED and SR represent the envelope overlap difference and signal-to-noise ratio of noise detection, respectively, and a and b represent the weighting factors corresponding to the envelope overlap difference and signal-to-noise ratio, respectively. The greater the envelope overlap difference of noise detection and the smaller the signal-to-noise ratio, the greater the noise interference degree.

[0064] It should be noted that when performing noise detection, combining the signal's time domain angle (envelope overlap difference) and frequency domain angle (signal-to-noise ratio) can provide more comprehensive information and improve the accuracy, robustness and adaptability of noise detection. This method can better understand the characteristics of the signal and improve the effect of noise detection.

[0065] It should be further explained that, since the signal-to-noise ratio can directly reflect the ratio of useful information to noise in a signal, in many cases, the signal-to-noise ratio is an important indicator for evaluating the degree of noise interference. Therefore, in setting the weighting factor corresponding to the envelope overlap difference and the signal-to-noise ratio in the calculation of noise interference, the weighting factor corresponding to the signal-to-noise ratio can be set slightly larger. For example, the weighting factors corresponding to the envelope overlap difference and the signal-to-noise ratio are 0.4 and 0.6 respectively.

[0066] The signal stability, signal strength and noise interference during the acquisition period are imported into the signal quality evaluation model to obtain the signal quality score of the acquisition period, and compared with the preset threshold. When the signal quality score is lower than the preset threshold, an abnormal prompt is given.

[0067] In a specific embodiment of the above operation, the effective value of each parameter of signal stability, signal strength and noise interference can be set in the signal quality evaluation model according to the signal characteristics (such as frequency) of the excitation signal. This is because the frequencies of some excitation signals are more susceptible to noise. Then, the signal stability, signal strength and noise interference during the acquisition period are compared with the corresponding effective values ​​to obtain a signal quality score.

[0068] When an abnormal prompt appears, the contact impedance time series set and contact movement time series set within the acquisition period of the abnormal prompt time are intercepted to identify the abnormal direction. The specific identification process is as follows: the contact impedance time series set within the acquisition period of the abnormal prompt time is compared with the normal contact impedance, and the time proportion value that does not meet the normal contact impedance is counted.

[0069] In the above, the normal contact impedance value depends on the specific application scenario. For brain wave signals, it is usually desired that the contact impedance is lower than 5 kiloohms (kΩ).

[0070] The contact impedance time series set and the contact movement time series set within the acquisition period of the abnormal prompt time are subjected to variation analysis to obtain the variation index corresponding to the contact impedance and contact movement. Specifically, the variation analysis is as follows: the mean value and standard deviation of the contact impedance time series set and the contact movement time series set are calculated respectively, and the variation index corresponding to the contact impedance and contact movement is calculated by dividing the standard deviation by the mean value and taking the percentage. The higher the variation index, the greater the fluctuation of the data.

[0071] The variation indexes corresponding to the contact impedance and contact movement are compared with the configured safety variation indexes respectively. At the same time, the time proportion value that does not meet the normal contact impedance is compared with the configured safety proportion value. The parameters higher than the safety variation index or higher than the safety proportion value are output as abnormal indications.

[0072] The safety variation index and safety ratio values ​​mentioned above are initially set, the purpose of which is to assist in the identification of abnormal directions.

[0073] For identification of specific abnormal directions mentioned above, see Figure 3 shown.

[0074] Furthermore, anomaly direction identification also includes anomaly time positioning, and the specific operation is as follows: within the collection period of the abnormal prompt time, the abnormal direction time series set is marked with several points in a two-dimensional coordinate system with time as the horizontal axis and the abnormal direction as the vertical axis to draw an abnormal direction change curve.

[0075] In the above operation example, when the abnormal direction is contact impedance fluctuation, the abnormal direction timing set is a contact impedance timing set, and when the abnormal direction is contact movement fluctuation, the abnormal direction timing set is a contact movement timing set.

[0076] For the whole process of collecting and using the above-mentioned EEG signals, contact impedance, and contact movement displacement, please refer to Figure 2 shown.

[0077] The slope of each point on the abnormal pointing change curve is obtained respectively, and the slopes of adjacent points are subjected to mutation identification. The mutation identification can be achieved by comparing the absolute value difference of the slopes of adjacent points with the critical absolute value difference of the slopes. When the absolute value difference of the slopes of an adjacent point is higher than the critical absolute value difference of the slope, it is identified that there is a mutation in the slope of the adjacent point, and the time period of the adjacent point on the horizontal axis is taken as the abnormal time.

[0078] The present invention can carry out targeted processing of the abnormal direction by locating the abnormal time.

[0079] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims

1. A method for real-time determination of signal quality in brain impedance measurement, characterized in that: The following steps are involved: placing current electrodes and voltage electrodes at designated locations on the head, and setting impedance monitoring equipment and displacement sensors at corresponding designated locations; A high-frequency alternating current signal is used to apply an excitation signal to the head through the current electrode, and the voltage electrode collects the voltage signal to form an electroencephalogram signal, and an impedance monitoring device and a displacement sensor collect the contact impedance and contact movement displacement between the current electrode and the head to form a contact impedance time series set and a contact movement time series set; The formed brain wave signal is filtered and then transformed into a spectrum, thereby identifying the periodic component and period length by analyzing the peak value in the spectrum diagram, and then selecting the acquisition time period according to the period length; The brain wave signals within the acquisition period are intercepted from the continuous brain wave signals and divided into a number of period windows; The signal stability is analyzed by calculating the standard deviation of the signal in each cycle window and comparing the standard deviation of the signals in adjacent cycle windows. Extract the signal amplitude range of each period window to analyze the signal strength; Perform noise detection on the signal of each period window to analyze the noise interference degree; The signal stability, signal strength and noise interference degree during the acquisition period are introduced into the signal quality evaluation model to obtain the signal quality score of the acquisition period, and compared with the predetermined threshold. When the signal quality score is lower than the predetermined threshold, an abnormal prompt is issued; When an abnormal prompt appears, the contact impedance time series set and the contact movement time series set within the acquisition period of the abnormal prompt time are intercepted to identify the abnormal direction; The abnormal pointing identification process is as follows: The contact impedance time series set and the contact movement time series set in the acquisition period of the abnormal prompt time are subjected to variation analysis to obtain the variation index corresponding to the contact impedance and the contact movement; The contact impedance time series set in the acquisition period of the abnormal prompt time is compared with the normal contact impedance, and the time proportion value that does not meet the normal contact impedance is counted; The variation indexes corresponding to the contact impedance and contact movement are compared with the configured safety variation indexes respectively. At the same time, the time proportion value that does not meet the normal contact impedance is compared with the configured safety proportion value. The parameters higher than the safety variation index or higher than the safety proportion value are output as abnormal indications.

