Parameter Determination Method, Parameter Determination Device, Storage Medium and Electronic Device

Abstract

A parameter determination method and a parameter determination device are disclosed, which relate to the technical field of medical data processing. The parameter determination method includes: determining a first parameter set corresponding to a to-be-tested object based on a first time interval, where the first time interval includes a nighttime interval, and the first parameter set includes a first heart rate characterization parameter and a first heart rate variability characterization parameter; determining a second parameter set corresponding to the to-be-tested object based on a second time interval, where the second time interval includes a daytime interval, and the second parameter set includes a second heart rate characterization parameter and a second heart rate variability characterization parameter. The present disclosure reduces the influence of interference factors in the parameter measurement process, and achieves the purpose of improving the accuracy of the obtained parameter set and the comparability over time. In addition, when the first parameter set and the second parameter set determined by the present disclosure are used to characterize the neurohormone level and / or state, the characterization accuracy and the comparability over time can be effectively improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of medical data processing, and particularly to a method for determining a parameter, an apparatus for determining a parameter, a computer-readable storage medium, and an electronic device. Background Art

[0002] As is well known, since neurohormones driven by the sympathetic and parasympathetic nerves are extremely susceptible to many factors such as circadian variations, disease progression, sleep conditions, and drugs, it is very difficult to accurately measure them.

[0003] Therefore, there is an urgent need for a method for determining a parameter to determine a parameter that can accurately characterize the level and / or state of neurohormones. Summary of the Invention

[0004] To solve the above technical problems, the present disclosure is proposed. Embodiments of the present disclosure provide a method for determining a parameter, an apparatus for determining a parameter, a computer-readable storage medium, and an electronic device.

[0005] In one aspect, an embodiment of the present disclosure provides a method for determining a parameter. The method includes: determining a first parameter set corresponding to a test subject based on a first time interval, the first time interval including a night interval, and the first parameter set including a first heart rate characterization parameter and a first heart rate variability characterization parameter; determining a second parameter set corresponding to the test subject based on a second time interval, the second time interval including a day interval, and the second parameter set including a second heart rate characterization parameter and a second heart rate variability characterization parameter.

[0006] In an embodiment of the present disclosure, the first time interval includes a plurality of first time periods, and the first heart rate characterization parameter includes a first average heart rate parameter and a first static heart rate parameter. Moreover, determining the first parameter set corresponding to the test subject based on the first time interval includes: determining the first average heart rate parameter corresponding to each of the plurality of first time periods based on the plurality of first time periods; determining the first static heart rate parameter based on the first average heart rate parameter corresponding to each of the plurality of first time periods.

[0007] In an embodiment of the present disclosure, determining the first static heart rate parameter based on the first average heart rate parameter corresponding to each of the plurality of first time periods includes: determining the smallest first average heart rate parameter among the first average heart rate parameters corresponding to each of the plurality of first time periods; determining the smallest first average heart rate parameter as the first static heart rate parameter.

[0008] In an embodiment of the present disclosure, the first heart rate variability characterization parameter includes a first static heart rate variability parameter. And, 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 the first static time period; determining the heart rate variability parameter corresponding to the first static time period as the first static heart rate variability parameter.

[0009] In an embodiment of the present disclosure, before determining the first average heart rate parameters corresponding to multiple first time periods based on the multiple first time periods, the method further includes: removing the non - stable time intervals corresponding to the multiple first time periods. Wherein, determining the first average heart rate parameters corresponding to the multiple first time periods based on the multiple first time periods includes: determining the first average heart rate parameters corresponding to the multiple first time periods based on the multiple first time periods after removing the non - stable time intervals.

[0010] In an embodiment of the present disclosure, the second time interval includes multiple second time periods, and the second heart rate characterization parameter includes a second average heart rate parameter and a second static heart rate parameter. And, determining the second parameter set corresponding to the test subject based on the second time interval includes: determining the second average heart rate parameters corresponding to the multiple second time periods based on the multiple second time periods; determining the second static heart rate parameter based on the second average heart rate parameters corresponding to the multiple second time periods.

[0011] In an embodiment of the present disclosure, determining the second static heart rate parameter based on the second average heart rate parameters corresponding to the multiple second time periods includes: determining the smallest second average heart rate parameter among the second average heart rate parameters corresponding to the multiple second time periods; determining the smallest second average heart rate parameter as the second static heart rate parameter.

[0012] In an embodiment of the present disclosure, the second heart rate variability characterization parameter includes a second static heart rate variability parameter. And, after determining the second static heart rate parameter based on the second average heart rate parameters corresponding to the multiple second time periods, the method further includes: determining the second time period corresponding to the second static heart rate parameter as the third static time period; determining the heart rate variability parameter corresponding to the third static time period as the second static heart rate variability parameter.

[0013] In an embodiment of the present disclosure, before determining the second average heart rate parameters corresponding to the multiple second time periods based on the multiple second time periods, the method further includes: removing the non - stable time intervals corresponding to the multiple second time periods. Wherein, determining the second average heart rate parameters corresponding to the multiple second time periods based on the multiple second time periods includes: determining the second average heart rate parameters corresponding to the multiple second time periods based on the multiple second time periods after removing the non - stable time intervals.

[0014] On the other hand, embodiments of the present disclosure provide a parameter determination device, which includes: a first determination module, configured to determine a first parameter set corresponding to a test subject based on a first time interval, the first time interval including a nighttime interval, and the first parameter set including a first heart rate characterization parameter and a first heart rate variability characterization parameter; a second determination module, configured to determine a second parameter set corresponding to the test subject based on a second time interval, the second time interval including a daytime interval, and the second parameter set including a second heart rate characterization parameter and a second heart rate variability characterization parameter.

[0015] On the other hand, embodiments of the present disclosure provide a computer-readable storage medium storing a computer program for executing the parameter determination method mentioned in the above embodiments.

[0016] On the other hand, embodiments of the present disclosure provide an electronic device, which includes: a processor and a memory for storing processor-executable instructions, wherein the processor is configured to execute the parameter determination method mentioned in the above embodiments.

[0017] The parameter determination method provided by the present disclosure reduces the influence of interference factors in the parameter measurement process by respectively determining the parameter sets (i.e., the first parameter set and the second parameter set) corresponding to the test subject in the first time interval and the second time interval, achieving the purpose of improving the accuracy of the obtained parameter sets and the comparability at different times. In addition, when the first parameter set and the second parameter set determined by the embodiments of the present disclosure are used to characterize the neurohormone level and / or state, the characterization accuracy can be effectively improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] By describing the embodiments of the present disclosure in more detail in conjunction with the drawings, the above and other objects, features, and advantages of the present disclosure will become more apparent. The drawings are used to provide a further understanding of the embodiments of the present disclosure and constitute a part of the specification, and are used to explain the present disclosure together with the embodiments of the present disclosure, and do not constitute a limitation to the present disclosure. In the drawings, the same reference numerals generally represent the same components or steps.

