State information determination method and apparatus, control method and apparatus

Abstract

A state information determination method and device, and a control method and device are disclosed, and relate to the technical field of medical data processing. The state information determination method comprises: determining a state evaluation parameter corresponding to a to-be-tested body based on a preset analysis time interval, and determining first state information corresponding to the to-be-tested body based on the state evaluation parameter. The state evaluation parameter comprises at least one of a first heart rate characteristic parameter, a first heart rate variability characteristic parameter, a second heart rate characteristic parameter and a second heart rate variability characteristic parameter. The first heart rate characteristic parameter and the first heart rate variability characteristic parameter correspond to a first time interval, and the second heart rate characteristic parameter and the second heart rate variability characteristic parameter correspond to a second time interval. The present disclosure can improve the accuracy of the determined first state information and the comparability at different times, thereby providing favorable conditions for assisting in determining the disease state of the to-be-tested body and assisting in predicting the progress and the like based on the determined first state information.

Description

Technical Field

[0001] This disclosure relates to the field of medical data processing technology, specifically to methods and apparatus for determining status information, control methods and apparatus, computer-readable storage media, and electronic devices. Background Technology

[0002] As is well known, parameters such as heart rate and heart rate variability of the test subject are easily affected by many factors such as diurnal variation, disease progression, sleep status and medication. Therefore, it is difficult to determine the state information (such as the state information of neurohormones of the test subject) and its comparability based on parameters such as heart rate and heart rate variability. Summary of the Invention

[0003] To address the aforementioned technical problems, this disclosure is proposed. Embodiments of this disclosure provide a method and apparatus for determining state information, a control method and apparatus, a computer-readable storage medium, and an electronic device.

[0004] In one aspect, embodiments of this disclosure provide a method for determining state information. The method includes: determining state evaluation parameters corresponding to a test subject based on a preset analysis time interval, and determining first state information corresponding to the test subject based on the state evaluation parameters. The state evaluation parameters include at least one of a first heart rate characterization parameter, a first heart rate variability characterization parameter, a second heart rate characterization parameter, and a second heart rate variability characterization parameter. The first heart rate characterization parameter and the first heart rate variability characterization parameter correspond to a first time interval, and the second heart rate characterization parameter and the second heart rate variability characterization parameter correspond to a second time interval.

[0005] In one embodiment of this disclosure, determining the first state information corresponding to the test object based on state evaluation parameters includes: determining the numerical change information corresponding to the test object based on the state evaluation parameters; and determining the first state information based on the numerical change information.

[0006] In one embodiment of this disclosure, before determining the first state information corresponding to the test object based on state evaluation parameters, the method further includes: determining the second state information corresponding to the test object based on the state evaluation parameters. Specifically, determining the first state information corresponding to the test object based on the state evaluation parameters includes: determining the first state information based on the state evaluation parameters and the second state information.

[0007] In one embodiment of this disclosure, the preset analysis time interval includes multiple preset analysis time periods. Furthermore, determining the second state information corresponding to the test object based on state evaluation parameters includes: determining the state evaluation level corresponding to each of the multiple preset analysis time periods based on the state evaluation parameters; determining the lowest level or baseline level among the state evaluation levels corresponding to each of the multiple preset analysis time periods that meets the conditions for a normal state level; and using the state information of the preset analysis time period corresponding to the lowest level or baseline level as the second state information.

[0008] In one embodiment of this disclosure, determining first state information based on state evaluation parameters and second state information includes: comparing state evaluation parameters and second state information to obtain a comparison result; and determining first state information based on the comparison result.

[0009] In one embodiment of this disclosure, the state evaluation parameters include a first heart rate characterization parameter, which includes a first average heart rate parameter and a first resting heart rate parameter. Furthermore, determining the state evaluation parameters corresponding to the test subject based on a preset analysis time interval includes: determining multiple first time intervals corresponding to the preset analysis time interval and the first time interval; determining a first average heart rate parameter corresponding to each of the multiple first time intervals; and determining a first resting heart rate parameter based on the first average heart rate parameter corresponding to each of the multiple first time intervals.

[0010] In one embodiment of this disclosure, determining a first static heart rate parameter based on the first average heart rate parameters corresponding to each of the multiple first time periods includes: determining the smallest first average heart rate parameter among the first average heart rate parameters corresponding to each of the multiple first time periods; and determining the smallest first average heart rate parameter as the first static heart rate parameter.

[0011] In one embodiment of this disclosure, the state evaluation parameters further include a first heart rate variability characterization parameter, which includes a first static heart rate variability parameter. Furthermore, after determining the first static heart rate parameter based on the first average heart rate parameters corresponding to multiple first time periods, the method further includes: determining the first time period corresponding to the first static heart rate parameter as a first static time period; and determining the heart rate variability parameter corresponding to the first static time period as the first static heart rate variability parameter.

[0012] In one embodiment of this disclosure, before determining the first average heart rate parameter corresponding to each of the multiple first time periods based on multiple first time periods, the method further includes: removing the non-stable time intervals corresponding to each of the multiple first time periods. Specifically, determining the first average heart rate parameter corresponding to each of the multiple first time periods based on the multiple first time periods includes: determining the first average heart rate parameter corresponding to each of the multiple first time periods based on the multiple first time periods after removing the non-stable time intervals.

[0013] In one embodiment of this disclosure, the state evaluation parameters include a second heart rate characterization parameter, which includes a second average heart rate parameter and a second resting heart rate parameter. Furthermore, determining the state evaluation parameters corresponding to the test subject based on a preset analysis time interval includes: determining multiple second time periods corresponding to the preset analysis time interval and the second time interval; determining a second average heart rate parameter corresponding to each of the multiple second time periods; and determining a second resting heart rate parameter based on the second average heart rate parameter corresponding to each of the multiple second time periods.

[0014] In one embodiment of this disclosure, determining a second static heart rate parameter based on the second average heart rate parameters corresponding to each of the multiple second time periods includes: determining the smallest second average heart rate parameter among the second average heart rate parameters corresponding to each of the multiple second time periods; and determining the smallest second average heart rate parameter as the second static heart rate parameter.

[0015] In one embodiment of this disclosure, the second heart rate variability characterization parameter further includes a second static heart rate variability parameter. Furthermore, after determining the second static heart rate parameter based on the second average heart rate parameters corresponding to multiple second time periods, the method further includes: determining the second time period corresponding to the second static heart rate parameter as a third static time period; and determining the heart rate variability parameter corresponding to the third static time period as the second static heart rate variability parameter.

[0016] In one embodiment of this disclosure, before determining the second average heart rate parameter corresponding to each of the multiple second time periods based on multiple second time periods, the method further includes: removing the non-stable time intervals corresponding to each of the multiple second time periods. Specifically, determining the second average heart rate parameter corresponding to each of the multiple second time periods based on the multiple second time periods includes: determining the second average heart rate parameter corresponding to each of the multiple second time periods based on the multiple second time periods after removing the non-stable time intervals.

[0017] In one embodiment of this disclosure, the first state information is the state information of the neurohormonal activity of the test subject.

[0018] In another aspect, embodiments of this disclosure provide a control method, the method comprising: acquiring first state information corresponding to a test object, wherein the first state information is obtained based on the state information determination method described in any of the above embodiments; and controlling the working state of a medical system and / or a medical device and / or a mobile device based on the first state information.

[0019] In another aspect, embodiments of this disclosure provide a state information determination device, which includes: a first determination module, configured to determine state evaluation parameters corresponding to a test subject based on a preset analysis time interval, wherein the state evaluation parameters include at least one of a first heart rate characterization parameter, a first heart rate variability characterization parameter, a second heart rate characterization parameter, and a second heart rate variability characterization parameter, wherein the first heart rate characterization parameter and the first heart rate variability characterization parameter correspond to a first time interval, and the second heart rate characterization parameter and the second heart rate variability characterization parameter correspond to a second time interval; and a second determination module, configured to determine first state information corresponding to the test subject based on the state evaluation parameters.

[0020] In another aspect, embodiments of this disclosure provide a control device, which includes: an acquisition module for acquiring first state information corresponding to a test object, wherein the first state information is obtained based on the state information determination method mentioned in any of the above embodiments; and a control module for controlling the working state of a medical system and / or a medical device based on the first state information.

