CN114668376B - Arm-worn artificial intelligence sphygmomanometer - Google Patents
Arm-worn artificial intelligence sphygmomanometer Download PDFInfo
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- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
- A61B5/021—Measuring pressure in heart or blood vessels
- A61B5/022—Measuring pressure in heart or blood vessels by applying pressure to close blood vessels, e.g. against the skin; Ophthalmodynamometers
- A61B5/0225—Measuring pressure in heart or blood vessels by applying pressure to close blood vessels, e.g. against the skin; Ophthalmodynamometers the pressure being controlled by electric signals, e.g. derived from Korotkoff sounds
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- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
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Abstract
The invention provides an arm-worn artificial intelligence sphygmomanometer, and belongs to the technical field of artificial intelligence. Comprising the following steps: the measuring module comprises a cuff, an air bag, an air charging unit, an air discharging unit, a pressure detecting circuit, a Korotkoff sound detecting circuit, a pulse wave detecting circuit, a first processor and a first wireless communication unit, wherein the air bag, the air charging unit, the air discharging unit, the pressure detecting circuit, the Korotkoff sound detecting circuit, the pulse wave detecting circuit, the first processor and the first wireless communication unit are arranged on the cuff; a mobile intelligent device based on a hong Mongolian operating system, which comprises an input unit, a second processor and a second wireless communication unit; the first processor controls the operation of the pressure detection circuit, the Korotkoff sound detection circuit, the inflation unit and/or the deflation unit according to the control signal of the second processor; the input unit receives control input of a user, and the second processor generates a control signal according to the control input; the second processor determines continuously varying blood pressure during arterial pulsation using a deep learning model based on cuff pressure information, pulse wave information, and Korotkoff sound information. The invention has high detection precision and can acquire real-time continuous blood pressure.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to an arm-worn artificial intelligence sphygmomanometer.
Background
The measurement principle of the current common sphygmomanometer mainly comprises the following two types: the Korotkoff sound method and the proportional coefficient method (also called oscillometric method or oscillation method) of the cuff oscillation wave, which both require a cuff balloon to be wound around the arm for inflation and pressurization, and the pressure is increased above the systolic pressure, and then the blood pressure is judged in the process of deflation and pressure relief. The Korotkoff sounds include artificial Korotkoff sounds and electronic Korotkoff sounds, which require experienced medical staff to use a stethoscope, a mercury manometer, a cuff, an inflation/deflation bladder to close the brachial artery with the stethoscope by binding the cuff in place on the upper arm of the subject, inflating the cuff with the inflation/deflation bladder to increase pressure until the blood flow of the arm is blocked, and then gradually reducing the cuff pressure by the inflation/deflation bladder to restore the blood flow of the arm, during which arterial blood flow pulsations of the arm produce a small to large and large to small Korotkoff sound change, and the change of Korotkoff sounds can be heard with the aid of the stethoscope and mercury manometer to determine systolic and diastolic pressures. The basic principle of the electronic Korotkoff sound method is to replace manual work by electronic technology, for example, inflation and deflation of a cuff are completed by an air pump, an electric control valve and the like, and listening is completed by an electronic pickup and a processor.
The basic process of the cuff oscillation wave is very similar to the Korotkoff sound method, the cuff is inflated and boosted to block the blood flow of the arm, then the cuff is deflated and depressurized gradually to restore the blood flow of the arm, the static pressure in the cuff and the pressure pulse wave generated by the pulsation of arterial blood are monitored, but the calculation method is to detect a group of pressure pulse waves with the amplitude from small to large, then the pressure pulse waves with the amplitude from small to large and the corresponding cuff pressure from large to small by detecting the pressure pulse wave generated by the pulsation change of the arterial blood of the arm in the deflation process and the corresponding cuff pressure, and calculate the systolic pressure and the diastolic pressure according to the amplitude proportion coefficient of the pressure pulse wave of the experience value by taking the cuff pressure corresponding to the maximum value of the pressure pulse wave as the average pressure.
However, the artificial Korotkoff sound method has high requirements on detection personnel, otherwise, the error is likely to be large, the method is not suitable for daily detection in families, the electronic Korotkoff sound method is easily influenced by other external sounds, and the pulse intensity of different people also has certain influence on the measurement result. In contrast, in the proportional coefficient method of the cuff oscillation wave, since the proportional coefficient is generally an empirical value (obtained through a lot of experiments), an error due to individual difference occurs. In addition, the systolic pressure and the diastolic pressure obtained by the Korotkoff sound method are blood pressures at two moments in the measuring process, the proportional coefficient method of the cuff oscillation wave is based on average blood pressure in the measuring process to obtain the systolic pressure and the diastolic pressure, and the two methods cannot acquire real-time continuous blood pressure.
