WO2022062237A1 - 智能穿戴式胎动监测*** - Google Patents

智能穿戴式胎动监测*** Download PDF

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WO2022062237A1
WO2022062237A1 PCT/CN2020/139817 CN2020139817W WO2022062237A1 WO 2022062237 A1 WO2022062237 A1 WO 2022062237A1 CN 2020139817 W CN2020139817 W CN 2020139817W WO 2022062237 A1 WO2022062237 A1 WO 2022062237A1
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fetal movement
channel
peak
data
index
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PCT/CN2020/139817
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English (en)
French (fr)
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贾朋飞
张莹莹
杨洪波
刘永峰
郭凯
吕甜甜
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永康国科康复工程技术有限公司
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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
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A61B5/1118—Determining activity level
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/43—Detecting, measuring or recording for evaluating the reproductive systems
    • A61B5/4306—Detecting, measuring or recording for evaluating the reproductive systems for evaluating the female reproductive systems, e.g. gynaecological evaluations
    • A61B5/4343—Pregnancy and labour monitoring, e.g. for labour onset detection
    • A61B5/4362—Assessing foetal parameters
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
    • A61B5/6802—Sensor mounted on worn items
    • 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
    • A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
    • A61B5/7207—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal of noise induced by motion artifacts
    • 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
    • A61B5/7235—Details of waveform analysis
    • 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
    • A61B5/7235—Details of waveform analysis
    • A61B5/725—Details of waveform analysis using specific filters therefor, e.g. Kalman or adaptive filters
    • 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
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2503/00—Evaluating a particular growth phase or type of persons or animals
    • A61B2503/02—Foetus

Definitions

  • the invention relates to the technical field of fetal movement detection, in particular to an intelligent wearable fetal movement monitoring system.
  • Fetal movement is the most intuitive feeling that pregnant women have about the health of the fetus in the uterus.
  • pregnant women can detect abnormal changes in fetal movement.
  • Most of the guidelines involving fetal movement self-monitoring behavior affirm the importance of fetal movement self-monitoring.
  • the "Guidelines for Pre-pregnancy and Pregnancy Health Care (2016)” recommend that pregnant women start counting fetal movements at 29-30 weeks of pregnancy1, and most foreign guidelines recommend pregnant women at 28 weeks. After that, monitor fetal movement every day 2 . Decreased fetal movement is often the first sign of fetal death3 .
  • a New Zealand case-control study of 155 stillbirths and 310 live births showed that sudden, intense fetal movements over a short period of time were associated with a nearly seven-fold increased risk of stillbirth. Sudden, frequent and intense fetal movement, especially the subsequent reduction or disappearance of fetal movement, is a manifestation of acute fetal distress .
  • the longest static time of the fetus is more than 50min, and the fetal activity is less than 10%, which means that the number of fetal movements is less. If such a situation occurs frequently, pregnant women and medical staff should pay great attention to it. Frequently reduced fetal movement is closely related to preterm birth or even stillbirth.
  • the monitoring of more forms of fetal movement indicators (such as the longest fetal static time, fetal activity, etc.) has important medical reference significance. level, it is difficult to fully reflect the status of the fetus.
  • the technical problem to be solved by the present invention is to provide an intelligent wearable fetal movement monitoring system in view of the above-mentioned deficiencies in the prior art.
  • an intelligent wearable fetal movement monitoring system comprising: a data acquisition module, an intelligent terminal, a data storage and analysis and determination module and a data management module deployed on a cloud server;
  • the data acquisition module includes N acquisition units arranged on the abdominal wall of the pregnant woman at intervals for collecting vibration signals of the abdominal wall of the pregnant woman, so as to perform parallel acquisition of N channel data;
  • the intelligent terminal is used for transmitting the multi-channel data collected by the data collection module to the data storage and analysis and determination module;
  • the data storage and analysis and determination module includes a data storage unit and an analysis and determination unit, the data storage unit receives and stores the multi-channel data sent by the intelligent terminal, and the analysis and determination unit analyzes and determines the multi-channel data to
  • the vibration signal distinguishes the fetal movement signal and the noise signal, and further distinguishes and determines the specific form of the fetal movement signal and the noise signal to form a classification result; wherein, the specific form of the fetal movement signal includes at least the percussion form of fetal movement and the strong form of fetal movement, and the noise signal.
  • the specific forms of noise include at least breathing noise, sneezing noise and body motion noise;
  • the data management module includes a statistical unit and an application terminal, and the data management module receives the classification result sent by the data storage and analysis and determination module, and accordingly forms a multi-dimensional fetal movement index per unit monitoring time;
  • the application terminal displays the multi-dimensional fetal movement index obtained by the statistical unit.
  • the collection unit is a wearable pressure sensor disposed on the abdominal wall of the pregnant woman.
  • the application terminal includes a management background and a personal user terminal.
  • the multi-dimensional fetal movement index includes at least the number of fetal movements per unit monitoring time, the form of fetal movement, the longest duration of fetal movement, the proportion of strong fetal movement in the longest duration, the longest fetal static time, and fetal activity;
  • the longest duration of the fetal movement is: the total time occupied by the interval with the longest duration among the determination unit intervals that are continuously determined as fetal movement signals within the unit monitoring time;
  • the proportion of strong fetal movement within the longest duration is: within the unit monitoring time, in the longest duration interval of fetal movement, the percentage of the interval determined to be a strong form of fetal movement;
  • the longest static time of the fetus is: the total time occupied by the interval with the longest duration among the determination unit intervals that are continuously determined to be noise signals within the unit monitoring time;
  • the fetal activity is: the percentage of the determination units determined as fetal movement signals in all determination units in the unit monitoring time in the unit monitoring time.
  • the method for performing fetal movement monitoring by the smart wearable fetal movement monitoring system comprises the following steps:
  • the intelligent terminal wirelessly transmits the multi-pass data collected by the data collection module to the data storage and analysis and determination module;
  • the data storage unit in the data storage and analysis and determination module receives and stores the multi-channel data sent by the intelligent terminal, and then the analysis and determination unit in the data storage and analysis and determination module analyzes and determines the multi-channel data,
  • the vibration signal is distinguished from the fetal movement signal and the noise signal, and the specific form of the fetal movement signal and the noise signal is further distinguished and determined to form a classification result;
  • the data management module receives the classification result sent by the data storage and analysis and determination module, and accordingly forms a multi-dimensional fetal movement index per unit monitoring time;
  • the management background and the personal user terminal in the application terminal display the obtained multi-dimensional fetal movement index for the user to obtain.
  • the method for analyzing and determining the multi-channel data by the analyzing and determining unit in the step S3 includes the following steps:
  • the data of the N channels received in the unit monitoring time is cut and segmented according to the timeline to form multiple independent judgment units, and then the multiple judgment units are divided one by one according to the timeline. Analyze and determine through the following steps;
  • the vibration signal is firstly judged by distinguishing the fetal movement signal and the noise signal, and then the specific form of the fetal movement signal and the noise signal is discriminated and judged to form a classification result, and then the analysis and judgement of the next judgment unit is carried out;
  • step 4) Repeat the above step 3) until the analysis and determination of all the determination units within the unit monitoring time are completed.
  • the step 1) specifically includes: first, the vibration signal is smoothed by a method including at least wavelet threshold denoising and Butterworth filtering, and then a baseline drift correction is performed by using an asymmetric least squares baseline correction method .
  • the step 3) specifically includes:
  • r max-min , r max-min meanRowValue max -meanRowValue min ,
  • meanRowValue max represents the maximum value of the mean value of the row vector
  • meanRowValue min represents the minimum value of the mean value of the row vector
  • index max represents the index of the maximum value of the row vector mean value, The index of the minimum value of the mean value of the row vector;
  • step 2-1) carry out the concrete classification of fetal movement signal and noise signal to the vibration signal of this determination unit according to the multi-channel feature obtained in step 2-1), including:
  • the Pearson correlation coefficient range is divided as follows: 0.8-1.0, very strong correlation; 0.6-0.8, strong correlation; 0.4-0.6, moderate correlation; 0.2-0.4, weak correlation; 0.0-0.2, very strong correlation Weak correlation or irrelevance; ⁇ 0, negative correlation; two thresholds A and B need to be determined and improved by comprehensively considering the number of channels N and the above statistical experience;
  • the peak-to-peak quotient of at least one channel is greater than the set threshold value P T , then it is determined as the fetal movement in the form of percussion, and the classification result is output, otherwise the following steps continue to be determined;
  • the threshold P T needs to be determined and perfected according to the sensing characteristics (sensitivity, linearity, etc.) of the sensing device; generally speaking, 2.0 ⁇ P T ⁇ 2.5;
  • the threshold value thresholdValue needs to be determined and perfected according to the sensing characteristics of the sensing device (response curve, linearity, etc.), and the proportional coefficient I needs to be determined and perfected according to the performance of the sample.
  • the proportional coefficient I needs to be determined and perfected according to the performance of the sample.
  • the acquisition units include four uniformly spaced laterally arranged on the abdominal wall of the pregnant woman, so as to perform parallel acquisition of 4-channel data.
  • the step 3) specifically includes:
  • cov(X, Y) represents the covariance between two variables X, Y, ⁇ X , ⁇ Y represents the standard deviation of the two variables X, Y; s0, s1, s2, s3 represent the four-channel passing through
  • Pearson correlation coefficients between the four channels namely ⁇ 0,1 , ⁇ 0,2 , ⁇ 0,3 , ⁇ 1,2 , ⁇ 1,3 , ⁇ 2 , 3.
  • ⁇ 0 1/3( ⁇ 0,1 + ⁇ 0,2 + ⁇ 0,3 );
  • ⁇ 1 1/3( ⁇ 0,1 + ⁇ 1,2 + ⁇ 1,3 );
  • ⁇ 2 1/3( ⁇ 0,2 + ⁇ 1,2 + ⁇ 2,3 );
  • ⁇ 3 1/3( ⁇ 0,3 + ⁇ 1,3 + ⁇ 2,3 );
  • r max-min meanRowValue max -meanRowValue min ,
  • meanRowValue max represents the maximum value of the mean value of the row vector
  • meanRowValue min represents the minimum value of the mean value of the row vector
  • index max represents the index of the maximum value of the row vector mean value, The index of the minimum value of the mean value of the row vector;
  • step 3-1) carry out the concrete classification of fetal movement signal and noise signal to the vibration signal of this judgment unit according to the multi-channel feature obtained in step 3-1), including:
  • the present invention deploys the data storage and analysis and determination module on the cloud server. Compared with the traditional method of embedding it in the hardware terminal or front-end software, the significant advantages of doing so are: good security and confidentiality, convenient iterative update of the algorithm, and easy calculation. Convenient performance expansion, good support for third-party libraries such as math library/AI algorithm library;
  • the present invention adopts the design method of multi-channel pressure sensor to collect the abdominal wall pressure signal of pregnant women, the number of channels is easy to expand, the extracted pressure signal is perfect and comprehensive, it is easy to distinguish and identify various signal features, and the accuracy and reliability are good;
  • the present invention can realize the obvious distinction and classification of different fetal movement forms such as tapping, strong, continuous and other noises, as well as noises such as body movement, sneezing, laughter, etc., through the fusion and comparison analysis algorithm based on the characteristics of multi-channel pressure signals.