[0019] Figure 1 Shown is a schematic diagram of a scenario applicable to the embodiments of the present disclosure.

[0020] Figure 2 Shown is another schematic diagram of a scenario applicable to the embodiments of the present disclosure.

[0021] Figure 3 Shown is a flowchart of a parameter determination method provided by an exemplary embodiment of the present disclosure.

[0022] Figure 4The figure shows a schematic flowchart of measuring a first parameter set corresponding to a test subject based on a first time interval provided by an exemplary embodiment of the present disclosure.

[0023] Figure 5 The figure shows a schematic flowchart of determining a first static heart rate parameter based on first average heart rate parameters corresponding to multiple first time periods provided by an exemplary embodiment of the present disclosure.

[0024] Figure 6 The figure shows a schematic flowchart of measuring a first parameter set corresponding to a test subject based on a first time interval provided by another exemplary embodiment of the present disclosure.

[0025] Figure 7 The figure shows a schematic flowchart of measuring a first parameter set corresponding to a test subject based on a first time interval provided by yet another exemplary embodiment of the present disclosure.

[0026] Figure 8 The figure shows a schematic flowchart of measuring a second parameter set corresponding to a test subject based on a second time interval provided by an exemplary embodiment of the present disclosure.

[0027] Figure 9 The figure shows a schematic flowchart of determining a second static heart rate parameter based on second average heart rate parameters corresponding to multiple second time periods provided by an exemplary embodiment of the present disclosure.

[0028] Figure 10 The figure shows a schematic flowchart of measuring a second parameter set corresponding to a test subject based on a second time interval provided by another exemplary embodiment of the present disclosure.

[0029] Figure 11 The figure shows a schematic structural diagram of a parameter determination device provided by an exemplary embodiment of the present disclosure.

[0030] Figure 12 The figure shows a schematic structural diagram of a first determination module provided by an exemplary embodiment of the present disclosure.

[0031] Figure 13 The figure shows a schematic structural diagram of a first determination module provided by another exemplary embodiment of the present disclosure.

[0032] Figure 14 The figure shows a schematic structural diagram of a first determination module provided by yet another exemplary embodiment of the present disclosure.

[0033] Figure 15 The figure shows a schematic structural diagram of a second determination module provided by an exemplary embodiment of the present disclosure.

[0034] Figure 16 The figure shows a schematic structural diagram of a second determination module provided by another exemplary embodiment of the present disclosure.

[0035] Figure 17 The following is a schematic structural diagram of an electronic device provided by an exemplary embodiment of the present disclosure. Detailed implementation manners

[0036] Hereinafter, exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all embodiments of the present disclosure. It should be understood that the present disclosure is not limited by the exemplary embodiments described herein.

[0037] Overview of the Application

[0038] As is well known, neurohormones driven by the sympathetic and parasympathetic nerves are extremely susceptible to multiple factors. Especially for heart disease patients suffering from heart failure or arrhythmia, their neurohormones are extremely susceptible to factors such as circadian changes, disease progression, sleep status, exercise or activity level, drugs, and mood changes. Therefore, in the prior art, heart rate (HR) and heart rate variability (HRV) are usually used as diagnostic bases or alternative measurement indicators that can characterize neurohormones. Specifically, heart rate refers to the number of heartbeats per minute. Heart rate variability refers to the change in the difference between successive heartbeat cycles. Heart rate calculation sources include but are not limited to surface electrocardiogram, endocardium, epicardium electrocardiogram, electrocardiogram from subcutaneous electrodes, or ventricular heart rate or atrial heart rate calculated through R waves and P waves. In addition, it should be understood that heart rate can also be calculated using other technologies, such as but not limited to photoplethysmography (PPG) technology.

[0039] However, whether it is heart rate or heart rate variability, they are still extremely susceptible to interference. For example, during different sleep stages at night, heart rate and / or heart rate variability often change, and this change may not necessarily be related to neurohormones. In addition, due to the low heart rate, patients with a pacemaker or implantable cardioverter-defibrillator (ICD) usually pace at night. In this case, the patient's true heart rate is usually "hidden", resulting in the true heart rate not being reflected in the measured heart rate data. Moreover, in the prior art, the measured daytime heart rate and daytime heart rate variability also include data during the exercise stage and the emotional excitement stage. However, the exercise stage and the emotional excitement stage can only characterize short-term states, and these short-term changes are also different at different times (days). At the same time, diseases, such as heart failure, have a significant impact on the neurohormone level and / or state of patients, but are relatively long-term in terms of time. Thus, there is an urgent need for a parameter determination method to determine a parameter that can accurately characterize the neurohormone level and / or state and make the determined parameter more comparable.

[0040] Based on the above-mentioned technical problems, the basic concept of the present disclosure is to propose a parameter determination method, a parameter determination device, a computer-readable storage medium, and an electronic device.

[0041] The parameter determination method provided by the present disclosure includes: determining a first parameter set corresponding to a test subject based on a first time interval, where the first time interval includes a night interval, and the first parameter set includes a first heart rate characterization parameter and a first heart rate variability characterization parameter; determining a second parameter set corresponding to the test subject based on a second time interval, where the second time interval includes a day interval, and the second parameter set includes a second heart rate characterization parameter and a second heart rate variability characterization parameter.

[0042] The parameter determination method provided by the present disclosure reduces the influence of interference factors in the parameter measurement process by respectively determining the parameter sets (i.e., the first parameter set and the second parameter set) corresponding to the test subject in the first time interval and the second time interval, achieving the purpose of improving the accuracy and comparability of the obtained parameter sets. In addition, when the first parameter set and the second parameter set determined by the present disclosure are used to characterize the neurohormone level and / or state, the characterization accuracy can be effectively improved.

[0043] After introducing the basic principle of the present disclosure, various non-limiting embodiments of the present disclosure will be specifically introduced with reference to the accompanying drawings.

[0044] Exemplary Scenarios

[0045] Figure 1 Shown is a schematic diagram of a scenario applicable to the embodiments of the present disclosure. As Figure 1 Shown, the scenario applicable to the embodiments of the present disclosure includes a server 1 and a medical device 2, where there is a communication connection relationship between the server 1 and the medical device 2.