Disclosure of Invention
Therefore, the technical problem to be solved by the embodiment of the invention is to overcome the defects that the blood pressure detection method in the prior art is limited in accuracy and cannot acquire real-time continuous blood pressure, so as to provide the arm-mounted artificial intelligence blood pressure instrument.
To this end, the present invention provides an arm-worn artificial intelligence blood pressure monitor comprising:
the mobile intelligent device comprises a measurement module and a mobile intelligent device based on a hong Mongolian operating system;
the measuring module comprises a cuff, an air bag, an air inflation unit, an air deflation unit, a pressure detection circuit, a Korotkoff sound detection circuit, a pulse wave detection circuit, a first processor and a first wireless communication unit, wherein the air bag, the air inflation unit, the air deflation unit, the pressure detection circuit, the Korotkoff sound detection circuit, the pulse wave detection circuit, the first processor and the first wireless communication unit are arranged on the cuff; the mobile intelligent device comprises an input unit, a second processor, a second wireless communication unit and a display unit;
the inflation unit and the deflation unit are used for inflating and deflating the air bag in the blood pressure detection process according to the control of the first processor; the pressure detection circuit is used for detecting cuff pressure information, the Korotkoff sound detection circuit is used for detecting Korotkoff sound information, the pulse wave detection circuit is used for detecting pulse wave information, and the first processor sends the cuff pressure information, the pulse wave information and the Korotkoff sound information to the second wireless communication unit through the first wireless communication unit; the first processor also controls the operation of the pressure detection circuit, the Korotkoff sound detection circuit, the inflation unit and/or the deflation unit according to the control signals transmitted by the second processor through the second wireless communication unit and the first wireless communication unit;
the input unit is used for receiving control input of a user, the second processor is used for generating the control signal according to the control input and sending the control signal to the first wireless communication unit through the second wireless communication unit, and the control input at least comprises starting and stopping; the second processor is further configured to determine a continuously changing blood pressure during arterial pulsation and display a continuous blood pressure waveform in the display unit using a deep learning model based on the cuff pressure information, pulse wave information, and the Korotkoff sound information received by the second wireless communication unit.
Optionally, the second processor determines a time interval between each korotkoff sound and a rising point on the nearest pulse wave according to the korotkoff sound information and the pulse wave information; inputting the pulse wave information, the Korotkoff sound information and the cuff pressure information to a first model branch of the deep learning model, inputting the output of the first model branch and the time interval to a second model branch of the deep learning model, and determining continuously variable blood pressure in the arterial pulsation process according to the output of the second model branch.
Optionally, the first model branch includes a first neural network combination and a second neural network combination, the input of the first neural network combination is the pulse wave information, the input of the second neural network combination is the korotkoff sound information and the cuff pressure information, and the output weighted sum of the first neural network combination and the second neural network combination obtains the output of the first model branch.
Optionally, the pulse wave information input to the first neural network combination is pulse wave information after being subjected to segmentation processing and preprocessing;
the segmentation processing adopts a sliding overlapping sampling strategy to obtain pulse wave information segments;
the preprocessing comprises continuous wavelet decomposition, denoising and demodulation processing of the pulse wave information segment to obtain a wavelet time-frequency diagram;
the first neural network combination comprises a convolutional neural network and a cyclic neural network, wherein the convolutional neural network comprises a first convolutional layer, a first pooling layer, a second convolutional layer and a second pooling layer which are sequentially connected; the circulating neural network comprises a gating circulating layer, a full-connection layer and an identification layer.
Optionally, the weight sequence loss function of the first neural network combination is:
wherein x' j ＝x j -max(x 1 ,…,x m ) M is the number of training data, W j Weight, x, being the weight of the weight loss function j Is the wavelet time-frequency diagram matrix of the j-th training data.
Optionally, the first neural network combination comprises a depth noise reduction automatic encoder and a support vector machine, the depth noise reduction automatic encoder comprises a plurality of layers of automatic encoders, a hidden layer of a former layer of automatic encoder is used as an input layer of a latter layer of automatic encoder, and the values of nodes of the hidden layer of each layer of automatic encoder are obtained by calculating the values of nodes of the input layer through linear weighted connection and summation and output to an excitation function;
the pulse wave information input to the first neural network combination is pulse wave information subjected to variation modal decomposition processing, and the pulse wave information is decomposed to different frequency bands through the variation modal decomposition processing, so that modal components belonging to different frequency bands are obtained.
