WO2015035764A1 - 生理参数处理方法、系统及监护设备 - Google Patents

生理参数处理方法、系统及监护设备 Download PDF

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WO2015035764A1
WO2015035764A1 PCT/CN2014/074131 CN2014074131W WO2015035764A1 WO 2015035764 A1 WO2015035764 A1 WO 2015035764A1 CN 2014074131 W CN2014074131 W CN 2014074131W WO 2015035764 A1 WO2015035764 A1 WO 2015035764A1
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physiological
signal
quality index
signal quality
thd
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French (fr)
Inventor
孙泽辉
喻娇
苏健伟
杨景明
谢超成
叶文宇
岑建
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Shenzhen Mindray Bio Medical Electronics Co Ltd
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Shenzhen Mindray Bio Medical Electronics Co Ltd
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7221—Determining signal validity, reliability or quality
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/25—Fusion techniques
    • G06F18/254—Fusion techniques of classification results, e.g. of results related to same input data
    • G06F18/256—Fusion techniques of classification results, e.g. of results related to same input data of results relating to different input data, e.g. multimodal recognition
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/02—Preprocessing
    • G06F2218/04—Denoising
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/12—Classification; Matching

Definitions

  • the invention relates to a medical device, in particular to a physiological parameter processing method, system and monitoring device. Background technique
  • a physiological parameter processing method includes the following steps:
  • the same physiological parameters obtained by the physiological signals are fused according to the signal quality index of each physiological signal to obtain physiological parameters after fusion.
  • a physiological parameter processing system comprising:
  • Receiving module acquiring two or more physiological signals
  • a signal quality index module respectively analyzing the two or more physiological signals to obtain a signal quality index of each physiological signal
  • the fusion module fuses the same physiological parameters obtained by the physiological signals according to the obtained signal quality index of each physiological signal, and obtains the physiological parameters after fusion.
  • two or more physiological signals and respective signal quality indexes are acquired, and two or more physiological parameters are fused according to a signal quality index, thereby avoiding obtaining only based on a single physiological signal.
  • the disadvantages caused by the parameters can be improved by using the physiological parameters of other physiological signals when the single physiological signal is disturbed.
  • Figure 1 is a flow chart of a physiological parameter processing method
  • FIG. 2 is a flow chart of obtaining a signal quality index by analyzing a physiological signal in an embodiment
  • FIG. 3 is a flow chart of obtaining a signal quality index by analyzing a physiological signal in another embodiment
  • FIG. 4 is a flowchart for correcting a comprehensive signal quality index.
  • Figure 5 is a schematic diagram of obtaining weights in the fusion process
  • Figure 6 is a schematic diagram of a physiological parameter processing system
  • FIG. 7 is a schematic diagram of a signal quality index module of an embodiment
  • FIG. 8 is a schematic diagram of a signal quality index module of another embodiment.
  • Figure 9 is a schematic diagram of a fusion module. detailed description
  • the physiological parameter processing method of an embodiment includes the following steps:
  • the physiological signal may be an electrocardiographic signal (ECG signal), an invasive blood pressure signal (IBP signal), a blood oxygen signal (SP02 signal), or the like. More than two physiological signals refer to two and more than two cases, for example, the number of physiological signals may be two, three, four, and the like.
  • the acquired physiological signal can be the original signal collected by the sensor or filtered or otherwise processed.
  • step S120 the two or more physiological signals are respectively analyzed to obtain a signal quality index (SQI, Signal Quality Index) of each physiological signal.
  • SQI is an evaluation of the quality of the signal, and can be calculated in various ways, for example. The calculation of a single item or a comprehensive calculation can also be corrected according to some prior judgments. The manner in which the signal quality index is obtained will be described in detail below.
  • Step S130 respectively processing the two or more physiological signals to obtain the same physiological parameters obtained by the physiological signals.
  • IBP algorithm analysis to obtain the corresponding physiological parameters, such as IBP signal physiological parameters (diastolic blood pressure, systolic blood pressure, mean pressure, pulse rate), SP02 signal physiological parameters (pulse rate, blood oxygen saturation) Degree), ECG physiological parameters (heart rate, arrhythmia), etc.
  • the same physiological parameters are: ECG heart rate value (HR), IBP pulse rate value (PR lbp ), SP02 pulse rate value (PR sp . 2 ), these same physiological parameters reflect the same physiological state, but the signal sources are different.
  • Step S140 according to the obtained signal quality index of each physiological signal, the same obtained by each physiological signal
  • the physiological parameters are fused to obtain physiological parameters after fusion.
  • the fusion may use a weighted average method to fuse the two or more physiological parameters of the same species, and determine the weight of the physiological parameters obtained by the corresponding physiological signals from the signal quality index of each physiological signal, and the signal quality index reflects the signal quality.
  • the weight of the physiological parameter of the physiological signal is greater than the weight of the physiological parameter of the physiological signal reflecting the poor signal quality of the signal quality index; the fusion can also be used to fuse the two or more physiological parameters by other statistical methods and Kalman filtering. .
  • the above physiological parameter processing method by acquiring two or more physiological signals and respective signal quality indexes, merging two or more physiological parameters according to the signal quality index, thereby avoiding the disadvantages caused by parameters obtained based on only a single physiological signal, When the single physiological signal is disturbed, it can be improved by using physiological parameters of other physiological signals.
  • the above physiological parameter processing method is not a technical solution proposed to obtain a diagnosis result or a health condition, and the physiological signal cannot be directly processed by the method to obtain the diagnosis result, but the obtained result is obtained.
  • the physiological parameters are then fused according to the quality index to avoid false alarms. That is to say, the above physiological parameter processing method is to adjust the existing physiological parameters, and the obtained result may also be an intermediate that does not reflect the diagnosis result or the health condition. Therefore, the above physiological parameter processing method is not a disease. Diagnosis and treatment.
  • the step of separately analyzing the physiological signals to obtain a signal quality index of each physiological signal may specifically include the following steps:
  • Step S210 respectively obtaining a sub-signal quality index of each physiological signal that characterizes a signal characteristic or state.
  • the physiological signal as the ECG signal as an example
  • the sub-SQI may be one or more of kSQI, bSQI, sSQL hSQL bslSQI.
  • kSQI characterizes ventricular fibrillation type: the larger it is, the more likely it is to be a noiseless QRS wave, typically 7; sSQI characterizes the ratio of effective signal to all signals: the larger it is, the more likely it is to be a noiseless QRS wave, typically 0.6; bslSQI characterizes the base drift size: the larger the value indicates that the base drift noise is smaller, then the smaller the influence on the algorithm, then it is divided into two levels according to the degree of influence it may have on the algorithm, of course, it can also be divided into multiple levels, Algorithm-dependent; hSQI characterizes the high-frequency noise size: the larger the value indicates that the higher the high-frequency noise, the smaller the influence on the algorithm, and can be divided into four levels according to the degree of influence it may have on the algorithm. , related to the specific algorithm; bSQI characterizes the integrated noise size: the smaller it indicates the more noise Small, then the smaller the impact on the algorithm, can be grade
  • kSQI can effectively characterize the type of ventricular fibrillation, which is defined as follows: Where X is the discrete signal or continuous signal that needs to be calculated, A and the mean and standard deviation of discrete signal X or continuous signal X, respectively, and ⁇ is the expected operation symbol in mathematics.
  • the bSQI characterizes the noise magnitude and is the Bob-Wave fluctuation matching signal quality index, which is defined as follows:
  • k is the QRS wave of the current analysis
  • w is the sliding analysis window (the width can be taken as 10s)
  • the current QRS wave (k) as the center
  • the left and right sides take 1/2 window width
  • N matehed is two different in w QRS wave detection algorithm (any two QRS wave detection algorithms, such as DF algorithm and LT algorithm) can detect the number of QRS wave matches
  • the QRS wave matching is based on the recommendation of the American National Standards Association for Advancement of Medical Devices (AAMI).
  • AAMI American National Standards Association for Advancement of Medical Devices
  • the significance of bSQI is that when the signal quality is good, the two algorithms used can correctly mark the QRS wave, and the bSQI value is high.
  • the interference occurs, the existence of the interference causes the DF and LT algorithms to have different false positives, and the bSQI value is low. .
  • the bSQI can characterize the noise.
  • the calculation of bSQI can also be applied to IBP and SP02, but the matching time window should be set according to the respective standards, for example, the above ECG is 150ms, and IBP and SP02 can be 200ms.
  • sSQI characterizes the ratio of the effective signal to all signals, and represents the ratio of the power spectral density of the QRS wave to the total power spectral density, as shown in the following equation:
  • the main energy of the QRS wave is concentrated in a frequency band with a width of about 10 Hz centered at 10 Hz.
  • the total energy upper limit is generally around 50 Hz, so the thdl in the formula can be selected.
  • thd2 can be selected as 14 Hz
  • thd3 can be selected as 50 Hz.
  • the energy of QRS wave is mainly concentrated in the frequency band about l () Hz and the width is about 10 Hz.
  • PSD power spectral density
  • the ratio of the power spectral density (PSD) value to the total PSD value can be used as a reference index for judging the quality of ECG signals.
  • the calculation of sSQI can be applied to IBP and SP02, but the calculated bandwidth should be set according to the respective standards. For example, the above ECG is 5 ⁇ 14Hz for 5 ⁇ 50Hz, and IBP can calculate 0 ⁇ 10Hz for 0 ⁇ 55Hz. Size, SP02 can calculate 0.2 ⁇ 12Hz accounted for 0.2 ⁇ 60Hz size.
  • hSQI 10 * min(QRS i —amplitude/hf— noise .
  • QRSi—amplitude means that the amplitude of the currently detected QRS wave is large
  • hf_noisei is the average of sum of 0.28s to 0.05s before the QRS wave.
  • sum(i)
  • bslSQI is an index that characterizes the size of the base drift, and its calculation is as follows:
  • QRSi-amplitude is the maximum and minimum difference in the QRS range (R-0.07s ⁇ : R+0.08s); baseline ⁇ amplitude is the maximum and minimum difference of the baseline judgment window period (R-ls ⁇ R+ls) .
  • the sub-SQI is not limited to the above five types, and any parameter that classifies the current signal or characterization of a certain state thereof may be used. Examples are as follows: SQI (time domain/frequency domain) characterizing energy, SQI (time domain/frequency domain) characterizing base drift, SQI (time domain/frequency domain) characterizing high frequency noise, SQI characterizing signal purity (time domain) / frequency domain), SQI (QRS wave energy ratio, amplitude ratio, etc.) characterizing QRS wave characteristics, characterizing the difference in detection results of different algorithms/same SQI, etc.
  • Step S220 calculating a sub-signal quality index of each physiological signal obtained, obtaining an integrated signal quality index of each physiological signal, and using the integrated signal quality index of each physiological signal as the signal quality index in step S120.
  • the comprehensive SQI values of the signals can be divided into the following five categories:
  • Category 4 The noise level is the highest, the human eye can't distinguish the physiological signal, and the algorithm can't analyze at all;
  • Category 3 The noise level is high, it is difficult for the human eye to distinguish the physiological signal, and the algorithm analysis is completely affected;
  • Class 2 The noise level is general, the human eye can easily distinguish the physiological signal, and the algorithm analysis is partially affected; Class 1: The noise level is very low, there is slight noise, but it has no effect on the algorithm analysis; Class 0: The signal quality is the best, the human eye can hardly see the noise, and the algorithm analysis is completely unaffected.
  • the integrated SQI classification is not limited to the above five categories, and may be four types, six types, etc., and may be a parameter that can characterize or distinguish the state of the current signal, may be a parameter for classifying the signal, or the like, or the above-mentioned parameters.
  • the integrated SQI can also be expressed as a large value when the signal quality is good, such as 4 types; when the signal quality is poor, the value is small, such as 0.
  • the SQI classification is performed with an ECG signal, it can be classified according to the following criteria, but it is not limited to such a standard. It can be any other standard that characterizes different degrees of noise or different types of noise.
  • Category 4 The signal quality is the worst, the human eye can't distinguish the QRS wave, and the algorithm can't analyze it at all;
  • Category 3 The noise level is high, and the human eye can basically distinguish the QRS wave, but the algorithm analysis affects both the QRS wave detection and the QRS wave classification noise.
  • Class 2 The noise level is general. The algorithm analyzes the QRS wave classification, but does not affect the noise of the QRS wave detection.
  • Class 1 The noise level is very low, does not affect the QRS wave detection, and does not affect the slight noise of the QRS wave classification;
  • ECGSQI comprehensive SQI for evaluating a physiological signal (for example, an ECG signal, that is, an electrocardiographic signal) can be obtained by judging (if a certain judgment condition is not satisfied, proceeding to the next judgment).
  • the threshold determination can be selected as follows:
  • Judgment 1 (kSQI> THD- K)&&( sSQI> THD S
  • hSQI> THD — Hl)&& bslSQI > THD BSL2, with 'GSQI 1;
  • the integrated SQI calculation is not limited to the above method, and the core is to obtain the influence on the algorithm analysis by some states or characteristics of the current signal.
  • the integrated SQI calculation method of another embodiment is as follows: When sSQI, kSQL hSQL bslSQI indicates that the signal quality is good, the bSQI result is trusted, and the integrated SQI is determined according to bSQI; when sSQI is low, due to the existence of abnormal spectrum distribution interference, The bSQI is distorted, and the integrated SQI is obtained by taking the kSQI and hSQL bslSQI as the wide value. When the above condition is not satisfied and the kSQI indicates that the signal quality is low, the bSQI is multiplied by the adjustment factor h to reduce the trust of the signal quality.
  • the specific calculation process and threshold settings are as follows:
  • the used sub-SQI may be the SQI (time domain/frequency domain) characterizing the energy, such as the above sSQI; the SQI characterizing the base drift (time) Domain/frequency domain), such as bslSQI above; SQI (time domain/frequency domain) characterizing high frequency noise, such as hSQI described above; SQI (time domain/frequency domain) characterizing signal purity; SQI characterizing QRS wave characteristics ( QRS wave energy ratio, amplitude ratio, etc.).
  • the step of separately analyzing the two or more physiological signals in step S120 to obtain a signal quality index of each physiological signal may specifically include the following steps:
  • Step S310 respectively obtaining a sub-signal quality index of each physiological signal that characterizes a signal characteristic or state. This step can be the same as step S210, and will not be described again.