  • the determination of fetal movement forms and the identification of noise are more comprehensive and complete;
  • the present invention can also include fetal movement forms (such as percussion, continuous, strong, etc.), the longest duration of fetal movement, the proportion of strong fetal movement in the longest duration, the longest fetal static time, and fetal activity. For the first time, these indicators quantitatively describe pregnant women's perception of fetal movement intensity, characteristics, and duration, and have important medical reference significance.
  • fetal movement forms such as percussion, continuous, strong, etc.
  • Fig. 1 is the principle block diagram of the intelligent wearable fetal movement monitoring system of the present invention
  • Fig. 2 is the schematic diagram of the 4-channel pressure sensor signal acquisition of the present invention
  • Fig. 3 is the working flow chart of the intelligent wearable fetal movement monitoring system of the present invention.
  • FIG. 4 is a preprocessing effect diagram of a breathing signal in a resting state in an embodiment of the present invention
  • Fig. 5 is the signal preprocessing effect diagram under the fetal movement trigger state in the embodiment of the present invention.
  • FIG. 6 is a waveform diagram of several representative signals enumerated in the present invention.
  • FIG. 7 is a matrix diagram formed by pressure data of four-channel signals in an embodiment of the present invention.
  • FIG. 8 is a flowchart of specific classification of fetal movement signals and noise signals in an embodiment of the present invention.
  • an intelligent wearable fetal movement monitoring system in this embodiment includes: a data acquisition module, an intelligent terminal, a data storage and analysis and determination module and a data management module deployed on a cloud server.
  • the data collection module includes N collection units arranged on the abdominal wall of the pregnant woman at intervals and used for collecting vibration signals of the abdominal wall of the pregnant woman, so as to perform parallel data collection of N channels.
  • the acquisition unit is a wearable pressure sensor disposed on the abdominal wall of pregnant women.
  • parallel multi-channel pressure sensors are used to collect the pressure distribution of the abdominal wall of pregnant women, which is convenient for expansion and maintenance.
  • the number of sensors can be flexibly configured according to the sensing characteristics of the device (such as load area, sensitivity, etc.), as long as it can reflect the pressure trend changes in the entire abdominal wall range. If the number of sensors is too small, there will be a lack of signal capture. Too many sensors will lead to redundant signal acquisition, large data volume, and increased cost.
  • the number of sensors should be at least not less than 3, which can cover the left, middle and right regions of the abdominal wall of the pregnant woman.
  • a 4-channel pressure sensor is used for description (as shown in Figure 2).
  • S0, S1, S2, and S3 respectively represent 4-channel pressure sensors, which are evenly distributed laterally on the abdominal wall of pregnant women.
  • This distribution method can minimize fetal movement.
  • the packaging area of the monitoring belt is small, and the skin area in direct contact with pregnant women is small, which can significantly improve the wearing comfort.
  • the fetal movement signal collected at the far position in the longitudinal direction will be slightly weakened, and this problem can be well solved by means of data preprocessing in the present invention.
  • S0, S1, S2, and S3 collect the normal abdominal wall breathing signals of pregnant women; when the fetal movement is in the form of "knocking", the triggered fetal movement is located at f0, and the fetal movement wave signal can be detected by S0 , S1 sensor acquisition, the signal strength received by S2, S3 is very weak; when the fetal movement form is "strong", it can trigger multiple areas of the abdominal wall of pregnant women, such as f1_a and f1_b areas, the fetal movement signal can be collected by S0, S3 sensors , the signal strength received by S1 and S2 is very weak.
  • the transmission between the sensor and the main control circuit can use a flexible cable or a fabric wire, which has the characteristics of invisibility, flattening, and flexibility.
  • the sensor can be integrated into existing products such as abdominal support belt, fetal monitoring belt or elastic fabric, or the circuit can be encapsulated in silicone material, which can significantly improve the comfort and breathability of pregnant women when they wear it for a long time.
  • the acquisition frequency of the sensor depends on the situation. Generally speaking, it is between 10Hz and 100Hz. If it is too low, it is not conducive to the extraction and analysis of signal characteristics. If it is too high, it will put forward greater requirements on data transmission stability, battery power supply, and server storage space.
  • the intelligent terminal is used for transmitting the multi-channel data collected by the data collection module to the data storage and analysis and determination module.
  • the smart terminal is selected as a smart phone or tablet, etc., and the smart terminal is connected to the sensor, data storage and analysis and determination module by wireless communication (Bluetooth or WIFI, etc.).
  • the data storage and analysis and determination module includes a data storage unit and an analysis and determination unit, the data storage unit receives and stores the multi-channel data sent by the intelligent terminal, and the analysis and determination unit analyzes and determines the multi-channel data to
  • the vibration signal distinguishes the fetal movement signal and the noise signal, and further distinguishes and determines the specific form of the fetal movement signal and the noise signal to form a classification result; wherein, the specific form of the fetal movement signal includes at least the percussion form of fetal movement and the strong form of fetal movement, and the noise signal.
  • the specific forms of noise include at least breathing noise, sneezing noise and body motion noise.
  • the data storage and analysis and determination module is deployed on the cloud server, and the intelligent terminal uses bluetooth or wifi technology to upload to the cloud server and store it;
  • the significant advantages of the traditional method in front-end software are: good security and confidentiality, convenient iterative update of algorithms, convenient expansion of computing performance, and good support for third-party libraries such as math library/AI algorithm library.
  • fetal movement needs to be monitored 3 times a day for 1 hour each time.
  • the invention stores the data in the cloud server, which not only facilitates the elastic expansion of the storage capacity, but also facilitates the security and privacy protection of the user data.
  • the data management module includes a statistical unit and an application terminal, and the classification result of the data storage and analysis and determination module is sent back to the data management module, and the data management module receives the classification result sent by the data storage and analysis and determination module, and according to This forms a multi-dimensional fetal movement indicator within the unit monitoring time.
  • the application terminal displays the multi-dimensional fetal movement indicators obtained by the statistical unit.
  • the application terminal may include a management background and a personal user terminal (such as a mobile phone or a computer, etc.)
  • the user terminal can be used by a single pregnant woman to know her fetal movement assessment.
  • the multi-dimensional fetal movement index includes at least the number of fetal movements within the unit monitoring time, the form of fetal movement, the longest duration of fetal movement, the proportion of strong fetal movement in the longest duration, the longest fetal static time and fetal activity, specifically:
  • the longest duration of fetal movement is: the total time occupied by the interval with the longest duration among the determination unit intervals continuously determined as fetal movement signals within a unit monitoring time.
  • the proportion of strong fetal movement within the longest duration is: the percentage of the interval determined to be a strong form of fetal movement in the longest duration interval of fetal movement within the unit monitoring time.
  • the longest duration of fetal movement is 60s.
  • the fetal movement form is “strong”
  • the longest static time of the fetus is: the total time occupied by the interval with the longest duration among the determination unit intervals that are continuously determined to be noise signals within the unit monitoring time.
  • the above fetal movement indicators can effectively characterize the subjective feelings of pregnant women on the intensity, characteristics and duration of fetal movement. The occurrence of too low and other conditions urgently need the attention of doctors or pregnant women.
  • the present invention can also include the fetal movement form (such as percussion, continuous, strong, etc.), the longest duration of fetal movement, the proportion of strong fetal movement in the longest duration, the longest fetal static time, and fetal activity.
  • fetal movement form such as percussion, continuous, strong, etc.
  • the longest duration of fetal movement such as percussion, continuous, strong, etc.
  • the proportion of strong fetal movement in the longest duration the longest fetal static time
  • fetal activity fetal activity
  • the method for performing fetal movement monitoring by the smart wearable fetal movement monitoring system includes the following steps:
  • the intelligent terminal wirelessly transmits the multi-pass data collected by the data collection module to the data storage and analysis and determination module;
  • the data storage unit in the data storage and analysis and determination module receives and stores the multi-channel data sent by the intelligent terminal, and then the analysis and determination unit in the data storage and analysis and determination module analyzes and determines the multi-channel data,
  • the vibration signal is distinguished from the fetal movement signal and the noise signal, and the specific form of the fetal movement signal and the noise signal is further distinguished and determined to form a classification result;
  • the data management module receives the classification result sent by the data storage and analysis and determination module, and accordingly forms a multi-dimensional fetal movement index per unit monitoring time;
  • the management background and the personal user terminal in the application terminal display the obtained multi-dimensional fetal movement index for the user to obtain.
  • the method that the analysis and determination unit analyzes and determines the multi-channel data comprises the following steps:
  • the data of the N channels received in the unit monitoring time is cut and segmented according to the timeline to form multiple independent judgment units, and then the multiple judgment units are divided one by one according to the timeline. Analyze and determine through the following steps;
  • the vibration signal is firstly judged to distinguish the fetal movement signal and the noise signal, and then the specific form of the fetal movement signal and the noise signal is discriminated and judged to form a classification result, and then the analysis and judgment of the next judgment unit is carried out;
  • step 4) Repeat the above step 3) until the analysis and determination of all the determination units within the unit monitoring time are completed.
  • step 1) specifically includes: first, the vibration signal is smoothed by a method including at least wavelet threshold denoising and Butterworth filtering, and then the baseline drift correction is performed by using the asymmetric least squares baseline correction method .
  • the hardware acquisition circuit has power frequency interference and high-frequency noise. Due to its own characteristics and changes in the external environment, the data collected by the pressure sensor has a baseline drift. The noise existing in these hardware modules is unavoidable. Such factors are important for subsequent feature extraction and model design. Great impact, good preprocessing of the collected data is an essential step in designing a good algorithm. Wavelet threshold denoising, Butterworth filtering and other means can achieve better smoothing effect, setting an ideal wavelet coefficient denoising threshold, or setting an appropriate Butterworth critical frequency can effectively solve the power frequency interference and For high-frequency noise problems, these parameters should be determined and improved according to the sampling frequency of the system and the trigger frequency of the effective signal.