Optionally, the koff sound information and the cuff pressure information combined by the second neural network are input into a plurality of koff sound information segments and a cuff pressure information segment consistent with a time segment thereof, each koff sound information segment comprises 1/n koff sounds, and n is a positive integer;
the second neural network combination includes a deep belief neural network and a LightGBM network; the deep confidence neural network is used for extracting the characteristic information of the Korotkoff sound information section, and the LightGBM network determines a corresponding blood pressure value according to the characteristic information of the Korotkoff sound information section and the cuff pressure information section;
the construction method of the deep confidence neural network comprises the following steps: the construction method of the deep confidence neural network comprises the following steps: stacking d limited Boltzmann machines, constructing a vector S for a Korotkoff sound information segment corresponding to one blood pressure value, taking the vector S as a display layer neuron of the first limited Boltzmann machine, encoding the S by an unsupervised learning method, and outputting a vector Q1 as a first characteristic extraction result of the Korotkoff sound information segment to form a hidden layer neuron; training the deep confidence neural network by using a large number of unlabeled Korotkoff sound information segment training set data through a Gibbs sampling and contrast divergence algorithm to obtain a weight matrix W1, and repeating the previous step by taking Q1 as the input of the next limited Boltzmann machine to extract the secondary characteristics of the Korotkoff sound information segment; and obtaining a final monitoring signal characteristic extraction result and the weight W= { W1, W2, … … Wd } of the deep confidence network through d restricted Boltzmann machines.
Optionally, the second model branch comprises an acceptance-v 3 neural network and an extreme learning machine; the admission-v 3 neural network determines the systolic pressure and the diastolic pressure of the detection object according to the cuff pressure value and the Korotkoff sound information; and the extreme learning machine corrects the blood pressure value output by the first model branch according to the time interval, the systolic pressure and the diastolic pressure.
Optionally, the output of the second model branch is a discrete blood pressure value; the second processor predicts the missing blood pressure values among the discrete blood pressure values by utilizing a residual error network and a prediction filter;
the input of the residual error network is the discrete blood pressure value, and the output is the coefficient of the prediction filter;
the prediction filter predicts by the following formula:
wherein P (t) is the time tBlood pressure value, P' (t+t) 0 ) For the prediction of (t+t) 0 ) Blood pressure value at time, A (t 0 ,t+t 0 ) Is (t+t) 0 ) Time corresponding t 0 Coefficient t 0 -r, -r+1, …, r, r being the radius of the prediction filter.
Optionally, the blood pressure meter further comprises a heart rate detection unit and/or a respiration detection unit;
the input of the second model branch further comprises first heart rate information and second heart rate information, and/or first respiratory rate and second respiratory rate;
the first heart rate information is heart rate information of the detected object in a calm state, and the second heart rate information is heart rate information detected by the heart rate detection unit in the detection process;
the first respiratory rate is respiratory rate of the detected object in a calm state, and the second respiratory rate is respiratory rate information detected by the respiratory detection unit in the detection process.
The technical scheme of the embodiment of the invention has the following advantages:
the arm-worn artificial intelligence blood pressure instrument provided by the embodiment of the invention determines the blood pressure of the detection object according to the pulse wave information and the Korotkoff sound information at the same time, thereby improving the detection precision. In addition, the blood pressure meter can continuously and uninterruptedly measure the blood pressure, can provide continuous arterial pressure waveforms in the arterial pulsation process, and can more accurately reflect the blood pressure condition of a detection object. In addition, because the measurement module and the mobile intelligent device are in wireless communication connection, the mobile intelligent terminal can be independent, for example, the mobile intelligent terminal can be realized by combining a smart phone or a tablet personal computer with a corresponding application program (APP), and the purchase cost of a user is reduced.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of an embodiment of an arm-worn artificial intelligence blood pressure monitor;
FIG. 2 is a functional block diagram of one specific example of a measurement module in an embodiment of the invention;
fig. 3 is a schematic block diagram of a specific example of a mobile smart device in an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made apparent and fully in view of the accompanying drawings, in which some, but not all embodiments of the invention are shown. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In describing the present invention, it should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. The use of the terms "comprises" and/or "comprising," when used in this specification, are intended to specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. The term "and/or" includes any and all combinations of one or more of the associated listed items. The terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," "outer," and the like refer to an orientation or positional relationship based on that shown in the drawings, merely for convenience of description and to simplify the description, and do not denote or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus are not to be construed as limiting the invention. The terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. The terms "mounted," "connected," "coupled," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; can be directly connected, can also be indirectly connected through an intermediate medium, and can also be the communication between the two elements; the connection may be wireless or wired. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
In addition, the technical features of the different embodiments of the present invention described below may be combined with each other as long as they do not collide with each other.
