This application is a U.S. national stage under 35 U.S.C. § 371 of International Application No. PCT/CN2017/094762, filed on Jul. 27, 2017, designating the United States of America, the contents of which are incorporated herein by reference in their entireties.

TECHNICAL FIELD

The present disclosure generally relates to systems and methods applicable in health-care related areas. More particularly, the present disclosure relates to systems and methods for determining blood pressure of a subject.

BACKGROUND

Blood pressure measurement can be categorized into invasive blood pressure measurement and non-invasive blood pressure measurement. Invasive blood pressure measurement is usually used in medical surgeries or medical research and needs to be conducted by the medical professionals. Non-invasive blood pressure measuring is an indirect blood pressure measuring method. A sphygmomanometer is a popular non-invasive blood pressure measuring device. It is composed of an inflatable cuff to collapse and then release the artery under the cuff in a controlled manner, and a mercury or mechanical manometer to measure the pressure. However, frequent measuring using the sphygmomanometer causes discomfort of the subject as frequent inflation oppresses the blood vessels of the subject. Further, the accuracy of the measured blood pressure is affected by the size of the cuff, elastic effects and the posture of the subject during the measurement.

Another non-invasive blood pressure measurement system uses the electrocardiosignal and the pulse wave signal to predict blood pressure of a subject. The system calculates the transmitting velocity of the pulse wave signal and determines a model for predicting the blood pressure. However, the subject needs to wear two sets of devices for measuring the electrocardiosignal and the pulse wave signal separately. Therefore, there is a need to provide a non-invasive blood pressure measurement system and method to efficiently predict the subject's blood pressure.

SUMMARY

According to one aspect of the present disclosure, a system for determining blood pressure may include at least one medium including a set of instructions, a communication platform connected to a network and at least one processor in communication with the at least one storage medium. When executing the set of instructions, the at least one processor may receive a request to determine blood pressure of a first subject from a terminal. The at least one processor may obtain data related to heart activity of the first subject in response to the request. The at least one processor may determine a personalized model for predicting blood pressure with respect to the first subject. The at least one processor may determine the blood pressure of the first subject using the personalized model based on the data related to heart activity of the first subject. The at least one processor may send the blood pressure of the first subject to the terminal in response to the request.

In some embodiments, the at least one processor may communicate with a sensor attached to the first subject. In some embodiments, the sensor may be configured to detect the heart activity of the first subject and generate a signal. The at least one processor may generate the data related to heart activity of the first subject based on the signal.

In some embodiments, the data related to the first subject includes at least one of age of the first subject, gender of the first subject, height of the first subject, weight of the first subject, posture of the first subject, time information associated with the request, whether or not the first subject has high blood pressure, and/or whether or not the first subject is under at least one medication.

In some embodiments, the at least one processor may obtain historical data related to heart activities of a plurality of second subjects. In some embodiments, the plurality of second subjects may include the first subject. In some embodiments, the historical data may include a plurality of historical blood pressure measurements with respect to the plurality of second subjects. The at least one processor may determine a preliminary model for predicting blood pressure based on the historical data related to heart activities of the plurality of second subjects. The at least one processor may generate the personalized model for predicting blood pressure with respect to the first subject based on the preliminary model for predicting blood pressure and at least part of the historical data related to heart activity of the first subject.

In some embodiments, the at least one processor may generate a first model for predicting first blood pressure based on the historical data related to heart activities of the plurality of second subjects.

In some embodiments, the at least one processor may generate a second model for predicting a plurality of residuals between the plurality of historical blood pressure measurements and values of the first blood pressure predicted by the first model based on the historical data related to heart activities of the plurality of second subjects. The at least one processor may determine the preliminary model for predicting blood pressure based on the first model, the second model, and at least part of the historical data related to heart activities of the plurality of second subjects.

In some embodiments, the at least one processor may transform the signal into a first representation in the frequency domain. The at least one processor may determine a first plurality of coefficients related to the first representation in the frequency domain. The at least one processor may generate the data related to heart activity of the first subject based on the first plurality of coefficients.

In some embodiments, the at least one processor may transform a portion of the signal into a second representation in the frequency domain. The at least one processor may determine a second plurality of coefficients related to the second representation in the frequency domain. The at least one processor may generate the data related to heart activity of the first subject based on the second plurality of coefficients.