[0046] Specifically, the medical device 2 is used to collect reference parameters of the test subject, where the reference parameters include but are not limited to heart rate parameters and heart rate variability parameters, etc. The server 1 is used to determine a first parameter set corresponding to the test subject based on a first time interval, and determine a second parameter set corresponding to the test subject based on a second time interval. Wherein, the first time interval includes a night interval, and the first parameter set includes a first heart rate characterization parameter and a first heart rate variability characterization parameter. The second time period includes a day interval, and the second parameter set includes a second heart rate characterization parameter and a second heart rate variability characterization parameter. That is, this scenario implements a parameter determination method.

[0047] Exemplarily, the server 1 determines the above-mentioned first parameter set and / or second parameter set according to the reference parameters collected by the medical device 2.

[0048] Since Figure 1The above-described scenario realizes a parameter determination method by using Server 1. Therefore, it can not only improve the adaptability of the scenario, but also effectively reduce the computational load of Medical Device 2.

[0049] It should be noted that the present disclosure is also applicable to another scenario. Figure 2 The following shows a schematic diagram of another scenario applicable to the embodiments of the present disclosure. Specifically, this scenario includes Medical Device 3, and Medical Device 3 includes a parameter acquisition module 301 and a calculation module 302.

[0050] Specifically, the parameter acquisition module 301 in Medical Device 3 is used to acquire the reference parameters of the test subject. The reference parameters include, but are not limited to, heart rate parameters and heart rate variability parameters, etc. The calculation module 302 is used to determine the first parameter set corresponding to the test subject based on the first time interval, and determine the second parameter set corresponding to the test subject based on the second time interval. The first time interval includes the night interval, and the first parameter set includes the first heart rate characterization parameter and the first heart rate variability characterization parameter. The second time period includes the day interval, and the second parameter set includes the second heart rate characterization parameter and the second heart rate variability characterization parameter. That is, this scenario realizes a parameter determination method.

[0051] Exemplarily, the calculation module 302 determines the above-mentioned first parameter set and / or second parameter set according to the reference parameters acquired by the parameter acquisition module 301.

[0052] Since Figure 2 the above-described scenario realizes a parameter determination method by using Medical Device 3 without performing data transmission operations with related devices such as a server, the above scenario can ensure the real-time nature of the parameter determination method.

[0053] It should be noted that the medical device mentioned in the above scenario can be either an implantable medical device (IMD) or a wearable medical device (WMD). The embodiments of the present disclosure do not make a unified limitation in this regard.

[0054] Exemplary Methods

[0055] Figure 3 The following shows a schematic flowchart of the parameter determination method provided by an exemplary embodiment of the present disclosure. As Figure 3 shown, the parameter determination method provided by the embodiments of the present disclosure includes the following steps.

[0056] Step S100: Determine the first parameter set corresponding to the test subject based on the first time interval.

[0057] Exemplarily, the first time interval includes a nighttime interval. For example, in the 24-hour system, the first time interval is the time interval from 18:00 of the current day to 6:00 of the next day. Preferably, the first time interval is the time interval from 22:00 of the current day to 6:00 of the next day. Preferably, within the first time interval, the test subject is in a resting or sleeping state to reduce interference, thereby improving the accuracy of the finally obtained first parameter set. Among them, the resting state means that the body is in a static state, and the sleeping state means that the subject has fallen asleep, the body is static and in a "lying" posture.

[0058] Exemplarily, the test subject mentioned in step S100 refers to a human body. That is, the first parameter set corresponding to the human body is determined. It should be understood that determining the first parameter set corresponding to the test subject mentioned in step S100 may mean calculating the first parameter set corresponding to the test subject.

[0059] In the embodiment of the present disclosure, the first parameter set includes a first heart rate characterization parameter and a first heart rate variability characterization parameter. Among them, the first heart rate characterization parameter refers to a parameter that can characterize the heart rate situation within the first time interval. The first heart rate variability characterization parameter refers to a parameter that can characterize the heart rate variability situation within the first time interval. For example, the first heart rate characterization parameter is obtained based on the heart rate measured in real time, and the first heart rate variability characterization parameter is obtained based on the heart rate variability measured in real time.

[0060] Step S200: Determine the second parameter set corresponding to the test subject based on the second time interval.

[0061] Exemplarily, the second time interval includes a daytime interval. For example, in the 24-hour system, the second time interval is the time interval from 6:00 of the current day to 18:00 of the current day. Preferably, the second time interval is the time interval from 8:00 of the current day to 20:00 of the current day. Preferably, within the second time interval, the test subject is in a resting state or a state of basically no movement or activity to reduce interference, thereby improving the accuracy and comparability of the finally obtained second parameter set. More preferably, within the second time interval, the test subject is in a resting or non-moving state in a non-standing body position. Exemplarily, the above-mentioned non-moving state can be determined by means of some relevant motion / body movement or other physiological parameter thresholds. For example, if the real-time body movement signal of the test subject is lower than a certain threshold, or if the real-time heart rate of the test subject does not exceed the preset exercise heart rate threshold, it can be determined that the test subject is in a non-moving state.

[0062] In the embodiments of the present disclosure, the second parameter set includes a second heart rate characterization parameter and a second heart rate variability characterization parameter. Among them, the second heart rate characterization parameter refers to a parameter that can characterize the heart rate situation within the second time interval. The second heart rate variability characterization parameter refers to a parameter that can characterize the heart rate variability situation within the second time interval. For example, the second heart rate characterization parameter is obtained based on the heart rate measured in real time, and the second heart rate variability characterization parameter is obtained based on the heart rate variability measured in real time.

[0063] In the actual application process, based on the first time interval, a first parameter set corresponding to the test subject is determined, and based on the second time interval, a second parameter set corresponding to the test subject is determined.

[0064] The parameter determination method provided by the embodiments of the present disclosure reduces the influence of interference factors in the parameter measurement process by respectively determining the parameter sets (i.e., the first parameter set and the second parameter set) corresponding to the test subject in the first time interval and the second time interval, achieving the purpose of improving the accuracy and comparability of the obtained parameter sets. In addition, when the first parameter set and the second parameter set determined by the embodiments of the present disclosure are used to characterize the neurohormone level and / or state, the characterization accuracy can be effectively improved.

[0065] Figure 4 The following is a schematic flowchart of measuring a first parameter set corresponding to a test subject based on a first time interval provided by an exemplary embodiment of the present disclosure. In Figure 3 Based on the embodiment shown, Figure 4 The embodiment shown is extended, Figure 4 The differences between the embodiment shown below and Figure 3 The embodiment shown will be emphatically described, and the same parts will not be repeated.