  • Figure 4 and Figure 5 show the system's preprocessing process for typical signals.
  • Figure 4 shows the preprocessing effect of the breathing signal in the resting state
  • Figure 5 shows the signal preprocessing effect when the fetal movement is triggered.
  • step 2) of this embodiment the batch of multi-channel pressure sensing data is cut into segments to form a single independent determination unit.
  • the time span of a single determination unit needs to be determined by comprehensively considering the fetal movement trigger time and the system sampling frequency. Generally speaking , the time span of a single determination unit can be 2-15 seconds. If the determination unit within this time span has fetal movement characteristics, it is determined as "true”, that is, fetal movement signal, otherwise it is determined as "false", that is, noise signal.
  • the 4-channel pressure sensing data is cut into segments to form multiple judgment units connected in sequence, and each judgment unit includes 4 channels collected in the same time segment. data.
  • step 3) of this embodiment the collected data is specifically classified into: percussion fetal movement, strong fetal movement, breathing noise, sneezing noise and body movement noise.
  • the waveforms of several representative signals are listed as follows (as shown in Figure 6).
  • the 4 curves in the figure represent the pressure data collected by the 4 sensing channels respectively.
  • Figure 6a shows the data collected by the 4-channel sensor when there is no fetal movement.
  • the 4-channel data is the abdominal wall pressure signal generated by the normal breathing of the pregnant woman, and each channel has obvious breathing signal characteristics;
  • Figure 6b is a "tapping" form of The data waveform generated by fetal movement, the curve channel indicated by the arrow in the figure has obvious pressure pulse signal;
  • Figure 6c is the data waveform generated by the "strong” fetal movement, the “strong” fetal movement can trigger multiple abdominal wall areas, the arrow in the figure The two curve channels referred to can monitor the obvious pressure jump process;
  • Figure 6d shows the data waveform generated by the "sneezing" noise.
  • the 4-channel data shows a significant pressure increase, and then quickly drops to around 0, and then returns to In the normal pressure data range, there are fewer regular peaks and troughs;
  • Figure 6e shows the data waveform generated by "body motion" noise.
  • the pressure average of the 4 channels will increase significantly at the same time, and slowly return to the normal value, and there is no regularity. Peaks and valleys.
  • the algorithms provided in some embodiments of the present invention can perform feature fusion on the multi-channel sensor data of each determination unit, using shallow features (including but not limited to time-domain features, frequency-frequency features, wavelet series, etc.) and similarity Algorithms (including but not limited to Pearson correlation coefficient, Hausdorff distance, Frechet distance) and AI algorithms (including but not limited to KNN, logistic regression, SVM, etc.) Two labels of "true” and “false” are indicated), and further determine what form of fetal movement and what form of noise it belongs to.
  • shallow features including but not limited to time-domain features, frequency-frequency features, wavelet series, etc.
  • similarity Algorithms including but not limited to Pearson correlation coefficient, Hausdorff distance, Frechet distance
  • AI algorithms including but not limited to KNN, logistic regression, SVM, etc.
  • the acquisition units include four uniformly spaced laterally arranged on the abdominal wall of the pregnant woman, so as to perform parallel acquisition of 4-channel data.
  • step 3 the specific steps of analyzing and determining the determination unit by multi-channel feature analysis are:
  • cov(X, Y) represents the covariance between two variables X, Y, ⁇ X , ⁇ Y represents the standard deviation of the two variables X, Y; s0, s1, s2, s3 represent the four-channel passing through
  • Pearson correlation coefficients between the four channels namely ⁇ 0,1 , ⁇ 0,2 , ⁇ 0,3 , ⁇ 1,2 , ⁇ 1,3 , ⁇ 2 , 3.
  • ⁇ 0 1/3( ⁇ 0,1 + ⁇ 0,2 + ⁇ 0,3 );
  • ⁇ 1 1/3( ⁇ 0,1 + ⁇ 1,2 + ⁇ 1,3 );
  • ⁇ 2 1/3( ⁇ 0,2 + ⁇ 1,2 + ⁇ 2,3 );
  • ⁇ 3 1/3( ⁇ 0,3 + ⁇ 1,3 + ⁇ 2,3 ).
  • the fetal movement signal is a weak physiological parameter signal.
  • the four pressure sensors will not be triggered at the same time, and the Pearson correlation coefficient between the four channels is obviously weaker.
  • the noise signals such as breathing, sneezing, and body movement will stimulate the four pressure sensors at the same time, and the waveforms have similar development trends. Accordingly, they have high Pearson correlation coefficients, ⁇ 0 , ⁇ 1 , ⁇ 2 , ⁇ 3 is larger.
  • ⁇ 0.4 is weak or irrelevant.
  • 3-1-3 Perform spectrum analysis and normalization on the four-channel signal, and calculate the average value amp n of the corresponding amplitudes of the four channel signals at different frequencies, and count the number of channels whose amp n is greater than the amplitude threshold thresholdAmp, denoted as count ( amp>thresholdAmp) ;
  • the normalized amplitude thresholdAmp is about 0.2.
  • the periodic regular breathing signal is expressed as a cosine signal, the main frequency count (amp>thresholdAmp) is 2, and the two frequencies correspond to the constant term and the cosine frequency respectively.
  • meanRowValue represents the mean value of the row vector
  • index represents the index of the row vector
  • r max-min meanRowValue max -meanRowValue min ,
  • meanRowValue max represents the maximum value of the mean value of the row vector
  • meanRowValue min represents the minimum value of the mean value of the row vector
  • index max represents the index of the maximum value of the row vector mean value, The index of the minimum value of the mean value of the row vector;
  • the threshold thresholdValue can be set to 3 times the breathing amplitude.
  • the signals are classified into five categories: noise (breathing), noise (body movement), noise (sneezing), fetal movement (knocking), and fetal movement (strong) through the above algorithm, and labels 0 (breathing), -1 (body motion), -2 (sneezing), 1 (tapping), 2 (strong), as shown in Figure 7.
  • the performance of the classification algorithm is also evaluated, and the method used is: calculating the precision and recall indexes of each class, and multiplying by the weight proportion of the class in the total number of samples, The precision and recall indicators of the entire classification model are obtained, and the F1 value of the entire classification model is derived to determine the performance of the algorithm: the higher the F1 value, the more ideal the classification model is.
  • TP means that the correct prediction is a positive example
  • FP means that the wrong prediction is a positive example
  • FN means that the wrong prediction is a negative example.
  • the meaning of precision is: the proportion of correctly predicted positive (TP) accounts for all predicted positives (TP+FP)
  • the meaning of recall is: correctly predicted positive (TP) accounts for all positive samples (TP) +FN) ratio;
  • w 0 represents the weight of label 0 (breath) in all samples, w -2 , w -1 , w 1 , w 2 And so on; precision 0 indicates the precision rate of the class identified as label 0 (breath), recall 0 indicates the recall rate of the class identified as label 0 (breath), and so on for other categories;
  • the horizontal axis represents the true class, and the vertical axis represents the predicted class.

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Abstract

一种智能穿戴式胎动监测***，包括：数据采集模块、智能终端、部署于云服务器上的数据存储与分析判定模块以及数据管理模块。该***将数据存储与分析判定模块部署于云服务器，相比将其嵌入到硬件终端或前端软件中的传统方式，这样做的显著优点是：安全保密性好、算法迭代更新方便、计算性能拓展方便、对于数学库/AI算法库等第三方库支持良好；该***除了胎动次数，还可对包括胎动形式(如敲击、持续、强烈等)、胎动最长持续时间、最长持续时间内强烈胎动占比、胎儿最长静态时间、胎儿活跃度在内的多项胎动评估指标进行监测，这些指标首次定量描述了孕妇对于胎动强度、特征、持续时间的感知情况，具有重要的医学参考意义。

Description

智能穿戴式胎动监测*** 技术领域
本发明涉及胎动检测技术领域，特别涉及一种智能穿戴式胎动监测***。
背景技术
胎动是孕妇对胎儿宫内健康状况最直观的感受，当胎儿发生宫内损伤、发育迟缓甚至死亡时，孕妇都可通过胎动的异常变化有所察觉。涉及胎动自我监测行为的指南多数肯定了胎动自我监测的重要性，我国在《孕前和孕期保健指南(2018)》推荐孕妇在孕29-30周开始数胎动 1，国外指南大多推荐孕妇在28周后开始每天监测胎动 2。胎动减少经常是胎儿夭折的第一信号 3，2018澳大利亚/新西兰临床实践指南指出，孕妇应不仅仅关注胎动次数，对于胎动强度、胎动特征、持续时间的关注度更加重要 4。一般而言，胎动最长持续时间超过5min，最长持续时间内强烈胎动占比超过80％，意味着胎儿在此时间段一直处于活动状态，且动作强烈程度过高。当胎动异常活跃、强烈，持续时间过长时，孕妇及医护人员要谨慎预防出现脐带绕颈，胎儿供氧不足等现象。新西兰的一项病例对照研究纳入155例死胎病例和310例活产病例，研究表明，短时间内突然变强的胎动与死胎风险增加有关，风险增加将近7倍。胎动突然频繁、强烈，特别是随之出现的胎动减少或消失是急性胎儿窘迫的表现，如强烈胎动未能缓解，要警惕进一步引起胎儿死亡 6。胎儿最长静态时间超过50min，胎儿活跃度低于10％意味胎儿胎动次数较少，频繁出现此类情况，则应引起孕妇及医护人员高度重视。频繁出现的胎动减少与胎儿早产甚至死胎现象密切相关，大多数死胎都伴随3-4天的频繁胎动减少现象，55％经历死胎的孕妇都能明显感知到频繁的胎动减少 7。胎动减少被认为与胎儿生长受限、死胎等不良结局相关，胎动减少者发生死胎的风险增加4倍 8，还与感染、神经发育异常、母胎输血、胎盘功能不全、脐带并发症和紧急分娩、早产等不良妊娠结局密切相关。围产儿出现脑损伤、新生儿缺血缺氧性脑病等不良妊娠结局的可能性增加 9；10。综合考量胎动次数、胎儿最长静态时 间、胎儿活跃度指标，当频繁出现胎动较少情况，不论超声评估结果如何，都意味着孕妇存在胎盘功能障碍的高风险 7，需要进行进一步的医学诊断。
所以，除了胎动次数外，对更多形式的胎动指标(如胎儿最长静态时间、胎儿活跃度等)的监测具有重要的医学参考意义，但现有产品普遍还停留在仅能监测胎动次数的水平，难以全面反应胎儿的状态。
引用文献：
[1]中华医学会妇产科学分会产科学组.孕前和孕期保健指南(2018)[J].中华妇产科杂志,2018,53(1):7-13.