[0021] In another aspect, embodiments of this disclosure provide a computer-readable storage medium storing a computer program for performing the state information determination method and / or control method mentioned in the above embodiments.

[0022] In another aspect, embodiments of this disclosure provide an electronic device comprising: a processor and a memory for storing processor-executable instructions, wherein the processor is configured to execute the state information determination method and / or control method mentioned in the above embodiments.

[0023] Since the embodiments of this disclosure do not directly determine the first state information of the test subject based on the heart rate and / or heart rate variability parameters corresponding to the test subject, but instead use the state evaluation parameters corresponding to the test subject to determine the first state information, the embodiments of this disclosure can improve the accuracy of the determined first state information and the comparability at different times, thereby providing favorable conditions for assisting in the prediction of disease progression of the test subject based on the determined first state information. Attached Figure Description

[0024] The above and other objects, features, and advantages of this disclosure will become more apparent from the more detailed description of the embodiments thereof in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this disclosure and form part of the specification. They are used together with the embodiments of this disclosure to explain the disclosure and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0025] Figure 1 The diagram shown is a scenario applicable to an embodiment of this disclosure.

[0026] Figure 2 The diagram shown illustrates another scenario applicable to the embodiments of this disclosure.

[0027] Figure 3 The diagram shown is a flowchart illustrating a state information determination method provided in an exemplary embodiment of this disclosure.

[0028] Figure 4 The diagram shown is a schematic flowchart illustrating the process of determining the first state information of the test object based on state evaluation parameters according to an exemplary embodiment of this disclosure.

[0029] Figure 5 The diagram shown is a flowchart illustrating a state information determination method provided in another exemplary embodiment of this disclosure.

[0030] Figure 6 The diagram shown is a schematic flowchart of a process for determining the second state information of the test object based on state evaluation parameters, provided by an exemplary embodiment of this disclosure.

[0031] Figure 7 The diagram shown is a schematic flowchart of a process for determining first state information based on state evaluation parameters and second state information, provided in an exemplary embodiment of this disclosure.

[0032] Figure 8 The diagram shown is a schematic flowchart of an exemplary embodiment of this disclosure, which describes the process of determining the state evaluation parameters of the test object based on a preset analysis time interval.

[0033] Figure 9 The diagram shown is a schematic flowchart of an exemplary embodiment of the present disclosure, which describes a process for determining a first static heart rate parameter based on first average heart rate parameters corresponding to multiple first time periods.

[0034] Figure 10 The diagram shown is a flowchart illustrating the process of determining the state evaluation parameters of the test object based on a preset analysis time interval, as provided in another exemplary embodiment of this disclosure.

[0035] Figure 11 The diagram shown is a flowchart illustrating the process of determining the state evaluation parameters of the test object based on a preset analysis time interval, according to another exemplary embodiment of this disclosure.

[0036] Figure 12 The diagram shown is a flowchart illustrating the process of determining the state evaluation parameters of the test object based on a preset analysis time interval, as provided in another exemplary embodiment of this disclosure.

[0037] Figure 13 The diagram shown is a flowchart illustrating a control method provided in an exemplary embodiment of this disclosure.

[0038] Figure 14 The diagram shown is a schematic representation of the state information determination device provided in an exemplary embodiment of this disclosure.

[0039] Figure 15 The diagram shown is a structural schematic of the second determining module provided in an exemplary embodiment of this disclosure.

[0040] Figure 16 The diagram shown is a structural schematic of a status information determination device provided in another exemplary embodiment of this disclosure.

[0041] Figure 17 The diagram shown is a structural schematic of a second state information determination module provided in an exemplary embodiment of this disclosure.

[0042] Figure 18 The diagram shown is a structural schematic of the first determining module provided in an exemplary embodiment of this disclosure.

[0043] Figure 19 The diagram shown is a structural schematic of a first determining module provided in another exemplary embodiment of this disclosure.

[0044] Figure 20 The diagram shown is a structural schematic of a first determining module provided in yet another exemplary embodiment of this disclosure.

[0045] Figure 21 The diagram shown is a structural schematic of the first determining module provided in another exemplary embodiment of this disclosure.

[0046] Figure 22 The diagram shown is a schematic representation of the structure of a control device provided in an exemplary embodiment of this disclosure.

[0047] Figure 23 The diagram shown is a structural schematic of an electronic device provided in an exemplary embodiment of this disclosure. Detailed Implementation

[0048] Hereinafter, exemplary embodiments according to the present disclosure will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of the present disclosure, and not all embodiments of the present disclosure, and it should be understood that the present disclosure is not limited to the exemplary embodiments described herein.

[0049] Application Overview

[0050] It is well known that neurohormones driven by the sympathetic and parasympathetic nervous systems are highly susceptible to multiple factors. This is especially true for heart disease patients with heart failure or arrhythmias, whose neurohormones are easily affected by diurnal variations, disease progression, sleep patterns, exercise or activity levels, body position, medications, and emotional changes. Therefore, in current technology, heart rate (HR) and heart rate variability (HRV) are commonly used as diagnostic criteria or alternative measurements to characterize neurohormones. Specifically, heart rate refers to the number of heartbeats per minute. Heart rate variability refers to the variation in the difference between successive heartbeat cycles. Sources for heart rate calculation include, but are not limited to, surface electrocardiograms (ECGs), endocardial ECGs, epicardial ECGs, ECGs from subcutaneous electrodes, or ventricular or atrial heart rates calculated from the R and P waves. Furthermore, it should be understood that heart rate can also be calculated using other techniques, such as, but not limited to, photoplethysmography (PPG).

[0051] However, both heart rate and heart rate variability remain highly susceptible to interference. For example, heart rate and / or heart rate variability frequently change during different sleep stages at night, and these changes are not necessarily related to neurohormones. Furthermore, sleep states are not always repetitive across different nighttime sleep stages. Additionally, due to lower heart rates, patients with pacemakers or implanted cardioverter defibrillators (ICDs) typically pace at night, in which case the patient's true heart rate is often "hidden," thus failing to be reflected in the measured heart rate data. Moreover, current techniques for measuring daytime heart rate and daytime heart rate variability include data from exercise, emotional excitement, and different body positions; however, exercise and emotional excitement only characterize short-term states, and these short-term variations differ across different times (days). Simultaneously, diseases such as heart failure have a significant, but relatively long-term, impact on a patient's neurohormonal levels and / or state. Therefore, it is evident that, in existing technologies, it is difficult to determine the state information of the test subject (such as the state information of the test subject's neurohormones) based on parameters such as heart rate and heart rate variability.

[0052] Based on the aforementioned technical problems, the basic concept of this disclosure is to propose a method and apparatus for determining state information, a control method and apparatus, a computer-readable storage medium, and an electronic device.

[0053] The method for determining state information provided in this disclosure includes: determining state evaluation parameters corresponding to the test subject based on a preset analysis time interval, wherein the state evaluation parameters include at least one of a first heart rate characterization parameter, a first heart rate variability characterization parameter, a second heart rate characterization parameter, and a second heart rate variability characterization parameter, wherein the first heart rate characterization parameter and the first heart rate variability characterization parameter correspond to a first time interval, and the second heart rate characterization parameter and the second heart rate variability characterization parameter correspond to a second time interval; and determining first state information corresponding to the test subject based on the state evaluation parameters.

[0054] Since this disclosure does not directly determine the first state information of the test subject based on the heart rate and / or heart rate variability parameters corresponding to the test subject, but uses the state evaluation parameters corresponding to the test subject to determine the first state information, this disclosure can improve the accuracy of the determined first state information and the comparability at different times, thereby providing favorable conditions for assisting in the prediction of disease progression of the test subject based on the determined first state information.

[0055] Having introduced the basic principles of this disclosure, various non-limiting embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.

[0056] Exemplary scenario

[0057] Figure 1 The diagram shown illustrates a scenario applicable to an embodiment of this disclosure. Figure 1 As shown, the scenario applicable to the embodiments of this disclosure includes server 1 and medical device 2, wherein there is a communication connection between server 1 and medical device 2.