  • Step S320 obtaining a comprehensive signal quality index of each physiological signal by calculating the obtained sub-signal quality index of each physiological signal.
  • This step may be the same as the method for calculating the integrated signal quality index in step S220, and will not be described again.
  • Step S330 correcting the integrated signal quality index of each physiological signal, and using the corrected integrated signal quality index as the signal quality index of the physiological signal. Because the comprehensive SQI values obtained under certain kinds of special signals are not accurate, after pre-determination, when these special types of signal situations occur, the comprehensive SQI strategy needs to be corrected, as shown in Figure 4,
  • the method of signal quality index includes the following steps:
  • step S410 it is determined whether it is noise. If it is noise, the integrated SQI value is 4; if it is not noise, the next judgment is made.
  • An example of whether it is noise judgment is as follows:
  • THD B 50;
  • THD S 50.
  • the judgment of the noise level can be based on the following two points: Whether the pacing signal detected by the noise determination within a predetermined time (for example, every second) is greater than a predetermined value of the noise determination (for example, greater than 10) or whether the high frequency noise of the signal is greater than the noise determination threshold. . Whether the high-frequency noise is too large can be judged by the sub-SQI calculated above, or the high-frequency noise can be obtained by some classical filtering methods, or by some classical statistical methods, such as a certain range (such as 1 second) The number of thresholds is counted to obtain a sign of high frequency noise. If the previously calculated sub-SQI is selected for judgment, judge sSQK THD S and bSQK THD B and kSQK THD K, if satisfied, considers the high frequency noise to be too large.
  • Step S420 determining whether it is saturated, if the sum of the time lengths of the ECG data within the current saturation determination time range (for example, 1 second) is greater than the preset saturation threshold exceeds the saturation determination time threshold (for example, 0.5 seconds), and the saturation determination time A valid QRS wave is not detected within the threshold range and is considered saturated; in the case of saturation, the integrated SQI value is 4. If it is not saturated, then go to the next judgment.
  • the saturation determination time threshold for example, 0.5 seconds
  • Step S430 determining whether it is a stop pulse, if the maximum and minimum difference of the electrocardiogram data within the current stop determination time range (for example, 2 seconds) is less than the stoppage amplitude threshold (such as 0.2 mv), or the stop determination time range is not detected.
  • the effective QRS wave is considered to be a stop; in the case of a stop, the integrated SQI is zero. If it is not for a stop, go to the next judgment.
  • step S440 it is judged whether it is ventricular fibrillation, and if it is ventricular fibrillation, the comprehensive SQI value is set to zero. If it is not ventricular fibrillation, the integrated SQI value is not modified. In this step, whether or not the ventricular fibrillation is judged based on the sub-signal quality index and the waveform shape parameter.
  • the sub-signal quality index includes: kSQI characterizing the type of ventricular fibrillation, sSQI characterizing the effective signal in all signal ratios; characterization of the waveform morphological parameters including: whether it is a wide wave (determined according to the width of the QRS wave, in this embodiment, if the QRS wave When the width is greater than 140ms, it is considered to be a wide wave), the wide-wave ratio in the window period, and the maximum value of the waveform.
  • some threshold values need to be set in advance, and judged based on these threshold values.
  • kSQI is a sub-SQI that characterizes the type of ventricular fibrillation. It has two thresholds.
  • the extreme threshold THD—K1 If it is less than this value, it is most likely ventricular fibrillation.
  • the other is the typical threshold THD K2. If it is less than the threshold, it may be Ventricular fibrillation.
  • the sSQI threshold THD_S indicates that if the threshold is exceeded, the current signal is less likely to be noisy. If it is lower than the threshold, the current signal may be noise.
  • Wide-wave ratio extreme threshold THD— WR1 greater than or equal to the threshold The current signal is most likely ventricular fibrillation; wide-wave ratio typical threshold THD_W 2, greater than or equal to the threshold The current signal may be ventricular fibrillation.
  • the wide wave threshold THD-WN which is greater than the threshold, may be ventricular fibrillation.
  • QRS wave threshold THD — Q greater than this threshold may be ventricular fibrillation.
  • Signal Difference Threshold THD—D the maximum value difference of the waveform is greater than the threshold is a valid ECG signal.
  • the threshold for ventricular fibrillation judgment is defined as follows:
  • the ventricular fibrillation index window period such as 4 seconds or 8 seconds
  • Step B Whether the QRS wave of the last 1 second is a wide wave, otherwise it is judged to be non-ventricular fibrillation; if yes, proceed to the next step.
  • the RR interval is considered to be uniform if one of the following two conditions is met: Condition 1: Use the current QRS wave RR interval to compare with the most recent 16 QRS wave RR intervals, if the current RR interval is more than half of the historical RR interval difference Less than 12.5%, it is considered to be uniform; Condition 2: The difference between the current RR interval and the most recent three historical RR intervals is less than 12.5%, which is also considered to be uniform.
  • the pre-judgment information used to correct the integrated signal quality index is not limited to the method shown in FIG. 4, and may be used as long as the judgment can be used to improve the comprehensive SQI result or directly assist the electrocardiographic analysis result by the judgment.
  • the four kinds of judgments presented in Figure 4, in these four cases, may result in erroneous integrated SQI results, so they need to be corrected. Different integrated SQI calculation methods may result in In this case, there will be cases where the comprehensive SQI calculation is not accurate. Therefore, in order to obtain a more accurate integrated SQ1, the appropriate deformation and modification can be made to Figure 4, for example, only one or two or three judgments are performed, etc. The order can also be modified, such as first performing saturation judgment, then performing noise judgment.
  • step S140 the fusion of the physiological parameters obtained by the physiological signals according to the obtained signal quality index of each physiological signal can be implemented in various ways.
  • the same physiological condition can be obtained by using the weighted average method.
  • the parameters are fused, and the weight of the same physiological parameter obtained by each physiological signal is determined by the signal quality index of each physiological signal.
  • the weight of the same physiological parameter obtained by each physiological signal can be determined according to the preset correspondence between the signal quality index and the weight, and the signal quality index reflects the physiological parameter of the physiological signal with good signal quality, and the signal quality index reflects the poor signal quality.
  • the weight of the physiological parameters of the physiological signal can be implemented in various ways.
  • the same physiological condition can be obtained by using the weighted average method.
  • the parameters are fused, and the weight of the same physiological parameter obtained by each physiological signal is determined by the signal quality index of each physiological signal.
  • the weight of the same physiological parameter obtained by each physiological signal can be determined according to the preset correspondence between the signal quality index and the weight, and the signal quality index
  • the integrated SQI and heart rate value (HR) of the ECG, the integrated SQI of the IBP, and the pulse rate value (PRibp), and the integrated SQI of the SP02 are obtained through steps S120 and S130.
  • the pulse rate value (PRspo2) the ECG heart rate weight (Cecg) can be calculated according to the integrated SQI of the ECG
  • the IBP pulse rate weight (Cibg) can be calculated according to the comprehensive SQI of the IBP
  • the SP02 pulse rate weight (Cspo2) can be calculated according to the comprehensive SQI of the SP02.
  • the final calculated fusion heart rate value according to the weight and heart rate/pulse rate
  • the pulse rate calculated by IBP and SP02 is a stable value, then a stable heart rate output value can be obtained according to the calculation formula of heart rate fusion, so that false alarms caused by ECG interference or heart rate hopping can be successfully reduced (such as arrhythmia, tachycardia, etc.), thus improving monitoring The anti-interference of the equipment and the accuracy of the alarm.
  • the parameter data displayed on the interface can be made more stable, and the user is not trusted to the device.
  • the weights may be fused by the other statistical methods and the Kalman filtering method to the two or more physiological parameters.
  • the weights are obtained by the following steps:
  • Step S 142 determining the fusion coefficient of the same physiological parameter obtained by each physiological signal according to the same physiological parameter obtained from each physiological signal and the confirmed physiological parameter.
  • the historical parameters of the different historical periods 1, 2, 3 obtained from the physiological signals a, b, c are Xl a, Xlb, Xlc. . . X2a, X2b X2c. . . ; X3a, X3b, X3c. . . .
  • the serial numbers 1, 2, and 3 indicate different historical periods
  • a, b, and c indicate different physiological signals.
  • the historically confirmed physiological parameters refer to the correct physiological parameters (such as XI, X2, X3, etc., where the serial numbers 1, 2, and 3 indicate the same historical period as the physiological parameters).
  • the fusion coefficients Pa, Pb, Pc of the same physiological parameters obtained by different physiological signals a, b, c can be determined. , thus making:
  • Step S 144 Determine the weight of the physiological parameter obtained by each physiological signal according to the signal quality index of each physiological signal and the fusion coefficient of the same physiological signal obtained.
  • the calculation formula of the weight Ca can be adjusted as needed, and is not limited to the above two calculation formulas.
  • the basic principle of the formula is that the physiological parameters obtained by the signal quality index reflecting the physiological signal with good signal quality are more important than the signal quality index reflecting the poor signal quality.
  • the physiological signal gives the weight of the physiological parameters.
  • a method of weighted averaging similar to Equation 1 can be used to perform the fusion of physiological parameters.
  • the proportion of each physiological parameter can be adjusted according to the past situation, thereby further making the physiological parameters obtained after the fusion close to the real situation.
  • a physiological parameter processing system of an embodiment including a receiving module 610, a signal quality index module 620, an obtaining module 630, and a fusion module 640.
  • the receiving module 610 is configured to acquire more than two physiological signals.
  • the physiological signals may be an electrocardiographic signal (ECG signal), an invasive blood pressure signal (IBP signal), a blood oxygen signal (SP02 signal), and the like. More than two physiological signals refer to two and more than two cases, for example, the number of physiological signals may be two, three, four, and the like.
  • the acquired physiological signal can be the original signal collected by the sensor or the filtered or other processed signal.
  • the signal quality index module 620 separately analyzes the two or more physiological signals to obtain a signal quality index (SQI, Signal Quality Index) of each physiological signal, and the SQI is an evaluation of the quality of the signal, which can be calculated in various ways. Obtaining, for example, a single item calculation or a comprehensive calculation, etc., can also be corrected based on some prior judgments. The manner in which the signal quality index is obtained will be described in detail below.
  • SQI Signal Quality Index
  • the obtaining module 630 is configured to respectively process the two or more physiological signals to obtain the same physiological parameters obtained by the physiological signals.
  • IBP algorithm analysis IBP signal physiological parameters (diastolic blood pressure, systolic blood pressure, mean pressure, pulse rate), SP02 signal physiological parameters (pulse rate, blood oxygen saturation) Degree), ECG physiological parameters (heart rate, arrhythmia), etc.
  • the same physiological parameters are: ECG heart rate value (HR), IBP pulse rate value (PR lbp ), SP02 pulse rate value (PR sp . 2 ), these same physiological parameters reflect the same physiological state, but the signal sources are different.
  • the fusion module 640 fuses the same physiological parameters obtained by the physiological signals according to the obtained signal quality index of each physiological signal, and obtains the physiological parameters after the fusion.
  • the fusion may use a weighted average method to fuse the two or more physiological parameters of the same species, and determine the weight of the physiological parameters obtained by the corresponding physiological signals from the signal quality index of each physiological signal, and the signal quality index reflects the signal quality.
  • the weight of the physiological parameter of the physiological signal is greater than the weight of the physiological parameter of the physiological signal reflecting the poor signal quality of the signal quality index; the fusion can also use other statistical methods, Kalman filtering method to the same physiological physiology obtained by each physiological signal The parameters are fused.
  • the above physiological parameter processing system integrates two or more physiological parameters according to a signal quality index by acquiring two or more physiological signals and respective signal quality indexes, thereby avoiding the disadvantages caused by parameters obtained based on only a single physiological signal.
  • the signal quality index module 620 includes a sub-signal quality index unit 622 and a first integrated signal quality index unit 624.
  • Sub-signal quality index unit 622 is used to obtain sub-signal quality indices for each of the physiological signals characterizing the signal characteristics or states, respectively.
  • the physiological signal as the ECG signal as an example, the sub-SQI may be one or more of kSQI, bSQL sSQI, hSQL bslSQI.
  • kSQI characterizes ventricular fibrillation type: the larger it is, the more likely it is to be a noiseless QRS wave, typically 7; sSQI characterizes the ratio of effective signal to all signals: the larger it is, the more likely it is to be a noiseless QRS wave, typically 0.6; bslSQI characterization base drift size: The larger the larger the base drift noise, the smaller the impact on the algorithm, then it can be divided into two levels according to its possible influence on the algorithm. Algorithm-dependent; hSQI characterizes the frequency of high-frequency noise: the larger the value indicates the lower the high-frequency noise, then the smaller the influence on the algorithm, the more it can be divided into four levels according to its possible influence on the algorithm. Multi-level, related to specific algorithm; bSQI Characterizes the integrated noise size: The smaller it is, the smaller the noise is, the smaller the impact on the algorithm can be, according to its degree of influence on the algorithm, related to the specific algorithm. among them:
  • kSQI J
  • X is the discrete signal or continuous signal to be calculated
  • ⁇ is the mean and standard deviation of the discrete signal X or the continuous signal X, respectively
  • E is the expected operation symbol in mathematics.
  • the bSQI characterizes the noise magnitude and is the Bob-Wave fluctuation matching signal quality index, which is defined as follows:
  • the QRS wave matching is based on the recommendation of the American National Standards Association for Advancement of Medical Devices (AAMI).
  • AAMI American National Standards Association for Advancement of Medical Devices
  • the significance of bSQI is that when the signal quality is good, the two algorithms used can correctly mark the QRS wave, and the bSQI value is high.
  • the interference occurs, the existence of the interference causes the DF and LT algorithms to have different false positives, and the bSQI value is low. .