[2]张雯,张静.孕妇胎动自我监测行为研究现状及指南建议[J].中国妇幼健康研究,2017,28(2):213-215.
[3]Jf F.A kick from within-fetal movement counting and the cancelled progress in antenatal care[J].J Perinat Med,2004,32(1):13-24.
[4]Daly L M,Gardener G,Bowring V,et al.Care of pregnant women with decreased fetal movements:Update of a clinical practice guideline for Australia and New Zealand[J].Australian and New Zealand Journal of Obstetrics and Gynaecology,2018,58(4):463-468.
[5]Ryo E,Nishihara K,Matsumoto S,et al.A new method for long-term home monitoring of fetal movement by pregnant women themselves[J].Med Eng Phys,2012,34(5):566-72.
[6]T S,Jmd T,Ea M,et al.Maternal perception of fetal activity and late stillbirth risk:findings from the auckland stillbirth study[J].Birth,2011,38(4):311-316.
[7]Scala C,Bhide A,Familiari A,et al.Number of episodes of reduced fetal movement at term:association with adverse perinatal outcome[J].Am J Obstet Gynecol,2015,213(5):678e1-6.
[8]王萌璐,陈倩.死胎的病因与预防[J].中国实用妇科与产科杂志,2017,33(11):1121-1125.
[9]M T,H F,S S,et al.Brain damage caused by severe fetomaternal hemorrhage[J].Pediatrics International,2010,52(2):301-304.
[10]黑明燕.孕晚期胎动减少与新生儿缺氧缺血性脑损伤[J].中华实用儿科 临床杂志,2015,30(4):311-316.
发明内容
本发明所要解决的技术问题在于针对上述现有技术中的不足，提供一种智能穿戴式胎动监测***。
为解决上述技术问题，本发明采用的技术方案是：一种智能穿戴式胎动监测***，包括：数据采集模块、智能终端、部署于云服务器上的数据存储与分析判定模块以及数据管理模块；
所述数据采集模块包括间隔设置在孕妇腹壁上的N个用于采集孕妇腹壁的振动信号的采集单元，以进行N个通道数据并行采集；
所述智能终端用于将所述数据采集模块采集的多通道数据传输至所述数据存储与分析判定模块；
所述数据存储与分析判定模块包括数据存储单元和分析判定单元，所述数据存储单元接收并存储所述智能终端发送的多通道数据，所述分析判定单元对多通道数据进行分析判定，以将振动信号进行胎动信号与噪声信号的区分，并进一步对胎动信号与噪声信号的具体形式进行区分判定，形成分类结果；其中，胎动信号的具体形式至少包括敲击形式胎动和强烈形式胎动，噪声信号的具体形式至少包括呼吸噪声、喷嚏噪声和体动噪声；
所述数据管理模块包括统计单元以及应用终端，所述数据管理模块接收所述数据存储与分析判定模块发送的分类结果，并据此形成单位监测时间内的多维胎动指标；
所述应用终端展示所述统计单元得到的多维胎动指标。
优选的是，所述采集单元为可穿戴设置在孕妇腹壁上的压力传感器。
优选的是，所述应用终端包括管理后台和个人用户终端。
优选的是，所述多维胎动指标至少包括在单位监测时间内的胎动次数、胎动形式、胎动最长持续时间、最长持续时间内强烈胎动占比、胎儿最长静态时间以及胎儿活跃度；
其中，所述胎动最长持续时间为：单位监测时间内，连续被判定为胎动信号的判定单元区间中，持续时间最长的区间所占的总时间；
所述最长持续时间内强烈胎动占比为：单位监测时间内，胎动最长持续时间区间中，被判定为强烈形式胎动的区间所占的百分比；
所述胎儿最长静态时间为：单位监测时间内，连续被判定为噪声信号的判定单元区间中，持续时间最长的区间所占的总时间；
所述胎儿活跃度为：单位监测时间内，被判定为胎动信号的判定单元占单位监测时间内的所有判定单元的百分比。
优选的是，所述智能穿戴式胎动监测***进行胎动监测的方法包括以下步骤：
S1、通过所述数据采集模块对孕妇腹壁进行N个通道的的振动信号数据的并行采集；
S2、所述智能终端将所述数据采集模块采集的多通数据无线传输至所述数据存储与分析判定模块；
S3、所述数据存储与分析判定模块中的数据存储单元接收并储存所述智能终端发送的多通道数据，然后所述数据存储与分析判定模块中的分析判定单元对多通道数据进行分析判定，将振动信号进行胎动信号与噪声信号的区分，并进一步对胎动信号与噪声信号的具体形式进行区分判定，形成分类结果；
S4、所述数据管理模块接收所述数据存储与分析判定模块发送的分类结果，并据此形成单位监测时间内的多维胎动指标；
S5、所述应用终端中的管理后台和个人用户终端对获得的多维胎动指标进行展示，以供用户获取。
优选的是，所述步骤S3中分析判定单元对多通道数据进行分析判定的方法包括以下步骤：
1)数据预处理；
2)对预处理后的数据，首先将单位监测时间内接收到的N个通道的数据按时间线进行数据切割分段，形成多个独立的判定单元，然后按时间线对多个判定单元逐一通过以下步骤进行分析判定；
3)通过多通道特征分析先对振动信号进行胎动信号与噪声信号的区分判定，然后再对胎动信号与噪声信号的具体形式进行区分判定，形成分类结果， 之后进行下一个判定单元的分析判定；
4)重复上述步骤3)直至完成单位监测时间内的所有判定单元的分析判定。
优选的是，所述步骤1)具体包括：先对振动信号采用至少包括小波阈值去噪、巴特沃斯滤波在内的方法进行平滑处理，然后采用非对称最小二乘基线校正方法进行基线漂移校正。
优选的是，所述步骤3)具体包括：
3-1)对单个判定单元进行多通道特征提取，包括：
3-1-1)提取N个通道之间的皮尔逊相关系数，然后计算每个通道对于其他通道皮尔逊相关系数的均值，记为皮尔逊相关系数均值ρ n；
3-1-2)计算N个通道中每个通道的峰峰值商：根据采样频率，设定峰峰值之间的最小水平单位距离，在此距离下，计算每一个通道种最大峰值与最小峰值的比值，记为peakDivision N；
3-1-3)对N个通道信号做频谱分析及归一化处理，并计算N个通道信号不同频率对应振幅的平均值amp n，统计amp n大于振幅阈值thresholdAmp的通道数量，记为count (amp>thresholdAmp)；
3-1-4)矩阵特征分析：单个判定单元内，将N个通道信号的振动信号数据构成M*N的矩阵,M表示采样频率f与判定单元时长t的乘积，即M＝f*t；对于该矩阵，以meanRowValue表示行向量的均值，index表示行向量的索引，取如下特征：
a)行向量均值最大值与最小值的差值
r max-min，r max-min＝meanRowValue max-meanRowValue min，
其中，meanRowValue max表示行向量均值最大值，meanRowValue min表示行向量均值最小值；
[根据细则26改正01.02.2021]　
b)行向量均值最大值的索引与行向量均值最小值的索引差index max-min,
Figure WO-DOC-FIGURE-1
其中，index max表示行向量均值最大值的索引，
Figure WO-DOC-FIGURE-2
示行向量均值最小值的索引；
2-2)依据步骤2-1)获得的多通道特征对该判定单元的振动信号进行胎动信号与噪声信号的具体分类，包括：
3-2-1)设定2个皮尔逊相关系数判定阈值A和B，其中，0＜A＜B，将所有通道的皮尔逊相关系数均值ρ n中的最小值ρ min与A和B进行比较，再根据比较结果按以下不同步骤进行判定；
依据统计学经验，皮尔逊相关系数值域等级划分如下：0.8-1.0，极强相关；0.6-0.8，强相关；0.4-0.6，中等程度相关；0.2-0.4，弱相关；0.0-0.2，极弱相关或不相关；<0，负相关；A，B两个阈值，需综合考虑通道数N及上述统计学经验，确定并完善；
3-2-2)ρ min＞B时，统计N个通道中每个通道的峰峰值商peakDivisionN；
I、若存在有至少一个通道的峰峰值商大于设定的阈值P T时，则判定为敲击形式胎动，输出分类结果，否则再按以下步骤继续进行判定；
根据实验结果，阈值P T需根据传感器件传感特性(灵敏度、线性度等)确定并完善；一般而言，2.0≤P T≤2.5；
II、统计count (amp>thresholdAmp)的数量，若count (amp>thresholdAmp)＝J时，则判定为呼吸噪声，输出分类结果，其中J为不大于N的正整数；否则再按以下步骤继续进行判定；
III、设定矩阵特征分析阈值thresholdValue，若r max-min＞thresholdValue且index max-min<I*f时，判定为喷嚏噪声，否则判定为体动噪声；输出分类结果，其中，I为比例系数，1≤I≤2.5；
根据实验结果，阈值thresholdValue需根据传感器件传感特性(响应曲线、线性度等)确定并完善，比例系数I需根据样本表现确定并完善，一般而言，1≤I≤2.5；
3-2-3)ρ min＜A时，若所有通道的皮尔逊相关系数中，小于A的皮尔逊相关系数的数量不大于k时，则判定为敲击形式胎动，否则判定为强烈形式胎动，输出分类结果；其中k>N/2；根据实验结果，k值需根据通道数N及样本表现确定并完善，一般而言，k>N/2；