Example 1
The present embodiment provides an arm-worn artificial intelligence sphygmomanometer, as shown in fig. 1, 2 and 3, comprising: a measurement module 1, a mobile intelligent device 2 based on a hong Monte operating system;
the measurement module 1 includes a cuff 11, an air bag (not shown in the figure) provided on the cuff 11, an inflation unit 12, a deflation unit 13, a pressure detection circuit 14, a Korotkoff sound detection circuit 15, a pulse wave detection circuit 16, a first processor 17, and a first wireless communication unit 18; the mobile smart device 2 comprises an input unit 21, a second processor 22, a second wireless communication unit 23 and a display unit 24;
the specific layout of the inflation unit 12, the deflation unit 13, the pressure detection circuit 14, the koff sound detection circuit 15, the pulse wave detection circuit 16, the first processor 17 and the first wireless communication unit 18 in the cuff 11 may be determined according to the detection requirement, the device size, the shape and other factors, which are not specifically shown in the figure.
The inflation unit 12 and the deflation unit 13 are used for inflating and deflating the air bag in the blood pressure detection process according to the control of the first processor 17; the pressure detection circuit 14 is configured to detect cuff pressure information, the koff sound detection circuit 15 is configured to detect koff sound information, the pulse wave detection circuit 16 is configured to detect pulse wave information, and the first processor 17 sends the cuff pressure information, the pulse wave information, and the koff sound information to the second wireless communication unit 23 through the first wireless communication unit 18; the first processor 17 further controls the operation of the pressure detection circuit 14, the Korotkoff sound detection circuit 15, the inflation unit 12 and/or the deflation unit 13 according to the control signals transmitted from the second processor 22 via the second wireless communication unit 23 and the first wireless communication unit 18;
the input unit 21 is configured to receive a control input from a user, and the second processor 22 is configured to generate the control signal according to the control input and send the control signal to the first wireless communication unit 18 through the second wireless communication unit 23, where the control input includes at least start and stop; the second processor 22 is further configured to determine a continuously changing blood pressure during arterial pulsation from the cuff pressure information, pulse wave information, and the korotkoff sound information received by the second wireless communication unit 23 using a deep learning model and display a continuous blood pressure waveform in the display unit 24.
That is, the second processor 22 may acquire the blood pressure changing in real time during the detection process, and may not only display the blood pressure at a certain time or several times during the detection process.
Specifically, the first wireless communication unit 18 and the second wireless communication unit 23 may be near field communication units, that is, the first wireless communication unit 18 and the second wireless communication unit 23 may implement near field communication. The detection module further comprises a power source, such as a battery or the like. The input unit 21 may include keys and/or a touch screen. The touch screen may also serve as the display unit 24. The inflator unit 12 includes an electric inflator. The pressure sensing circuit 14 may include a capacitive pressure sensor that may be disposed within the bladder or at a communication port of the bladder that functions to translate a change in air pressure into a change in capacitor capacity. The Korotkoff sound detection circuit 15 may include a microphone.
The arm-worn artificial intelligence sphygmomanometer provided by the embodiment determines the blood pressure of the detection object according to the pulse wave information and the Korotkoff sound information at the same time, so that the detection precision is improved. In addition, the blood pressure meter can continuously and uninterruptedly measure the blood pressure, can provide continuous arterial pressure waveforms in the arterial pulsation process, and can more accurately reflect the blood pressure condition of a detection object. In addition, since the measurement module 1 and the mobile intelligent device 2 are in wireless communication connection, the mobile intelligent terminal can be independent, for example, can be realized by combining a smart phone or a tablet computer with a corresponding application program (APP), and the purchase cost of a user is reduced.
Optionally, the second processor 22 determines a time interval between each korotkoff sound and a rising point on the nearest pulse wave according to the korotkoff sound information and the pulse wave information; inputting the pulse wave information, the Korotkoff sound information and the cuff pressure information to a first model branch of the deep learning model, inputting the output of the first model branch and the time interval to a second model branch of the deep learning model, and determining continuously variable blood pressure in the arterial pulsation process according to the output of the second model branch.
Specifically, in the primary deflation detection, the time interval between each Korotkoff sound and the rising point of the nearest pulse wave is different, the time interval corresponding to the first Korotkoff sound is longest, and the time interval corresponding to the following Korotkoff sound is shorter and shorter.
For the blood pressure measurement method based on the Korotkoff sound alone and the blood pressure measurement method based on the pulse wave alone, cross-domain multiple arterial beats are needed, and continuous blood pressure in the process of each beat cannot be obtained. In this embodiment, the discrete blood pressure values during the multiple beats are estimated based on the Yu Maibo wave information and the korotkoff sound information, and corrected based on the time interval, the cuff pressure value and the korotkoff sound information, and finally the second processor 22 obtains a continuous blood pressure value according to the discrete blood pressure values.