2. A method for real-time determination of signal quality in brain impedance measurement according to claim 1, characterized in that: The analysis signal stability is as follows: The standard deviation of the signal in the next cycle window in the adjacent cycle window is obtained by taking the absolute value of the difference between the standard deviation of the signal in the previous cycle window and the period length to obtain the rate of change of the signal standard deviation in the adjacent cycle window; The number of adjacent period windows that can be divided into the statistical collection period is then calculated, and the maximum value of the signal standard deviation change rate of each adjacent period window is taken. The subtrahend is taken after dividing the maximum value by the maximum signal standard deviation change rate, and the value 1 is taken as the minuend, thereby constructing a subtraction operation to obtain the signal stability.

3. A method for real-time determination of signal quality in brain impedance measurement according to claim 1, characterized in that: The analysis of signal intensity is described in the following process: The signal amplitude range of each period window in the acquisition period is used to form a signal amplitude variation curve in a two-dimensional coordinate system with the period window as the horizontal axis and the signal amplitude range as the vertical axis; An inflection point is marked in the signal amplitude change curve, and a horizontal segment is extracted from the signal amplitude change curve based on the marked inflection point, and then an average signal amplitude range of the horizontal segment is calculated as the main signal amplitude range; The signal strength is calculated by comparing the subject signal amplitude range with the reference signal amplitude range.

4. A method for real-time determination of signal quality in brain impedance measurement according to claim 1, characterized in that: The noise detection is implemented as follows: Extract the signal envelope of each period window, and then compare the signal envelopes of adjacent period windows, mark the non-overlapping areas, and obtain the envelope length and time interval corresponding to the non-overlapping areas; The envelope lengths and time intervals corresponding to the non-overlapping areas formed by all adjacent periodic windows are respectively calculated to obtain the non-overlapping envelope length difference and the non-overlapping time interval difference, and then the two differences are added to obtain the envelope overlap difference of noise detection; The signal in each period window is transformed into a spectrum, and the signal-to-noise ratio of each period window is calculated from the obtained spectrum diagram, and the minimum signal-to-noise ratio is taken as the signal-to-noise ratio of noise detection.

5. A method for real-time determination of signal quality in brain impedance measurement according to claim 4, characterized in that: The difference in the length of the non-coinciding envelope lines is calculated as follows: Substitute the envelope lengths corresponding to each non-overlapping area into the calculation formula Calculate the difference in length DD of the non-coinciding envelope lines long , where l i It represents the envelope length corresponding to the i-th non-overlapping area, i represents the non-overlapping area number, i=1,2,......,n, n represents the number of non-overlapping areas.

6. A method for real-time determination of signal quality in brain impedance measurement according to claim 4, characterized in that: The non-overlapping time interval difference is calculated as follows: Extract the start time and end time from the time interval corresponding to each non-overlapping area, and obtain the duration; Compare the start times corresponding to the non-overlapping areas to obtain the start interval duration; Compare the end times corresponding to the non-overlapping areas to obtain the end interval duration; Substitute the duration of each non-overlapping area into the calculation formula, combined with the start interval duration and the end interval duration. Get the non-overlapping time interval difference DD time , where Δt start , Δt end Respectively represent the start interval length, end interval length, DN i represents the duration of the i-th non-overlapping region, and T represents the cycle length.

7. A method for real-time determination of signal quality in brain impedance measurement according to claim 4, characterized in that: The process of analyzing the noise interference is as follows: Substitute the envelope overlap difference and signal-to-noise ratio of noise detection into the analytical formula The noise interference degree NL is obtained, where ED and SR represent the envelope overlap difference and signal-to-noise ratio of noise detection respectively, and a and b represent the weighting factors corresponding to the envelope overlap difference and signal-to-noise ratio respectively.

8. A method for real-time determination of signal quality in brain impedance measurement according to claim 1, characterized in that: The variation analysis was performed as follows: The mean value and standard deviation of the contact impedance time series set and the contact movement time series set are calculated respectively, and the variation index corresponding to the contact impedance and contact movement is calculated using the mean value and standard deviation.

9. A method for real-time determination of signal quality in brain impedance measurement according to claim 1, characterized in that: The abnormal direction identification also includes abnormal time location, and the specific operations are as follows: During the collection period of the abnormal prompt time, the abnormal direction time series set is marked with several points in a two-dimensional coordinate system with time as the horizontal axis and the abnormal direction as the vertical axis to draw an abnormal direction change curve; The slope of each point on the abnormal pointing change curve is obtained respectively, and the slopes of adjacent points are identified for mutation. If a mutation is found in the slope of an adjacent point, the time period of the adjacent point on the horizontal axis is taken as the abnormal time.

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