3-2-4)A＜ρ min＜B时，再统计N个通道中每个通道的峰峰值商peakDivisionN，若存在有至少一个通道的峰峰值商大于设定的阈值P T时，则再按以下步骤a)进行判定，否则按以下步骤b)进行判定；
a)若所有通道的皮尔逊相关系数中，小于A的皮尔逊相关系数的数量不大于k时，则判定为敲击形式胎动，否则判定为强烈形式胎动，输出分类结果；其中k>N/2；
b)设定矩阵特征分析阈值thresholdValue，若r max-min＞thresholdValue且index max-min<I*f时，判定为喷嚏噪声，否则判定为体动噪声；输出分类结果，其中，I为比例系数，1≤I≤2.5。
优选的是，所述采集单元包括沿横向均匀间隔设置在孕妇腹壁上的4个，以进行4通道数据并行采集。
优选的是，所述步骤3)具体包括：
3-1)对单个判定单元进行多通道特征提取，包括：
3-1-1)提取4个通道之间的皮尔逊相关系数，皮尔逊相关系数计算公式为：
ρ X,Y＝cov(X,Y)/σ Xσ Y＝E((X-μ X)(Y-μ Y))/σ Xσ Y；
其中，cov(X,Y)表示两个变量X，Y之间的协方差，σ X，σ Y表示两个变量X，Y的标准差；以s0,s1,s2,s3分别表示四通道经过预处理之后的振动信号数据，则四通道之间共有6个皮尔逊相关系数，即ρ 0,1，ρ 0,2，ρ 0,3，ρ 1,2，ρ 1,3，ρ 2,3，计算每个通道对于其他通道皮尔逊相关系数的均值，即：
ρ 0＝1/3(ρ 0,1+ρ 0,2+ρ 0,3)；
ρ 1＝1/3(ρ 0,1+ρ 1,2+ρ 1,3)；
ρ 2＝1/3(ρ 0,2+ρ 1,2+ρ 2,3)；
ρ 3＝1/3(ρ 0,3+ρ 1,3+ρ 2,3)；
3-1-2)计算4个通道中每个通道的峰峰值商：根据采样频率，设定峰峰值之间的最小水平单位距离，在此距离下，计算每一个通道中最大峰值与最小峰值的比值，记为peakDivision 0、peakDivision 1、peakDivision 2、peakDivision 3；
3-1-3)对四通道信号做频谱分析及归一化处理，并计算4个通道信号不同频率对应振幅的平均值amp n，统计amp n大于振幅阈值thresholdAmp的通道数量，记为count (amp>thresholdAmp)；
3-1-4)矩阵特征分析：单个判定单元内，将4个通道信号的振动信号构 成M*4矩阵，M表示采样频率f与判定单元时长t的乘积，即M＝f*t；对于该矩阵，以meanRowValue表示行向量的均值，index表示行向量的索引，取如下特征：
a)行向量均值最大值与最小值的差值r max-min，
r max-min＝meanRowValue max-meanRowValue min，
其中，meanRowValue max表示行向量均值最大值，meanRowValue min表示行向量均值最小值；
[根据细则26改正01.02.2021]　
b)行向量均值最大值的索引与行向量均值最小值的索引差index max-min,
Figure WO-DOC-FIGURE-3
其中，index max表示行向量均值最大值的索引，
Figure WO-DOC-FIGURE-4
示行向量均值最小值的索引；
3-2)依据步骤3-1)获得的多通道特征对该判定单元的振动信号进行胎动信号与噪声信号的具体分类，包括：
3-2-1)设定2个皮尔逊相关系数判定阈值A和B，其中，A＝0.4，B＝0.6；，将所有通道的皮尔逊相关系数均值ρ n中的最小值ρ min与A和B进行比较，再根据比较结果按以下不同步骤进行判定；
3-2-2)(ρ 0、ρ 1、ρ 2、ρ 3) min＞0.6时，统计4个通道中每个通道的峰
峰值商peakDivisionN；
I、若存在有至少一个通道的峰峰值商大于设定的阈值P T时，P T＝2，则判定为敲击形式胎动，输出分类结果，否则再按以下步骤继续进行判定；
II、统计count (amp>thresholdAmp)的数量，若count (amp>thresholdAmp)＝J时，J＝2，则判定为呼吸噪声，输出分类结果；否则再按以下步骤继续进行判定；
III、设定矩阵特征分析阈值thresholdValue，若r max-min＞thresholdValue且index max-min<I*f时，判定为喷嚏噪声，否则判定为体动噪声；输出分类结果，其中，I＝1.5；
3-2-3)ρ min＜A时，若所有通道的皮尔逊相关系数中，小于A的皮尔逊相关系数的数量不大于k时，则判定为敲击形式胎动，否则判定为强烈形式胎动，输出分类结果；其中k＝3；
3-2-4)A＜ρ min＜B时，再统计N个通道中每个通道的峰峰值商peakDivisionN，若存在有至少一个通道的峰峰值商大于设定的阈值2时，则再按以下步骤a)进行判定，否则按以下步骤b)进行判定；
a)若所有通道的皮尔逊相关系数中，小于A的皮尔逊相关系数的数量不大于k时，则判定为敲击形式胎动，否则判定为强烈形式胎动，输出分类结果；其中k＝3；
b)设定矩阵特征分析阈值thresholdValue，若r max-min＞thresholdValue且index max-min<I*f时，判定为喷嚏噪声，否则判定为体动噪声；输出分类结果，其中，I＝1.5。
本发明的有益效果是：
1、本发明将数据存储与分析判定模块部署于云服务器，相比将其嵌入到硬件终端或前端软件中的传统方式，这样做的显著优点是：安全保密性好、算法迭代更新方便、计算性能拓展方便、对于数学库/AI算法库等第三方库支持良好；
2、本发明采用多通道压力传感器的设计方式采集孕妇腹壁压力信号，通道数量易于拓展，提取的压力信号完善、全面，便于区分识别多种信号特征，准确性及可信度较好；
3、本发明通过基于多通道压力信号特征的融合对比分析算法，可实现对于敲击、强烈、持续等不同胎动形式及体动、喷嚏、笑声等噪声明显的区分分类，本发明的方法对于胎动监测过程中胎动形式的判定及噪声的识别更加全面完善；
4、本发明除了胎动次数，还可对包括胎动形式(如敲击、持续、强烈等)、胎动最长持续时间、最长持续时间内强烈胎动占比、胎儿最长静态时间、胎儿活跃度在内的多项胎动评估指标进行监测，这些指标首次定量描述了孕妇对于胎动强度、特征、持续时间的感知情况，具有重要的医学参考意义。
附图说明
图1为本发明的智能穿戴式胎动监测***的原理框图；
图2为本发明的4通道压力传感器信号采集的示意图；
图3为本发明的智能穿戴式胎动监测***的工作流程图；
图4为本发明的实施例中静息状态下呼吸信号的预处理效果图；
图5为本发明的实施例中胎动触发状态下的信号预处理效果图；
图6为本发明列举的几种代表性信号的波形图；
图7为本发明的实施例中的四通道信号的压力数据构成的矩阵图；
图8为本发明的实施例中进行胎动信号与噪声信号的具体分类的流程图。
具体实施方式
下面结合实施例对本发明做进一步的详细说明，以令本领域技术人员参照说明书文字能够据以实施。
应当理解，本文所使用的诸如“具有”、“包含”以及“包括”术语并不排除一个或多个其它元件或其组合的存在或添加。
参照图1，本实施例的一种智能穿戴式胎动监测***，包括：数据采集模块、智能终端、部署于云服务器上的数据存储与分析判定模块以及数据管理模块。
1、数据采集模块
所述数据采集模块包括间隔设置在孕妇腹壁上的N个用于采集孕妇腹壁的振动信号的采集单元，以进行N个通道数据并行采集。
本实施例中，采集单元为可穿戴设置在孕妇腹壁上的压力传感器，本实施例中采用并行多通道压力传感器采集孕妇腹壁压力分布情况，便于拓展、维护。传感器数量可根据器件传感特性(如负载面积、灵敏度等)灵活配置，只要能反映全部腹壁范围内的压力趋势变化即可。传感器数量过少存在信号捕获缺失现象，传感器数量过多则会导致信号采集冗余、数据量大、成本增加。在优选的实施例中，传感器数量至少应不低于3个，能够覆盖到孕妇腹壁左、中、右3个区域。以下实施例中，以4通道压力传感器进行说明(如图2)，S0,S1,S2,S3分别表示4通道的压力传感器，横向均匀分布在孕妇腹壁，此种分布方式可最大程度地减少胎动监测带的封装面积，与孕妇直接接触的 皮肤面积少，能够明显提升穿戴舒适性。传感器在此种分布方式下，纵向较远位置采集到的胎动信号会稍微减弱，本发明中通过数据预处理手段能够很好解决这一问题。当不存在胎动的情况下，S0,S1,S2,S3采集的是孕妇的正常腹壁呼吸信号；当胎动形式为“敲击”时，所触发的胎动位于f0处，此胎动波信号可被S0，S1传感器采集，S2，S3接收的信号强度则非常微弱；当胎儿胎动形式为“强烈”时，可触发孕妇腹壁多个区域，如f1_a及f1_b区域，此胎动信号可被S0,S3传感器采集，S1,S2接收的信号强度则非常微弱。
在优选的实施例中，传感器与主控电路之间的传输可采用柔性排线或织物导线，此种线路具有隐形化、扁平化、柔性化等特点。传感器可集成在现有的托腹带、胎监带或弹性织布等产品中，也可将线路封装在硅胶材质中，能够显著提升孕妇长时间穿戴下的舒适性、透气性。
传感器采集频率可视情况而定，一般而言在10Hz～100Hz，过低不利于信号特征的提取分析，过高会对数据传输稳定性、电池供能、服务器存储空间提出较大要求。
2、智能终端
所述智能终端用于将所述数据采集模块采集的多通道数据传输至所述数据存储与分析判定模块。智能终端选择为智能手机或平板等，智能终端与传感器、数据存储与分析判定模块无线通信连接(蓝牙或WIFI等)。
3、数据存储与分析判定模块
所述数据存储与分析判定模块包括数据存储单元和分析判定单元，所述数据存储单元接收并存储所述智能终端发送的多通道数据，所述分析判定单元对多通道数据进行分析判定，以将振动信号进行胎动信号与噪声信号的区分，并进一步对胎动信号与噪声信号的具体形式进行区分判定，形成分类结果；其中，胎动信号的具体形式至少包括敲击形式胎动和强烈形式胎动，噪声信号的具体形式至少包括呼吸噪声、喷嚏噪声和体动噪声。
数据存储与分析判定模块部署于云服务器上，智能终端利用蓝牙或wifi技术上传至云服务器并存储；本发明中将数据存储与分析判定模块部署于云服务器，相比将其嵌入到硬件终端或前端软件中的传统方式，这样做的显著优点是：安全保密性好、算法迭代更新方便、计算性能拓展方便、对于数学 库/AI算法库等第三方库支持良好。