[0066] As Figure 4 shown, in the parameter determination method provided by the embodiments of the present disclosure, the first time interval includes a plurality of first time periods, and the first heart rate characterization parameter includes a first average heart rate parameter and a first static heart rate parameter. And the step of determining the first parameter set corresponding to the test subject based on the first time interval includes the following steps.

[0067] Step S110, based on a plurality of first time periods, determine the first average heart rate parameter corresponding to each of the plurality of first time periods.

[0068] Exemplarily, the first time period is a time window with a time length of 60 seconds. In other words, step S110 means that based on the plurality of time windows included in the first time interval, the first average heart rate parameter corresponding to each of the plurality of time windows is respectively determined.

[0069] Optionally, the first mean heart rate (MHR) parameter corresponding to each time window refers to the average heart rate measured within that time window.

[0070] Step S120: Determine a first static heart rate parameter based on the first mean heart rate parameters corresponding to multiple first time periods.

[0071] In an embodiment of the present disclosure, the first static heart rate parameter is a sleep rest heart rate (Sleep RHR) parameter.

[0072] Since the first static heart rate parameter is determined based on the first mean heart rate parameters corresponding to multiple first time periods, the obtained first static heart rate parameter can fully consider the characteristics of the first mean heart rate parameters corresponding to multiple first time periods. Thus, the obtained first static heart rate parameter can better characterize the heart rate condition of the test subject in the first time interval.

[0073] The parameter determination method provided by the embodiments of the present disclosure realizes the purpose of determining the first parameter set corresponding to the test subject based on the first time interval by determining the first mean heart rate parameters corresponding to multiple first time periods based on multiple first time periods and determining the first static heart rate parameter based on the first mean heart rate parameters corresponding to multiple first time periods. Compared with the real-time heart rate, the first mean heart rate parameter and the first static heart rate parameter can more accurately characterize the heart rate condition of the test subject in the first time interval. Therefore, when predicting the neurohormone level and / or state of the test subject in the first time interval based on the first mean heart rate parameter and the first static heart rate parameter obtained by the embodiments of the present disclosure, a more accurate neurohormone prediction result can be obtained.

[0074] In another embodiment of the present disclosure, the determination process of the first mean heart rate parameter is as follows: determine the heart rate variability parameters corresponding to multiple first time periods based on multiple first time periods; determine the first mean heart rate parameter based on the heart rate variability parameters corresponding to multiple first time periods. For example, first determine the first time period corresponding to the highest heart rate variability parameter among the heart rate variability parameters corresponding to multiple first time periods, and then determine the mean heart rate parameter corresponding to this first time period as the first mean heart rate parameter. Here, other statistical descriptions can also be used for the heart rate variability parameters, such as the 75% value, etc. In addition, it should be noted that the mean heart rate parameter can also be other statistical descriptions, such as the 25% value, etc.

[0075] Similarly, the embodiments of the present disclosure can also first remove the unstable time intervals in the first time interval, where the specific meaning of the unstable time interval can be referred to Figure 7 in the illustrated embodiment, and the embodiments of the present disclosure will not be elaborated here.

[0076] Figure 5 The figure shows a schematic flowchart of determining a first static heart rate parameter based on first average heart rate parameters corresponding to multiple first time periods provided by an exemplary embodiment of the present disclosure. In Figure 4 Based on the illustrated embodiment, Figure 5 the illustrated embodiment is extended, and the following focuses on Figure 5 the differences between the illustrated embodiment and Figure 4 the illustrated embodiment, and the same parts will not be elaborated again.

[0077] As Figure 5 shown, in the parameter determination method provided by the embodiment of the present disclosure, the steps of determining the first static heart rate parameter based on the first average heart rate parameters corresponding to multiple first time periods include the following steps.

[0078] Step S121, determine the smallest first average heart rate parameter among the first average heart rate parameters corresponding to multiple first time periods.

[0079] Step S122, determine the smallest first average heart rate parameter as the first static heart rate parameter.

[0080] For example, the number of first time periods included in the first time interval is 5, which are the first time period a, the first time period b, the first time period c, the first time period d, and the first time period e. Correspondingly, the first average heart rate parameter corresponding to the first time period a is 80 beats per minute, the first average heart rate parameter corresponding to the first time period b is 85 beats per minute, the first average heart rate parameter corresponding to the first time period c is 90 beats per minute, the first average heart rate parameter corresponding to the first time period d is 75 beats per minute, and the first average heart rate parameter corresponding to the first time period e is 95 beats per minute. Then, the first average heart rate parameter corresponding to the first time period d (75 beats per minute) is the smallest first average heart rate parameter. Thus, as described in step S122, the first average heart rate parameter corresponding to the first time period d (75 beats per minute) can be determined as the first static heart rate parameter.

[0081] The parameter determination method provided by the embodiments of the present disclosure realizes 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 the first average heart rate parameters corresponding to multiple first time periods and determining the smallest first average heart rate parameter as the first static heart rate parameter. Since the smallest first average heart rate parameter among the 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, the embodiments of the present disclosure can further enrich the effective information contained in the first parameter set, thereby improving the prediction accuracy and comparability of subsequent neurohormone levels and / or states.

[0082] Based on the Figure 5 embodiment shown, another embodiment of the present disclosure is extended. In the embodiment of the present disclosure, before the step of determining the smallest first average heart rate parameter among the first average heart rate parameters corresponding to multiple first time periods, it further includes: selecting a preset time interval in the first time interval, where the length of the preset time interval is less than that of the first time interval. And, in the embodiment of the present disclosure, the step of determining the smallest first average heart rate parameter among the first average heart rate parameters corresponding to multiple first time periods includes: determining the smallest first average heart rate parameter among the first average heart rate parameters corresponding to the first time periods included in the preset time interval. With such a setting, it is possible to effectively remove the average heart rate parameters corresponding to some atypical time intervals (such as) in the first time interval, thereby further improving the accuracy of the determined smallest first average heart rate parameter.

[0083] Figure 6 Shown is a schematic flowchart of measuring the first parameter set corresponding to the test subject based on the first time interval provided by another exemplary embodiment of the present disclosure. Based on the Figure 4 embodiment shown, Figure 6 the embodiment shown is extended. Below, the differences between the Figure 6 embodiment shown and the Figure 4 embodiment shown are emphasized, and the same parts will not be described again.

[0084] As Figure 6 shown, in the parameter determination method provided by the embodiment of the present disclosure, after the step of determining the first static heart rate parameter based on the first average heart rate parameters corresponding to the multiple first time periods, the following steps are further included.

[0085] Step S130, determining the first time period corresponding to the first static heart rate parameter as the first static time period.