  • the bSQI can characterize the noise.
  • the calculation of bSQI can also be applied to IBP and SP02, but the matching time window should be set according to the respective standards, for example, the above ECG is 150ms, and IBP and SP02 can be 200ms.
  • sSQI characterizes the ratio of the effective signal to all signals, and represents the ratio of the power spectral density of the QRS wave to the total power spectral density, as shown in the following equation:
  • the main energy of the QRS wave is concentrated in a frequency band with a width of about 10 Hz centered at 10 Hz.
  • the total energy upper limit is generally about 50 Hz, so the thdl in the formula can be selected as 5 Hz, thd2. It can be selected as 14Hz, and thd3 can be selected as 50Hz.
  • the energy of the QRS wave is mainly concentrated in a frequency band of about 10 Hz centered on the width of 10 Hz.
  • the ratio of the power spectral density (PSD) value to the total PSD value can be used as the judgment of the electrocardiogram. Reference indicator for signal quality.
  • the calculation of sSQI can be applied to IBP and SP02, but the calculated bandwidth should be set according to the respective standards. For example, the above ECG is 5 ⁇ 14Hz for 5 ⁇ 50Hz, and IBP can calculate 0 ⁇ 10Hz for 0 ⁇ 55Hz. Size, SP02 can calculate 0.2 12Hz accounted for 0.2 ⁇ 60Hz size.
  • QRSi—amplitude means that the current detected QRS wave amplitude is larger than 'J
  • hf_noisei is the average of sum of 0.28s ⁇ 0.05s before the QRS wave
  • sum (i)
  • bslSQI is an index that characterizes the size of the base drift, and its calculation is as follows: bslSQI - 10 * min(QRS i ⁇ amplitude/baseline, ⁇ amplitude)
  • QRS the amplitude is the maximum and minimum difference in the QRS range (R-().()7s ⁇ R+().()8s); baseline ⁇ amplitude is the baseline judgment window period (R-ls ⁇ R+ The maximum and minimum difference of ls ).
  • the sub-SQI is not limited to the above five types, and any parameter that classifies the current signal or characterization of a certain state thereof may be used. Examples are as follows: SQI (time domain/frequency domain) characterizing energy, SQI (time domain/frequency domain) characterizing base drift, SQI (time domain/frequency domain) characterizing high frequency noise, SQI characterizing signal purity (time domain) / frequency domain), SQI (QRS wave energy ratio, amplitude ratio, etc.) characterizing QRS wave characteristics, characterizing the difference in detection results of different algorithms/same SQI, etc.
  • the first integrated signal quality index unit 624 calculates the integrated signal quality index of each physiological signal by calculating the obtained sub-signal quality index of each physiological signal, and obtains the integrated signal quality index of each physiological signal as the signal quality index module 620. Signal quality index.
  • the comprehensive SQI values of the signals can be divided into the following five categories:
  • Category 4 The noise level is the highest, the human eye can't distinguish the physiological signal, and the algorithm can't analyze at all;
  • Category 3 The noise level is high, it is difficult for the human eye to distinguish the physiological signal, and the algorithm analysis is completely affected;
  • Class 2 The noise level is general, the human eye can easily distinguish the physiological signal, and the algorithm analysis is partially affected;
  • Class 1 The noise level is very low, there is slight noise, but it has no effect on the algorithm analysis
  • Class 0 The signal quality is the best, the human eye can hardly see the noise, and the algorithm analysis is completely unaffected.
  • the integrated SQI classification is not limited to the above five categories, and may be four types, six types, etc., and may be a parameter that can characterize or distinguish the state of the current signal, may be a parameter for classifying the signal, or the like, or the above-mentioned parameters.
  • the SQI classification is performed with an ECG signal, it can be classified according to the following criteria, but it is not limited to such a standard. It can be any other standard that characterizes different degrees of noise or different types of noise.
  • Category 4 The signal quality is the worst, the human eye can't distinguish the QRS wave, and the algorithm can't analyze it at all;
  • Category 3 The noise level is high, and the human eye can basically distinguish the QRS wave, but the algorithm analysis affects both the QRS wave detection and the QRS wave classification noise.
  • Class 2 The noise level is general. The algorithm analyzes the QRS wave classification, but does not affect the noise of the QRS wave detection. Class 1: The noise level is very low, does not affect the QRS wave detection, and does not affect the slight noise of the QRS wave classification.
  • ECGSQI comprehensive SQI for evaluating a physiological signal
  • a physiological signal for example, an ECG signal, that is, an electrocardiographic signal
  • the threshold determination can be selected as follows:
  • the integrated SQI calculation is not limited to the above method, and the core is to obtain the influence on the algorithm analysis by some states or characteristics of the current signal.
  • the integrated SQI of another embodiment is calculated as follows: When sSQI, kSQK hSQL bslSQI indicates that the signal quality is good, the bSQI result is trusted, and the integrated SQI is determined according to bSQI; when sSQI is low, due to the existence of abnormal spectrum distribution interference, The bSQI is distorted, and the integrated SQI is obtained by taking the threshold of kSQI and hSQL bslSQI; when the above situation is not satisfied and the kSQI indicates that the signal quality is low, the trust degree of the signal quality is reduced by multiplying the bSQI by the adjustment factor h.
  • the specific calculation process and threshold settings are as follows:
  • the sub-SQI used may be the SQI (time domain/frequency domain) characterizing the energy, such as the above sSQI; the SQI characterizing the base drift (time) Domain/frequency domain), such as bslSQI above; SQI (time domain/frequency domain) characterizing high frequency noise, such as hSQI described above; SQI (time domain/frequency domain) characterizing signal purity; SQI characterizing QRS wave characteristics ( QRS wave energy ratio, amplitude ratio, etc.).
  • the signal quality index module 620 includes a sub-signal quality index unit 623, a second integrated signal quality index unit 625, and a correction unit 626.
  • the sub-signal quality index unit 623 and the second integrated signal quality index unit 625 correspond to the sub-signal quality index unit 622 and the first integrated signal quality index unit 624 of the foregoing embodiment, and are not described again.
  • the correcting unit 626 is configured to correct the integrated signal quality index of each physiological signal, and use the corrected integrated signal quality index as the signal quality index of the physiological signal. Because the comprehensive SQI values obtained under certain types of special signals are not accurate, after prior judgment, in the case of these special types of signals, the strategy of synthesizing SQI needs to be corrected, as shown in Figure 4, the correction unit The process of correcting the integrated signal quality index by 626 is as follows: In step S410, it is determined whether it is noise. If it is noise, the integrated SQI value is 4; if it is not noise, the next judgment is made. An example of whether it is noise judgment is as follows:
  • THD B 50;
  • THD S 50.
  • the judgment of the noise level can be based on the following two points: Whether the pacing signal detected by the noise determination within a predetermined time (for example, every second) is greater than a predetermined value of the noise determination (for example, greater than 10) or whether the high frequency noise of the signal is greater than the noise determination threshold. . Whether the high-frequency noise is too large can be judged by the sub-SQI calculated above, or the high-frequency noise can be obtained by some classical filtering methods, or by some classical statistical methods, such as a certain range (such as 1 second) The number of thresholds is counted to obtain a sign of high frequency noise. If the previously calculated sub-SQI is selected for judgment, it is judged that sSQK THD S and bSQK THD B and kSQK THD K, if satisfied, the high frequency noise is considered too large.
  • Step S420 determining whether it is saturated, if the sum of the time lengths of the ECG data within the current saturation determination time range (for example, 1 second) is greater than the preset saturation threshold exceeds the saturation determination time threshold (for example, 0.5 seconds), and the saturation determination time A valid QRS wave is not detected within the broad range and is considered saturated; in the case of saturation, the integrated SQI value is 4. If it is not saturated, then go to the next judgment.
  • the saturation determination time threshold for example, 0.5 seconds
  • Step S430 determining whether it is a stoppage, if the maximum difference of the electrocardiographic data within the current stop determination time range (for example, 2 seconds) is less than the stoppage width threshold (such as 0.2 mv), or the stop determination time range is not A valid QRS wave is detected as a stop; in the case of a stop, the integrated SQI is zero. If it is not for a stop, go to the next judgment.
  • the stoppage width threshold such as 0.2 mv
  • step S44 it is judged whether it is ventricular fibrillation, and if it is ventricular fibrillation, the integrated SQI value is set to zero. If it is not ventricular fibrillation, the comprehensive SQI value is not modified. In this step, whether or not the ventricular fibrillation is judged based on the sub-signal quality index and the waveform shape parameter.
  • the sub-signal quality index includes: kSQI characterizing the type of ventricular fibrillation, sSQI characterizing the effective signal in all signal ratios; characterization of the waveform morphological parameters including: whether it is a wide wave (determined according to the width of the QRS wave, in this embodiment, if the QRS wave When the width is greater than 140ms, it is considered to be a wide wave), the wide-wave ratio in the window period, and the maximum value of the waveform.
  • some thresholds need to be set in advance, and judgment is made based on these thresholds.
  • kSQI is a sub-SQI that characterizes the type of ventricular fibrillation, which has two thresholds, One is the extreme threshold THD_ 1 If it is less than this value, it is most likely ventricular fibrillation, and the other is the typical threshold THD K2. If it is less than the threshold, it may be ventricular fibrillation.
  • the sSQI threshold THD S indicates that if the threshold is exceeded, the current signal is less likely to be noisy. If the threshold is lower than the threshold, the current signal may be noise.
  • Wide-wave ratio extreme threshold THD WR 1 greater than or equal to the threshold current signal is most likely ventricular fibrillation; wide-wave ratio typical threshold THD_WR2, greater than or equal to the threshold current signal may be ventricular fibrillation.
  • the wide-wave number threshold THD-WN which is greater than the threshold, may be ventricular fibrillation.
  • the QRS wave number threshold THD_Q which is greater than the threshold, may be ventricular fibrillation.
  • the signal difference threshold THD_D the waveform maximum value difference is greater than the threshold is a valid ECG signal.
  • the threshold for ventricular fibrillation judgment is defined as follows:
  • THD S 60;
  • THD— WR1 0.6
  • THD— WR2 0.5
  • THD_D 0.2mv
  • the ventricular fibrillation index window period such as 4 seconds or 8 seconds
  • Step B Whether the QRS wave of the last 1 second is a wide wave, otherwise it is judged to be non-ventricular fibrillation; if yes, proceed to the next step.
  • the pre-judgment information used in the correction of the integrated signal quality index is not limited to the method shown in FIG. 4, and may be used as long as the judgment can be used to improve the comprehensive SQI result or directly assist the electrocardiographic analysis result by the judgment.
  • the four kinds of judgments presented in Figure 4, in these four cases, may result in erroneous integrated SQI results, so they need to be corrected.
  • Different integrated SQI calculation methods may lead to cases where the integrated SQI calculation is not accurate in other cases. Therefore, in order to obtain a more accurate integrated SQI, the appropriate deformation and modification can be performed on FIG.
  • the fusion module 640 can perform the fusion of the same physiological parameters obtained by the physiological signals according to the obtained signal quality index of each physiological signal, and can be implemented in various manners, for example, the weighted average method can be used to physiology of the two or more of the same species. The parameters are fused, and the weight of the same physiological parameter obtained by each physiological signal is determined by the signal quality index of each physiological signal.
  • the weight of the same physiological parameter obtained by each physiological signal can be determined according to the preset correspondence between the signal quality index and the weight, and the signal quality index reflects the physiological parameter of the physiological signal with good signal quality, and the weight of the physiological parameter is greater than the signal quality index, and the signal quality is poor.
  • the weight of the physiological parameters of the physiological signal For example, taking the ECG, IBP, and SP02 signals as examples, the integrated SQI and heart rate value (HR) of the ECG, the integrated SQI and pulse rate value (PRibp) of the IBP, and the integrated SQI and pulse of the SP02 are obtained through steps S120 and S130.
  • the ECG heart rate weight (Cecg) can be calculated according to the integrated SQI of the ECG
  • the IBP pulse rate weight (Cib) can be calculated according to the comprehensive SQI of the IBP
  • the SP02 pulse rate weight (Cspo2) can be calculated according to the comprehensive SQI of the SP02.
  • the final calculated fusion heart rate value is obtained based on the weight and heart rate/pulse rate, where:
  • the correspondence between the preset signal quality index and the weight is as follows:
  • the integrated SQI 2 or 3 calculated by the current ECG signal
  • the calculated heart rate may always be in a transition state due to noise interference; and the current IBP and SP02 signal quality
  • the parameter data displayed on the interface can be made more stable, and the user is not trusted to the device.
  • the weights may be fused by the other statistical methods and the Kalman filtering method to the two or more physiological parameters.
  • the fusion module 640 includes a coefficient unit 642 and a weight unit 644, and obtains weights as follows:
  • the coefficient unit 642 determines the fusion coefficient of the same physiological parameter obtained from each physiological signal based on the same physiological parameter obtained from each physiological signal and the confirmed physiological parameter. It is assumed here that the historical parameters of the different historical periods 1, 2, 3 obtained from the physiological signals a, b, c are Xla, Xlb, Xlc...; X2a, X2b, X2c...; X3a, X3b, X3c.... Among them, the serial numbers 1, 2, and 3 indicate different historical periods, and a, b, and c indicate different physiological signals.
  • the historically confirmed physiological parameters refer to the correct physiological parameters (such as XI, X2, X3, etc., where the serial numbers 1, 2, and 3 indicate the same historical period as the physiological parameters).
  • the fusion coefficients Pa, Pb, Pc of the same physiological parameters obtained by different physiological signals a, b, c can be determined.
  • the weighting index of each physiological signal of the weighting unit 644 and the fusion coefficient of the same physiological signal obtained by the weighting unit 644 determine the weight of the physiological parameter obtained by each physiological signal.
  • the calculation formula of the weight Ca can be adjusted as needed, and is not limited to the above two calculation formulas.
  • the basic principle of the formula is that the physiological parameters obtained by the signal quality index reflecting the physiological signal with good signal quality are more important than the signal quality index reflecting the poor signal quality.
  • the weight of the physiological parameters obtained by the physiological signal is more important than the signal quality index reflecting the poor signal quality.
  • Equation 1 After the weights are calculated, a weighted average similar to Equation 1 can be used to fuse the physiological parameters.
  • the proportion of each physiological parameter can be adjusted according to the past situation, so that the physiological parameters obtained after the fusion are closer to the real situation.