一般来说，胎儿胎动情况需每天监测3次，每次1小时。随着终端用户的增多及单位监测时长的增加，数据量对存储空间的要求会越来越高。本发明将数据存储至云服务器，不仅便于存储容量的弹性拓展，也有利于对用户数据进行安全隐私保护，这些因素对于建立胎动数据库具有重要意义，目前母胎健康领域并不存在权威全面的胎动数据库。
4、数据管理模块
所述数据管理模块包括统计单元以及应用终端，数据存储与分析判定模块部的分类结果发回至数据管理模块，所述数据管理模块接收所述数据存储与分析判定模块发送的分类结果，并据此形成单位监测时间内的多维胎动指标。
所述应用终端展示所述统计单元得到的多维胎动指标，应用终端可包括管理后台和个人用户终端(如手机或电脑等)，管理后台可供医院使用，方便对所有孕妇胎动数据进行管理，个人用户终端可供单个孕妇用户使用，可了解本人的胎动评估情况。
所述多维胎动指标至少包括在单位监测时间内的胎动次数、胎动形式、胎动最长持续时间、最长持续时间内强烈胎动占比、胎儿最长静态时间以及胎儿活跃度，具体的：
所述胎动最长持续时间为：单位监测时间内，连续被判定为胎动信号的判定单元区间中，持续时间最长的区间所占的总时间。如：测量数据总长1小时，单个判定单元为6s，此1小时内最多有10个判定单元连续被判定为“真”，则胎动最长持续时间为(6s*10＝)60s。
所述最长持续时间内强烈胎动占比为：单位监测时间内，胎动最长持续时间区间中，被判定为强烈形式胎动的区间所占的百分比。如：胎动最长持续时间为60s，在这10个连续为“真”的判定单元中，胎动形式为“强烈”的共有3个判定单元，则最长持续时间内强烈胎动占比为3/10＝30％。
所述胎儿最长静态时间为：单位监测时间内，连续被判定为噪声信号的判定单元区间中，持续时间最长的区间所占的总时间。如：测量数据总长1小时，单个判定单元为6s，此1小时内最多有100个判定单元连续被判定为 “假”，即未检测到胎动信号，则胎儿最长静态时间为(6s*100＝)10min。
所述胎儿活跃度为：单位监测时间内，被判定为胎动信号的判定单元占单位监测时间内的所有判定单元的百分比。如：测量数据总长1小时，共600个待判定单元，此1小时内，共有150个判定单元被判定为“真”，则胎儿活跃度为150/600＝25％。
以上胎动指标可有效表征孕妇对于胎动强度、特征、持续时间的主观感受，胎动最长持续时间过长、最长持续时间内强烈胎动占比过高、胎儿最长静态时间过长、胎儿活跃度过低等情况的产生都亟需引起医生或孕妇的注意。
本发明除了胎动次数，还可对包括胎动形式(如敲击、持续、强烈等)、胎动最长持续时间、最长持续时间内强烈胎动占比、胎儿最长静态时间、胎儿活跃度在内的多项胎动评估指标进行监测，这些指标首次定量描述了孕妇对于胎动强度、特征、持续时间的感知情况，具有重要的医学参考意义。
在一种实施例中，参照图3，所述智能穿戴式胎动监测***进行胎动监测的方法包括以下步骤：
S1、通过所述数据采集模块对孕妇腹壁进行N个通道的的振动信号数据的并行采集；
S2、所述智能终端将所述数据采集模块采集的多通数据无线传输至所述数据存储与分析判定模块；
S3、所述数据存储与分析判定模块中的数据存储单元接收并储存所述智能终端发送的多通道数据，然后所述数据存储与分析判定模块中的分析判定单元对多通道数据进行分析判定，将振动信号进行胎动信号与噪声信号的区分，并进一步对胎动信号与噪声信号的具体形式进行区分判定，形成分类结果；
S4、所述数据管理模块接收所述数据存储与分析判定模块发送的分类结果，并据此形成单位监测时间内的多维胎动指标；
S5、所述应用终端中的管理后台和个人用户终端对获得的多维胎动指标进行展示，以供用户获取。
进一步的，所述步骤S3中，分析判定单元对多通道数据进行分析判定的 方法包括以下步骤：
1)数据预处理；
2)对预处理后的数据，首先将单位监测时间内接收到的N个通道的数据按时间线进行数据切割分段，形成多个独立的判定单元，然后按时间线对多个判定单元逐一通过以下步骤进行分析判定；
3)通过多通道特征分析先对振动信号进行胎动信号与噪声信号的区分判定，然后再对胎动信号与噪声信号的具体形式进行区分判定，形成分类结果，之后进行下一个判定单元的分析判定；
4)重复上述步骤3)直至完成单位监测时间内的所有判定单元的分析判定。
在本实施例中，步骤1)具体包括：先对振动信号采用至少包括小波阈值去噪、巴特沃斯滤波在内的方法进行平滑处理，然后采用非对称最小二乘基线校正方法进行基线漂移校正。
硬件采集电路存在工频干扰及高频噪声，压力传感器由于自身特性及外环境变化，采集的数据存在基线漂移，这些硬件模块本身存在的噪声难以避免，此类因素对于后续特征提取及模型设计具有很大影响，对采集的数据进行良好的预处理是设计良好算法的必须步骤。小波阈值去噪、巴特沃斯滤波等手段能够取得较好的平滑效果，设定理想的小波系数去噪阈值，或者设定合适的巴特沃斯临界频率，可有效解决***存在的工频干扰及高频噪声问题，这些参数要根据***的采样频率及有效信号的触发频率，确定并完善。对于基线漂移，本发明采用非对称最小二乘平滑方法进行校正，其非对称性参数及平滑度参数视情况而定。本***对于典型信号的预处理过程如图4和图5所示，图4表示静息状态下呼吸信号的预处理效果，图5表示胎动触发情况下的信号预处理效果。
在本实施例步骤2)中，将批量的多通道压力传感数据切割分段，形成单个独立的判定单元，单个判定单元的时间跨度需综合考虑胎动触发时间和***采样频率确定，一般而言，单个判定单元的时间跨度可在2-15秒，此时间跨度内的判定单元若存在胎动特征，则判定为“真”，即胎动信号，否则判定为“假”，即噪声信号。以4通道传感数据为例，按采集时间顺序，将4通 道压力传感数据切割分段形成依次连接的多个判定单元，每个判定单元中包括4个通道在相同的时间片段内采集的数据。
在本实施例步骤3)中将采集的数据进行具体分类：敲击形式胎动、强烈形式胎动、呼吸噪声、喷嚏噪声和体动噪声。以4通道传感数据为例，列举几种代表性信号的波形如下(如图6)，图中4条曲线分别表示4个传感通道采集的压力数据。图6a表示未发生胎动时4通道传感器采集的数据，此时4通道的数据为孕妇正常呼吸产生的腹壁压力信号，每一通道都具备明显的呼吸信号特征；图6b为“敲击”形式的胎动所产生数据波形，图中箭头所指的曲线通道有明显的压力脉冲信号；图6c为“强烈”形式的胎动所产生的数据波形，“强烈”胎动可触发多个腹壁区域，图中箭头所指的两条曲线通道都可监测出明显的压力跃升过程；图6d表示“喷嚏”噪声所产生的数据波形，4通道数据表现出明显地压力提升，随后迅速降到0值附近，再回到正常压力数据范围，规律性波峰波谷较少；图6e表示“体动”噪声所产生的数据波形，4通道的压力均值会同时出现明显提升，并慢慢恢复到正常值，不存在规律性波峰波谷。本发明的一些实施例中提供的算法可将每个判定单元的多通道传感数据进行特征融合，采用浅层特征(包括但不限于时域特征、频频特征、小波级数等)与相似度算法(包括但不限于皮尔逊相关系数、Hausdorff距离、Frechet距离)及AI算法(包括但不限于KNN、逻辑回归、SVM等)相结合的方式，分析识别此单个判定单元是否存在胎动信号(用“真”“假”两种标签进行表示)，且进一步判定属于何种胎动形式以及何种形式的噪声。
在一种优选的实施例中，所述采集单元包括沿横向均匀间隔设置在孕妇腹壁上的4个，以进行4通道数据并行采集。
步骤3)中，通过多通道特征分析对判定单元进行分析判定的具体步骤为：
3-1)对单个判定单元进行多通道特征提取，包括：
3-1-1)提取4个通道之间的皮尔逊相关系数，皮尔逊相关系数计算公式为：
ρ X,Y＝cov(X,Y)/σ Xσ Y＝E((X-μ X)(Y-μ Y))/σ Xσ Y；
其中，cov(X,Y)表示两个变量X，Y之间的协方差，σ X，σ Y表示两个变量X，Y的标准差；以s0,s1,s2,s3分别表示四通道经过预处理之后的振动信号数据，则四通道之间共有6个皮尔逊相关系数，即ρ 0,1，ρ 0,2，ρ 0,3，ρ 1,2，ρ 1,3，ρ 2,3，计算每个通道对于其他通道皮尔逊相关系数的均值，即：
ρ 0＝1/3(ρ 0,1+ρ 0,2+ρ 0,3)；
ρ 1＝1/3(ρ 0,1+ρ 1,2+ρ 1,3)；
ρ 2＝1/3(ρ 0,2+ρ 1,2+ρ 2,3)；
ρ 3＝1/3(ρ 0,3+ρ 1,3+ρ 2,3)。
其中，胎动信号是弱生理参数信号，一般而言，不会同时触发到四个压力传感器，四通道之间的皮尔逊相关系数明显较弱,ρ 0，ρ 1，ρ 2，ρ 3数值较小；而呼吸、喷嚏、体动等噪声信号会同时刺激到四个压力传感器，波形具有相似的发展趋势，相应地，具备较高的皮尔逊相关系数，ρ 0，ρ 1，ρ 2，ρ 3数值较大。皮尔逊相关系数ρ>＝0.6属于强相关，0.4＝<ρ<＝0.6属于中等程度相关，ρ<0.4属于弱相关或不相关。
3-1-2)计算4个通道中每个通道的峰峰值商：根据采样频率，设定峰峰值之间的最小水平单位距离，在此距离下，计算每一个通道中最大峰值与最小峰值的比值，记为peakDivision 0、peakDivision 1、peakDivision 2、peakDivision 3。
3-1-3)对四通道信号做频谱分析及归一化处理，并计算4个通道信号不同频率对应振幅的平均值amp n，统计amp n大于振幅阈值thresholdAmp的通道数量，记为count (amp>thresholdAmp)；一般而言，归一化处理后的振幅阈值thresholdAmp为0.2左右。周期规律性呼吸信号表现为余弦信号，主频数量count (amp>thresholdAmp)为2，两个频率分别对应常数项及余弦频率。
3-1-4)矩阵特征分析：单个判定单元内，将4个通道信号的振动信号构成M*4矩阵，M表示采样频率f与判定单元时长t的乘积，即M＝f*t；如：采样频率f＝20Hz,单个判定单元时长为t＝10s，则四通道信号的压力数据构成200*4矩阵，见图7。
对于该矩阵，以meanRowValue表示行向量的均值，index表示行向量的索引，取如下特征：
a)行向量均值最大值与最小值的差值r max-min，
r max-min＝meanRowValue max-meanRowValue min，