[0086] Step S140, determining the heart rate variability parameter corresponding to the first static time period as the first static heart rate variability parameter.

[0087] In an embodiment of the present disclosure, the first static heart rate variability parameter is a Sleep Rest Heart Rate Variability (Sleep RHRV) parameter.

[0088] In the actual application process, based on multiple first time periods, multiple first average heart rate parameters corresponding to the multiple first time periods are determined, and based on the multiple first average heart rate parameters corresponding to the multiple first time periods, a first static heart rate parameter is determined. Then, the first time period corresponding to the first static heart rate parameter is determined as the first static time period, and the heart rate variability parameter corresponding to the first static time period is determined as the first static heart rate variability parameter. Then, based on the second time interval, a second parameter set corresponding to the test subject is determined.

[0089] Since the determined first static heart rate variability parameter is determined based on the first static heart rate parameter, therefore, compared with the prior art, the embodiment of the present disclosure can improve the ability and comparability of the determined first static heart rate variability parameter to characterize the neurohormone level and / or state of the test subject in the first time interval.

[0090] It should be noted that in another embodiment of the present disclosure, the first static heart rate variability parameter can also 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, multiple first heart rate variability parameters corresponding to the multiple first time periods are determined, and then based on the multiple first heart rate variability parameters corresponding to the multiple first time periods, the first static heart rate variability parameter is determined. Then, 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. Among them, the step of determining the first static heart rate variability parameter based on the multiple first heart rate variability parameters corresponding to 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 heart rate variability parameters corresponding to the multiple first time periods. Here, other statistical descriptions can also be used for the heart rate variability parameter in addition to the highest one, such as but not limited to the 75% value, etc. In addition, it should be noted that other statistical descriptions can also be used for the heart rate variability parameter, such as the 25% value, etc.

[0091] Figure 7 The following shows a flowchart of determining a first parameter set corresponding to a test subject based on a first time interval provided by still another exemplary embodiment of the present disclosure. In Figure 4 Based on the embodiment shown, Figure 7 The following embodiment is extended, and the following will focus on describingFigure 7 The differences between the illustrated embodiments and Figure 4 the illustrated embodiments will not be elaborated as the similarities are already mentioned.

[0092] As Figure 7 shown, in the parameter determination method provided by the embodiments of the present disclosure, before determining the first average heart rate parameters corresponding to multiple first time periods based on the multiple first time periods, the following steps are further included.

[0093] Step S105: Remove the unstable time intervals corresponding to each of the multiple first time periods.

[0094] Exemplarily, the unstable time intervals mentioned in step S105 refer to, for example, the snoring time interval and / or the time interval with unstable breathing frequency (such as apnea, sleep apnea).

[0095] In an embodiment of the present disclosure, the unstable time intervals further include the time intervals corresponding to special stages during sleep when the body activity level is relatively high. For example, the special stage during sleep is the rapid eye movement (REM) stage, and the heart rate changes significantly during dreaming. Also, for example, the unstable time intervals further include the time intervals corresponding to events such as premature atrial contraction (PAC), premature ventricular contraction (PVC), atrial tachycardia or atrial arrhythmia (such as atrial fibrillation, etc.), and ventricular tachycardia (VT).

[0096] Moreover, in the parameter determination method provided by the embodiments of the present disclosure, the step of determining the first average heart rate parameters corresponding to multiple first time periods based on the multiple first time periods includes the following steps.

[0097] Step S115: Based on the multiple first time periods after removing the unstable time intervals, determine the first average heart rate parameters corresponding to each of the multiple first time periods.

[0098] Since the specificity of the unstable time intervals is relatively high and they cannot well represent the typical situation of the test subject, therefore, the embodiments of the present disclosure improve the characterization ability of parameters such as the determined first average heart rate parameters and the first static heart rate variability parameters by removing the unstable time intervals.

[0099] Figure 8 The following shows a schematic flowchart of measuring the second parameter set corresponding to the test subject based on the second time interval provided by an exemplary embodiment of the present disclosure. On the basis of Figure 3 the illustrated embodiment, it is extended toFigure 8 In the embodiments shown below, the following will be described in detail Figure 8 the differences between the embodiments shown and Figure 3 the embodiments shown, and the similarities will not be elaborated.

[0100] As Figure 8 shown, in the parameter determination method provided by the embodiments of the present disclosure, the second time interval includes a plurality of second time periods, and the second heart rate characterization parameters include a second average heart rate parameter and a second static heart rate parameter. Moreover, the step of determining the second parameter set corresponding to the test subject based on the second time interval includes the following steps.

[0101] Step S210: Determine the second average heart rate parameter corresponding to each of the plurality of second time periods based on the plurality of second time periods.

[0102] Exemplarily, the second time period is a time window with a time length of 60 seconds. In other words, step S210 means determining the second average heart rate parameter corresponding to each of the plurality of time windows based on the plurality of time windows included in the second time interval.

[0103] Optionally, the second average heart rate (Mean Heart Rate, MHR) parameter corresponding to each time window refers to the average heart rate measured within that time window.

[0104] Step S220: Determine the second static heart rate parameter based on the second average heart rate parameters corresponding to the plurality of second time periods.

[0105] In an embodiment of the present disclosure, the second static heart rate parameter is the Day Rest Heart Rate (Day RHR) parameter.

[0106] Since the second static heart rate parameter is determined based on the second average heart rate parameters corresponding to the plurality of second time periods, the obtained second static heart rate parameter can fully consider the characteristics of the second average heart rate parameters corresponding to the plurality of second time periods. Thus, it can be seen that the obtained second static heart rate parameter can better characterize the heart rate condition of the test subject in the second time interval.

[0107] The parameter determination method provided by the embodiments of the present disclosure realizes the purpose of determining the second parameter set corresponding to the test subject based on the second time interval by determining the second average heart rate parameters corresponding to multiple second time periods respectively and determining the second static heart rate parameter based on the second average heart rate parameters corresponding to multiple second time periods respectively. Compared with the real-time heart rate, the second average heart rate parameter and the second static heart rate parameter can more accurately characterize the heart rate situation of the test subject in the second time interval. Therefore, when predicting the neurohormone level and / or state of the test subject in the second time interval based on the second average heart rate parameter and the second static heart rate parameter obtained by the embodiments of the present disclosure, a more accurate neurohormone prediction result can be obtained.