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Abstract

一种生理参数处理方法，包括如下步骤：获取两个以上生理信号；分别对所述两个以上生理信号进行分析，得到各生理信号的信号质量指数；分别对所述两个以上生理信号进行处理，获得由各生理信号得到的同种生理参数；及根据得到的各生理信号的信号质量指数对由各生理信号得到的同种生理参数进行融合，得到融合后的该生理参数。上述生理参数处理方法，通过获取两个以上生理信号及各自的信号质量指数，根据信号质量指数对由各生理参数得到同种生理参数进行融合，从而避免仅基于单个生理信号获得的参数带来的弊端，在该单个生理信号受到干扰时，可以利用其他生理信号的生理参数来完善。

Description

说明书
发明名称: 生理参数处理方法、 系统及监护 i殳备 技术领域
本发明涉及医疗设备，特别是涉及一种生理参数处理方法、系统及监护设备。 背景技术
对人体生命体征信号进行处理获得生理参数具有广泛的用途。 然而， 这些生 命体征信号经常受到诸如噪声、 伪迹等的干扰造成生理参数出现错误。 以监护设 备为例， 心律失常的错误估计会导致相关的误报警， 这些误报警降低了病人和医 护人员对仪器的满意度， 更为严重的是医护人员对监护设备报警的信任度降低， 从而可能忽略真正的危急情况， 大大削弱了监护效果。 发明内容
基于此， 有必要针对信号受扰导致的误报警问题， 提供一种生理参数处理方 法、 系统及监护设备。
一种生理参数处理方法， 包括如下步骤：
获取两个以上生理信号；
分别对所述两个以上生理信号进行分析， 得到各生理信号的信号质量指数； 分别对所述两个以上生理信号进行处理， 获得由各生理信号得到的同种生理 参数； 及
根据所述各生理信号的信号质量指数对所述由各生理信号得到的同种生理 参数进行融合， 得到融合后的生理参数。
一种生理参数处理系统， 包括：
接收模块， 获取两个以上生理信号；
信号质量指数模块， 分别对所述两个以上生理信号进行分析， 得到各生理信 号的信号质量指数；
获取模块， 分别对所述两个以上生理信号进行处理， 获得由各生理信号得到 的同种生理参数； 及
融合模块，根据得到的各生理信号的信号质量指数对由各生理信号得到的同 种生理参数进行融合， 得到融合后的生理参数。
此外， 还提出了一种包含上述生理参数处理系统的监护设备。
在上述生理参数处理方法、 系统及监护设备中， 通过获取两个以上生理信号 及各自的信号质量指数， 根据信号质量指数对两个以上同种生理参数进行融合， 从而避免仅基于单个生理信号获得的参数带来的弊端， 在该单个生理信号受到干 扰时， 可以利用其他生理信号的生理参数来完善。 附图说明
为了更清楚地说明本发明实施方式， 下面将对实施方式中所需要使用的附图 作简单地介绍， 显而易见地， 下面描述中的附图仅仅是本发明的一些实施方式， 对于本领域普通技术人员来讲， 在不付出创造性劳动的前提下， 还可以根据这些 附图获得其他的附图。
图 1为生理参数处理方法流程图；
图 2为一实施方式中对生理信号进行分析获得信号质量指数的流程图； 图 3为另一实施方式中对生理信号进行分析获得信号质量指数的流程图； 图 4为修正综合信号质量指数的流程图；
图 5为融合过程中获得权重的示意图；
图 6为生理参数处理系统示意图；
图 7为一实施方式的信号质量指数模块的示意图；
图 8为另一实施方式的信号质量指数模块的示意图； 及
图 9为融合模块的示意图。 具体实施方式
以下说明提供了用于完全理解分 τ 犯力 3 v ^m丁个々 J¾ :
施的特定细节。 然而， 本领域的技术人员应该理解， 无需这样的细节亦可实践本 发明。 在一些实例中， 为了避免不必要地混淆对实施方式的描述， 没有详细示出 或描述公知的结构和功能。 除非上下文清楚地要求， 否则， 贯穿本说明书和权利 要求， 用语"包括"、 "包含 "等应以包含性的意义来解释而不是排他性或穷尽性的 意义， 即， 其含义为"包括， 但不限于，，。 在本详细描述部分中， 使用单数或复数 的用语也分别包括复数或单数。
传统的生理参数处理策略都基于单个生理信号进行研究和改进, 用于提高单 个生理信号噪声情况下信号的生理参数估计， 例如经典滤波、 机器自学习、 卡尔 曼滤波、 变换域分析等， 但这些技术都未能很好的解决噪声影响的问题， 且有些 算法计算复杂， 需要占用大量的资源而难以工程实现。 同时， 也有从多个生理数 据源中选择合理的生理数据的方法，但也未能很好的解决噪声带来的误报警或漏 报警问题， 适用范围较小。
如图 1所示， 一实施方式的生理参数处理方法， 包括如下步骤：
步骤 S110，获取两个以上生理信号。生理信号可以是心电信号（ECG信号）、 有创血压信号（IBP信号）、 血氧信号 （SP02信号）等。 两个以上生理信号指的 是包括两个和超过两个的情况， 比如生理信号的数量可以是两个、三个、四个等。 获取到的生理信号可以为传感器采集到的原始信号或者经过滤波或者其它处理 后的信号。
步骤 S120,分别对所述两个以上生理信号进行分析，得到各生理信号的信号 质量指数（SQI, Signal Quality Index )„ SQI是表现信号质量好坏的评价， 可以采 用多种方式计算获得， 例如单项的计算或是综合的计算等， 还可以根据一些预先 的判断进行修正。 信号质量指数的获得方式将在下文详细描述。
步骤 S130, 分别对所述两个以上生理信号进行处理， 获得， 由各生理信号得 到的同种生理参数。 例如进行 IBP算法分析、 SP02算法分析、 ECG算法分析， 得到相应的生理参数，例如 IBP信号生理参数（舒张压、收缩压、平均压、脉率）、 SP02信号生理参数（脉率、 血氧饱和度）、 ECG生理参数（心率、 心律失常） 等， 在这些生理参数中， 例如同种生理参数有： ECG的心率值（HR )、 IBP的 脉率值（PRlbp )、 SP02的脉率值 (PRsp。2), 这些同种生理参数反映了相同的生理状 态， 只是信号源不同。
步骤 S140,根据得到的各生理信号的信号质量指数对由各生理信号得到的同 种生理参数进行融合， 得到融合后的生理参数。 融合可以用加权平均的方法对所 述两个以上同种的生理参数进行融合， 由各生理信号的信号质量指数确定由各对 应生理信号得到的生理参数的权重， 信号质量指数反映信号质量好的生理信号的 生理参数的权重大于信号质量指数反映信号质量差的生理信号的生理参数的权 重; 融合也可以用其他统计的方法、 卡尔曼滤波的方法对所述两个以上同种生理 参数进行融合。
上述生理参数处理方法， 通过获取两个以上生理信号及各自的信号质量指 数， 根据信号质量指数对两个以上同种生理参数进行融合， 从而避免仅基于单个 生理信号获得的参数带来的弊端， 在该单个生理信号受到千扰时， 可以利用其他 生理信号的生理参数来完善。
需要说明的是， 上述生理参数处理方法， 并非以获得诊断结果或健康状况为 直接目所提出的技术方案， 通过该方法也不能对生理信号进行处理直接获得诊断 结果， 而是获取已经处理得到的生理参数后根据质量指数进行融合， 避免错误报 警。 也就是说， 上述生理参数处理方法， 是对已有的生理参数进行调整， 所得到 的结果也可以是不反映诊断结果或健康状况的中间^ t, 因此， 上述生理参数处 理方法不属于疾病的诊断和治疗方法。 如图 2所示， 步骤 S120中， 分别对所述生理信号进行分析 , 得到各生理信 号的信号质量指数的步骤可以具体包括如下步骤:
步骤 S210, 分别获得表征信号特征或状态的各生理信号的子信号质量指数。 以生理信号为心电信号为例， 子 SQI可以是 kSQI、 bSQI、 sSQL hSQL bslSQI 中的一种或多种。 kSQI表征室颤类型: 其越大那么越有可能是无噪声的 QRS波， 一般为 7; sSQI表征有效信号占所有信号的比例： 其越大那么越有可能是无噪声 的 QRS波, 一般为 0.6; bslSQI表征基漂大小： 其越大表明基漂噪声越小, 那么 其对算法影响越小， 那么根据其可能对算法的影响程度分为两级， 当然也可以分 为多级， 跟具体的算法相关； hSQI 表征高频噪声大小: 其越大表明高频噪声越 小， 那么其对算法影响越小， 可根据其可能对算法的影响程度分为四级， 当然也 可以分为多级， 跟具体算法相关； bSQI表征综合噪声大小： 其越小表明噪声越 小，那么对算法影响越小，可以根据其对算法的影响程度分级，跟具体算法相关。 其中：
kSQI能有效表征室颤类型 , 其定义如下：
Figure imgf000007_0001
其中， X为需要计算的离散信号或连续信号， A和 分别为离散信号 X或连 续信号 X的均值和标准差， Ε为数学里的期望运算符号。
bSQI表征噪声大小， 为逐博波动匹配信号质量指数, 其定义如下：
*丄 .
Figure imgf000007_0002
其中， k为当前分析的 QRS波， w为滑动分析窗口 （宽度可取 10s ), 以当前 QRS波（ k )为中心， 左右各取 1/2窗宽， Nmatehed为在 w中两种不同的 QRS波检 测算法（任意两种 QRS波检测算法都可以，例如 DF算法和 LT算法）检出的 QRS 波匹配数目， Nall为在 w 中两种算法各自检测出的 QRS波的数目并集总和， 即 Nall = Ni+N2- Nmatched, Ni为在 w中 QRS波检测算法 1检测出的 QRS波数目 , N2 为在 w中 QRS波检测算法 2检测出的 QRS波数目。 QRS波匹配是根据美国国家 标准医疗器械促进协会（AAMI )的推荐标准， 当两种算法对同一 QRS波位置标 注在 150ms之内时，认为是同一个 QRS波。 bSQI的意义在于， 当信号质量好时， 使用的两种算法都可以正确标注 QRS波， bSQI值高； 当干扰发生时， 干扰的存 在使 DF和 LT算法产生了不同的误判， bSQI值低。 也就是说该 bSQI能表征噪 声的好坏。 bSQI的计算同样可应用于 IBP和 SP02, 但其匹配的时间窗应根据各 自的标准来设定， 例如上述 ECG为 150ms, IBP和 SP02可为 200ms。
sSQI表征有效信号占所有信号的比例， 表示 QRS波的功率谱密度值占总的 功率谱密度的值比例， 如下式所示：
Figure imgf000007_0003
其中以心电信号为例， QRS波主要的能量集中在以 10Hz为中心的、 宽度约 为 10Hz 的频带内， 总的能量上限一般在 50Hz左右， 所以公式中的 thdl可以选 为 5Hz, thd2可以选为 14Hz, thd3可以选为 50Hz。 根据对心电信号的谱分析，
QRS波的能量主要集中在约以 l ()Hz为中心的、 宽度约为 l OHz的频带内, 该功 率谱密度 (PSD)值占总 PSD值的比例可以作为判断心电信号质量的参考指标。 sSQI的计算可应用于 IBP和 SP02, 但其计算的带宽应根据各自的标准来设定， 例如上述 ECG为计算 5〜14Hz占 5〜50Hz的大小 , IBP可计算 0〜10Hz占 0〜55Hz 的大小， SP02可计算 0.2〜12Hz占 0.2〜60Hz的大小。
hSQI是表征高频噪声大小的指数， 其计算如下式所示：
hSQI = 10 * min(QRSi—amplitude/hf— noise . 式中 QRSi— amplitude是指当前检测到的 QRS波幅度大'〗、； hf_noisei是 QRS 波之前 0.28s〜0.05s的 sum的平均值， 而 sum(i)=|hf(i)|+|hf(i- l)|+...+|hf(i- 5)|), hf 是将 ECG信号经过如下的一个高通滤波器： hf(i)= X(i)- χ(ί- 1)+ χ(ί- 2)得到的值，其 中 χ就是原始的心电波形或经过处理后的心电波形数据。
bslSQI是表征基漂大小的指数， 其计算如下式所示：
bslSQI = 10 * m (QRSi― amplitude/baseline,― amplitude)
其中， QRSi— amplitude是 QRS波范围内 （ R-0.07s〜： R+0.08s ) 的最大最小值 差； baseline^ amplitude是基线判断窗口期（R-ls〜R+ls ) 的最大最小值差。
子 SQI不限于上述的 5种，任何对当前信号分类或者表征其某种状态的参数 都可以。举例如下： 表征能量的 SQI (时域 /频域）、表征基漂的 SQI (时域 /频域）、 表征高频噪声的 SQI (时域 /频域）、表征信号纯度的 SQI (时域 /频域）、表征 QRS 波特征的 SQI ( QRS波能量比例、 幅度比例等等）、 表征不同算法检测结果差异 / 相同的 SQI等。
步骤 S220,通过对获得的各生理信号的子信号质量指数进行计算，得到各生 理信号的综合信号质量指数， 将各生理信号的综合信号质量指数作为步骤 S120 中的信号质量指数。
按照噪声水平， 可以将信号的综合 SQI值分为以下五类：
4类： 噪声水平最高， 人眼分辨不出生理信号， 算法完全无法分析；
3类： 噪声水平较高， 人眼较难能分辨出生理信号， 算法分析完全受影响；
2类： 噪声水平一般， 人眼能较容易分辨出生理信号， 算法分析部分受影响； 1类： 噪声水平很低， 有轻微的噪声， 但对算法分析无任何影响； 0类： 信号质量最好， 人眼几乎看不出噪声， 算法分析完全不受影响。
综合 SQI分类不限于上述的 5类， 可以是 4类、 6类等， 还可以是能表征或 者区别当前信号的状态的参数， 可以是对信号的分类的参数等等， 或者是上述所 述参数的组合或者采用一定数学方法计算得到的值。综合 SQI也可以表示为信号 质量好时数值大， 比如 4类； 信号质量差时数值小， 比如 0类。
如果以心电信号进行 SQI分类， 可以按照如下标准进行分类, 但也不限于这 样的标准， 可以是以其他任何表征不同程度噪声或不同类型噪声对算法产生不同 影响的标准：
4类： 信号质量最差， 人眼分辨不出 QRS波， 算法完全无法分析；
3类： 噪声水平较高，人眼基本能分辨出 QRS波，但算法分析时既影响 QRS 波检测 , 也影响 QRS波分类的噪声；
2类： 噪声水平一般， 算法分析时影响 QRS波分类， 但不影响 QRS波检测 的噪声；
1类： 噪声水平很低， 不影响 QRS波检测， 也不影响 QRS波分类的轻微噪 声 ；
0类： 信号质量最好， 人眼几乎看不出噪声， 对算法分析无任何影响。 基于以上 5个子 SQI, 通过如下判断（如果不满足某个判断条件， 则进入下 一个判断 )可以得到评价生理信号 (例如 ECG信号， 即心电信号 )的综合 SQI (本 例中简称为 ECGSQI), 其中， 阈值确定可按如下实例选择：
① kSQI相关阈值： THD— K = 7;
② bSQI相关阈值： THD B：:: 80;