其中，meanRowValue max表示行向量均值最大值，meanRowValue min表示行向量均值最小值；
b)行向量均值最大值的索引与行向量均值最小值的索引差index max-min,
Figure PCTCN2020139817-appb-000005
其中，index max表示行向量均值最大值的索引，
Figure PCTCN2020139817-appb-000006
示行向量均值最小值的索引；
其中，当r max-min大于某一阈值thresholdValue且索引差值index max-min<1.5f时，能有效区分出喷嚏信号，一般而言，阈值thresholdValue可设定为呼吸幅值的3倍。
3-2)依据步骤3-1)获得的多通道特征对该判定单元的振动信号进行胎动信号与噪声信号的具体分类，参照图8，包括：
3-2-1)设定2个皮尔逊相关系数判定阈值A和B，其中，A＝0.4，B＝0.6；，将所有通道的皮尔逊相关系数均值ρ n中的最小值ρ min与A和B进行比较，再根据比较结果按以下不同步骤进行判定；
3-2-2)(ρ 0、ρ 1、ρ 2、ρ 3) min＞0.6时，统计4个通道中每个通道的峰
峰值商peakDivisionN；
I、若存在有至少一个通道的峰峰值商大于设定的阈值P T时，P T＝2，则判定为敲击形式胎动，输出分类结果，否则再按以下步骤继续进行判定；
依据统计学经验，皮尔逊相关系数值域等级划分如下：0.8-1.0，极强相关；0.6-0.8，强相关；0.4-0.6，中等程度相关；0.2-0.4，弱相关；0.0-0.2，极弱相关或不相关；<0，负相关；A，B两个阈值，需综合考虑通道数N及上述统计学经验，确定并完善；本实施例中选择P T＝2；
II、统计count (amp>thresholdAmp)的数量，若count (amp>thresholdAmp)＝J时，J＝2，则判定为呼吸噪声，输出分类结果；否则再按以下步骤继续进行判定；依据频域分析公式，呼吸信号表现为余弦波形，主频数量J＝2；
III、设定矩阵特征分析阈值thresholdValue，若r max-min＞thresholdValue且index max-min<I*f时，判定为喷嚏噪声，否则判定为体动噪声；输出分类结果，其中，I＝1.5；根据实验结果，根据实验结果，阈值thresholdValue 需根据传感器件传感特性(响应曲线、线性度等)确定并完善，比例系数I需根据样本表现确定并完善，一般而言，1≤I≤2.5；本实施例中选择I＝1.5；
3-2-3)ρ min＜A时，若所有通道的皮尔逊相关系数中，小于A的皮尔逊相关系数的数量不大于k时，则判定为敲击形式胎动，否则判定为强烈形式胎动，输出分类结果；其中k＝3；根据实验结果，k值需根据通道数N及样本表现确定并完善，一般而言，k>N/2；本实施例中选择k＝3；
3-2-4)A＜ρ min＜B时，再统计N个通道中每个通道的峰峰值商peakDivisionN，若存在有至少一个通道的峰峰值商大于设定的阈值2时，则再按以下步骤a)进行判定，否则按以下步骤b)进行判定；
a)若所有通道的皮尔逊相关系数中，小于A的皮尔逊相关系数的数量不大于k时，则判定为敲击形式胎动，否则判定为强烈形式胎动，输出分类结果；其中k＝3；
b)设定矩阵特征分析阈值thresholdValue，若r max-min＞thresholdValue且index max-min<I*f时，判定为喷嚏噪声，否则判定为体动噪声；输出分类结果，其中，I＝1.5。
本实施例中，通过上述算法将信号分为噪声(呼吸)、噪声(体动)、噪声(喷嚏)、胎动(敲击)、胎动(强烈)等5类，分别用标签0(呼吸)、-1(体动)、-2(喷嚏)、1(敲击)、2(强烈)表示，如图7。本实施例中还对该分类算法的性能进行了评估，采用的方法为：计算出每一类的精确率precision及召回率recall指标，并乘以该类在总样本数中的权重占比，得出整个分类模型的精确率Precision、召回率Recall指标，从而推导出整个分类模型的F1值，以此判定算法性能：F1值越高，说明分类模型越理想。
精确率
Figure PCTCN2020139817-appb-000007
召回率
Figure PCTCN2020139817-appb-000008
Precision＝precision 0*w 0+precision -2*w -2+precision -1*w -1+precision 1*w 1+precision 2*w 2        (3)
Recall＝recall 0*w 0+recall -2*w -2+recall -1*w -1+recall 1*w 1+recall 2*w 2           (4)
Figure PCTCN2020139817-appb-000009
上述公式，(1)、(2)分别表示精确率、召回率的算法，TP表示正确预测为正例，FP表示错误预测为正例，FN表示错误预测为负例。精确率的意义是：正确被预测为正(TP)占所有被预测为正的(TP+FP)的比例，召回率的意义是：正确被预测为正(TP)占样本所有正例(TP+FN)的比例；
(3)、(4)表示本发明设计算法的精确率Precision、召回率Recall计算公式，w 0表示标签0(呼吸)占所有样本的权重，w -2、w -1、w 1、w 2以此类推；precision 0表示识别为标签0(呼吸)这一类的精确率，recall 0表示识别为标签0(呼吸)这一类的召回率，其他类别以此类推；
(5)表示本发明算法F1值的计算公式。
评估结果：
随机抽取某孕妇的胎动监测数据，以孕妇怀孕天数为30周2天，32周7天，36周4天共1498组数据为例，本算法模型对于5类信号的分类结果统计如下表1，横轴表示真实类别，纵轴表示预测类别。以下以标签0(呼吸)为例进行说明：测试集实际共有949例标签0(呼吸)，第一行第一列表示：实际为标签0(呼吸)，预测为标签0(呼吸)的共有880例；第一行第二列表示：实际为标签0(呼吸)，预测为标签-2(喷嚏)的有0例；第一行第三列表示：实际为标签0(呼吸)，预测为标签-1(体动)的有0例；第一行第四列表示：实际为标签0(呼吸)，预测为标签1(敲击)的有50例；第一行第五列表示：实际为标签0(呼吸)，预测为标签2(强烈)的有19例。
根据上述评估方法，权重处理后计算得出本算法分类模型的精确率为：0.920，召回率为：0.912，F1值为：0.916。胎动判定属于弱生理信号监测，且噪声干扰较多，本分类方法在兼顾胎动形式判定及噪声种类识别的同时，达到如此性能，充分说明本分类方法表现优秀。
表1
实际/预测 呼吸0 喷嚏-2 体动-1 敲击1 强烈2
呼吸0 880 0 0 50 19
喷嚏-2 0 16 1 0 0
体动-1 3 0 98 9 7
敲击1 22 0 0 294 10
强烈2 1 0 3 7 78
尽管本发明的实施方案已公开如上，但其并不仅仅限于说明书和实施方式中所列运用，它完全可以被适用于各种适合本发明的领域，对于熟悉本领域的人员而言，可容易地实现另外的修改，因此在不背离权利要求及等同范围所限定的一般概念下，本发明并不限于特定的细节。

Claims (10)

  1. 一种智能穿戴式胎动监测***，其特征在于，包括：数据采集模块、智能终端、部署于云服务器上的数据存储与分析判定模块以及数据管理模块；
    所述数据采集模块包括间隔设置在孕妇腹壁上的N个用于采集孕妇腹壁的振动信号的采集单元，以进行N个通道数据并行采集；
    所述智能终端用于将所述数据采集模块采集的多通道数据传输至所述数据存储与分析判定模块；
    所述数据存储与分析判定模块包括数据存储单元和分析判定单元，所述数据存储单元接收并存储所述智能终端发送的多通道数据，所述分析判定单元对多通道数据进行分析判定，以将振动信号进行胎动信号与噪声信号的区分，并进一步对胎动信号与噪声信号的具体形式进行区分判定，形成分类结果；其中，胎动信号的具体形式至少包括敲击形式胎动和强烈形式胎动，噪声信号的具体形式至少包括呼吸噪声、喷嚏噪声和体动噪声；
    所述数据管理模块包括统计单元以及应用终端，所述数据管理模块接收所述数据存储与分析判定模块发送的分类结果，并据此形成单位监测时间内的多维胎动指标；
    所述应用终端展示所述统计单元得到的多维胎动指标。
  2. 根据权利要求1所述的智能穿戴式胎动监测***，其特征在于，所述采集单元为可穿戴设置在孕妇腹壁上的压力传感器。
  3. 根据权利要求1所述的智能穿戴式胎动监测***，其特征在于，所述应用终端包括管理后台和个人用户终端。
  4. 根据权利要求3所述的智能穿戴式胎动监测***，其特征在于，所述多维胎动指标至少包括在单位监测时间内的胎动次数、胎动形式、胎动最长持续时间、最长持续时间内强烈胎动占比、胎儿最长静态时间以及胎儿活跃度；
    其中，所述胎动最长持续时间为：单位监测时间内，连续被判定为胎动信号的判定单元区间中，持续时间最长的区间所占的总时间；
    所述最长持续时间内强烈胎动占比为：单位监测时间内，胎动最长持续 时间区间中，被判定为强烈形式胎动的区间所占的百分比；
    所述胎儿最长静态时间为：单位监测时间内，连续被判定为噪声信号的判定单元区间中，持续时间最长的区间所占的总时间；
    所述胎儿活跃度为：单位监测时间内，被判定为胎动信号的判定单元占单位监测时间内的所有判定单元的百分比。
  5. 根据权利要求4所述的智能穿戴式胎动监测***，其特征在于，所述智能穿戴式胎动监测***进行胎动监测的方法包括以下步骤：
    S1、通过所述数据采集模块对孕妇腹壁进行N个通道的的振动信号数据的并行采集；
    S2、所述智能终端将所述数据采集模块采集的多通数据无线传输至所述数据存储与分析判定模块；
    S3、所述数据存储与分析判定模块中的数据存储单元接收并储存所述智能终端发送的多通道数据，然后所述数据存储与分析判定模块中的分析判定单元对多通道数据进行分析判定，将振动信号进行胎动信号与噪声信号的区分，并进一步对胎动信号与噪声信号的具体形式进行区分判定，形成分类结果；
    S4、所述数据管理模块接收所述数据存储与分析判定模块发送的分类结果，并据此形成单位监测时间内的多维胎动指标；