[0108] Figure 9 The following is a schematic flowchart of determining the second static heart rate parameter based on the second average heart rate parameters corresponding to multiple second time periods respectively provided by an exemplary embodiment of the present disclosure. On the basis of the embodiment shown in Figure 8 an extended embodiment shown in Figure 9 is derived. The following focuses on describing Figure 9 the differences between the embodiment shown in Figure 8 and the embodiment shown in

[0109] As shown in Figure 9 in the parameter determination method provided by the embodiments of the present disclosure, the step of determining the second static heart rate parameter based on the second average heart rate parameters corresponding to multiple second time periods respectively includes the following steps.

[0110] Step S221, determine the smallest second average heart rate parameter among the second average heart rate parameters corresponding to multiple second time periods respectively.

[0111] Step S222, determine the smallest second average heart rate parameter as the second static heart rate parameter.

[0112] For example, the number of second time periods included in the second time interval is 3, which are the second time period a, the second time period b, and the second time period c respectively. Correspondingly, the second average heart rate parameter corresponding to the second time period a is 80 beats per minute, the second average heart rate parameter corresponding to the second time period b is 85 beats per minute, and the second average heart rate parameter corresponding to the second time period c is 90 beats per minute. Then, the second average heart rate parameter (80 beats per minute) corresponding to the second time period a is the smallest second average heart rate parameter. Thus, as described in step S222, the second average heart rate parameter (80 beats per minute) corresponding to the second time period a can be determined as the second static heart rate parameter. It should be noted that other statistical descriptions, such as the 25% value, etc., can also be applied to the second static heart rate parameter.

[0113] Similarly, since the smallest second average heart rate parameter among the second average heart rate parameters corresponding to multiple second time periods can, to a certain extent, characterize the heart rate characteristics of the test subject in the second time interval, the embodiments of the present disclosure can further enrich the effective information contained in the second parameter set, thereby improving the prediction accuracy and comparability of subsequent neurohormone levels and / or states.

[0114] Based on the Figure 8 embodiment shown, another embodiment of the present disclosure is extended. In the embodiment of the present disclosure, before the step of determining the second average heart rate parameter corresponding to each of the multiple second time periods based on the multiple second time periods, it further includes: removing the unstable time intervals corresponding to each of the multiple second time periods. Wherein, 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 unstable time intervals.

[0115] For example, the test subject is emotionally excited (due to arguments or other reasons), etc. Also, for example, the unstable time intervals further include the time intervals corresponding to events such as premature atrial contraction (PAC), premature ventricular contraction (PVC), atrial tachycardia or atrial arrhythmia (such as atrial fibrillation, etc.), ventricular tachycardia (VT), etc. Therefore, the embodiments of the present disclosure can further improve the prediction accuracy and comparability of subsequent neurohormone levels and / or states. Wherein, the specific meaning of the unstable time interval can refer to the Figure 7 embodiment shown, and the embodiments of the present disclosure will not be elaborated herein.

[0116] Figure 10 Shown is a schematic flowchart of measuring the second parameter set corresponding to a test subject based on a second time interval provided by another exemplary embodiment of the present disclosure. In the Figure 8 embodiment shown, another Figure 10 embodiment shown is extended. Below, the differences between the Figure 10 embodiment shown and the Figure 8 embodiment shown will be emphasized, and the same parts will not be elaborated.

[0117] As Figure 10 shown, in the parameter determination method provided by the embodiments of the present disclosure, the second heart rate variability characterization parameter includes a second static heart rate variability parameter. And, after 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 further included.

[0118] Step S230: Determine the second time period corresponding to the second static heart rate parameter as the third static time period.

[0119] Step S240: Determine the heart rate variability parameter corresponding to the third static time period as the second static heart rate variability parameter.

[0120] In an embodiment of the present disclosure, the second static heart rate variability parameter is the Day Rest Heart Rate Variability (Day RHRV) parameter.

[0121] In the actual application process, based on the first time interval, determine the first parameter set corresponding to the test subject, then based on multiple second time periods, determine the second average heart rate parameters corresponding to each of the multiple second time periods, and based on the second average heart rate parameters corresponding to each of the multiple second time periods, determine the second static heart rate parameter. Subsequently, determine the second time period corresponding to the second static heart rate parameter as the third static time period, and determine the heart rate variability parameter corresponding to the third static time period as the second static heart rate variability parameter.

[0122] Since the determined second static heart rate variability parameter is determined based on the second static heart rate parameter, compared with the prior art, the embodiment of the present disclosure can improve the ability of the determined second static heart rate variability parameter to characterize the neurohormone level and / or state of the test subject in the second time interval.

[0123] In an embodiment of the present disclosure, the first average heart rate parameter and / or the second average heart rate parameter mentioned in the above embodiment can be replaced with other statistical descriptions, such as corresponding heart rate median and / or quartiles (such as 75% or 25%) and other statistical index parameters. Or, based on the above embodiment, supplement the first parameter set and / or the second parameter set with corresponding heart rate median and / or quartiles (such as 75% or 25%) and other statistical index parameters. By setting like this, the flexibility of the parameter determination method can be further improved, and the effective information contained in the corresponding parameter set can be further enriched.

[0124] In an embodiment of the present disclosure, the first heart rate variability characterization parameter mentioned in the above embodiment includes the corresponding heart rate variability parameter, and / or, the second heart rate variability characterization parameter mentioned in the above embodiment includes, but is not limited to, the heart rate variability parameters commonly used in the literature. And, the included heart rate variability parameter is obtained using the standard deviation information of the heart rate (or R - R or P - P interval).

[0125] In Figure 3Another embodiment of the present disclosure is extended based on the illustrated embodiment. In the embodiment of the present disclosure, after the step of determining the second parameter set corresponding to the test subject based on the second time interval (i.e., step S200), it further includes: determining the neurohormone level and / or status corresponding to the test subject based on the first parameter set and the second parameter set.

[0126] The embodiment of the present disclosure can improve the characterization accuracy of the neurohormone level and / or status by means of the first parameter set and the second parameter set, thereby providing a prerequisite for better assisting doctors in disease diagnosis operations.

[0127] Exemplary Devices

[0128] Figure 11 The following shows a schematic structural diagram of a parameter determination device provided by an exemplary embodiment of the present disclosure. As Figure 11 shown, the parameter determination device provided by the embodiment of the present disclosure includes:

[0129] A first determination module 100, configured to determine a first parameter set corresponding to the test subject based on the first time interval;

[0130] A second determination module 200, configured to determine a second parameter set corresponding to the test subject based on the second time interval.

[0131] Figure 12 The following shows a schematic structural diagram of the first determination module provided by an exemplary embodiment of the present disclosure. Based on the Figure 11 illustrated embodiment, an Figure 12 illustrated embodiment is extended. Below, the differences between the Figure 12 illustrated embodiment and the Figure 11 illustrated embodiment are emphasized, and the same parts will not be elaborated.

