KR20220014242A - Prediction method and system of hypertension using machine learning, computer program for the same - Google Patents

Abstract

The present invention relates to a hypertension prediction method and system using machine learning. It relates to a hypertension prediction method and system using machine learning that receive the ballistic signal of a patient from a heart ballistic sensor installed in a bed, extract biometric information, input it into a plurality of machine learning (machine learning) algorithms, determine a hypertension alert level from a calculated hypertension prediction probability, and provide it to the patient.

Description

Hypertension prediction method and system using machine learning, and a computer program for performing the same

The present invention relates to a method and system for predicting hypertension using machine learning, receiving a patient's trajectory signal from a trajectory sensor installed in a bed, extracting biometric information, and inputting it into a plurality of machine learning (machine learning) algorithms to calculate The present invention relates to a hypertension prediction method and system using machine learning that provides a patient with a high blood pressure warning level from the predicted hypertension prediction probabilities.

Blood pressure is the pressure exerted by the blood flowing through the blood vessels on the walls of the blood vessels, and is one of the important vital signs. Since high blood pressure, in which blood pressure is abnormally high, or hypotension, in which blood pressure is abnormally low, is not only a disease that needs to be managed by itself, but also acts as a cause or risk factor for various other diseases, accurate monitoring of blood pressure is very important for maintaining health. can do.

Meanwhile, the invention of chronic diseases resulting from the improvement of living standards and westernized dietary habits is increasing worldwide, and among them, the number of hypertensive patients is steadily increasing. High blood pressure is caused by various environmental factors such as bad eating habits, family history, drinking, smoking, lack of exercise, and stress, and can be broadly divided into two types.

One is 'essential hypertension', which occurs without a causal disease, and the other is 'secondary hypertension', which occurs secondary to kidney disease, vascular abnormality, or endocrine disease. The incidence of secondary hypertension among all hypertensive patients is estimated to be less than 5%, and among them, hypertension due to kidney disease accounts for a high frequency. Conversely, if essential hypertension persists, the probability of developing kidney disease increases. Therefore, in order to prevent the onset of other diseases and to treat high blood pressure, it is necessary to consistently determine high blood pressure.

Hypertension is usually determined by measuring a blood pressure level at a hospital or at home using a blood pressure measuring device, and determining hypertension according to the measured blood pressure level. However, if there is no blood pressure measuring device, the above-described determination method cannot be used, and there is a problem in that it is difficult to measure blood pressure immediately in an emergency.

On the other hand, a technique for checking blood pressure by acquiring biometric information in an unconstrained method rather than a structure worn on a patient's body like a blood pressure measuring device is disclosed. However, it was not possible to objectively predict the onset of hypertension because it was simply to acquire and confirm biometric information.

Unexamined Patent Publication No. 10-2005-0057593 (June 16, 2005, Hypertension Risk Diagnosis Method) Unexamined Patent Publication No. 10-2014-0039408 (April 2014.04.02. Apparatus and method for generating high blood pressure judgment model using body type information, device and method for high blood pressure judgment using high blood pressure judgment model)

The present invention was devised to solve the above problems, attaching a ballistic sensor to a patient's bed or mattress to acquire biometric information in an unconstrained way without measuring blood pressure through the patient's body, and The purpose of the present invention is to provide a method and system for predicting hypertension using machine learning that can not only check the blood pressure level of a patient but also predict the onset of hypertension.

The hypertension prediction method using machine learning of the present invention for solving the above problems is a method for predicting hypertension using a patient's ballistic signal obtained from a ballistic sensor installed on a bed or mattress, wherein a computer detects hypertension receiving an input of the patient's age and gender and ballistic trajectory signals to be predicted; calculating each average by extracting biometric information from the trajectory signal, and inputting it into a plurality of pre-trained machine learning algorithms according to age and gender to calculate a patient's hypertension prediction probability; and determining and outputting a high blood pressure alert level from the calculated hypertension prediction probabilities.

Here, calculating the hypertension prediction probability; It further includes; removing noise from the previously collected trajectory signal.

In this case, the step of removing the noise; scaling the size of the trajectory signal; extracting a trajectory signal corresponding to a preset root mean square maximum value and a preset root mean square minimum value range from the scaled trajectory signal; and extracting a trajectory signal corresponding to a preset minimum amplitude value and a maximum amplitude value range from the extracted trajectory signal.