Optionally, the first model branch includes a first neural network combination and a second neural network combination, the input of the first neural network combination is the pulse wave information, the input of the second neural network combination is the korotkoff sound information and the cuff pressure information, and the output weighted sum of the first neural network combination and the second neural network combination obtains the output of the first model branch.
In this embodiment, the first neural network combination is configured to determine a blood pressure value that changes in each beat based on pulse wave information, the second neural network combination is configured to determine a blood pressure value of each beat based on koff sound information and the cuff pressure information, and then the outputs of the first neural network combination and the second neural network combination are weighted and summed to obtain the blood pressure value that changes in each beat.
Optionally, the pulse wave information input to the first neural network combination is pulse wave information after being subjected to segmentation processing and preprocessing;
the segmentation processing adopts a sliding overlapping sampling strategy to obtain pulse wave information segments;
the preprocessing comprises continuous wavelet decomposition, denoising and demodulation processing of the pulse wave information segment to obtain a wavelet time-frequency diagram;
the first neural network combination comprises a convolutional neural network and a cyclic neural network, wherein the convolutional neural network comprises a first convolutional layer, a first pooling layer, a second convolutional layer and a second pooling layer which are sequentially connected; the circulating neural network comprises a gating circulating layer, a full-connection layer and an identification layer.
Specifically, the first convolution layer performs convolution operation on the input, and the convolution kernel output value is c i ＝f(w·x i +a), where w is the convolution kernel weight, a is the offset, x i The vector matrix of the wavelet time-frequency diagram is the ith pulse wave information band, and i represents the ith pulse wave information band. The pooling layer extracts a maximum value from each feature vector output by the convolution layer.
The gating circulation layer has two inputs, one is characteristic information (extracted by the convolutional neural network) x corresponding to the current pulse wave information band t The other is the hidden state h transferred by the last node t-1 This hidden state includes information about the previous node, in combination with x t And h t-1 Will get the output y of the current hidden node t And hidden state h passed to the next node t . The gating circulation layer has weightA set gate (r gate) and an update gate (z gate).
The convolutional neural network is used for extracting characteristics of a wavelet time-frequency diagram of each pulse wave information band, and the convolutional neural network is used for identifying a blood pressure value corresponding to each pulse wave information band.
Optionally, the weight sequence loss function of the first neural network combination is:
wherein x' j ＝x j -max(x 1 ,…,x m ) M is the number of training data, W j Weight, x, being the weight of the weight loss function j Is the wavelet time-frequency diagram matrix of the j-th training data.
Optionally, the first neural network combination comprises a depth noise reduction automatic encoder and a support vector machine, the depth noise reduction automatic encoder comprises a plurality of layers of automatic encoders, a hidden layer of a former layer of automatic encoder is used as an input layer of a latter layer of automatic encoder, and the values of nodes of the hidden layer of each layer of automatic encoder are obtained by calculating the values of nodes of the input layer through linear weighted connection and summation and output to an excitation function;
the pulse wave information input to the first neural network combination is pulse wave information subjected to variation modal decomposition processing, and the pulse wave information is decomposed to different frequency bands through the variation modal decomposition processing, so that modal components belonging to different frequency bands are obtained.
The depth noise reduction auto encoder may be trained by:
constructing training samples (X, Y), wherein X is a modal component of a pulse wave in different frequency bands, and Y is a corresponding blood pressure value;
performing unsupervised greedy layer-by-layer training on the depth noise reduction automatic encoder, and training the automatic encoders of the first layer-1 and the first layer through a contrast divergence algorithm;
performing supervised fine tuning on a depth noise reduction automatic encoder: turning over the depth noise reduction automatic encoder which is subjected to unsupervised greedy training in the previous step to obtain an automatic encoder with doubled layer number; the automatic encoder is trained by BP algorithm to fine tune the depth noise reduction automatic encoder.
In the embodiment, the deep noise reduction automatic encoder can accurately extract the characteristic information in the pulse wave information, provide better data basis for the subsequent support vector machine blood pressure identification, reduce the identification difficulty and improve the identification precision.