[0058] Specifically, medical device 2 is used to collect baseline parameters of the test subject, including but not limited to heart rate parameters and heart rate variability parameters. Server 1 is used to determine the state evaluation parameters corresponding to the test subject based on a preset analysis time interval, and to determine the first state information corresponding to the test subject based on the state evaluation parameters. The state evaluation parameters include at least one of a first heart rate characterization parameter, a first heart rate variability characterization parameter, a second heart rate characterization parameter, and a second heart rate variability characterization parameter. The first heart rate characterization parameter and the first heart rate variability characterization parameter correspond to a first time interval, and the second heart rate characterization parameter and the second heart rate variability characterization parameter correspond to a second time interval. That is, this scenario implements a method for determining state information.

[0059] For example, server 1 determines the aforementioned status evaluation parameters based on the baseline parameters collected by medical device 2.

[0060] Furthermore, server 1 is also used to acquire the first state information corresponding to the test object, and control the working state of the medical system and / or medical device 2 and / or mobile device based on the first state information. That is, this scenario implements a control method.

[0061] because Figure 1 The scenario shown above utilizes server 1 to implement a state information determination method and / or control method. Therefore, it can not only improve the adaptability of the scenario, but also effectively reduce the computational load of medical device 2.

[0062] It should be noted that this disclosure also applies to another scenario. Figure 2 The diagram shown illustrates another scenario applicable to the embodiments of this disclosure. Specifically, this scenario includes a medical device 3, which includes a parameter acquisition module 301 and a calculation module 302.

[0063] Specifically, the parameter acquisition module 301 in medical device 3 is used to acquire baseline parameters of the test subject, including but not limited to heart rate parameters and heart rate variability parameters. The calculation module 302 is used to determine the state evaluation parameters corresponding to the test subject based on a preset analysis time interval, and to determine the first state information corresponding to the test subject based on the state evaluation parameters. The state evaluation parameters include at least one of a first heart rate characterization parameter, a first heart rate variability characterization parameter, a second heart rate characterization parameter, and a second heart rate variability characterization parameter. The first heart rate characterization parameter and the first heart rate variability characterization parameter correspond to a first time interval, and the second heart rate characterization parameter and the second heart rate variability characterization parameter correspond to a second time interval. That is, this scenario implements a method for determining state information.

[0064] For example, the calculation module 302 determines the aforementioned state evaluation parameters based on the reference parameters collected by the parameter acquisition module 301.

[0065] Furthermore, the computing module 302 is also used to acquire the first state information corresponding to the test object, and control the working state of the medical system and / or medical device 3 and / or mobile device based on the first state information. That is, this scenario implements a control method.

[0066] because Figure 2 The scenario described above utilizes medical device 3 to implement the state information determination method and / or control method without requiring data transmission operations with servers or other related devices. Therefore, the scenario above can guarantee the real-time performance of the state information determination method and / or control method.

[0067] It should be noted that the medical devices mentioned in the above scenarios can be either implantable medical devices (IMDs) or wearable medical devices (WMDs), and this disclosure does not impose a uniform limitation on either.

[0068] Exemplary methods

[0069] Figure 3 The diagram shown is a flowchart illustrating a state information determination method provided in an exemplary embodiment of this disclosure. Figure 3 As shown in the embodiments of this disclosure, the method for determining status information includes the following steps.

[0070] Step S100: Based on the preset analysis time interval, determine the state evaluation parameters corresponding to the test object.

[0071] In this embodiment of the disclosure, the state evaluation parameters include at least one of a first heart rate characterization parameter, a first heart rate variability characterization parameter, a second heart rate characterization parameter, and a second heart rate variability characterization parameter. The first heart rate characterization parameter and the first heart rate variability characterization parameter correspond to a first time interval, and the second heart rate characterization parameter and the second heart rate variability characterization parameter correspond to a second time interval.

[0072] For example, the first time interval includes the nighttime interval. For instance, in a 24-hour clock, the first time interval is from 18:00 of the current day to 6:00 of the next day. Preferably, the first time interval is from 22:00 of the current day to 6:00 of the next day. Preferably, during the first time interval, the test subject is in a resting or sleeping state to reduce interference and thus improve the accuracy of the final state evaluation parameters. Here, a resting state refers to a state where the body is at rest, and a sleeping state refers to a state where the body is relatively still and in a "lying" position after falling asleep.

[0073] For example, the second time interval includes a daytime interval. For instance, in a 24-hour clock, the second time interval is from 6:00 AM to 6:00 PM of the current day. Preferably, the second time interval is from 8:00 AM to 8:00 PM of the current day. Preferably, during the second time interval, the test subject is in a resting or minimally active state to reduce interference and thus improve the accuracy and comparability of the final state evaluation parameters. More preferably, during the second time interval, the test subject is in a resting or non-active state in a non-standing position. For example, the aforementioned non-active state can be determined using relevant exercise / body movement or other physiological parameter thresholds. For example, if the test subject's real-time body movement signal is below a certain threshold or does not exceed a preset exercise heart rate threshold, the test subject can be determined to be in a non-active state.

[0074] For example, the test subject mentioned in step S100 refers to the human body. That is, determining the state evaluation parameters corresponding to the human body. It should be understood that determining the state evaluation parameters corresponding to the test subject mentioned in step S100 can mean calculating the state evaluation parameters corresponding to the test subject.

[0075] It should be noted that the preset analysis time interval mentioned in step S100 may include one or more first time intervals and / or one or more second time intervals. For example, the preset analysis time interval may be a week-long time interval including seven calendar days.

[0076] Step S200: Determine the first state information corresponding to the test object based on the state evaluation parameters.

[0077] For example, the first state information mentioned in step S200 refers to information that can characterize the state of the test subject (such as the state of the heart). For instance, the first state information is the absolute trend change information and / or relative trend change information corresponding to the state evaluation parameters, determined based on the state evaluation parameters. The absolute trend change information can be information about how the value changes over time, and the relative trend change information can be information about how the value changes relative to the baseline value.

[0078] In practical applications, the state evaluation parameters corresponding to the test object are first determined based on the preset analysis time interval, and then the first state information corresponding to the test object is determined based on the state evaluation parameters.

[0079] Since the embodiments of this disclosure do not directly determine the first state information of the test subject based on the heart rate and / or heart rate variability parameters corresponding to the test subject, but instead use the state evaluation parameters corresponding to the test subject to determine the first state information, the embodiments of this disclosure can improve the accuracy of the determined first state information and the comparability at different times, thereby providing favorable conditions for assisting in the prediction of disease progression of the test subject based on the determined first state information.

[0080] Figure 4 The diagram shown is a schematic flowchart illustrating the process of determining the first state information of the test object based on state evaluation parameters according to an exemplary embodiment of this disclosure. Figure 3 Extending from the illustrated embodiment Figure 4 The illustrated embodiment will be described in detail below. Figure 4 The illustrated embodiments and Figure 3 The differences between the embodiments shown are not repeated here, and the similarities are not repeated here.

[0081] like Figure 4 As shown, in the state information determination method provided in this embodiment, the step of determining the first state information corresponding to the test object based on state evaluation parameters includes the following steps.

[0082] Step S210: Determine the numerical change information corresponding to the test object based on the state evaluation parameters.

[0083] For example, the numerical change information includes absolute value change trend information. For instance, the absolute value change trend information includes three types: an absolute upward trend within a preset time range, an absolute downward trend within a preset time range, and a relatively stable trend within a preset time range.

[0084] In one embodiment of this disclosure, a trend line characterizing the aforementioned absolute value changes is calculated using linear regression (absolute value and time). Other statistical analysis methods can also be used similarly.

[0085] For example, the status assessment parameters include a first heart rate characterization parameter, which includes a first average heart rate parameter and a first resting heart rate parameter. Correspondingly, the absolute value change trend information can include the absolute value change trend information of the first average heart rate parameter and the absolute value change trend information of the first resting heart rate parameter, or the change trend of a combination of their absolute values.

[0086] It should be understood that the status assessment parameters are not limited to the first average heart rate parameter and the first resting heart rate parameter mentioned above. The specific parameters included in the status assessment parameters and the specific meaning of each parameter can be found in the following embodiments (e.g., Figures 8 to 12 Example (Example).