Meanwhile, determining and outputting the high blood pressure alert level may include: extracting a hypertension prediction probability having a highest value among the calculated hypertension prediction probabilities; and outputting a high blood pressure warning level by determining whether the extracted high blood pressure prediction probability falls within a set threshold range, wherein the high blood pressure warning level is classified into relief, interest, and attention, and the high blood pressure prediction probability is 0% To 10%, "safe", when the hypertension prediction probability is 11% to 30%, "interest", and when the hypertension prediction probability is 31% or more, it is determined and outputted as "caution".

And the hypertension prediction system using machine learning of the present invention is a bed; a ballistic sensor installed on the bed to acquire a ballistic signal of the patient; a server storing the computer program of claim 8 for predicting hypertension by receiving the ballistic signal acquired from the ballistic sensor; and a smart device in which an application for receiving and outputting hypertension prediction results from the server is installed.

According to the method and system for predicting hypertension using machine learning of the present invention having the above configuration, it is possible to receive the patient's ballistic signal from the ballistic sensor installed in the bed, check the blood pressure level in real time, as well as predict the occurrence of hypertension. Therefore, there is an advantage that the patient does not need to wear a measuring device, and in particular, it has the advantage of high accuracy because the hypertension probability extracted through a plurality of machine learning (machine learning) algorithms is compared with each other to determine whether hypertension occurs.

In addition, the patient can check the blood pressure level just by lying or sitting in bed, thereby predicting the onset of hypertension, thereby diagnosing hypertension early and performing health management of the patient.

1 is a schematic configuration diagram showing a hypertension prediction system using a ballistic signal according to an embodiment of the present invention;
2 is a flowchart of a method for predicting hypertension using a ballistic signal according to an embodiment of the present invention;
3 is a flowchart of a method for removing noise of a deep trajectory signal according to an embodiment of the present invention;
4 is a graph showing a trajectory signal from which noise has been removed according to an embodiment of the present invention;
5 is a ROC-Curve graph showing a performance index of a trained artificial neural network according to an embodiment of the present invention;
6 is a ROC-Curve graph showing a performance index of a learned logistic regression analysis according to an embodiment of the present invention.

Hereinafter, a method and system for predicting hypertension using machine learning according to a preferred embodiment of the present invention will be described in detail with reference to the accompanying drawings.

As shown in the figure, the hypertension prediction method and system using machine learning according to the present invention receive a patient's age, gender, and ballistic signal from a ballistic sensor installed in a bed, extract biometric information, and perform a plurality of machine learning methods. This is a hypertension prediction method and system that can check whether hypertension develops from the high blood pressure probability output by inputting biometric information into the (machine learning) algorithm.

In addition, the method for predicting hypertension according to an embodiment of the present invention is performed by a computer, and the computer stores a computer program for executing the method for predicting hypertension by operating the computer.

In addition, the computer refers to a computing device in a broad sense including a smart device such as a smart phone or a tablet PC as well as a general personal computer.

In addition, the computer program may be stored in a separate recording medium or stored in a cloud server connected to the Internet, and the recording medium is specially designed and configured for the present invention, or a person having ordinary knowledge in the field of computer software It may be known to and available for use.

For example, the recording medium may include a hard disk, a magnetic medium such as a floppy disk and a magnetic tape, an optical recording medium such as a CD or DVD, a magneto-optical recording medium capable of both magnetic and optical recording, ROM, RAM, and flash memory. and the like, alone or in combination, may be a hardware device specially configured to store and execute program instructions.

In addition, the computer program may be a computer program composed of program instructions, a local data file, a local data structure, etc. alone or in combination, and may be executed by a computer using an interpreter as well as machine code such as generated by a compiler. It may be a computer program written in a high-level language code that can be

1 shows a hypertension prediction system according to an embodiment of the present invention. Referring to FIG. 1 , the hypertension prediction system of the present invention includes a bed 10, a ballistic sensor 20, a server 30 and a smart device ( 40) consists of

The bed 10 is composed of a bed frame 11 and a mattress 12, and provides a space for the patient to lie down.

The ballistic sensor 20 may be installed in the bed 10 , measure the ballistic signal of the patient, and transmit it to the server 30 .

In addition, the trajectory sensor 20 may be installed on the side or lower surface of the bed frame 11 , and may be used by being attached to the mattress 12 in addition to the bed frame 11 .

On the other hand, the trajectory signal is an electrical recording of the body movement generated according to the physical movement of the heart, and represents the amount of movement of the blood ejected by the heart, and the trajectory sensor 20 is placed on the bed 10 by the movement of the body. Measure the generated micro-vibration.

The server 30 is installed with the computer program, and receives a ballistic signal from the ballistic sensor 20 through a communication network to predict whether hypertension develops.