Optionally, the koff sound information and the cuff pressure information combined by the second neural network are input into a plurality of koff sound information segments and a cuff pressure information segment consistent with a time segment thereof, each koff sound information segment comprises 1/n koff sounds, and n is a positive integer;
the second neural network combination includes a deep belief neural network and a LightGBM network; the deep confidence neural network is used for extracting the characteristic information of the Korotkoff sound information section, and the LightGBM network determines a corresponding blood pressure value according to the characteristic information of the Korotkoff sound information section and the cuff pressure information section;
the construction method of the deep confidence neural network comprises the following steps: stacking d limited Boltzmann machines, constructing a vector S for a Korotkoff sound information segment corresponding to one blood pressure value, taking the vector S as a display layer neuron of the first limited Boltzmann machine, encoding the S by an unsupervised learning method, and outputting a vector Q1 as a first characteristic extraction result of the Korotkoff sound information segment to form a hidden layer neuron; training the deep confidence neural network by using a large number of unlabeled Korotkoff sound information segment training set data through a Gibbs sampling and contrast divergence algorithm to obtain a weight matrix W1, and repeating the previous step by taking Q1 as the input of the next limited Boltzmann machine to extract the secondary characteristics of the Korotkoff sound information segment; and obtaining a final monitoring signal characteristic extraction result and the weight W= { W1, W2, … … Wd } of the deep confidence network through d restricted Boltzmann machines.
Specifically, the number of the restricted boltzmann machines is determined according to the number of the pieces of the koff sound information inputted and the number of the output feature extraction results. The Light GBM model is a learning algorithm based on a decision tree, and has the advantages of faster training speed, higher accuracy and high data processing capacity.
In this embodiment, a real-time blood vessel pressure value is identified according to the korotkoff sound information and the cuff pressure information, wherein the blood vessel pressure identification is mainly based on the korotkoff sound information, but since the korotkoff sound is affected by the cuff pressure, that is, the cuff pressure is different, even if the same korotkoff sound exists, the present embodiment also corrects the blood vessel pressure value identified based on the korotkoff sound information by using the cuff pressure information.
It should be noted here that, when the outputs of the first neural network combination and the second neural network combination are weighted and summed, only the blood pressure values at the same time or in the same period (the period where there is an intersection may be considered as the same period) are weighted and summed, and if only one neural network combination has an output corresponding to the blood pressure value at a time or period, the weighted and summed processing is not required.
Optionally, the second model branch comprises an acceptance-v 3 neural network and an extreme learning machine; the admission-v 3 neural network determines the systolic pressure and the diastolic pressure of the detection object according to the cuff pressure value and the Korotkoff sound information; and the extreme learning machine corrects the blood pressure value output by the first model branch according to the time interval, the systolic pressure and the diastolic pressure.
In this embodiment, the input of the second model branch includes not only the output of the first model branch and the time interval, but also the cuff pressure value and the korotkoff sound information.
Specifically, the admission-v 3 neural network identifies a first time point corresponding to a systolic pressure and a second time point corresponding to a diastolic pressure according to the Korotkoff sound information, takes a pressure value corresponding to the first time point in the cuff pressure values as the systolic pressure, and takes a pressure value corresponding to the second time point in the cuff pressure values as the diastolic pressure.
Specifically, the second model branch corrects the blood pressure value at the time corresponding to the systolic pressure according to the systolic pressure, and the second model branch corrects the blood pressure value at the time corresponding to the diastolic pressure according to the diastolic pressure. For example, the blood pressure may be corrected by means of weighted summation, and the weights may be learned by training.
When the blood pressure value is determined according to the pulse wave information, the blood pressure value is influenced by the individual blood vessel condition, and the detection aiming at the individual blood vessel condition needs a professional detection instrument, some or even invasive detection, which does not meet the daily blood pressure detection requirement, so that the blood pressure value determined by the pulse wave information is corrected by utilizing the time interval between the Korotkoff sound capable of reflecting the individual blood vessel condition and the rising point on the pulse wave in the embodiment, thereby not only reducing the influence of the individual blood vessel condition on the blood pressure detection, improving the blood pressure detection precision, but also not increasing the detection cost.
In addition, before the Korotkoff sound information is input into the acceptance-v 3 neural network, frame windowing processing is performed on the Korotkoff sound information to obtain Korotkoff sound information frames, the length of each Korotkoff sound information frame is smaller than or equal to the duration of one Korotkoff sound, short-time Fourier transformation is performed on the Korotkoff sound information frames to obtain corresponding sound spectrum diagram data, and then the acceptance-v 3 neural network identifies whether the corresponding Korotkoff sound information frame is a Korotkoff sound information frame corresponding to systolic pressure and a Korotkoff sound information frame corresponding to diastolic pressure based on the sound spectrum diagram data.
Optionally, the output of the second model branch is a discrete blood pressure value; the second processor 22 predicts missing blood pressure values between the discrete blood pressure values using a residual network and a prediction filter;
the input of the residual error network is the discrete blood pressure value, and the output is the coefficient of the prediction filter;
the prediction filter predicts by the following formula:
wherein P (t) is the time tBlood pressure value, P' (t+t) 0 ) For the prediction of (t+t) 0 ) Blood pressure value at time, A (t 0 ,t+t 0 ) Is (t+t) 0 ) Time corresponding t 0 Coefficient t 0 -r, -r+1, …, r, r being the radius of the prediction filter.