    S5、所述应用终端中的管理后台和个人用户终端对获得的多维胎动指标进行展示，以供用户获取。
  6. 根据权利要求5所述的智能穿戴式胎动监测***，其特征在于，所述步骤S3中分析判定单元对多通道数据进行分析判定的方法包括以下步骤：
    1)数据预处理；
    2)对预处理后的数据，首先将单位监测时间内接收到的N个通道的数据按时间线进行数据切割分段，形成多个独立的判定单元，然后按时间线对多个判定单元逐一通过以下步骤进行分析判定；
    3)通过多通道特征分析先对振动信号进行胎动信号与噪声信号的区分判定，然后再对胎动信号与噪声信号的具体形式进行区分判定，形成分类结果，之后进行下一个判定单元的分析判定；
    4)重复上述步骤3)直至完成单位监测时间内的所有判定单元的分析判定。
  7. 根据权利要求6所述的智能穿戴式胎动监测***，其特征在于，所述步骤1)具体包括：先对振动信号采用至少包括小波阈值去噪、巴特沃斯滤波在内的方法进行平滑处理，然后采用非对称最小二乘基线校正方法进行基线漂移校正。
  8. 根据权利要求6所述的智能穿戴式胎动监测***，其特征在于，所述步骤3)具体包括：
    3-1)对单个判定单元进行多通道特征提取，包括：
    3-1-1)提取N个通道之间的皮尔逊相关系数，然后计算每个通道对于其他通道皮尔逊相关系数的均值，记为皮尔逊相关系数均值ρ n；
    3-1-2)计算N个通道中每个通道的峰峰值商：根据采样频率，设定峰峰值之间的最小水平单位距离，在此距离下，计算每一个通道种最大峰值与最小峰值的比值，记为peakDivision N；
    3-1-3)对N个通道信号做频谱分析及归一化处理，并计算N个通道信号不同频率对应振幅的平均值amp n，统计amp n大于振幅阈值thresholdAmp的通道数量，记为count (amp>thresholdAmp)；
    3-1-4)矩阵特征分析：单个判定单元内，将N个通道信号的振动信号数据构成M*N的矩阵,M表示采样频率f与判定单元时长t的乘积，即M＝f*t；对于该矩阵，以meanRowValue表示行向量的均值，index表示行向量的索引，取如下特征：
    a)行向量均值最大值与最小值的差值r max-min，r max-min＝meanRowValue max-meanRowValue min，其中，meanRowValue max表示行向量均值最大值，meanRowValue min表示行向量均值最小值；
    b)行向量均值最大值的索引与行向量均值最小值的索引差index max-min,
    Figure PCTCN2020139817-appb-100001
    其中，index max表示行向量均值最大值的索引，
    Figure PCTCN2020139817-appb-100002
    示行向量均值最小值的索引；
    2-2)依据步骤2-1)获得的多通道特征对该判定单元的振动信号进行胎动信号与噪声信号的具体分类，包括：
    3-2-1)设定2个皮尔逊相关系数判定阈值A和B，其中，0＜A＜B，将所有通道的皮尔逊相关系数均值ρ n中的最小值ρ min与A和B进行比较，再根据比较结果按以下不同步骤进行判定；
    3-2-2)ρ min＞B时，统计N个通道中每个通道的峰峰值商peakDivisionN；
    I、若存在有至少一个通道的峰峰值商大于设定的阈值P T时，则判定为敲击形式胎动，输出分类结果，否则再按以下步骤继续进行判定，其中，2.0≤P T≤2.5；
    II、统计count (amp>thresholdAmp)的数量，若主频数量count (amp>thresholdAmp)＝J时，则判定为呼吸噪声，输出分类结果，J为正整数；否则再按以下步骤继续进行判定；
    III、设定矩阵特征分析阈值thresholdValue，若r max-min＞thresholdValue且index max-min<I*f时，判定为喷嚏噪声，否则判定为体动噪声；输出分类结果，其中，I为比例系数，1≤I≤2.5；
    3-2-3)ρ min＜A时，若所有通道的皮尔逊相关系数中，小于A的皮尔逊相关系数的数量不大于k时，则判定为敲击形式胎动，否则判定为强烈形式胎动，输出分类结果；其中k>N/2；
    3-2-4)A＜ρ min＜B时，再统计N个通道中每个通道的峰峰值商peakDivisionN，若存在有至少一个通道的峰峰值商大于设定的阈值P T时，则再按以下步骤a)进行判定，否则按以下步骤b)进行判定；
    a)若所有通道的皮尔逊相关系数中，小于A的皮尔逊相关系数的数量不大于k时，则判定为敲击形式胎动，否则判定为强烈形式胎动，输出分类结果；其中k>N/2；
    b)设定矩阵特征分析阈值thresholdValue，若r max-min＞thresholdValue且index max-min<I*f时，判定为喷嚏噪声，否则判定为体动噪声；输出分类结果，其中，I为比例系数，1≤I≤2.5。
  9. 根据权利要求8所述的智能穿戴式胎动监测***，其特征在于，所述采集单元包括沿横向均匀间隔设置在孕妇腹壁上的4个，以进行4通道数据并行采集。
  10. 根据权利要求9所述的智能穿戴式胎动监测***，其特征在于，所述步骤3)具体包括：
    3-1)对单个判定单元进行多通道特征提取，包括：
    3-1-1)提取4个通道之间的皮尔逊相关系数，皮尔逊相关系数计算公式为：
    ρ X,Y＝cov(X,Y)/σ Xσ Y＝E((X-μ X)(Y-μ Y))/σ Xσ Y；
    其中，cov(X,Y)表示两个变量X，Y之间的协方差，σ X，σ Y表示两个变量X，Y的标准差；以s0,s1,s2,s3分别表示四通道经过预处理之后的振动信号数据，则四通道之间共有6个皮尔逊相关系数，即ρ 0,1，ρ 0,2，ρ 0,3，ρ 1,2，ρ 1,3，ρ 2,3，计算每个通道对于其他通道皮尔逊相关系数的均值，即：
    ρ 0＝1/3(ρ 0,1+ρ 0,2+ρ 0,3)；
    ρ 1＝1/3(ρ 0,1+ρ 1,2+ρ 1,3)；
    ρ 2＝1/3(ρ 0,2+ρ 1,2+ρ 2,3)；
    ρ 3＝1/3(ρ 0,3+ρ 1,3+ρ 2,3)；
    3-1-2)计算4个通道中每个通道的峰峰值商：根据采样频率，设定峰峰值之间的最小水平单位距离，在此距离下，计算每一个通道中最大峰值与最小峰值的比值，记为peakDivision 0、peakDivision 1、peakDivision 2、peakDivision 3；
    3-1-3)对四通道信号做频谱分析及归一化处理，并计算4个通道信号不同频率对应振幅的平均值amp n，统计amp n大于振幅阈值thresholdAmp的通道数量，记为count (amp>thresholdAmp)；
    3-1-4)矩阵特征分析：单个判定单元内，将4个通道信号的振动信号构成M*4矩阵，M表示采样频率f与判定单元时长t的乘积，即M＝f*t；对于该矩阵，以meanRowValue表示行向量的均值，index表示行向量的索引，取如下特征：
    a)行向量均值最大值与最小值的差值r max-min，
    r max-min＝meanRowValue max-meanRowValue min，
    其中，meanRowValue max表示行向量均值最大值，meanRowValue min表示行向量均值最小值；
    b)行向量均值最大值的索引与行向量均值最小值的索引差index max-min,
    Figure PCTCN2020139817-appb-100003
    其中，index max表示行向量均值最大值的索引，
    Figure PCTCN2020139817-appb-100004
    示行向量均值最小值的索引；
    3-2)依据步骤3-1)获得的多通道特征对该判定单元的振动信号进行胎动信号与噪声信号的具体分类，包括：
    3-2-1)设定2个皮尔逊相关系数判定阈值A和B，其中，A＝0.4，B＝0.6；，将所有通道的皮尔逊相关系数均值ρ n中的最小值ρ min与A和B进行比较，再根据比较结果按以下不同步骤进行判定；
    3-2-2)(ρ 0、ρ 1、ρ 2、ρ 3) min＞0.6时，统计4个通道中每个通道的峰峰值商peakDivisionN；
    I、若存在有至少一个通道的峰峰值商大于设定的阈值P T时，P T＝2，则判定为敲击形式胎动，输出分类结果，否则再按以下步骤继续进行判定；
    II、统计count (amp>thresholdAmp)的数量，若count (amp>thresholdAmp)＝J时，J＝2，则判定为呼吸噪声，输出分类结果；否则再按以下步骤继续进行判定；
    III、设定矩阵特征分析阈值thresholdValue，若r max-min＞thresholdValue且index max-min<I*f时，判定为喷嚏噪声，否则判定为体动噪声；输出分类结果，其中，I＝1.5；
    3-2-3)ρ min＜A时，若所有通道的皮尔逊相关系数中，小于A的皮尔逊相关系数的数量不大于k时，则判定为敲击形式胎动，否则判定为强烈形式胎动，输出分类结果；其中k＝3；
    3-2-4)A＜ρ min＜B时，再统计N个通道中每个通道的峰峰值商peakDivisionN，若存在有至少一个通道的峰峰值商大于设定的阈值2时，则再按以下步骤a)进行判定，否则按以下步骤b)进行判定；
    a)若所有通道的皮尔逊相关系数中，小于A的皮尔逊相关系数的数量不大于k时，则判定为敲击形式胎动，否则判定为强烈形式胎动，输出分类结果；其中k＝3；
    b)设定矩阵特征分析阈值thresholdValue，若r max-min＞thresholdValue且index max-min<I*f时，判定为喷嚏噪声，否则判定为体动噪声；输出分类结果，其中，I＝1.5。
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