The smart device 40 receives the predicted occurrence of hypertension and biometric information from the server 30 through a communication network, and outputs it so that the patient can monitor it.

In addition, the smart device 40 may be installed with an application capable of monitoring the occurrence of hypertension by displaying and monitoring biometric information.

Hereinafter, the hypertension prediction method of the present invention will be described in detail with reference to FIG. 2 .

2 is a flowchart of a method for predicting hypertension according to an embodiment of the present invention. Referring to FIG. 2 , the method for predicting hypertension of the present invention largely includes a step (S1000) of receiving a patient's age, sex, and ballistic signal, the hypertension probability and calculating (S2000) and determining and outputting a high blood pressure alert level (S3000).

First, the hypertension prediction method of the present invention receives a patient's ballistic signal from the ballistic sensor 20 and also receives the patient's age and gender information (S1000).

The ballistic signal may be a ballistic signal in a state in which the patient is awake or a ballistic signal obtained while the patient is sleeping.

Next, the noise of the input trajectory signal is removed (S2000).

On the other hand, since the input ballistic signal is affected by the bed's spring constant and damping factor when the patient lies or moves on the bed, there is a problem of bringing the signal amplitude modulation and attenuation time constant. should be done

3 is a flowchart illustrating a method for removing noise from a deep trajectory signal according to an embodiment of the present invention, and FIG. 4 is a graph showing a trajectory signal from which noise has been removed according to an embodiment of the present invention.

Referring to FIGS. 3 to 4 , in order to remove the noise of the deep trajectory signal, first, the signal level of the input deep trajectory signal is scaled ( S2100 ).

In this case, it is preferable to adjust the scaling of the signal size to be “10um/s ² ”, thereby preventing an overflow of appropriate resolution and numbers.

Next, a deep trajectory signal corresponding to a preset minimum root mean square (RMS) value and a maximum root mean square (RMS) value is extracted from the scaled ballistic signal ( S2200 ).

Next, by extracting a signal corresponding to a preset minimum amplitude value and a maximum amplitude value from the extracted trajectory signal, noise removal of the finally input trajectory signal is completed ( S2300 ).

On the other hand, the preset minimum root mean square (RMS) value, maximum root mean square (RMS) value, minimum amplitude value, and maximum amplitude value are average values extracted from the ballistic signals of patients lying or sitting on the bed, and the patient or around the bed. It can be set in various ways according to the environment of

Next, the high blood pressure prediction probability is output by inputting the noise-removed trajectory signal to the machine learning algorithm (S3000).

In detail, first, biometric information is extracted from the trajectory signal from which the noise has been removed (S3100).

Here, the biometric information includes a heart rate, a respiration rate, a relative stroke volume, and a heart rate variability for a preset time, and includes various methods can be extracted.

Next, an average value of the extracted biometric information is calculated (S3200). In this case, it is preferable to calculate each according to age and gender.

Next, the average values of the calculated biometric information are input to a plurality of machine learning algorithms learned in advance according to age and sex, and the hypertension prediction probability is calculated and output (S3000).

The machine learning algorithm is an algorithm that allows a computer to learn by itself from input data, and in the present invention, a high blood pressure probability is calculated using a machine learning algorithm that can classify data into one of two types.

Specifically, the machine learning algorithm may use a classification technique algorithm such as regression analysis, logistic regression analysis, decision tree, Bayesian classification, artificial neural network, SVM (Suppoter Vector Machine), K-nearest neighbor, and the like, and the present invention uses an artificial neural network with the best classification performance and a logistic regression analysis algorithm.

In addition, in the present invention, 20,000 biometric information (heart rate, heart rate variability) data sets for learning along with age and gender were collected from hypertensive patients and normal people, respectively, and the artificial neural network and logistic regression analysis algorithm were respectively learned.

In addition, 20,000 pieces of predictive biometric information (heart rate, heart rate variability) data sets were separately collected from hypertensive patients and normal people along with age and gender, and entered into the learned artificial neural network and logistic regression analysis algorithm to evaluate classification performance. Through this, parameters that can have high classification performance were optimized.

In addition, the artificial neural network was trained 150 times in four layer layers having 64 neurons in each layer.

5 is a ROC-Curve graph showing a performance indicator of a trained artificial neural network according to an embodiment of the present invention, and FIG. 6 is a ROC-Curve graph showing a performance indicator of a learned logistic regression analysis according to an embodiment of the present invention. It is a graph.