In other alternative embodiments, the second processor 22 may also perform a curve fitting process, such as a polynomial fitting, on the discrete blood pressure values.
Optionally, the blood pressure meter further comprises a heart rate detection unit and/or a respiration detection unit;
the input of the second model branch further comprises first heart rate information and second heart rate information, and/or first respiratory rate and second respiratory rate;
the first heart rate information is heart rate information of the detected object in a calm state, and the second heart rate information is heart rate information detected by the heart rate detection unit in the detection process;
the first respiratory rate is respiratory rate of the detected object in a calm state, and the second respiratory rate is respiratory rate information detected by the respiratory detection unit in the detection process.
In this embodiment, in the blood pressure detection process, correction may be performed based on the heart rate variation and the respiratory rate variation of the detection object as the blood pressure value, so as to reduce the erroneous judgment of hypertension caused by factors such as exercise, tension, etc.
In other alternative embodiments, the first heart rate information and the second heart rate information, and/or the first respiratory rate and the second respiratory rate may not be used as inputs of the second model branch, that is, the heart rate variation and the respiratory rate variation of the detected subject are not corrected to the blood pressure value, but the second processor 22 outputs corresponding prompt information according to the first heart rate information and the second heart rate information, and/or the first respiratory rate and the second respiratory rate, and displays the prompt information while displaying the continuous blood pressure waveform, where the prompt information may indicate that the detected subject is not in a calm state at present, and the blood pressure value cannot be used as a diagnostic basis.
It is apparent that the above examples are given by way of illustration only and are not limiting of the embodiments. Other variations or modifications of the above teachings will be apparent to those of ordinary skill in the art. It is not necessary here nor is it exhaustive of all embodiments. While still being apparent from variations or modifications that may be made by those skilled in the art are within the scope of the invention.
Claims (9)
1. An arm-worn artificial intelligence sphygmomanometer, comprising: the mobile intelligent device comprises a measurement module and a mobile intelligent device based on a hong Mongolian operating system; the measuring module comprises a cuff, an air bag, an air inflation unit, an air deflation unit, a pressure detection circuit, a Korotkoff sound detection circuit, a pulse wave detection circuit, a first processor and a first wireless communication unit, wherein the air bag, the air inflation unit, the air deflation unit, the pressure detection circuit, the Korotkoff sound detection circuit, the pulse wave detection circuit, the first processor and the first wireless communication unit are arranged on the cuff; the mobile intelligent device comprises an input unit, a second processor, a second wireless communication unit and a display unit; the inflation unit and the deflation unit are used for inflating and deflating the air bag in the blood pressure detection process according to the control of the first processor; the pressure detection circuit is used for detecting cuff pressure information, the Korotkoff sound detection circuit is used for detecting Korotkoff sound information, the pulse wave detection circuit is used for detecting pulse wave information, and the first processor sends the cuff pressure information, the pulse wave information and the Korotkoff sound information to the second wireless communication unit through the first wireless communication unit; the first processor also controls the operation of the pressure detection circuit, the Korotkoff sound detection circuit, the inflation unit and/or the deflation unit according to the control signals transmitted by the second processor through the second wireless communication unit and the first wireless communication unit; the input unit is used for receiving control input of a user, the second processor is used for generating the control signal according to the control input and sending the control signal to the first wireless communication unit through the second wireless communication unit, and the control input at least comprises starting and stopping; the second processor is further configured to determine a blood pressure continuously changing during arterial pulsation according to the cuff pressure information, the pulse wave information, and the korotkoff sound information received by the second wireless communication unit using a deep learning model and display a continuous blood pressure waveform in the display unit; the second processor determines the time interval between each Korotkoff sound and the nearest rising point of the pulse wave according to the Korotkoff sound information and the pulse wave information; inputting the pulse wave information, the Korotkoff sound information and the cuff pressure information to a first model branch of the deep learning model, inputting the output of the first model branch and the time interval to a second model branch of the deep learning model, and determining continuously variable blood pressure in the arterial pulsation process according to the output of the second model branch.
2. The blood pressure meter of claim 1, wherein the first model branch comprises a first neural network combination and a second neural network combination, the input of the first neural network combination is the pulse wave information, the input of the second neural network combination is the korotkoff sound information and the cuff pressure information, and the output weighted sum of the first neural network combination and the second neural network combination results in the output of the first model branch.