[0132] As Figure 12 shown, in the parameter determination device provided by the embodiment of the present disclosure, the first determination module 100 includes:

[0133] A first average heart rate parameter determination unit 110, configured to determine first average heart rate parameters corresponding to multiple first time periods based on the multiple first time periods;

[0134] A first static heart rate parameter determination unit 120, configured to determine a first static heart rate parameter based on the first average heart rate parameters corresponding to the multiple first time periods.

[0135] In an embodiment of the present disclosure, the first static heart rate parameter determination unit 120 is further configured to determine the smallest first average heart rate parameter among the first average heart rate parameters corresponding to the multiple first time periods, and determine the smallest first average heart rate parameter as the first static heart rate parameter.

[0136] Figure 13 The following is a schematic structural diagram of a first determination module provided by another exemplary embodiment of the present disclosure. On the basis of the Figure 12 embodiment shown, the Figure 13 embodiment shown is extended. The differences between the Figure 13 embodiment shown and the Figure 12 embodiment shown will be described in detail below, and the same parts will not be repeated.

[0137] As Figure 13 shown, in the parameter determination device provided by the embodiments of the present disclosure, the first determination module 100 further includes:

[0138] A first static time period determination unit 130, configured to determine the first time period corresponding to the first static heart rate parameter as the first static time period;

[0139] A first static heart rate variability parameter determination unit 140, configured to determine the heart rate variability parameter corresponding to the first static time period as the first static heart rate variability parameter.

[0140] Figure 14 The following is a schematic structural diagram of a first determination module provided by yet another exemplary embodiment of the present disclosure. On the basis of the Figure 12 embodiment shown, the Figure 14 embodiment shown is extended. The differences between the Figure 14 embodiment shown and the Figure 12 embodiment shown will be described in detail below, and the same parts will not be repeated.

[0141] As Figure 14 shown, the parameter determination device provided by the embodiments of the present disclosure further includes:

[0142] An unstable time interval removal unit 105, configured to remove the unstable time intervals corresponding to each of the multiple first time periods.

[0143] Moreover, in the parameter determination device provided by the embodiments of the present disclosure, the first average heart rate parameter determination unit 110 includes:

[0144] A first average heart rate parameter determination subunit 115, configured to determine 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 unstable time intervals.

[0145] Figure 15 The following is a schematic structural diagram of a second determination module provided by an exemplary embodiment of the present disclosure. On the basis of the Figure 12 embodiment shown, the Figure 15 embodiment shown is extended. The differences between the Figure 15 embodiment shown and the Figure 12The differences of the illustrated embodiments will not be elaborated, and the same parts will not be repeated.

[0146] As Figure 15 shown, in the parameter determination device provided in the embodiments of the present disclosure, the second determination module 200 includes:

[0147] A second average heart rate parameter determination unit 210, configured to determine second average heart rate parameters corresponding to multiple second time periods based on the multiple second time periods;

[0148] A second static heart rate parameter determination unit 220, configured to determine a second static heart rate parameter based on the second average heart rate parameters corresponding to the multiple second time periods.

[0149] In an embodiment of the present disclosure, the second static heart rate parameter determination unit 220 is further configured to determine the smallest second average heart rate parameter among the second average heart rate parameters corresponding to the multiple second time periods, and determine the smallest second average heart rate parameter as the second static heart rate parameter.

[0150] Figure 16 Shown is a schematic structural diagram of the second determination module provided by another exemplary embodiment of the present disclosure. Based on the Figure 15 illustrated embodiment, the Figure 16 illustrated embodiment is extended. The following focuses on Figure 16 the differences between the Figure 15 illustrated embodiment and the

[0151] As Figure 16 shown, in the parameter determination device provided in the embodiments of the present disclosure, the second determination module 200 further includes:

[0152] A third static time period determination unit 230, configured to determine the second time period corresponding to the second static heart rate parameter as the third static time period;

[0153] A second static heart rate variability parameter determination unit 240, configured to determine the heart rate variability parameter corresponding to the third static time period as the second static heart rate variability parameter.

[0154] It should be understood that Figures 11 to 16The first determination module 100 and the second determination module 200 in the provided parameter determination device, as well as the first average heart rate parameter determination unit 110, the first static heart rate parameter determination unit 120, the first static time period determination unit 130, the second static time period determination unit 135, and the first static heart rate variability parameter determination unit 140 included in the first determination module 100, and the second average heart rate parameter determination unit 210, the second static heart rate parameter determination unit 220, the third static time period determination unit 230, and the second static heart rate variability parameter determination unit 240 included in the second determination module 200, and the operations and functions of the first static heart rate variability parameter determination subunit 141 included in the first static heart rate variability parameter determination unit 140 may refer to the above Figures 3 to 10 The provided parameter determination method will not be described in detail here to avoid repetition.

[0155] In addition, it should be noted that the parameter determination device mentioned in the above embodiments may have the parameter determination method mentioned in the above embodiments by itself or be combined with existing medical devices / instruments to implement the parameter determination method mentioned in the above embodiments by means of the reference parameters collected by the data collection function of the existing medical devices / instruments and / or the judgment function of the existing medical devices / instruments. Furthermore, the first parameter set and the second parameter set determined by means of the parameter determination method are used to achieve the purpose of defining and detecting arrhythmia events (including but not limited to PVC or PAC, non-sustained VT, atrial fibrillation (AF), ventricular tachycardia (VT), and ventricular fibrillation (VF), etc.).

[0156] Next, reference will be made to Figure 17 to describe the electronic device according to an embodiment of the present disclosure. Figure 17 The following shows a schematic structural diagram of an electronic device provided by an exemplary embodiment of the present disclosure.

[0157] As Figure 17 shown, the electronic device 1700 includes one or more processors 1701 and a memory 1702.

[0158] The processor 1701 may be a central processing unit (CPU) or other forms of processing units with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 1700 to perform desired functions.

[0159] The memory 1702 may include one or more computer program products, and the computer program products 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, etc. 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 media, and the processor 1701 may run the program instructions to implement the parameter determination methods of the various embodiments of the present disclosure described above and / or other desired functions. Various contents such as heart rate information may also be stored in the computer-readable storage media.

[0160] In one example, the electronic device 1700 may further include: an input device 1703 and an output device 1704, and these components are interconnected through a bus system and / or other forms of connection mechanisms (not shown).

[0161] The input device 1703 may include, for example, a keyboard, a mouse, and so on.