③ sSQI相关阈值： THD S = 60;
④ hSQI相关阈值： THD H 1 = 400; THD H2 = 300; THD H3 = 200; THD H4 = 150;
⑤ bslSQI相关阈值： THD BSL1 = 40; THD BSL2 = 20。 判断 1: (kSQI> THD— K)&&( sSQI> THD S ||hSQI> THD— Hl)&& bslSQI > THD BSL2, 有 'GSQI = 1；
判断 2: (kSQI> THD_K)&&( sSQI > THD S \\ hSQl > THD— Hl)&& bslSQI <= THD BSL2, 有 ECGSQI = 2;
判断 3: (bslSQI<= THD_BSL2)&&(hSQI<= THD H3), 有 ECGSQI = 2; 判断 4: (bslSQI <= THD— BSL2)&&(hSQI<= THD H4) , 有 ECGSQI = 3； 判断 5: (THD_BSL2< bslSQI <= THD BSLl )&&(hSQl> THD H2) , 有 ECGSQI = 1;
判断 6： (THD_BSL2< bslSQI <= THD— BSLI)&&( THD— H3<hSQI<= THD— H2), 有 ECGSQI = 2;
判断 7: (THD_BSL2< bslSQI <= THD_BSLI)&&( hSQI<= THD H3), 有 ECGSQI = 3;
判断 8: (bslSQI > THD_BSL2)&&(hSQI> THD H4). 有 ECGSQI = 0;
判断 9: (bslSQI > THD— BSL2)&&(hSQI<= THD— H4), 有 ECGSQI = 1； 判断 10: 如果 bSQI>THD— B, 且 ECGSQI >=3, 有 ECGSQI = 2;
上述判断全不符合则 ECGSQI=0。
容易理解， 综合 SQI计算不限于上述方法， 核心是通过当前信号的一些状态 或者特征来得到其对算法分析的影响。 例如， 另一个实施方式的综合 SQI计算方 式如下： 当 sSQI、 kSQL hSQL bslSQI表明信号质量好时， 信任 bSQI结果， 根 据 bSQI来确定综合 SQI;当 sSQI低时，由于异常谱分布干扰的存在，会造成 bSQI 失真， 通过 kSQI, hSQL bslSQI取阔值的方式来得到综合 SQI; 当上述情况不 满足且 kSQI指示信号质量低时，通过 bSQI乘以调整因子 h降低对信号质量的信 任度。 具体的计算过程及阈值设定如下：
其中， 阈值设定为：
THD K = 7
THD B1 = 80、 THD B2 = 60、 THD B3 = 40、 THD B4 = 20
THD S = 60
TIIDJ11 = 300 TIIDJ12 = 200、 TI1D II3 = 150 THD BSLl = 50、 THD BSL2 = 30、 THD BSL3 = 15,
判断步骤如下：
判断 1： 若满足 ( kSQI >= THD ) && (sSQI >= THD S) && (hSQI >= THD H2) && (bslSQI >= THD_BSL2), 根据 bSQI进行综合 SQI (本例 中简称为 ECGSQI)分类， 如下：
4 if bSQI < TIID _ B4
3 if TIID― B4 <= bSQI < TIID B3
ECGSQI 2 if THD _ 53 <= bSQI < THD B2
1 if THD _ B2 <= bSQI < THD _B\
0 if TIID B\ <= hSQI 判断 2: 若判断 1不满足， 则进行以下判断： 若（sSQKTHD— S), 通 过 kSQI、 hSQI、 bslSQI取阈值的方式来得到综合 SQI
4 if (kSQI < THD— K、 & hSQI < THD— i 3) & 8L、MSQI < THD― BSL3)
3 if (kSQI < THD _K)& & (THD _H2> hSQI >= THD _H3) & & (THD _BSL2 > bslSQI >= THD _ BSL3)
ECGSQI --
2 if (kSQI < THD _K、8L & _H\> hSQI >= THD _H )Sc & (THD _BSL\ > bslSQI >= THD _ BSL 2) 1 if (kSQI >= THD QSL & >= THD— & MpslSQl >= THD― BSLX) 判断 3: 若判断 2不满足， 则进行以下判断： 若（kSQ THD— K), 根 据 bSQI乘以调整因子 h降低对信号质量的信任度， h是根据实验调整 的经验系数， 本实施方式中 h= 1.1。
4 ifbSQI<THD BA*h
3 if THD B4 *h<= bSQI < THD B3 * h
ECGSQI 2 if THD B3 *h<= bSQI < THD _B2 * h
1 if TIID B2 *h <= bSQI < THD B\ * h
0 ifTHD Bl*h<= bSQI 判断 4: 若上述条件都不满足， ECGSQI = 0。 上面描述了 ECG综合 SQI的计算， 对于 1PB和 SP02的综合 SQI的计算， 所用到的子 SQI可以是表征能量的 SQI (时域 /频域）， 例如上述的 sSQI; 表征基 漂的 SQI (时域 /频域）， 例如上述的 bslSQI; 表征高频噪声的 SQI (时域 /频域）， 例如上述的 hSQI; 表征信号纯度的 SQI (时域 /频域）； 表征 QRS波特征的 SQI (QRS波能量比例、 幅度比例等等）。 IBP和 SP02都可以计算得到综合 SQI, 但 由于其生理信号与 ECG的差异其计算的思路可以套用上面 , 但其具体计算参数 可以根据需要调整。 在另一个实施方式中, 如图 3所示， 对于 ECG信号， 步骤 S120中分别对所 述两个以上生理信号进行分析， 得到各生理信号的信号质量指数的步骤可以具体 包括如下步骤：
步骤 S310， 分别获得表征信号特征或状态的各生理信号的子信号质量指数。 本步骤可以与步骤 S210相同， 不再赘述。
步骤 S320,通过对获得的各生理信号的子信号质量指数进行计算，得到各生 理信号的综合信号质量指数。 本步骤可以与步骤 S220中计算综合信号质量指数 的方法相同， 不再赘述。
步骤 S330,修正各生理信号的综合信号质量指数,并将修正后的综合信号质 量指数作为该生理信号的信号质量指数。 因为在某几类特殊信号情况下得到的综 合 SQI值并不准确， 经过预先的判断， 在这几类特殊信号情况发生时， 综合 SQI 的策略需要进行修正处理， 如图 4所示， 修正综合信号质量指数的方法包括如下 步骤：
步骤 S410, 判断其是否为噪声， 若为噪声， 则置综合 SQI值为 4; 若不为噪 声， 则到下一个判断。 是否为噪声判断的一个例子如下：
相关阈值说明：
kSQI相关阈值: THD = 7;
bSQI相关阈值： THD B = 50;
sSQI相关阈值： THD S = 50。
噪声水平的判断可以基于以下 2点： 噪声判断预定时间内 （比如每秒钟）检 出的起搏信号是否大于噪声判断预定值（比如大于 10个）或者信号的高频噪声 是否大于噪声判断阈值。 高频噪声是否太大可由前面计算出的子 SQI进行判断， 也可以由一些经典的滤波方法得到高频噪声的标志， 或由一些经典的统计方法， 比如一段范围内 （比如 1秒）跨越一定阈值的次数统计得到高频噪声的标志。 如 果选择前面计算的子 SQI进行判断，判断 sSQK THD S且 bSQK THD B且 kSQK THD K, 如果满足， 则认为高频噪声太大。
步骤 S420, 判断其是否饱和， 如果当前饱和判断时间范围内 （比如 1秒）的 心电数据大于预先设置的饱和阈值的时间长度总和超过饱和判断时间阈值 ( 比 如 0.5秒），且该饱和判断时间阈值范围内没有检测到有效 QRS波则认为是饱和； 在饱和情况下置综合 SQI值为 4。 若不为饱和， 则到下一个判断。
步骤 S430, 判断其是否为停搏， 如果当前停搏判断时间范围（比如 2秒）内 心电数据最大最小值差小于停搏幅度阈值（比如 0.2mv ),或该停搏判断时间范围 内没有检测到有效 QRS波则认为是停搏；在停搏情况下置综合 SQI为 0。若不为 停搏， 则到下一个判断。
步骤 S440, 判断其是否为室颤， 若为室颤， 则置综合 SQI值为 0。 若不为室 颤， 则不修改综合 SQI值。 本步骤中， 根据子信号质量指数及波形形态参数得到 是否为室颤的判断。 子信号质量指数包括： 表征室颤类型的 kSQI、 表征有效信 号占所有信号比例的 sSQI; 表征波形形态参数包括： 是否为宽波（根据 QRS波 的宽度来判断， 本实施方式中， 如果 QRS波宽度大于 140ms, 则认为是宽波）、 窗口期内的宽波比例、波形最值差。 室颤的判断过程中，需要预先设定一些阔值， 并根据这些阈值进行判断。例如： kSQI是表征室颤类型的子 SQI,其有两个阈值， 一为极端阈值 THD— K1 若小于该值则极有可能是室颤， 另一个为典型阈值 THD K2若小于该阈值可能是室颤。 sSQI阈值 THD— S表示若超过该阈值则当前 信号为噪声的可能性较小， 若低于该阈值当前信号可能是噪声。 宽波比例极端阈 值 THD— WR1 , 大于等于该阈值当前信号极有可能是室颤； 宽波比例典型阈值 THD_W 2, 大于等于该阈值当前信号可能是室颤。 宽波个数阈值 THD— WN, 大 于该阈值可能是室颤。 QRS波个数阈值 THD— Q, 大于该阈值可能是室颤。 信号 差值阈值 THD— D, 波形最值差大于该阈值是有效的心电信号。
是否为室颤判断的一个例子如下：
室颤判断的阈值定义如下：
kSQI相关阈值： THD_K1 = 7; THD K2 = 4;
sSQI相关阈值： THD— S = 60；
宽波比例阈值： THD— WR1 = 0.6; THD_WR2 = 0.5; 宽波个数阈值： THD WN = 10;
Q S波个数阔值： THD Q = 3；
信号差值阈值： THD D = 0.2mv„
室颤判断具体步骤如下：
步骤 A: 判断 VF窗（室颤指数窗口期， 比如 4秒或 8秒） 内检出的 QRS波 个数 <=1； 且 VF窗内心电信号的最大最小值差〉 THD D; 且 kSQK THD 2; 若上述条件都满足则判定为室颤 , 否则进入下一步骤。
步骤 B: 最近 1秒的 QRS波是否为宽波， 若否则判定为非室颤； 是则进入 下一步骤。
步骤 C: 判断最近 4秒检出的 QRS波 R 间隔是否不均匀； 且最近 4秒内检 出的 QRS波宽波比例 > THD— W 1 , 宽波个数> THD— WN; 且 kSQI<= THD K1； 若上述三个条件都满足则判定为室颤， 否则进入下一步骤。 满足下面两个条件之 一则认为 RR间隔匀齐 ： 条件 1 : 用当前 QRS波 RR间期与最近的 16个 QRS 波 RR间期比较， 若当前 RR间期与一半以上的历史 RR间期差异小于 12.5%, 则认为是匀齐； 条件 2:当前 RR间期与最近的三个历史 RR间期差异小于 12.5%, 也认为匀齐。
步骤 D:判断最近 4秒检出的 QRS波 RR间隔是否不均匀；且 kSQK THD K2 且 sSQI>= THD— S; 若上述条件都满足则判定为室颤， 否则进入下一步骤。
步骤 E: 判断最近 4秒检出的 QRS波 RR间隔是否不均匀； 且最近 4秒内检 出的 QRS波宽波比例 > THD— WR2, 宽波个数> THD_WN; 且 kSQK THD K1且 sSQI>= THD_S; 若上述条件都满足则判定为室颤， 否则进入下一步骤。
步骤 F: 判断最近 4秒检出的 QRS波 RR间隔是否均匀； 且 kSQK THD K2 且 sSQI>= THD S; 或者当前检出的 QRS波个数 >= THD— Q。 若上述条件都满足 则判定为室颤， 否则判定为非室颤。
修正综合信号质量指数所采用的预判信息不限于图 4所示的方式， 只要是通 过该判断可以用来改善综合 SQI结果，或者通过判断来直接辅助心电分析结果的 都可以采用。 图 4中提出的 4种判断， 在这 4种情况下上述方法可能会得到错误 的综合 SQI结果， 因此需要将其修正。 不同的综合 SQI计算方法可能会导致在其 它情况下也会有综合 SQI计算不准的情况， 因此为了得到更加准确的综合 SQ1 , 可以对图 4进行适当的变形和修改， 比如只进行一种或两种或三种判断等， 判断 的次序也可以进行修改， 比如先进行饱和判断， 再进行噪声判断等。 步骤 S 140中， 根据得到的各生理信号的信号质量指数对由各生理信号得到 同种生理参数进行融合可以采用多种方式实现， 例如可以用加权平均的方法对由 各生理信号得到同种生理参数进行融合， 由各生理信号的信号质量指数确定由各 各生理信号得到的同种生理参数的权重。 可以根据预设的信号质量指数与权重的 对应关系确定由各生理信号得到的同种生理参数的权重 , 信号质量指数反映信号 质量好的生理信号的生理参数的权重大于信号质量指数反映信号质量差的生理 信号的生理参数的权重。 例如， 仍然以 ECG、 IBP、 SP02三种信号为例， 通过 步骤 S 120和 S 130获得 ECG的综合 SQI及心率值 ( HR )、 IBP的综合 SQI及脉 率值 ( PRibp )、 SP02的综合 SQI及脉率值 (PRspo2)后 , 可以根据 ECG的综合 SQI计算 ECG心率权重（ Cecg )、根据 IBP的综合 SQI计算 IBP脉率权重（ Cibp )、 根据 SP02的综合 SQI计算 SP02脉率权重 ( Cspo2 ), 再根据权重及心率 /脉率 得到最终计算的融合心率值, 其中：
预设的信号质量指数与权重的对应关系如下：
100 SO/ = 01
80 SQI = 2
50 SQI = 3
0 SQI = 4
MR 4- C * PR + C * PR
HR
c + c + Γ
(公式 1 )
若当前 ECG信号计算得到的综合 SQI = 2或 3 , 其计算得到的心率由于噪声 的干扰可能一直都处于跳变状态； 而当前 IBP及 SP02的信号质量较好， 其综合 SQI=0或 1 , 这时 IBP及 SP02计算得到的脉率都为稳定值， 那么根据心率融合 的计算公式可以得到较稳定的心率输出值， 这样就可以成功减少由于 ECG本身 干扰或心率跳变带来的误报警（例如心律不齐、 心动过速等）， 从而提高了监护 设备的抗干扰性和报警的准确性。 同时， 还可以使界面显示的参数数据更稳定， 避免使用者对设备产生不信任。