3. The blood pressure monitor of claim 2, wherein the pulse wave information input to the first neural network combination is pulse wave information after being subjected to a segmentation process and a preprocessing process; the segmentation processing adopts a sliding overlapping sampling strategy to obtain pulse wave information segments; the preprocessing comprises continuous wavelet decomposition, denoising and demodulation processing of the pulse wave information segment to obtain a wavelet time-frequency diagram; the first neural network combination comprises a convolutional neural network and a cyclic neural network, wherein the convolutional neural network comprises a first convolutional layer, a first pooling layer, a second convolutional layer and a second pooling layer which are sequentially connected; the circulating neural network comprises a gating circulating layer, a full-connection layer and an identification layer.
4. A blood pressure monitor according to claim 3, wherein the weight sequence loss function of the first neural network combination is:
wherein x is j ′＝x j -max(x 1 ,…,x m ) M is the number of training data, W j Weight, x, being the weight of the weight loss function j Is the wavelet time-frequency diagram matrix of the j-th training data.
5. The blood pressure monitor of claim 2, wherein the first neural network combination comprises a depth noise reduction automatic encoder and a support vector machine, the depth noise reduction automatic encoder comprises a plurality of layers of automatic encoders, a hidden layer of a former layer of automatic encoder is used as an input layer of a later layer of automatic encoder, and the value of each node of the hidden layer of each layer of automatic encoder is calculated by summing the value of each node of the input layer through linear weighted connection and outputting the summed value to an excitation function; the pulse wave information input to the first neural network combination is pulse wave information subjected to variation modal decomposition processing, and the pulse wave information is decomposed to different frequency bands through the variation modal decomposition processing, so that modal components belonging to different frequency bands are obtained.
6. The blood pressure monitor according to claim 2, wherein the koff sound information and the cuff pressure information input to the second neural network combination are a plurality of koff sound information segments each including 1/n koff sounds and a cuff pressure information segment in accordance with a time period thereof, n being a positive integer; the second neural network combination includes a deep belief neural network and a LightGBM network; the deep confidence neural network is used for extracting the characteristic information of the Korotkoff sound information section, and the LightGBM network determines a corresponding blood pressure value according to the characteristic information of the Korotkoff sound information section and the cuff pressure information section; the construction method of the deep confidence neural network comprises the following steps: stacking d restricted boltzmann machines, for one of the blood pressuresVector S is constructed by the Korotkoff sound information segment corresponding to the value, S is encoded by an unsupervised learning method as a display layer neuron of the first limited Boltzmann machine, and vector Q is output 1 The first characteristic extraction result of the Korotkoff sound information section is used for forming hidden layer neurons; training the deep confidence neural network by using a large number of unlabeled Korotkoff sound information segment training set data through Gibbs sampling and contrast divergence algorithm to obtain a weight matrix W 1 By Q 1 Repeating the previous step as the input of the next limited Boltzmann machine, and carrying out secondary feature extraction of the Korotkoff sound information section; obtaining a final monitoring signal characteristic extraction result and weight W= { W of the deep confidence network through d restricted Boltzmann machines 1 ，W 2 ，……，W d }。
7. The blood pressure meter of claim 1, wherein the second model branch comprises an acceptance-v 3 neural network and an extreme learning machine; the admission-v 3 neural network determines the systolic pressure and the diastolic pressure of the detection object according to the cuff pressure value and the Korotkoff sound information; and the extreme learning machine corrects the blood pressure value output by the first model branch according to the time interval, the systolic pressure and the diastolic pressure.
8. The blood pressure meter of claim 1, wherein the output of the second model branch is a discrete blood pressure value; the second processor predicts the missing blood pressure values among the discrete blood pressure values by utilizing a residual error network and a prediction filter; the input of the residual error network is the discrete blood pressure value, and the output is the coefficient of the prediction filter; the prediction filter predicts by the following formula:
wherein P (t) is the blood pressure value at time t, P' (t+t) 0 ) For the prediction of (t+t) 0 ) Time of dayIs a blood pressure value of A (t) 0 ,t+t 0 ) Is (t+t) 0 ) Time corresponding t 0 Coefficient t 0 -r, -r+1, …, r, r being the radius of the prediction filter.
9. The blood pressure monitor of claim 1, further comprising a heart rate detection unit and/or a respiration detection unit; the input of the second model branch further comprises first heart rate information and second heart rate information, and/or first respiratory rate and second respiratory rate; the first heart rate information is heart rate information of the detected object in a calm state, and the second heart rate information is heart rate information detected by the heart rate detection unit in the detection process; the first respiratory rate is respiratory rate of the detected object in a calm state, and the second respiratory rate is respiratory rate information detected by the respiratory detection unit in the detection process.
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