5 to 6 , the ROC-Curve graph is a graph showing better performance as the area increases, and the trained artificial neural network showed 93% prediction accuracy and 016% error, and the learned logistic regression analysis It can be seen that both machine learning algorithms show excellent performance with a prediction accuracy of 96%.

Accordingly, the heart rate and heart rate variability extracted from the heart ballistic signal can be considered as discriminatory data for judging hypertension.

Meanwhile, the artificial neural network and the logistic regression analysis algorithm finally output each probability corresponding to hypertension, and in the present invention, the output probability is defined as the hypertension probability.

Next, after outputting the hypertension probability through the plurality of machine learning algorithms, the hypertension alert level is determined from the hypertension probability ( S4000 ).

In order to determine the high blood pressure alert level, first, the highest hypertension prediction probability is extracted by comparing the output hypertension prediction probabilities ( S4100 ).

Here, the reason for extracting the highest hypertension prediction probability is to minimize the error in judging hypertension. Rather than arbitrarily setting an error range, select the largest value among the hypertension prediction probabilities obtained through analysis. This is to perform high blood pressure prediction with high reliability and accuracy by judging hypertension.

Next, it is determined whether the extracted hypertension probability falls within a preset threshold range to classify the hypertension alert level (S4200)

The high blood pressure alert level may be classified as "safe", "interest", and "caution", and when the hypertension probability is 0% to 10%, it is determined as "safe", and the hypertension probability is 11% to 30% In the case of "interest", when the hypertension probability is 31% or more, it is judged as "caution" and output.

Through these steps, it is possible to diagnose whether a patient develops hypertension, and the output result can be transmitted to a smart device for monitoring.

Therefore, the method and system for predicting hypertension using the ballistic signal of the present invention can predict the patient's hypertension by receiving the ballistic signal from the ballistic sensor installed in the bed, so there is no need to measure the patient by wearing a measuring device, It is possible to predict whether a patient has high blood pressure simply by lying in bed or sitting in bed, which has the advantage of diagnosing high blood pressure at an early stage and performing health management of the patient.

10: Bed 20: Deep ballistic sensor
30: server 40: smart device

Claims (9)

In the method of predicting hypertension using a patient's ballistic signal obtained from a ballistic sensor installed on a bed or mattress,
receiving, by the computer, the age and gender of the patient for which hypertension is to be predicted, and the ballistic signal;
calculating each average by extracting biometric information from the trajectory signal, and inputting it into a plurality of pre-trained machine learning algorithms according to age and sex to calculate a patient's hypertension prediction probability; and
determining and outputting a high blood pressure alert level from the calculated hypertension prediction probabilities;
Hypertension prediction method using machine learning, characterized in that it comprises a.
According to claim 1,
calculating the hypertension prediction probability; Before,
Removing the noise of the collected trajectory signal; Hypertension prediction method using machine learning, characterized in that it further comprises.
3. The method of claim 2,
removing the noise;
scaling the magnitude of the trajectory signal;
extracting a trajectory signal corresponding to a preset root mean square maximum value and a preset root mean square minimum value range from the scaled trajectory signal; and
A method for predicting hypertension using machine learning, comprising: extracting a ballistic signal corresponding to a preset minimum amplitude value and a maximum amplitude value range from the extracted ballistic signal.
3. The method of claim 2,
The method for predicting hypertension using machine learning, wherein the biometric information is heart rate, respiration rate, relative stroke volume, and heart rate variability.
According to claim 1,
The step of determining and outputting the high blood pressure alert level:
extracting a hypertension prediction probability having a highest value among the calculated hypertension prediction probabilities; and
and outputting a hypertension warning level by determining whether the extracted hypertension prediction probability falls within a set threshold range.
6. The method of claim 5,
The high blood pressure alert level is classified as reassurance, concern, attention,
When the hypertension prediction probability is 0% to 10%, it is judged as “safe”, when the hypertension prediction probability is 11% to 30%, it is “interested”, and when the hypertension prediction probability is 31% or more, it is judged and outputted as “caution”. Hypertension prediction method using machine learning.
According to claim 1,
The plurality of machine learning algorithms are an artificial neural network (ANN) and a logistic regression analysis algorithm.
A computer program for performing the hypertension prediction method using the ballistic signal of any one of claims 1 to 7.
bed;
a trajectory sensor installed on the bed to acquire a trajectory signal of the patient;
a server storing the computer program of claim 8 for predicting hypertension by receiving the ballistic signal acquired from the ballistic sensor; and
Hypertension prediction system using machine learning, characterized in that it includes a smart device installed with an application that receives and outputs the hypertension prediction result from the server.

Images