当然， 除了预设的信号质量指数与权重的对应关系外， 权重也可以用其他统 计的方法、 卡尔曼滤波的方法对所述两个以上同种生理参数进行融合。 例如， 如图 5所示， 权重通过如下步骤获得：
步骤 S 142 , 根据历史由各生理信号得到的同种生理参数和经确认的生理参 数， 确定由各生理信号得到的同种生理参数的融合系数。 这里假设历史由各生理 信号 a、 b、 c得到的不同历史时期 1、 2、 3的同种生理参数为 Xl a、 Xlb、 Xlc. . . ; X2a、 X2b X2c. . . ; X3a、 X3b、 X3c. . .。 其中， 序号 1、 2、 3表示不同的历史时 期， a、 b、 c表示不同的生理信号。 历史的经确认的生理参数是指经过专家确认 后的正确的生理参数（比如 XI、 X2、 X3等, 其中的序号 1、 2、 3表示与生理参 数相同的历史时期）。 通过一定的数学方法， 比如神经网络方法、 Bayes (贝叶斯） 方法、 卡尔曼滤波法等， 可以确定由不同生理信号 a、 b、 c得到的同种生理参数 的融合系数 Pa、 Pb、 Pc, 从而使得：
Pa *Xla+Pb*Xlb+Pc*Xl c=Xl ;
Pa *X2a+Pb*X2b+Pc*X2c=X2;
Pa *X3a+Pb*X3b+Pc*X3c=X3„
步骤 S 144 ,根据各生理信号的信号质量指数及其得到的同种生理信号的融合 系数， 确定由各生理信号得到的生理参数的权重。 确定生理参数权重的方式有多 种， 取决于信号质量指数的表示形式， 比如由生理信号 a得到的生理参数 Xa的 系数为 Pa, 信号质量指数为 SQIa (在本实施方式中， 信号质量越好， SQI数值 越小）， 生理参数 Xa的权重 Ca可以表示为： Ca= Pa/ (SQIa + 1)A2或 Ca = Pa / (SQIa+ 1)等。 权重 Ca的计算公式可以根据需要进行调整， 不限于上述两种计算 公式，公式的基本原理为由信号质量指数反映信号质量好的生理信号得到的生理 参数的权重大于由信号质量指数反映信号质量差的生理信号得到的生理参数的 权重。
计算获得权重后， 可以使用与公式 1相似的加权平均的方法来进行生理参数 的融合。 根据历史的生理参数来确定权重， 可以根据以往的情况来调整各生理参数的 比重， 从而进一步使得融合后获得的生理参数接近真实情况。 如图 6所示，还提供了一实施方式的生理参数处理系统， 包括接收模块 610、 信号质量指数模块 620、 获取模块 630和融合模块 640。
接收模块 610用于获取两个以上生理信号。 生理信号可以是心电信号（ECG 信号）、 有创血压信号 ( IBP 信号 )、 血氧信号 ( SP02信号）等。 两个以上生理 信号指的是包括两个和超过两个的情况，比如生理信号的数量可以是两个、三个、 四个等。 获取到的生理信号可以为传感器采集到的原始信号或者经过滤波或者其 它处理后的信号。
信号质量指数模块 620分别对所述两个以上生理信号进行分析获，得到各生 理信号的信号质量指数（ SQI, Signal Quality Index ), SQI是表现信号质量好坏的 评价， 可以采用多种方式计算获得， 例如单项的计算或是综合的计算等， 还可以 根据一些预先的判断进行修正。 信号质量指数的获得方式将在下文详细描述。
获取模块 630用于分别对所述两个以上生理信号进行处理, 获得由各生理信 号得到的同种生理参数。 例如进行 IBP算法分析、 SP02算法分析、 ECG算法分 析， 得到相应的生理参数, 例如 IBP信号生理参数（舒张压、 收缩压、 平均压、 脉率）、 SP02信号生理参数（脉率、 血氧饱和度）、 ECG生理参数（心率、 心律 失常）等，在这些生理参数中，例如同种生理参数有： ECG的心率值（HR )、 IBP 的脉率值 ( PRlbp )、 SP02的脉率值 (PRsp。2), 这些同种生理参数反映了相同的生 理状态， 只是信号源不同。
融合模块 640根据得到的各生理信号的信号质量指数对由各生理信号得到的 同种生理参数进行融合， 得到融合后的生理参数。 融合可以用加权平均的方法对 所述两个以上同种的生理参数进行融合， 由各生理信号的信号质量指数确定由各 对应生理信号得到的生理参数的权重， 信号质量指数反映信号质量好的生理信号 的生理参数的权重大于信号质量指数反映信号质量差的生理信号的生理参数的 权重； 融合也可以用其他统计的方法、 卡尔曼滤波的方法对所述由各生理信号得 到的同种生理参数进行融合。 上述生理参数处理系统， 通过获取两个以上生理信号及各自的信号质量指 数， 根据信号质量指数对两个以上同种生理参数进行融合， 从而避免仅基于单个 生理信号获得的参数带来的弊端， 在该单个生理信号受到干扰时， 可以利用其他 生理信号的生理参数来完善。 如图 7所示， 信号质量指数模块 620包括子信号质量指数单元 622和第一综 合信号质量指数单元 624。
子信号质量指数单元 622用于分别获得表征信号特征或状态的各生理信号的 子信号质量指数。以生理信号为心电信号为例，子 SQI可以是 kSQI、 bSQL sSQI、 hSQL bslSQI中的一种或多种。 kSQI表征室颤类型： 其越大那么越有可能是无噪 声的 QRS波， 一般为 7; sSQI表征有效信号占所有信号的比例： 其越大那么越 有可能是无噪声的 QRS波， 一般为 0.6; bslSQI表征基漂大小： 其越大表明基漂 噪声越小，那么其对算法影响越小，那么根据其可能对算法的影响程度分为两级， 当然也可以分为多级， 跟具体的算法相关； hSQI表征高频噪声大小： 其越大表 明高频噪声越小， 那么其对算法影响越小， 可才艮据其可能对算法的影响程度分为 四级， 当然也可以分为多级， 跟具体算法相关； bSQI 表征综合噪声大小： 其越 小表明噪声越小， 那么对算法影响越小， 可以根据其对算法的影响程度分级， 跟 具体算法相关。 其中：
kSQI能有效表征室颤类型的特征， 其定义如下： kSQI = J
σ ； 其中， X为需要计算的离散信号或连续信号， Α和 分别为离散信号 X或连 续信号 X的均值和标准差， E为数学里的期望运算符号。
bSQI表征噪声大小， 为逐博波动匹配信号质量指数， 其定义如下：
bSQ =
Figure imgf000018_0001
其中， k为当前分析的 QRS波， w为滑动分析窗口 （宽度可取 10s ), 以当前 QRS波（k )为中心， 左右各取 1/2窗宽， Nmatched为在 w中两种不同的 QRS波检 测算法（任意两种 QRS波检测算法都可以，例如 DF算法和 LT算法）检出的 QRS 波匹配数目， Nall为在 w中两种算法各自检测出的 QRS波的数目并集总和， 即 Nall = Ni+Nr Nmatched, 为在 w中 QRS波检测算法 1检测出的 QRS波数目， N2 为在 w中 QRS波检测算法 2检测出的 QRS波数目。 QRS波匹配是根据美国国家 标准医疗器械促进协会（ AAMI )的推荐标准， 当两种算法对同一 QRS波位置标 注在 150ms之内时，认为是同一个 QRS波。 bSQI的意义在于， 当信号质量好时， 使用的两种算法都可以正确标注 QRS波， bSQI值高； 当干扰发生时， 干扰的存 在使 DF和 LT算法产生了不同的误判 , bSQI值低。 也就是说该 bSQI能表征噪 声的好坏。 bSQI的计算同样可应用于 IBP和 SP02, 但其匹配的时间窗应根据各 自的标准来设定， 例如上述 ECG为 150ms, IBP和 SP02可为 200ms .
sSQI表征有效信号占所有信号的比例， 表示 QRS波的功率谱密度值占总的 功率谱密度的值比例， 如下式所示：
Figure imgf000019_0001
其中以心电信号为例， QRS波主要的能量集中在以 10Hz为中心的、 宽度约 为 10Hz 的频带内， 总的能量上限一般在 50Hz左右， 所以公式中的 thdl可以选 为 5 Hz, thd2可以选为 14Hz, thd3可以选为 50Hz。 根据对心电信号的谱分析， QRS波的能量主要集中在约以 10Hz为中心的、 宽度约为 10Hz的频带内， 该功 率谱密度 (PSD)值占总 PSD值的比例可以作为判断心电信号质量的参考指标。 sSQI的计算可应用于 IBP和 SP02, 但其计算的带宽应根据各自的标准来设定， 例如上述 ECG为计算 5〜14Hz占 5〜50Hz的大小， IBP可计算 0〜10Hz占 0〜55Hz 的大小 , SP02可计算 0.2 12Hz占 0.2〜60Hz的大小。
hSQI是表征高频噪声大小的指数， 其计算如下式所示：
hSQI - 10 * imniORS, amplitudc/ f noise, ) · 式中 QRSi— amplitude是指当前检测到的 QRS波幅度大 'J、； hf_noisei是 QRS 波之前 0.28s~0.05s 的 sum的平均值， 而 sum(i)=|hf(i)|+|lif(i-l)|+...+|lif(i-5)|)， hf 是将 ECG信号经过如下的一个高通滤波器： hf(i)= X(i)- χ(ί-1)+ χ(ί- 2)得到的值，其 中 χ就是原始的心电波形或经过处理后的心电波形数据。
bslSQI是表征基漂大小的指数, 其计算如下式所示： bslSQI - 10 * min(QRSi― amplitude/baseline,― amplitude)
其中， QRS,— amplitude是 QRS波范围内 ( R-().()7s~R+().()8s ) 的最大最小值 差; baseline^ amplitude是基线判断窗口期（R- ls〜R+ls ) 的最大最小值差。
子 SQI不限于上述的 5种，任何对当前信号分类或者表征其某种状态的参数 都可以。 举例如下: 表征能量的 SQI (时域 /频域）、表征基漂的 SQI (时域 /频域 )、 表征高频噪声的 SQI (时域 /频域）、表征信号纯度的 SQI (时域 /频域）、表征 QRS 波特征的 SQI ( QRS波能量比例、 幅度比例等等）、 表征不同算法检测结果差异 / 相同的 SQI等。
第一综合信号质量指数单元 624通过对获得的各生理信号的子信号质量指数 进行计算， 得到各生理信号的综合信号质量指数， 将各生理信号的综合信号质量 指数作为信号质量指数模块 620获得的信号质量指数。
按照噪声水平， 可以将信号的综合 SQI值分为以下五类:
4类： 噪声水平最高 , 人眼分辨不出生理信号, 算法完全无法分析；
3类: 噪声水平较高 , 人眼较难能分辨出生理信号， 算法分析完全受影响；
2类： 噪声水平一般 , 人眼能较容易分辨出生理信号， 算法分析部分受影响;
1类: 噪声水平很低 , 有轻微的噪声， 但对算法分析无任何影响；
0类: 信号质量最好 , 人眼几乎看不出噪声， 算法分析完全不受影响。
综合 SQI分类不限于上述的 5类， 可以是 4类、 6类等， 还可以是能表征或 者区别当前信号的状态的参数， 可以是对信号的分类的参数等等， 或者是上述所 述参数的组合或者采用一定数学方法计算得到的值。
如果以心电信号进行 SQI分类， 可以按照如下标准进行分类， 但也不限于这 样的标准， 可以是以其他任何表征不同程度噪声或不同类型噪声对算法产生不同 影响的标准：
4类： 信号质量最差， 人眼分辨不出 QRS波， 算法完全无法分析；
3类： 噪声水平较高，人眼基本能分辨出 QRS波，但算法分析时既影响 QRS 波检测 , 也影响 QRS波分类的噪声；
2类： 噪声水平一般， 算法分析时影响 QRS波分类， 但不影响 QRS波检测 的噪声； 1类： 噪声水平很低， 不影响 QRS波检测， 也不影响 QRS波分类的轻微噪
基于以上 5个子 SQI, 通过如下判断 （如果不满足某个判断条件, 则进入下 一个判断）可以得到评价生理信号（例如 ECG信号， 即心电信号 )的综合 SQI (本 例中简称为 ECGSQI), 其中， 阈值确定可按如下实例选择：
① kSQI相关阔值： THD = 7；
② bSQI相关阈值： THD— B = 80；
③ sSQI相关阈值： THD— S = 60；
④ hSQI相关阈值： THD— Hl = 400; THD_H2 = 300; THD— H3 = 200;
THD_H4 = 150;
⑤ bslSQI相关阈值： THD— BSL1 = 40; THD— BSL2 = 20。
判断 1 : (kSQI> THD— K)&&( sSQI> THD— S ||hSQI> THD— Hl)&& bslSQI > THD— BSL2, 有 ECGSQI = 1 ;
判断 2: (kSQI> THD— K)&&( sSQI > THD— S || hSQI > THD— Hl)&& bslSQI <= THD_BSL2, ECGSQI = 2;
判断 3: (bslSQI<= THD— BSL2)&&(hSQI<= THD_H3), 有 ECGSQI = 2; 判断 4: (bslSQI <= THD— BSL2)&&(hSQI<= THD— H4), 有 ECGSQI = 3; 判断 5 : (THD_BSL2< bslSQI <= THD— BSLl)&&(hSQI> THD_H2) , 有 ECGSQI = 1 ;
判断 6： (THD_BSL2< bslSQI <= THD— BSL1)&&( THD— H3<hSQI<= THD_H2), 有 ECGSQI = 2;
判断 7: (THD_BSL2< bslSQI <= THD— BSL1)&&( hSQI<= THD H3), 有 ECGSQI = 3;
判断 8: (bslSQI > THD— BSL2)&&(hSQI> THD_H4), 有 ECGSQI = 0;
判断 9: (bslSQI > THD— BSL2)&&(hSQI<= THD— H4), 有 ECGSQI = 1 ; 判断 10: 如果 bSQl〉THD— B, JL ECGSQI >=3, 有 ECGSQI = 2; 上述判断全不符合则 ECGSQI=0。
容易理解， 综合 SQI计算不限于上述方法， 核心是通过当前信号的一些状态 或者特征来得到其对算法分析的影响。 例如， 另一个实施方式的综合 SQI计算方 式如下： 当 sSQI、 kSQK hSQL bslSQI表明信号质量好时， 信任 bSQI结果， 根 据 bSQI来确定综合 SQI;当 sSQI低时，由于异常谱分布干扰的存在，会造成 bSQI 失真， 通过 kSQI、 hSQL bslSQI取阈值的方式来得到综合 SQI; 当上述情况不 满足且 kSQI指示信号质量低时，通过 bSQI乘以调整因子 h降低对信号质量的信 任度。 具体的计算过程及阈值设定如下：
其中， 阈值设定为：
THD K = 7