[0087] For example, the numerical trend information is the relationship between a correlation coefficient of a state evaluation parameter and its corresponding preset coefficient threshold. Alternatively, the numerical trend information can be the relationship between the slope (or average value) of a state evaluation parameter and its corresponding preset slope threshold (or preset average value threshold). Preferably, when the slope (or average value) of a state evaluation parameter is less than its corresponding preset slope threshold (or preset average value threshold), the absolute value trend information of the state evaluation parameter can be defined as a downward trend within a preset time range.

[0088] It should be noted that the specific values ​​of the preset coefficient threshold, preset slope threshold, and preset average value threshold mentioned above can all be set according to the actual situation, and this disclosure does not impose a uniform limitation on them.

[0089] Step S220: Determine the first state information based on the numerical change information.

[0090] For example, if the numerical change information includes numerical change trend information, then the determined first state information can be state information related to "showing an upward trend within a preset time range".

[0091] In one embodiment of this disclosure, the numerical change trend includes three types: "increasing," "decreasing," and "stable." In a simpler case, such as when the numerical value corresponds to heart rate variability, these three trends correspond to the subject's "improving," "deteriorating," and "stable" states, respectively. If the numerical value corresponds to resting heart rate, these three trends correspond to the subject's "deteriorating," "improving," and "stable" states, respectively. If the corresponding numerical value is a certain combination of the two, then the three trends will correspond to the three states according to the specific algorithm of the combination.

[0092] The state information determination method provided in this disclosure achieves the purpose of determining the first state information corresponding to the test object based on state evaluation parameters by determining the numerical change information corresponding to the test object based on state evaluation parameters, and then determining the first state information based on the numerical change information. Since the numerical change information can reflect the true state of the test object to a certain extent, this disclosure embodiment can effectively reduce the difference between the determined first state information and the true state, thereby improving the accuracy of the determined first state information.

[0093] Figure 5 The diagram shown is a flowchart illustrating a state information determination method provided in another exemplary embodiment of this disclosure. Figure 3 Extending from the illustrated embodiment Figure 5 The illustrated embodiment will be described in detail below. Figure 5 The illustrated embodiments and Figure 3 The differences between the embodiments shown are not repeated here, and the similarities are not repeated here.

[0094] like Figure 5 As shown, in the state information determination method provided in this embodiment, before determining the first state information corresponding to the test object based on the state evaluation parameters, the following steps are also included.

[0095] Step S150: Determine the second state information corresponding to the test object based on the state evaluation parameters.

[0096] For example, the second state information mentioned in step S150 is the normal state information corresponding to the test subject. Correspondingly, the first state information mentioned in step S230 below is the real-time state information corresponding to the test subject. Preferably, the normal state information refers to the state information corresponding to the test subject being in a healthy, stable disease state, or when the disease condition does not change significantly.

[0097] Furthermore, in the state information determination method provided in this embodiment, the step of determining the first state information corresponding to the test object based on state evaluation parameters includes the following steps.

[0098] Step S230: Determine the first state information based on the state evaluation parameters and the second state information.

[0099] In practical applications, the state evaluation parameters corresponding to the test object are first determined based on the preset analysis time interval. Then, the first state information corresponding to the test object is determined based on the determined state evaluation parameters and the second state information.

[0100] and Figure 3 Compared to the illustrated embodiments, the embodiments of this disclosure combine the determined second state information to assist in determining the first state information. Compared to determining the first state information solely based on state evaluation parameters, the embodiments of this disclosure can further improve the accuracy of the determined first state information. Especially when the second state information is the "normal" or disease-stable state information corresponding to the test subject, the embodiments of this disclosure can fully consider the individual characteristics of the test subject, thereby achieving the goal of improving the accuracy of the determined first state information.

[0101] Figure 6 The diagram shown is a schematic flowchart illustrating the process of determining the second state information corresponding to the test object based on state evaluation parameters, according to an exemplary embodiment of this disclosure. Figure 5 Extending from the illustrated embodiment Figure 6 The illustrated embodiment will be described in detail below. Figure 6 The illustrated embodiments and Figure 5 The differences between the embodiments shown are not repeated here, and the similarities are not repeated here.

[0102] like Figure 6As shown, in the state information determination method provided in this embodiment, the preset analysis time interval includes multiple preset analysis time periods. Furthermore, the step of determining the second state information corresponding to the test object based on state evaluation parameters includes the following steps.

[0103] Step S151: Based on the state evaluation parameters, determine the state evaluation level corresponding to each of the multiple preset analysis time periods.

[0104] For example, the preset analysis time period mentioned above is a time window of preset time length.

[0105] It should be noted that the aforementioned status assessment levels can be set by the user according to the specific values ​​of the status assessment parameters corresponding to each preset analysis time period.

[0106] Step S152: Determine the lowest level or baseline level (value) that meets the normal state level conditions among the state evaluation levels corresponding to each of the multiple preset analysis time periods.

[0107] For example, the normal state level condition mentioned in step S152 refers to a universally applicable normal state level. For instance, if the test subject is an adult, then the normal state level is the normal state level corresponding to an adult. Or, if the test subject is a patient (e.g., a patient with chronic heart failure), then the "normal state" level is the stable disease state level corresponding to the patient. The state information in this state becomes the baseline level (value). In one embodiment of this disclosure, the state information determining the baseline level can be determined by a doctor or other relevant personnel based on the patient's condition, medication, and / or remote patient information (e.g., weight, symptoms, etc.) and obtained through a certain algorithm. One method for collecting patient condition and medication information is through the hospital's HIS system or the patient's electronic medical record, either automatically or manually.

[0108] Preferably, the normal state level mentioned in step S152 corresponds to the state evaluation level mentioned in step S151, so that the state evaluation level mentioned in step S151 can be identified based on the normal state level mentioned in step S152.

[0109] In another embodiment of this disclosure, the step of determining the lowest level or baseline level that meets the normal state level conditions among the state evaluation levels corresponding to each of the multiple preset analysis time periods includes: determining the lowest level or baseline level (value) among the state evaluation levels that continuously meet the normal state level conditions among the multiple preset analysis time periods.

[0110] Step S153: Use the status information of the preset analysis time period corresponding to the lowest level or baseline level as the second status information.

[0111] Since this embodiment uses the state information of the lowest level or baseline level that meets the normal state level conditions as the state information of the preset analysis time period, this embodiment can greatly improve the accuracy of the determined second state information, reduce the probability of misjudgment, and ultimately improve the accuracy of the first state information determined based on the state evaluation parameters and the second state information.

[0112] Figure 7 The diagram shown is a schematic flowchart illustrating the process of determining first state information based on state evaluation parameters and second state information, according to an exemplary embodiment of this disclosure. Figure 5 Extending from the illustrated embodiment Figure 7 The illustrated embodiment will be described in detail below. Figure 7 The illustrated embodiments and Figure 5 The differences between the embodiments shown are not repeated here, and the similarities are not repeated here.

[0113] like Figure 7 As shown, in the state information determination method provided in this embodiment, the step of determining the first state information based on state evaluation parameters and second state information includes the following steps.

[0114] Step S231: Compare the state evaluation parameters and the second state information to obtain the comparison result.

[0115] Step S232: Determine the first state information based on the comparison results.

[0116] This embodiment of the present disclosure achieves the purpose of determining the first state information based on the state evaluation parameters and the second state information by comparing the state evaluation parameters and the second state information to obtain the comparison result, and then determining the first state information based on the comparison result.

[0117] Figure 8 The diagram illustrates a flowchart of an exemplary embodiment of this disclosure, illustrating the process of determining state evaluation parameters corresponding to a test object based on a preset analysis time interval. Figure 3 Extending from the illustrated embodiment Figure 8 The illustrated embodiment will be described in detail below. Figure 8 The illustrated embodiments and Figure 3 The differences between the embodiments shown are not repeated here, and the similarities are not repeated here.

[0118] like Figure 8 As shown, in the state information determination method provided in this embodiment, the state evaluation parameters include a first heart rate characterization parameter, which includes a first average heart rate parameter and a first resting heart rate parameter. Furthermore, the step of determining the state evaluation parameters corresponding to the test subject based on a preset analysis time interval includes the following steps.