[0162] The output device 1704 may output various information to the outside, including the determined first parameter set and second parameter set, etc. The output device 1704 may include, for example, a display, a speaker, a printer, and a communication network and its connected remote output devices, and so on.

[0163] Of course, for simplicity, Figure 17 only some of the components related to the present disclosure in the electronic device 1700 are shown, and components such as buses, input / output interfaces, etc. are omitted. In addition, according to specific application scenarios, the electronic device 1700 may further include any other appropriate components.

[0164] In addition to the above methods and devices, the embodiments of the present disclosure may also be computer program products, which include computer program instructions that, when run by a processor, cause the processor to execute the steps in the parameter determination methods according to the various embodiments of the present disclosure described above in this specification.

[0165] The computer program product can be written in any combination of one or more programming languages for executing the program code of the operations of the embodiments of the present disclosure. The programming languages include object-oriented programming languages such as Java, C++, etc., and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, executed as an independent software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0166] In addition, an embodiment of the present disclosure can also be a computer-readable storage medium having computer program instructions stored thereon. When the computer program instructions are run by a processor, the processor is caused to execute the steps in the parameter determination method according to various embodiments of the present disclosure described above in this specification.

[0167] The computer-readable storage medium can adopt any combination of one or more readable media. The readable media can be a readable signal medium or a readable storage medium. The readable storage medium can, for example, include but is not limited to an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0168] The basic principles of the present disclosure have been described above in conjunction with specific embodiments. However, it should be noted that the advantages, benefits, effects, etc. mentioned in the present disclosure are only examples and not limitations, and it cannot be considered that these advantages, benefits, effects, etc. are essential for each embodiment of the present disclosure. In addition, the above-described specific details are only for illustrative purposes and for ease of understanding, rather than limitations. The above details do not limit the present disclosure to necessarily adopt the above specific details for implementation.

[0169] The block diagrams of the devices, apparatuses, equipment, and systems involved in this disclosure are only exemplary examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "comprising," "including," "having," etc. are open-ended terms that mean "including but not limited to" and can be used interchangeably with each other. The words "or" and "and" used herein refer to the phrase "and / or" and can be used interchangeably with it, unless the context clearly indicates otherwise. The phrase "such as" used herein refers to the phrase "such as but not limited to" and can be used interchangeably with it.

[0170] It should also be noted that in the devices, equipment, and methods of this disclosure, each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent solutions of this disclosure.

[0171] 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 can 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 the broadest scope consistent with the principles and novel features disclosed herein.

[0172] The above description has been given for purposes of illustration and description. In addition, this description is not intended to limit the embodiments of this disclosure to the forms disclosed herein. Although multiple example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions, and sub-combinations thereof.

Claims

1. A method for determining parameters, characterized in that, Including: Based on a first time interval, determining a first parameter set corresponding to a test subject, the first time interval including a nighttime interval, the first parameter set including a first heart rate characterization parameter and a first heart rate variability characterization parameter, the first heart rate characterization parameter including a first average heart rate parameter and a first static heart rate parameter, and the first heart rate variability characterization parameter including a first static heart rate variability parameter; Based on a second time interval, determining a second parameter set corresponding to the test subject, the second time interval including a daytime interval, the second parameter set including a second heart rate characterization parameter and a second heart rate variability characterization parameter, the second heart rate characterization parameter including a second average heart rate parameter and a second static heart rate parameter, and the second heart rate variability characterization parameter including a second static heart rate variability parameter, wherein the first time interval includes a plurality of first time periods, and the determining, based on the first time interval, the first parameter set corresponding to the test subject includes: Based on the plurality of first time periods, determining first heart rate variability parameters corresponding to the plurality of first time periods respectively; Based on the first heart rate variability parameters corresponding to the plurality of first time periods respectively, determining the first static heart rate variability parameter, wherein the first static heart rate variability parameter includes a sleep and rest heart rate variability parameter; Determining the first time period corresponding to the first static heart rate variability parameter as a first static time period; Determining the first average heart rate parameter corresponding to the first static time period as the first static heart rate parameter, wherein the first static heart rate parameter includes a sleep and rest heart rate parameter; And, the second time interval includes a plurality of second time periods, and the determining, based on the second time interval, the second parameter set corresponding to the test subject includes: Based on the plurality of second time periods, determining second heart rate variability parameters corresponding to the plurality of second time periods respectively; Based on the second heart rate variability parameters corresponding to the plurality of second time periods respectively, determining the second static heart rate variability parameter, wherein the second static heart rate variability parameter includes a daytime rest heart rate variability parameter; Determining the second time period corresponding to the second static heart rate variability parameter as a third static time period; Determining the second average heart rate parameter corresponding to the third static time period as the second static heart rate parameter; wherein the second static heart rate parameter includes a daytime rest heart rate parameter.

2. The parameter determination method according to claim 1, characterized in that The determining the first static heart rate variability parameter based on the first heart rate variability parameters corresponding to the plurality of first time periods respectively includes: Determining the highest first heart rate variability parameter among the first heart rate variability parameters corresponding to the plurality of first time periods respectively; Determining the highest first heart rate variability parameter as the first static heart rate variability parameter.

3. The parameter determination method according to claim 1, characterized in that The determining the second static heart rate variability parameter based on the second heart rate variability parameters corresponding to the plurality of second time periods respectively includes: Determine the highest second heart rate variability parameter among the second heart rate variability parameters corresponding to each of the multiple second time periods; Determine the highest second heart rate variability parameter as the second static heart rate variability parameter.

4. The parameter determination method according to claim 1, 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.

5. The parameter determination method according to claim 1, characterized in that The second time interval includes the time interval from 8:00 on the current day to 20:00 on the current day.

6. The parameter determination method according to claim 1, characterized in that, After determining the second parameter set corresponding to the test subject based on the second time interval, it further includes: Determine the neurohormone level corresponding to the test subject based on the first parameter set and the second parameter set.

7. The parameter determination method according to claim 1, characterized in that After determining the second parameter set corresponding to the test subject based on the second time interval, it further includes: Determine the neurohormone state corresponding to the test subject based on the first parameter set and the second parameter set.

8. The method according to claim 1, wherein Before determining the first heart rate variability parameter corresponding to each of the multiple first time periods based on the multiple first time periods, it further includes: Remove the unstable time intervals corresponding to each of the multiple first time periods.

9. A computer-readable storage medium storing a computer program for executing the parameter determination method according to any one of claims 1 to 8 above.

10. An electronic device, the electronic device comprising: A processor; A memory for storing executable instructions of the processor; The processor for executing the parameter determination method according to any one of claims 1 to 8 above.

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