THD B1 = 80、 THD B2 = 60、 THD B3 = 40、 THD_B4 = 20
THD S = 60
THD H1 = 300、 THD H2 = 200、 THD H3 = 150
THD BSL1 = 50、 THD— BSL2 = 30、 THD— BSL3 = 15。 判断步骤如下：
判断 1： 若满足（ kSQI >= THD— K ) && (sSQI >= THD— S) && (hSQI >= THD_H2) && (bslSQI >= THD_BSL2), 根据 bSQI进行综合 SQI (本例 中简称为 ECGSQI )分类， 如下：
4 if bSQI < THD B
3 if THD― B4 <= bSQI < THD― B3
ECGSQI = { 2 if THD _ 53 <= bSQI < THD _ B2
1 if THD _ B2 <= bSQI < THD _ Bl
0 if THD— B\ <= bSQI 判断 2: 若判断 1不满足， 则进行以下判断： 若（sSQK THD— S ), 通 过 kSQI、 hSQL bslSQI取阈值的方式来得到综合 SQI 4 if (kSQI < THD _K)& & (hSQI < THD _ H3) & & (bslSQI < THD _ BSL3)
3 if (kSQI < THD— K、& & (THD _H2 > hSQI >= THD _ H3) & &(THD _ BSL2 > bslSQI >= THD _ BSL3)
ECGSQI：
if (kSQI < THD 、 & (7ΪΖ)—m> hSQI >= THD— & &{THD― BSLi > bslSQI >= THD―
1 if (kSQI >= THD _ ) & Sc tSQI >= THD _ HI) & &{bslSQI >= THD _ BSL\) 判断 3: 若判断 2不满足， 则进行以下判断： 若 (kSQKTHD K), 根 据 bSQI乘以调整因子 h降低对信号质量的信任度, h是根据实验调整 的经验系数， 本实施方式中 h=l.l。
4 if hSQI < TUD _B4*
3 if THD B4 * h 二- hSQI < THD B3-
ECGSQI 2 if THD _B *h<= bSOl < TUD B2-
1 if THD _B2*h <= bSOl < THD _BV
0 ifTHD B\*h<= hSQI 判断 4: 若上述条件都不满足， ECGSQI = 0。
上面描述了 ECG综合 SQI的计算， 对于 IPB和 SP02的综合 SQI的计算， 所用到的子 SQI可以是表征能量的 SQI (时域 /频域）， 例如上述的 sSQI; 表征基 漂的 SQI (时域 /频域）， 例如上述的 bslSQI; 表征高频噪声的 SQI (时域 /频域）, 例如上述的 hSQI; 表征信号纯度的 SQI (时域 /频域）； 表征 QRS波特征的 SQI ( QRS波能量比例、 幅度比例等等）。 IBP和 SP02都可以计算得到综合 SQI, 但 由于其生理信号与 ECG的差异其计算的思路可以套用上面， 但其具体计算参数 可以根据需要调整。 在另一个实施方式中， 如图 8所示， 对于 ECG信号， 信号质量指数模块 620 包括子信号质量指数单元 623、 第二综合信号质量指数单元 625和修正单元 626。 其中子信号质量指数单元 623和第二综合信号质量指数单元 625与前述实施方式 的子信号质量指数单元 622和第一综合信号质量指数单元 624相应 , 不再赘述。
修正单元 626用于修正各生理信号的综合信号质量指数， 并将修正后的综合 信号质量指数作为该生理信号的信号质量指数。 因为在某几类特殊信号情况下得 到的综合 SQI值并不准确， 经过预先的判断， 在这几类特殊信号情况发生时， 综 合 SQI的策略需要进行修正处理， 如图 4所示， 修正单元 626修正综合信号质量 指数的过程如下： 步骤 S410, 判断其是否为噪声， 若为噪声， 则置综合 SQI值为 4; 若不为噪 声， 则到下一个判断。 是否为噪声判断的一个例子如下：
相关阈值说明：
kSQI相关阈值: THD = 7;
bSQI相关阈值： THD B = 50;
sSQI相关阈值： THD S = 50。
噪声水平的判断可以基于以下 2点: 噪声判断预定时间内 （比如每秒钟）检 出的起搏信号是否大于噪声判断预定值 (比如大于 10个）或者信号的高频噪声 是否大于噪声判断阈值。 高频噪声是否太大可由前面计算出的子 SQI进行判断， 也可以由一些经典的滤波方法得到高频噪声的标志， 或由一些经典的统计方法， 比如一段范围内 （比如 1秒）跨越一定阈值的次数统计得到高频噪声的标志。 如 果选择前面计算的子 SQI进行判断，判断 sSQK THD S且 bSQK THD B且 kSQK THD K, 如果满足， 则认为高频噪声太大。
步骤 S420, 判断其是否饱和， 如果当前饱和判断时间范围内 （比如 1秒）的 心电数据大于预先设置的饱和阈值的时间长度总和超过饱和判断时间阈值 ( 比 如 0.5秒），且该饱和判断时间阔值范围内没有检测到有效 QRS波则认为是饱和； 在饱和情况下置综合 SQI值为 4。 若不为饱和， 则到下一个判断。
步骤 S430, 判断其是否为停搏， 如果当前停搏判断时间范围（比如 2秒）内 心电数据最大最小值差小于停搏幅度阔值（比如 0.2mv ),或该停搏判断时间范围 内没有检测到有效 QRS波则认为是停搏；在停搏情况下置综合 SQI为 0。若不为 停搏， 则到下一个判断。
步骤 S440, 判断其是否为室颤， 若为室颤， 则置综合 SQI值为 0。 若不为室 颤， 则不修改综合 SQI值。 本步骤中， 根据子信号质量指数及波形形态参数得到 是否为室颤的判断。 子信号质量指数包括： 表征室颤类型的 kSQI、 表征有效信 号占所有信号比例的 sSQI; 表征波形形态参数包括： 是否为宽波（根据 QRS波 的宽度来判断， 本实施方式中， 如果 QRS波宽度大于 140ms, 则认为是宽波）、 窗口期内的宽波比例、波形最值差。 室颤的判断过程中，需要预先设定一些阈值， 并根据这些阈值进行判断。例如： kSQI是表征室颤类型的子 SQI,其有两个阈值， 一为极端阈值 THD_ 1 若小于该值则极有可能是室颤， 另一个为典型阈值 THD K2若小于该阔值可能是室颤。 sSQI阈值 THD S表示若超过该阔值则当前 信号为噪声的可能性较小， 若低于该阈值当前信号可能是噪声。 宽波比例极端阈 值 THD WR 1 , 大于等于该阈值当前信号极有可能是室颤； 宽波比例典型阈值 THD_WR2, 大于等于该阈值当前信号可能是室颤。 宽波个数阈值 THD— WN, 大 于该阈值可能是室颤。 QRS波个数阈值 THD— Q, 大于该阈值可能是室颤。 信号 差值阈值 THD— D, 波形最值差大于该阈值是有效的心电信号。
是否为室颤判断的一个例子如下：
室颤判断的阈值定义如下：
kSQI ? 关阈值： THD_K1 = 7; THD K2 = 4;
sSQI相关阈值： THD S = 60;
宽波比例阈值： THD— WR1 = 0.6; THD— WR2 = 0.5;
宽波个数阈值： THD— WN = 10;
QRS波个数阈值： THD— Q = 3；
信号差值阈值： THD_D = 0.2mv„
室颤判断具体步骤如下：
步骤 A: 判断 VF窗（室颤指数窗口期， 比如 4秒或 8秒） 内检出的 QRS波 个数 <=1； 且 VF窗内心电信号的最大最小值差> THD— D; 且 kSQK THD— K2; 若上述条件都满足则判定为室颤， 否则进入下一步骤。
步骤 B: 最近 1秒的 QRS波是否为宽波， 若否则判定为非室颤； 是则进入 下一步骤。
步骤 C: 判断最近 4秒检出的 QRS波 R 间隔是否不均匀； 且最近 4秒内检 出的 QRS波宽波比例〉 THD— W 1 , 宽波个数> THD_WN; 且 kSQI<= THD K1； 若上述三个条件都满足则判定为室颤， 否则进入下一步骤。 满足下面两个条件之 一则认为 RR间隔匀齐 ： 条件 1 : 用当前 QRS波 RR间期与最近的 16个 QRS 波 RR间期比较， 若当前 RR间期与一半以上的历史 R 间期差异小于 12.5%， 则认为是匀齐； 条件 2:当前 RR间期与最近的三个历史 RR间期差异小于 12.5%, 也认为匀齐。 步骤 D:判断最近 4秒检出的 QRS波 RR间隔是否不均匀；且 kSQK THD K2 且 sSQI〉= THD— S; 若上述条件都满足则判定为室颤， 否则进入下一步骤。
步骤 E: 判断最近 4秒检出的 QRS波 RR间隔是否不均匀； 且最近 4秒内检 出的 QRS波宽波比例〉 THD— WR2, 宽波个数> THD_WN; 且 kSQK THD K1且 sSQI>= THD_S; 若上述条件都满足则判定为室颤， 否则进入下一步骤。
步骤 F: 判断最近 4秒检出的 QRS波 R 间隔是否均匀； 且 kSQK THD K2 且 sSQI>= THD S; 或者当前检出的 QRS波个数 >= THD— Q。 若上述条件都满足 则判定为室颤， 否则判定为非室颤。
修正综合信号质量指数所釆用的预判信息不限于图 4所示的方式， 只要是通 过该判断可以用来改善综合 SQI结果，或者通过判断来直接辅助心电分析结果的 都可以采用。 图 4中提出的 4种判断， 在这 4种情况下上述方法可能会得到错误 的综合 SQI结果， 因此需要将其修正。 不同的综合 SQI计算方法可能会导致在其 它情况下也会有综合 SQI计算不准的情况， 因此为了得到更加准确的综合 SQI, 可以对图 4进行适当的变形和修改， 比如只进行一种或两种或三种判断等， 判断 的次序也可以进行修改， 比如先进行饱和判断， 再进行噪声判断等。 融合模块 640根据得到的各生理信号的信号质量指数对由各生理信号得到的 同种生理参数进行融合可以采用多种方式实现， 例如可以用加权平均的方法对所 述两个以上同种的生理参数进行融合， 由各生理信号的信号质量指数确定由各生 理信号得到的同种生理参数的权重。 可以根据预设的信号质量指数与权重的对应 关系确定由各生理信号得到的同种生理参数的权重，信号质量指数反映信号质量 好的生理信号的生理参数的权重大于信号质量指数反映信号质量差的生理信号 的生理参数的权重。 例如， 仍然以 ECG、 IBP, SP02三种信号为例， 通过步骤 S120和 S130获得 ECG的综合 SQI及心率值（ HR )、 IBP的综合 SQI及脉率值 ( PRibp )、 SP02的综合 SQI及脉率值 (PRspo2)后， 可以根据 ECG的综合 SQI 计算 ECG心率权重（Cecg )、 根据 IBP的综合 SQI计算 IBP脉率权重（ Cibp )、 根据 SP02的综合 SQI计算 SP02脉率权重（Cspo2 )， 再根据权重及心率 /脉率 得到最终计算的融合心率值， 其中： 预设的信号质量指数与权重的对应关系如下：
100 SQ1 = 01
80 SQI = 2
C
50 SQI = 3
0 SQI = 4 最终融合心率的计算公式如下：
Hp _ Cecg * HR + Clbp * PRlbp + Cspo2 * PRspo2
ce + ibp + cspo2 (公式 i ) 若当前 ECG信号计算得到的综合 SQI = 2或 3， 其计算得到的心率由于噪声 的干扰可能一直都处于跳变状态； 而当前 IBP及 SP02的信号质量较好， 其综合 SQI=0或 1 , 这时 IBP及 SP02计算得到的脉率都为稳定值， 那么根据心率融合 的计算公式可以得到较稳定的心率输出值， 这样就可以成功减少由于 ECG本身 干扰或心率跳变带来的误报警（例如心律不齐、 心动过速等）， 从而提高了监护 设备的抗干扰性和报警的准确性。 同时， 还可以使界面显示的参数数据更稳定， 避免使用者对设备产生不信任。
当然， 除了预设的信号质量指数与权重的对应关系外， 权重也可以用其他统 计的方法、 卡尔曼滤波的方法对所述两个以上同种生理参数进行融合。 例如， 如图 9所示， 融合模块 640包括系数单元 642及权重单元 644, 并通过如下方式 获得权重：
系数单元 642根据历史由各生理信号得到的同种生理参数和经确认的生理参 数， 确定由各生理信号得到的同种生理参数的融合系数。 这里假设历史由各生理 信号 a、 b、 c得到的不同历史时期 1、 2、 3的同种生理参数为 Xla、 Xlb、 Xlc...; X2a、 X2b、 X2c...; X3a、 X3b、 X3c...。 其中， 序号 1、 2、 3表示不同的历史时 期， a、 b、 c表示不同的生理信号。 历史的经确认的生理参数是指经过专家确认 后的正确的生理参数（比如 XI、 X2、 X3等， 其中的序号 1、 2、 3表示与生理参 数相同的历史时期）。 通过一定的数学方法， 比如神经网络方法、 Bayes (贝叶斯） 方法、 卡尔曼滤波法等， 可以确定由不同生理信号 a、 b、 c得到的同种生理参数 的融合系数 Pa、 Pb、 Pc, 从而使得：
Pa *Xla+Pb*Xlb+Pc*Xlc=Xl ; Pa *X2a+Pb*X2b+Pc*X2c=X2;
Pa *X3a+Pb*X3b+Pc*X3c=X3。
权重单元 644各生理信号的信号质量指数及其得到的同种生理信号的融合系 数，确定由各生理信号得到的生理参数的权重。确定生理参数权重的方式有多种， 取决于信号质量指数的表示形式, 比如由生理信号 a得到的生理参数 Xa的系数 为 Pa, 信号质量指数为 SQIa (在本实施方式中， 信号质量越好， SQI数值越小）， 生理参数 Xa的权重 Ca可以表示为： Ca= Pa/ (SQIa + 1 )Λ2或 Ca Pa I (SQIa+ 1 ) 等。 权重 Ca的计算公式可以根据需要进行调整， 不限于上述两种计算公式， 公 式的基本原理为由信号质量指数反映信号质量好的生理信号得到的生理参数的 权重大于信号质量指数反映信号质量差的生理信号得到的生理参数的权重。
计算获得权重后， 可以使用与公式 1相似的加权平均的方法来进行生理参数 的融合。
根据历史的生理参数来确定权重， 可以根据以往的情况来调整各生理参数的 比重， 从而进一步使得融合后获得的生理参数接近真实情况。
上述生理参数处理系统可以应用在监护设备中。 以上所述实施方式仅表达了 本发明的几种实施方式， 其描述较为具体和详细， 但并不能因此而理解为对本发 明专利范围的限制。 应当指出的是， 对于本领域的普通技术人员来说, 在不脱离 本发明构思的前提下， 还可以做出若干变形和改进， 这些都属于本发明的保护范 围。 因此， 本发明专利的保护范围应以所附权利要求为准。