[0119] Step S110: Determine multiple first time periods corresponding to the preset analysis time interval and the first time interval.

[0120] For example, the first time period is a time window with a length of 60 seconds. In other words, step S110 refers to determining the first average heart rate parameter corresponding to each of the multiple time windows included in the preset analysis time interval and corresponding to the first time interval.

[0121] Step S120: Based on multiple first time periods, determine the first average heart rate parameter corresponding to each of the multiple first time periods.

[0122] Optionally, the first mean heart rate (MHR) parameter for each time window refers to the average heart rate measured within that time window.

[0123] Step S130: Determine the first static heart rate parameter based on the first average heart rate parameters corresponding to each of the multiple first time periods.

[0124] In one embodiment of this disclosure, the first static heart rate parameter is the sleep rest heart rate (Sleep RHR) parameter.

[0125] Since the first static heart rate parameter is determined based on the first average heart rate parameters corresponding to each of the multiple first time periods, the obtained first static heart rate parameter can fully take into account the characteristics of the first average heart rate parameters corresponding to each of the multiple first time periods. Therefore, the obtained first static heart rate parameter can better characterize the heart rate of the test subject in the first time interval.

[0126] Compared with real-time heart rate, the first average heart rate parameter and the first static heart rate parameter can more accurately characterize the heart rate of the test subject in the first time interval. Therefore, when predicting the neurohormone level and / or state (i.e., first state information) of the test subject in the first time interval based on the first average heart rate parameter and the first static heart rate parameter obtained in the embodiments of this disclosure, more accurate neurohormone prediction results can be obtained.

[0127] exist Figure 8 This disclosure extends the illustrated embodiment to include another embodiment. In this embodiment, before determining the first average heart rate parameter corresponding to each of the multiple first time periods based on multiple first time periods, the method further includes: removing the non-stable time intervals corresponding to each of the multiple first time periods. Specifically, determining the first average heart rate parameter corresponding to each of the multiple first time periods based on the multiple first time periods includes: determining the first average heart rate parameter corresponding to each of the multiple first time periods based on the multiple first time periods after removing the non-stable time intervals.

[0128] For example, an unstable time interval refers to a time interval such as a snoring time interval and / or a time interval with unstable respiratory rate (e.g., sleep apnea).

[0129] In one embodiment of this disclosure, the non-steady time intervals also include time intervals corresponding to specific stages of sleep when physical activity is high. For example, a specific stage of sleep is the rapid eye movement (REM) sleep stage, such as when dreaming, when heart rate changes significantly. Furthermore, the non-steady time intervals also include time intervals corresponding to events such as premature atrial contractions (PAC), premature ventricular contractions (PVC), atrial tachycardia / atrial arrhythmias (such as atrial fibrillation), and ventricular tachycardia (VT).

[0130] Since the non-stationary time intervals are highly specific and cannot well characterize the typical condition of the test subject, the embodiments of this disclosure improve the characterization ability of the determined first average heart rate parameter and first static heart rate parameter by removing the non-stationary time intervals.

[0131] Figure 9 The diagram illustrates a flowchart of an exemplary embodiment of this disclosure, illustrating the process of determining a first static heart rate parameter based on first average heart rate parameters corresponding to multiple first time periods. Figure 8 Extending from the illustrated embodiment Figure 9 The illustrated embodiment will be described in detail below. Figure 9 The illustrated embodiments and Figure 8 The differences between the embodiments shown are not repeated here, and the similarities are not repeated here.

[0132] like Figure 9 As shown, in the state information determination method provided in this embodiment, the step of determining the first static heart rate parameter based on the first average heart rate parameter corresponding to each of the multiple first time periods includes the following steps.

[0133] Step S131: Determine the smallest first average heart rate parameter among the first average heart rate parameters corresponding to each of the multiple first time periods.

[0134] Step S132: The smallest first average heart rate parameter is determined as the first static heart rate parameter.

[0135] For example, the number of first time periods is 5, namely first time period a, first time period b, first time period c, first time period d, and first time period e. Correspondingly, the first average heart rate parameter for first time period a is 80 beats / minute, the first average heart rate parameter for first time period b is 85 beats / minute, the first average heart rate parameter for first time period c is 90 beats / minute, the first average heart rate parameter for first time period d is 75 beats / minute, and the first average heart rate parameter for first time period e is 95 beats / minute. Therefore, the first average heart rate parameter for first time period d (75 beats / minute) is the smallest. Thus, as described in step S132, the first average heart rate parameter for first time period d (75 beats / minute) can be determined as the first resting heart rate parameter.

[0136] This embodiment of the disclosure achieves the purpose of determining the first static heart rate parameter based on the first average heart rate parameters corresponding to multiple first time periods by determining the smallest first average heart rate parameter among multiple first time periods and defining the smallest first average heart rate parameter as the first static heart rate parameter. Since the smallest first average heart rate parameter among the multiple first average heart rate parameters corresponding to multiple first time periods can characterize the heart rate characteristics of the test subject in the first time interval to a certain extent, this embodiment of the disclosure can improve the prediction accuracy and comparability of the first state information.

[0137] Figure 10 The diagram shown is a flowchart illustrating a process for determining the state evaluation parameters of a test object based on a preset analysis time interval, as provided in another exemplary embodiment of this disclosure. Figure 8 Extending from the illustrated embodiment Figure 10 The illustrated embodiment will be described in detail below. Figure 10 The illustrated embodiments and Figure 8 The differences between the embodiments shown are not repeated here, and the similarities are not repeated here.

[0138] like Figure 10 As shown, in the state information determination method provided in this embodiment, after the step of determining the first static heart rate parameter based on the first average heart rate parameter corresponding to each of the multiple first time periods, the following steps are also included.

[0139] Step S140: Determine the first time period corresponding to the first static heart rate parameter as the first static time period.

[0140] Step S150: Determine the heart rate variability parameter corresponding to the first static time period as the first static heart rate variability parameter.

[0141] In one embodiment of this disclosure, the first static heart rate variability parameter is the sleep-rest heart rate variability (Sleep RHRV) parameter.

[0142] Since the determined first static heart rate variability parameter is based on the first static heart rate parameter, the embodiments of this disclosure can improve the ability of the determined first static heart rate variability parameter to characterize the state information (such as neurohormonal levels and / or state) of the test subject in a first time interval compared with the prior art.

[0143] It should be noted that, in another embodiment of this disclosure, the first static heart rate variability parameter can be determined first, and then the first static heart rate parameter can be determined (this also applies to the second static heart rate variability parameter and the second static heart rate parameter; in other words, the second static heart rate variability parameter is determined first, and then the second static heart rate parameter is determined). For example, based on multiple first time periods, the first heart rate variability parameter corresponding to each of the multiple first time periods is determined, and then the first static heart rate variability parameter is determined based on the first heart rate variability parameter corresponding to each of the multiple first time periods. Subsequently, the first time period corresponding to the first static heart rate variability parameter is determined as the first static time period, and the average heart rate parameter corresponding to the first static time period is determined as the first static heart rate parameter. The step of determining the first static heart rate variability parameter based on the first heart rate variability parameter corresponding to each of the multiple first time periods can be executed as determining the first static heart rate variability parameter based on the first time period corresponding to the highest first heart rate variability parameter among the multiple first time periods. Here, the heart rate variability parameter can be described using other statistical methods besides the highest value, such as, but not limited to, the 75% value. In addition, it should be noted that the average heart rate parameter can also be described by other statistics, such as the 25% value.

[0144] Figure 11 The diagram illustrates a flowchart of another exemplary embodiment of this disclosure, illustrating the process of determining state evaluation parameters corresponding to the test object based on a preset analysis time interval. Figure 3 Extending from the illustrated embodiment Figure 11 The illustrated embodiment will be described in detail below. Figure 11 The illustrated embodiments and Figure 3 The differences between the embodiments shown are not repeated here, and the similarities are not repeated here.

[0145] like Figure 11 As shown, in the state information determination method provided in this embodiment, the state evaluation parameters include a second heart rate characterization parameter, which includes a second average heart rate parameter and a second resting heart rate parameter. Furthermore, the step of determining the state evaluation parameters corresponding to the test subject based on a preset analysis time interval includes the following steps.