According to another aspect of the present disclosure, a method implemented on a computing device having at least one processor, storage, and a communication platform connected to a network for determining blood pressure may include: receiving, by the at least one processor, a request to determine blood pressure of a first subject from a terminal; obtaining, by the at least one processor, data related to heart activity of the first subject in response to the request; determining, by the at least one processor, a personalized model for predicting blood pressure with respect to the first subject; determining, by the at least one processor, the blood pressure of the first subject using the personalized model based on the data related to heart activity of the first subject; and sending, by the at least one processor, the blood pressure of the first subject to the terminal in response to the request.

In some embodiments, the method may further include communicating with a sensor attached to the first subject and generating a signal and generating the data related to heart activity of the first subject based on the signal. In some embodiments, the sensor may be configured to detect the heart activity of the first subject.

In some embodiments, the method may further include obtaining historical data related to heart activities of a plurality of second subjects, determining a preliminary model for predicting blood pressure based on the historical data related to heart activities of the plurality of second subjects, and generating the personalized model for predicting blood pressure with respect to the first subject based on the preliminary model for predicting blood pressure and at least part of the historical data related to heart activity of the first subject. In some embodiments, the plurality of second subjects may include the first subject. In some embodiments, the historical data may include a plurality of historical blood pressure measurements with respect to the plurality of second subjects.

In some embodiments, the method may further include generating a first model for predicting first blood pressure based on the historical data related to heart activities of the plurality of second subjects.

In some embodiments, the method may further include generating a second model for predicting a plurality of residuals between the plurality of historical blood pressure measurements and values of the first blood pressure predicted by the first model based on the historical data related to heart activities of the plurality of second subjects and determining the preliminary model for predicting blood pressure based on the first model, the second model, and at least part of the historical data related to heart activities of the plurality of second subjects.

In some embodiments, the method may further include transforming the signal into a first representation in the frequency domain, determining a first plurality of coefficients related to the first representation in the frequency domain, and generating the data related to heart activity of the first subject based on the first plurality of coefficients.

In some embodiments, the method may further include transforming a portion of the signal into a second representation in the frequency domain, determining a second plurality of coefficients related to the second representation in the frequency domain, and generating the data related to heart activity of the first subject based on the second plurality of coefficients.

According to another aspect of the present disclosure, a system for determining blood pressure may include at least one medium including a set of instructions, a communication platform connected to a network and at least one processor in communication with the at least one storage medium. When executing the set of instructions, the at least one processor may receive a request to determine blood pressure of a first subject from a terminal. The at least one processor may obtain data related to heart activity of the first subject in response to the request. The at least one processor may obtain a personalized model for predicting blood pressure with respect to the first subject from a cloud server. The at least one processor may determine the blood pressure of the first subject using the personalized model based on the data related to heart activity of the first subject. The at least one processor may send the blood pressure of the first subject to the terminal in response to the request.

According to another aspect of the present disclosure, a system for determining blood pressure may include at least one medium including a set of instructions, a communication platform connected to a network and at least one processor in communication with the at least one storage medium. When executing the set of instructions, the at least one processor may receive a request to determine blood pressure of a first subject from a terminal. The at least one processor may obtain data related to heart activity of the first subject in response to the request. The at least one processor may send the data related to heart activity of the first subject to a cloud server. The at least one processor may receive the blood pressure of the first subject determined by the cloud server. In some embodiments, the blood pressure of the first subject may be determined by determining a personalized model for predicting blood pressure with respect to the first subject and determining the blood pressure of the first subject using the personalized model based on the data related to heart activity of the first subject. The at least one processor may send the blood pressure of the first subject to the terminal in response to the request.

According to another aspect of the present disclosure, a non-transitory computer-readable medium, comprising at least one set of instructions for determine blood pressure. When executed by at least one processor, the at least one set of instructions may direct the at least one processor to: receive a request to determine blood pressure of a first subject from a terminal; obtain data related to heart activity of the first subject in response to the request; determine a personalized model for predicting blood pressure with respect to the first subject; determine the blood pressure of the first subject using the personalized model based on the data related to heart activity of the first subject; and send the blood pressure of the first subject to the terminal in response to the request.

Additional features will be set forth in part in the description which follows, and in part will become apparent to those skilled in the art upon examination of the following and the accompanying drawings or may be learned by production or operation of the examples. The features of the present disclosure may be realized and attained by practice or use of various aspects of the methodologies, instrumentalities and combinations set forth in the detailed examples discussed below.