Claims

权利要求书
1、 一种生理参数处理方法， 其特征在于， 包括如下步骤：
获取两个以上生理信号；
分别对所述两个以上生理信号进行分析， 得到各生理信号的信号质量指数; 分别对所述两个以上生理信号进行处理 , 获得由各生理信号得到的同种生理 参数； 及
根据所述各生理信号的信号质量指数对所述由各生理信号得到的同种生理 参数进行融合， 得到融合后的生理参数。
2、 根据权利要求 1 所述的生理参数处理方法， 其特征在于， 在所述根据所 述各生理信号的信号质量指数对所述由各生理信号得到同种生理参数进行融合 的步骤中： 用加权平均的方法对所述由各生理信号得到的同种生理参数进行融 合， 由所述各生理信号的信号质量指数确定由各生理信号得到的同种生理参数的 权重。
3、 根据权利要求 2所述的生理参数处理方法， 其特征在于， 根据预设的信 号质量指数与权重的对应关系确定由各生理信号得到的同种生理参数的权重，信 号质量指数反映信号质量好的生理信号的生理参数的权重大于信号质量指数反 映信号质量差的生理信号的生理参数的权重。
4、 根据权利要求 2所述的生理参数处理方法， 其特征在于， 所述由各生理 信号得到的生理参数的权重的步骤包括：
根据历史由各生理信号得到的同种生理参数和经确认的生理参数， 确定由各 生理信号得到的同种生理参数的融合系数；
根据各生理信号的信号质量指数及其得到的同种生理信号的融合系数， 确定 由各生理信号得到的同种生理参数的权重。
5、 根据权利要求 1 所述的生理参数处理方法， 其特征在于， 所述分别对所 述两个以上生理信号进行分析 , 得到各生理信号的信号质量指数的步骤包括： 分别获得表征信号特征或状态的各生理信号的子信号质量指数；
通过对获得的各生理信号的子信号质量指数进行计算， 得到各生理信号的综 合信号质量指数， 将所述各生理信号的综合信号质量指数作为该生理信号的信号 质量指数。
6、 根据权利要求 1 所述的生理参数处理方法， 其特征在于， 所述分别对所 述两个以上生理信号进行分析， 得到各生理信号的信号质量指数的步骤包括: 分别获得表征信号特征或状态的各生理信号的子信号质量指数；
通过对获得的各生理信号的子信号质量指数进行计算， 得到各生理信号的综 合信号质量指数；
修正各生理信号的综合信号质量指数， 并将修正后的综合信号质量指数作为 该生理信号的信号质量指数。
7、 根据权利要求 5 所述的生理参数处理方法， 其特征在于， 所述子信号质 量指数包括表征能量大小、 表征信号纯度、 表征 QRS 波特征、 表征室颤类型、 表征有效信号占所有信号的比例、 表征基漂大小、 表征高频噪声大小、 表征噪声 大小、 表征不同算法检测结果差异 /相同中的一种或多种。
8、 一种生理参数处理系统， 其特征在于, 包括：
接收模块， 获取两个以上生理信号;
信号质量指数模块, 分别对所述两个以上生理信号进行分忻, 得到各生理信 号的信号质量指数；
获取模块， 分别对所述两个以上生理信号进行处理， 获得由各生理信号得到 的同种生理参数； 及
融合模块， 根据所述各生理信号的信号质量指数对所述由各生理信号得到的 同种生理参数进行融合， 得到融合后的生理参数。
9、 根据权利要求 8所述的生理参数处理系统， 其特征在于， 所述融合模块 用加权平均的方法对由各生理信号得到的同种生理参数进行融合， 由各生理信号 的信号质量指数确定由各生理信号得到的同种生理参数的权重。
10、 根据权利要求 9所述的生理参数处理系统, 其特征在于， 所述融合模块 根据预设的信号质量指数与权重的对应关系确定由各生理信号得到的同种生理 参数的权重， 信号质量指数反映信号质量好的生理信号的生理参数的权重大于信 号质量指数反映信号质量差的生理信号的生理参数的权重。
1 1、 根据权利要求 9所述的生理参数处理系统， 其特征在于， 所述融合模块 包括：
系数单元， 根据历史由各生理信号得到的同种生理参数和经确认的生理参 数， 确定由各生理信号得到的同种生理参数的融合系数；
权重单元 , 根据各生理信号的信号质量指数及其得到的同种生理信号的融合 系数， 确定由各生理信号得到的同种生理参数的权重。
12、 根据权利要求 8所述的生理参数处理系统, 其特征在于， 所述信号质量 指数模块包括：
子信号质量指数单元， 分别获得表征信号特征或状态的各生理信号的子信号 质量指数；
第一综合信号质量指数单元， 通过对获得的各生理信号的子信号质量指数进 行计算， 得到各生理信号的综合信号质量指数， 将所述各生理信号的综合信号质 量指数作为该生理信号的信号质量指数。
13、 根据权利要求 8所述的生理参数处理系统， 其特征在于， 所述信号质量 指数模块包括：
子信号质量指数单元， 分别获得表征信号特征或状态的各生理信号的子信号 质量指数；
第二综合信号质量指数单元， 通过对获得的各生理信号的子信号质量指数进 行计算， 得到各生理信号的综合信号质量指数；
修正单元， 修正各生理信号的综合信号质量指数， 并将修正后的综合信号质 量指数作为该生理信号的信号质量指数。
14、 根据权利要求 12 所述的生理参数处理系统， 其特征在于， 所述子信号 质量指数包括表征能量大小、 表征信号纯度、 表征 QRS波特征、 表征室颤类型、 表征有效信号占所有信号的比例、 表征基漂大小、 表征高频噪声大小、 表征噪声 大小、 表征不同算法检测结果差异 /相同中的一种或多种。
15、 一种包含权利要求 8所述的生理参数处理系统的监护设备。
PCT/CN2014/074131 2013-09-13 2014-03-26 生理参数处理方法、系统及监护设备 Ceased WO2015035764A1 (zh)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP3417765A4 (en) * 2016-02-18 2019-10-09 Shenzhen Mindray Bio-Medical Electronics Co., Ltd METHOD, DEVICE AND SYSTEM FOR COMBINING PROCESSING OF PHYSIOLOGICAL PARAMETER SIGNALS

Families Citing this family (17)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104921723B (zh) * 2015-05-15 2017-12-01 华南理工大学 一种基于多模态脑机接口的意识状态检测系统
CN105266776B (zh) * 2015-09-06 2017-11-10 陈才维 一种用于人体健康监测的方法
US9787676B2 (en) * 2015-09-29 2017-10-10 Anhui Huami Information Technology Co., Ltd. Multi-modal biometric identification
US10321836B2 (en) 2015-11-30 2019-06-18 Physio-Control, Inc. Context scores to enhance accuracy of ECG readings
CN105411567B (zh) * 2015-12-15 2018-04-24 北京世纪今科医疗器械有限公司 一种心电图数据分析方法及系统
CN105769172B (zh) * 2016-02-25 2018-11-27 苏州脉吉医疗技术有限公司 适用于传感器的信号处理方法
ES3029533T3 (en) * 2016-06-29 2025-06-24 Hoffmann La Roche Method for providing a signal quality degree associated with an analyte value measured in a continuous monitoring system
CN106777862A (zh) * 2016-11-02 2017-05-31 深圳市元征科技股份有限公司 一种基于体域网的数据处理方法和装置
WO2019019119A1 (en) 2017-07-27 2019-01-31 Vita-Course Technologies (Hainan) Co., Ltd. SYSTEMS AND METHODS FOR DETERMINING THE BLOOD PRESSURE OF A SUBJECT
CN107693000A (zh) * 2017-10-23 2018-02-16 山东大学 基于心电和脉搏信号融合的心率估计方法、装置和系统
CN114073498A (zh) * 2020-08-13 2022-02-22 深圳迈瑞生物医疗电子股份有限公司 监护设备及其生理参数处理方法
CN114190906A (zh) * 2020-09-18 2022-03-18 深圳迈瑞生物医疗电子股份有限公司 一种生理参数监测设备及其监测方法
CN113395189B (zh) * 2021-06-30 2023-03-24 重庆长安汽车股份有限公司 一种车载以太网sqi信号质量测试方法及系统
CN114388122A (zh) * 2021-07-23 2022-04-22 Oppo广东移动通信有限公司 参数融合处理方法、装置、可穿戴设备及存储介质
CN115381421A (zh) * 2022-08-26 2022-11-25 深圳市骁巨智能科技有限公司 一种基线漂移脉搏波峰提取的方法及其装置
CN116421197B (zh) * 2023-03-22 2025-12-23 深圳市科曼医疗设备有限公司 决策规则的确定方法及装置、设备及存储介质
CN119791628A (zh) * 2024-11-29 2025-04-11 北京津发科技股份有限公司 基于人因智能的生理信号质量评估方法、电子设备和介质

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101683260A (zh) * 2008-09-28 2010-03-31 深圳迈瑞生物医疗电子股份有限公司 一种节律性生理信号的优化处理方法及装置
CN102283642A (zh) * 2011-06-10 2011-12-21 中国科学院深圳先进技术研究院 一种基于躯感网的可连续测量多生理参数的穿戴式系统
CN102316792A (zh) * 2009-02-17 2012-01-11 皇家飞利浦电子股份有限公司 用于自动获取和存档临床上有意义的体征的系统和方法

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5626140A (en) * 1995-11-01 1997-05-06 Spacelabs Medical, Inc. System and method of multi-sensor fusion of physiological measurements
US8346349B2 (en) * 2008-01-16 2013-01-01 Massachusetts Institute Of Technology Method and apparatus for predicting patient outcomes from a physiological segmentable patient signal
CN102270264B (zh) * 2010-06-04 2014-05-21 中国科学院深圳先进技术研究院 生理信号质量评估系统及方法
US8577440B2 (en) * 2011-03-29 2013-11-05 Covidien Lp Method and system for positioning a sensor

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101683260A (zh) * 2008-09-28 2010-03-31 深圳迈瑞生物医疗电子股份有限公司 一种节律性生理信号的优化处理方法及装置
CN102316792A (zh) * 2009-02-17 2012-01-11 皇家飞利浦电子股份有限公司 用于自动获取和存档临床上有意义的体征的系统和方法
CN102283642A (zh) * 2011-06-10 2011-12-21 中国科学院深圳先进技术研究院 一种基于躯感网的可连续测量多生理参数的穿戴式系统

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP3417765A4 (en) * 2016-02-18 2019-10-09 Shenzhen Mindray Bio-Medical Electronics Co., Ltd METHOD, DEVICE AND SYSTEM FOR COMBINING PROCESSING OF PHYSIOLOGICAL PARAMETER SIGNALS
US11478198B2 (en) 2016-02-18 2022-10-25 Shenzhen Mindray Bio-Medical Electronics Co., Ltd. Physiological parameter signal fusion processing method, apparatus, and system

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