[0146] Step S160: Determine multiple second time periods corresponding to the preset analysis time interval and the second time interval.

[0147] For example, the second time period is a time window with a length of 60 seconds. In other words, step S210 refers to determining the second average heart rate parameter corresponding to each of the multiple time windows included in the preset analysis time interval and corresponding to the second time interval.

[0148] Step S170: Based on multiple second time periods, determine the second average heart rate parameter corresponding to each of the multiple second time periods.

[0149] Optionally, the second mean heart rate (MHR) parameter for each time window refers to the average heart rate measured within that time window.

[0150] Step S180: Determine the second static heart rate parameter based on the second average heart rate parameter corresponding to each of the multiple second time periods.

[0151] In one embodiment of this disclosure, the second static heart rate parameter is the day rest heart rate (Day RHR) parameter.

[0152] Since the second static heart rate parameter is determined based on the second average heart rate parameters corresponding to each of the multiple second time periods, the obtained second static heart rate parameter can fully take into account the characteristics of the second average heart rate parameters corresponding to each of the multiple second time periods. Therefore, the obtained second static heart rate parameter can better characterize the heart rate of the test subject in the second time interval.

[0153] Compared with real-time heart rate, the second average heart rate parameter and the second static heart rate parameter can more accurately characterize the heart rate status of the test subject in the second time interval. Therefore, when the state information (such as neurohormonal level and / or state information) of the test subject in the second time interval is determined based on the second average heart rate parameter and the second static heart rate parameter obtained in the embodiments of this disclosure, more accurate results can be obtained.

[0154] exist Figure 11 This disclosure extends the illustrated embodiment to include another embodiment. In this embodiment, before determining the second average heart rate parameter corresponding to each of the multiple second time periods, the method further includes removing the non-stable time intervals corresponding to each of the multiple second time periods. Specifically, determining the second average heart rate parameter corresponding to each of the multiple second time periods includes determining the second average heart rate parameter corresponding to each of the multiple second time periods based on the multiple second time periods after removing the non-stable time intervals.

[0155] exist Figure 11 This disclosure extends the illustrated embodiment to include another embodiment. In this embodiment, the step of determining a second resting heart rate parameter based on the second average heart rate parameters corresponding to each of the multiple second time periods includes: determining the smallest second average heart rate parameter among the second average heart rate parameters corresponding to each of the multiple second time periods; and determining the smallest second average heart rate parameter as the second resting heart rate parameter. Other statistical descriptions, such as the 25% value, can also be applied.

[0156] For example, there are three second time periods: second time period a, second time period b, and second time period c. Correspondingly, the second average heart rate parameter for second time period a is 80 beats / minute, for second time period b it is 85 beats / minute, and for second time period c it is 90 beats / minute. Therefore, the second average heart rate parameter for second time period a (80 beats / minute) is the smallest. Thus, the second average heart rate parameter for second time period a (80 beats / minute) can be determined as the second resting heart rate parameter.

[0157] Similarly, since the smallest second average heart rate parameter among the multiple second time periods can characterize the heart rate characteristics of the test subject in the second time interval to a certain extent, the embodiments of this disclosure can further enrich the effective information contained in the state assessment parameters. When determining the neurohormone levels and / or state information of the test subject based on the state assessment parameters, the embodiments of this disclosure can further improve the prediction accuracy and comparability of neurohormone levels and / or state information.

[0158] Figure 12 The diagram shown is a schematic representation of a process for determining the state evaluation parameters of a test object based on a preset analysis time interval, as provided in another exemplary embodiment of this disclosure. Figure 11 Extending from the illustrated embodiment Figure 12 The illustrated embodiment will be described in detail below. Figure 12 The illustrated embodiments and Figure 11 The differences between the embodiments shown are not repeated here, and the similarities are not repeated here.

[0159] like Figure 12 As shown, in the state information determination method provided in this embodiment, after the step of determining the second static heart rate parameter based on the second average heart rate parameter corresponding to each of the multiple second time periods, the following steps are also included.

[0160] Step S191: Determine the second time period corresponding to the second static heart rate parameter as the third static time period.

[0161] Step S192: The heart rate variability parameter corresponding to the third static time period is determined as the second static heart rate variability parameter.

[0162] In one embodiment of this disclosure, the second static heart rate variability parameter is the day-resting heart rate variability (Day RHRV) parameter.

[0163] Since the determined second static heart rate variability parameter is based on the second static heart rate parameter, the embodiments of this disclosure can improve the ability of the determined second static heart rate variability parameter to characterize the state information (such as neurohormonal levels and / or state information) of the test subject in the second time interval compared with the prior art.

[0164] Figure 13 The diagram shown is a flowchart illustrating a control method provided in an exemplary embodiment of this disclosure. Figure 13 As shown, the control method provided in this embodiment includes the following steps.

[0165] Step S300: Obtain the first state information corresponding to the test object.

[0166] For example, the first state information mentioned in step S300 is obtained based on the state information determination method described in any of the above embodiments.

[0167] Step S400: Control the working status of the medical system and / or medical device and / or mobile device based on the first status information.

[0168] For example, the medical system mentioned in step S400 refers to a medical system that can be installed in a medical device.

[0169] For example, the operating state of a medical device includes a state in which it uses warning light sources of different colors (such as yellow and / or red) to provide an alarm. It should be noted that the alarm of a medical device may also be achieved without the aid of a warning light source, but rather through vibration, sound, or a combination thereof.

[0170] For example, if the first state information is an absolute trend change, and this absolute trend change consistently increases within a preset time period, then the yellow warning light of the medical device can be activated to provide an alert. Optionally, when the yellow warning light illuminates twice consecutively, the red warning light of the medical device can be activated to provide a more focused warning.

[0171] In another embodiment of this disclosure, controlling the working state of the medical device includes controlling the on / off state of the medical device, and controlling the on / off state of other devices that are communicatively connected to the medical device, etc.

[0172] In one embodiment of this disclosure, the mobile device includes, but is not limited to, mobile devices such as mobile phones and tablets belonging to patients or their family members, doctors, and nurses. For example, controlling the working state of the mobile device based on the first state information includes, but is not limited to, controlling the emergency information notification state of the mobile device based on the first state information, so that patients and related personnel can be informed of the patient's condition in a timely manner.

[0173] In practical applications, the first state information corresponding to the test object is first obtained, and then the working state of the medical system and / or medical device is controlled based on the first state information.

[0174] The control method provided in this disclosure achieves the purpose of timely and effective notification and / or warning of the status of the test subject based on the status information of the test subject by controlling the working status of the medical system and / or medical device based on the first status information.

[0175] Exemplary device

[0176] Figure 14 The diagram shown is a structural schematic of a status information determination device provided in an exemplary embodiment of this disclosure. Figure 14 As shown, the status information determination device provided in this embodiment includes:

[0177] The first determining module 100 is used to determine the state evaluation parameters corresponding to the test object based on a preset analysis time interval;

[0178] The second determining module 200 is used to determine the first state information corresponding to the test object based on the state evaluation parameters.

[0179] Figure 15 The diagram shown is a structural schematic of the second determining module provided in an exemplary embodiment of this disclosure. Figure 14 Extending from the illustrated embodiment Figure 15 The illustrated embodiment will be described in detail below. Figure 15 The illustrated embodiments and Figure 14 The differences between the embodiments shown are not repeated here, and the similarities are not repeated here.

[0180] like Figure 15 As shown, in the status information determining device provided in this embodiment, the second determining module 200 includes:

[0181] The numerical change information determination unit 210 is used to determine the numerical change information corresponding to the test object based on the state evaluation parameters.

[0182] The first state information determination unit 220 is used to determine the first state information based on the numerical change information.

[0183] Figure 16 The diagram shown is a structural schematic of a state information determination device provided in another exemplary embodiment of this disclosure. Figure 14 Extending from the illustrated embodiment Figure 16 The illustrated embodiment will be described in detail below. Figure 16 The illustrated embodiments and Figure 14 The differences between the embodiments shown are not repeated here, and the similarities are not repeated here.

[0184] like Figure 16 As shown, the status information determination device provided in this embodiment of the disclosure further includes:

[0185] The second state information determination module 150 is used to determine the second state information corresponding to the test object based on the state evaluation parameters.

[0186] Furthermore, in this embodiment of the disclosure, the second determining module 200 includes:

[0187] The determining unit 230 is used to determine the first state information based on the state evaluation parameters and the second state information.

[0188] In one embodiment of this disclosure, the determining unit 230 is further configured to compare the state evaluation parameters and the second state information to obtain a comparison result, and determine the first state information based on the comparison result.

[0189] Figure 17 The diagram shown is a structural schematic of a second state information determination module provided in an exemplary embodiment of this disclosure. Figure 16 Extending from the illustrated embodiment Figure 17 The illustrated embodiment will be described in detail below. Figure 17 The illustrated embodiments and Figure 16 The differences between the embodiments shown are not repeated here, and the similarities are not repeated here.

[0190] like Figure 17 As shown, in the state information determining device provided in this embodiment, the second state information determining module 150 includes:

[0191] The state assessment level determination unit 151 is used to determine the state assessment level corresponding to each of the multiple preset analysis time periods based on the state assessment parameters.

[0192] The level determination unit 152 is used to determine the lowest level or baseline level (value) that meets the conditions for a normal state level among the state evaluation levels corresponding to multiple preset analysis time periods.

[0193] The second state information determination unit 153 is used to take the state information of the preset analysis time period corresponding to the lowest level or the baseline level as the second state information.

[0194] Figure 18The diagram shown is a structural schematic of a first determining module provided in an exemplary embodiment of this disclosure. Figure 14 Extending from the illustrated embodiment Figure 18 The illustrated embodiment will be described in detail below. Figure 18 The illustrated embodiments and Figure 14 The differences between the embodiments shown are not repeated here, and the similarities are not repeated here.

[0195] like Figure 18 As shown, in the status information determination device provided in this embodiment, the first determination module 100 includes:

[0196] The first time period determination unit 110 is used to determine multiple first time periods corresponding to the preset analysis time interval and the first time interval;

[0197] The first average heart rate parameter determination unit 120 is used to determine the first average heart rate parameter corresponding to each of the multiple first time periods based on multiple first time periods.

[0198] The first static heart rate parameter determination unit 130 is used to determine the first static heart rate parameter based on the first average heart rate parameter corresponding to each of the multiple first time periods.

[0199] In one embodiment of this disclosure, the first static heart rate parameter determination unit 130 is further configured to determine the smallest first average heart rate parameter among the first average heart rate parameters corresponding to each of the plurality of first time periods, and to determine the smallest first average heart rate parameter as the first static heart rate parameter.

[0200] Figure 19 The diagram shown is a structural schematic of a first determining module provided in another exemplary embodiment of this disclosure. Figure 18 Extending from the illustrated embodiment Figure 19 The illustrated embodiment will be described in detail below. Figure 19 The illustrated embodiments and Figure 18 The differences between the embodiments shown are not repeated here, and the similarities are not repeated here.

[0201] like Figure 19 As shown, in the status information determination device provided in this embodiment, the first determination module 100 further includes:

[0202] The first static time period determination unit 140 is used to determine the first time period corresponding to the first static heart rate parameter as the first static time period.

[0203] The first static heart rate variability parameter determination unit 150 is used to determine the heart rate variability parameter corresponding to the first static time period as the first static heart rate variability parameter.

[0204] Figure 20The diagram shown is a structural schematic of a first determining module provided in yet another exemplary embodiment of this disclosure. Figure 14 Extending from the illustrated embodiment Figure 20 The illustrated embodiment will be described in detail below. Figure 20 The illustrated embodiments and Figure 14 The differences between the embodiments shown are not repeated here, and the similarities are not repeated here.

[0205] like Figure 20 As shown, in the status information determination device provided in this embodiment, the first determination module 100 includes:

[0206] The second time period determination unit 160 is used to determine multiple second time periods corresponding to the preset analysis time interval and the second time interval;

[0207] The second average heart rate parameter determination unit 170 is used to determine the second average heart rate parameter corresponding to each of the multiple second time periods based on multiple second time periods.

[0208] The second static heart rate parameter determination unit 180 is used to determine the second static heart rate parameter based on the second average heart rate parameter corresponding to each of the multiple second time periods.

[0209] Figure 21 The diagram shown is a structural schematic of a first determining module provided in yet another exemplary embodiment of this disclosure. Figure 20 Extending from the illustrated embodiment Figure 21 The illustrated embodiment will be described in detail below. Figure 21 The illustrated embodiments and Figure 20 The differences between the embodiments shown are not repeated here, and the similarities are not repeated here.

[0210] like Figure 21 As shown, in the status information determination device provided in this embodiment, the first determination module 100 further includes:

[0211] The third static time period determination unit 191 is used to determine the second time period corresponding to the second static heart rate parameter as the third static time period.

[0212] The second static heart rate variability parameter determination unit 192 is used to determine the heart rate variability parameter corresponding to the third static time period as the second static heart rate variability parameter.

[0213] Figure 22 The diagram shown is a structural schematic of a control device provided in an exemplary embodiment of this disclosure. Figure 22 As shown, the control device provided in this embodiment includes:

[0214] The acquisition module 300 is used to acquire the first state information corresponding to the test object;

[0215] The control module 400 is used to control the working status of the medical system and / or medical device based on the first status information.

[0216] It should be understood that Figures 14 to 21 The operation and functions of the first determination module 100, the second state information determination module 150, and the second determination module 200 in the provided state information determination device, as well as the first time period determination unit 110, the first average heart rate parameter determination unit 120, the first static heart rate parameter determination unit 130, the first static time period determination unit 140, the first static heart rate variability parameter determination unit 150, the second time period determination unit 160, the second average heart rate parameter determination unit 170, the second static heart rate parameter determination unit 180, the third static time period determination unit 191, and the second static heart rate variability parameter determination unit 192 included in the first determination module 100, the state evaluation level determination unit 151, the level determination unit 152, and the second state information determination unit 153 included in the second state information determination module 150, and the absolute change information determination unit 210, the first state information determination unit 220, and the determination unit 230 included in the second determination module 200, can be referred to the above. Figures 3 to 12 The methods for determining the provided status information will not be elaborated here to avoid repetition.

[0217] Furthermore, it should be understood that Figure 22 The operation and functions of the acquisition module 300 and control module 400 in the provided control device can be referred to the above. Figure 13 The control methods provided will not be elaborated here to avoid repetition.

[0218] Below, for reference Figure 23 To describe an electronic device according to embodiments of the present disclosure. Figure 23 The diagram shown is a structural schematic of an electronic device provided in an exemplary embodiment of this disclosure.

[0219] like Figure 23 As shown, the electronic device 2300 includes one or more processors 2301 and memory 2302.

[0220] The processor 2301 may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 2300 to perform desired functions.

[0221] The memory 2302 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 2301 may execute the program instructions to implement the state information determination method, control method, and / or other desired functions of the various embodiments of this disclosure described above. Various contents, such as state evaluation information, may also be stored in the computer-readable storage medium.

[0222] In one example, the electronic device 2300 may also include an input device 2303 and an output device 2304, which are interconnected via a bus system and / or other forms of connection mechanism (not shown).

[0223] The input device 2303 may include, for example, a keyboard, a mouse, etc.

[0224] The output device 2304 can output various information to the outside, including determined first state information. The output device 2304 may include, for example, a display, a speaker, a vibrator, a printer, and a communication network and its connected remote output devices (such as SMS, WeChat, audio, video, email, mobile phones, etc.).

[0225] Of course, for the sake of simplicity, Figure 23 Only some of the components of the electronic device 2300 relevant to this disclosure are shown, omitting components such as buses, input / output interfaces, etc. In addition, the electronic device 2300 may include any other suitable components depending on the specific application.

[0226] In addition to the methods and devices described above, embodiments of this disclosure may also be computer program products, which include computer program instructions that, when executed by a processor, cause the processor to perform the steps in the state information determination method and control method according to various embodiments of this disclosure described above.

[0227] The computer program product can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of this disclosure. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on a user's computing device, partially on a user's computing device, as a standalone software package, partially on a user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0228] Furthermore, embodiments of this disclosure may also be computer-readable storage media storing computer program instructions thereon, which, when executed by a processor, cause the processor to perform the steps in the state information determination method and control method according to various embodiments of this disclosure described above.

[0229] The computer-readable storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may, for example, include, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0230] The basic principles of this disclosure have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this disclosure are merely examples and not limitations, and should not be considered as essential features of each embodiment of this disclosure. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the scope of this disclosure to the necessity of employing the aforementioned specific details for implementation.

[0231] The block diagrams of devices, apparatuses, devices, and systems disclosed herein are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0232] It should also be noted that in the apparatus, devices, and methods of this disclosure, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered as equivalent solutions to this disclosure.

[0233] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of this disclosure. Therefore, this disclosure is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features disclosed herein.

[0234] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this disclosure to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations therein.

Claims

1. A method for determining state information, characterized in that, include: Based on a preset analysis time interval, the state evaluation parameters corresponding to the test subject are determined. The state evaluation parameters include a first heart rate characterization parameter and a first heart rate variability characterization parameter. The first heart rate characterization parameter and the first heart rate variability characterization parameter correspond to a first time interval. The preset analysis time interval includes the first time interval. The second state information corresponding to the test object is determined based on the state evaluation parameters; The first state information is determined based on the state evaluation parameters and the second state information. Wherein, the first heart rate characterization parameter includes a first resting heart rate parameter, and the first heart rate variability characterization parameter includes a first resting heart rate variability parameter; the step of determining the state evaluation parameters corresponding to the test subject based on a preset analysis time interval includes: Based on multiple first time periods, determine the first heart rate variability parameter corresponding to each of the multiple first time periods; The first static heart rate variability parameter is determined based on the first heart rate variability parameter corresponding to each of the plurality of first time periods; The first time period corresponding to the first static heart rate variability parameter is defined as the first static time period; The average heart rate parameter corresponding to the first static time period is determined as the first static heart rate parameter, wherein the first time interval corresponds to multiple first time periods; The preset analysis time interval includes multiple preset analysis time periods, and the step of determining the second state information corresponding to the test object based on the state evaluation parameters includes: Based on the state evaluation parameters, the state evaluation level corresponding to each of the multiple preset analysis time periods is determined; Determine the lowest level or baseline level that meets the normal state level condition among the state assessment levels corresponding to each of the multiple preset analysis time periods; wherein, the test subject is a patient, and the normal state level condition is the disease stability state level condition corresponding to the test subject. The status information of the preset analysis time period corresponding to the lowest level or the baseline level is used as the second status information.

2. The method according to claim 1, characterized in that, Determining the first state information based on the state evaluation parameters and the second state information includes: The state evaluation parameters and the second state information are compared to obtain the comparison result; The first state information is determined based on the comparison results.

3. The method according to claim 1, characterized in that, The state evaluation parameters also include a second heart rate characterization parameter and a second heart rate variability characterization parameter. The first time interval is the nighttime interval, the second time interval is the daytime interval, the second heart rate characterization parameter and the second heart rate variability characterization parameter correspond to the second time interval, and the preset analysis time interval includes the second time interval. The second heart rate characterization parameter includes a second average heart rate parameter and a second resting heart rate parameter. The step of determining the state evaluation parameters corresponding to the test subject based on a preset analysis time interval also includes: Determine multiple second time periods corresponding to the preset analysis time interval and the second time interval; Based on the plurality of second time periods, determine the second average heart rate parameter corresponding to each of the plurality of second time periods; The second static heart rate parameter is determined based on the second average heart rate parameter corresponding to each of the plurality of second time periods.

4. The method according to claim 3, characterized in that, Determining the second static heart rate parameter based on the second average heart rate parameters corresponding to each of the plurality of second time periods includes: Determine the smallest second average heart rate parameter among the second average heart rate parameters corresponding to each of the plurality of second time periods; The minimum second average heart rate parameter is determined as the second static heart rate parameter.

5. The method according to claim 3, characterized in that, The second heart rate variability characterization parameter includes a second resting heart rate variability parameter. After determining the second resting heart rate parameter based on the second average heart rate parameters corresponding to each of the plurality of second time periods, it further includes: The second time period corresponding to the second static heart rate parameter is determined as the third static time period; The heart rate variability parameter corresponding to the third static time period is determined as the second static heart rate variability parameter.

6. The method according to claim 3, characterized in that, Before determining the second average heart rate parameter corresponding to each of the plurality of second time periods based on the plurality of second time periods, the method further includes: Remove the unstable time intervals corresponding to each of the multiple second time periods; The step of determining the second average heart rate parameter corresponding to each of the plurality of second time periods based on the plurality of second time periods includes: Based on multiple second time periods after removing the non-stable time intervals, the second average heart rate parameter corresponding to each of the multiple second time periods is determined.

7. The method according to claim 3, characterized in that, The first time interval includes the time interval from 22:00 on the current day to 6:00 on the next day, and the second time interval includes the time interval from 8:00 on the current day to 20:00 on the current day.

8. The method according to claim 1, wherein, The first static heart rate variability parameter is the sleep-resting heart rate variability parameter, and the first static heart rate parameter is the sleep-resting heart rate parameter.

9. The method according to claim 1, characterized in that, The determination of the first static heart rate variability parameter based on the first heart rate variability parameters corresponding to each of the plurality of first time periods includes: Determine the highest first heart rate variability parameter among the first heart rate variability parameters corresponding to each of the plurality of first time periods; The highest first heart rate variability parameter is determined as the first static heart rate variability parameter.

10. A control method, characterized in that, include: Obtain first state information corresponding to the test object, wherein the first state information is obtained based on the state information determination method according to any one of claims 1 to 9; The operating status of the medical system and / or medical device and / or mobile device is controlled based on the first status information.

11. A state information determining device, characterized in that, include: The first determining module is used to determine the state evaluation parameters corresponding to the test subject based on a preset analysis time interval. The state evaluation parameters include a first heart rate characterization parameter and a first heart rate variability characterization parameter. The first heart rate characterization parameter and the first heart rate variability characterization parameter correspond to a first time interval. The preset analysis time interval includes the first time interval. The second state information determination module is used to determine the second state information corresponding to the test object based on the state evaluation parameters. The second determining module includes a determining unit, used to determine the first state information based on the state evaluation parameters and the second state information; Wherein, the first heart rate characterization parameter includes a first static heart rate parameter, and the first heart rate variability characterization parameter includes a first static heart rate variability parameter; when the first determining module determines the state evaluation parameters corresponding to the test subject based on a preset analysis time interval in the execution step, it is used to determine the first heart rate variability parameter corresponding to each of the multiple first time intervals based on multiple first time intervals; determine the first static heart rate variability parameter based on the first heart rate variability parameter corresponding to each of the multiple first time intervals; determine the first time interval corresponding to the first static heart rate variability parameter as the first static time interval; determine the average heart rate parameter corresponding to the first static time interval as the first static heart rate parameter, wherein the first time interval corresponds to multiple first time intervals; The preset analysis time interval includes multiple preset analysis time periods, and the second status information determination module includes: The state assessment level determination unit is used to determine the state assessment level corresponding to each of the multiple preset analysis time periods based on the state assessment parameters. The level determination unit is used to determine the lowest level or baseline level that meets the normal state level conditions among the state evaluation levels corresponding to multiple preset analysis time periods; wherein, the test subject is a patient, and the normal state level conditions are the disease stability state level conditions corresponding to the test subject. The second state information determination unit is used to take the state information of the preset analysis time period corresponding to the lowest level or baseline level as the second state information.

12. A control device, characterized in that, include: An acquisition module is used to acquire first state information corresponding to the test object, wherein the first state information is obtained based on the state information determination method according to any one of claims 1 to 9; The control module is used to control the working status of the medical system and / or medical device based on the first status information.

13. A computer-readable storage medium storing a computer program for executing the state information determination method according to any one of claims 1 to 9, or executing the control method according to claim 10.

14. An electronic device, the electronic device comprising: processor; Memory used to store the processor's executable instructions; The processor is configured to execute the state information determination method according to any one of claims 1 to 9, or to execute the control method according to claim 10.

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