Parameter determination method, parameter determination device, storage medium and electronic equipment
By determining heart rate and heart rate variability parameters separately at night and during the day, and eliminating non-stationary time intervals, the problem of inaccurate neurohormone measurement was solved, the accuracy and comparability of the parameter set were improved, and precise characterization of neurohormone levels and states was achieved.
Patent Information
- Application Number
- CN202511177118.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2020-12-30
- Publication Date
- 2025-11-14
AI Technical Summary
Existing technologies are insufficient for accurately measuring the levels and states of neurohormones driven by the sympathetic and parasympathetic nervous systems. The results are inaccurate due to factors such as diurnal variations, disease progression, sleep patterns, and medications.
By determining the parameter sets of the test subjects at night and during the day, including heart rate and heart rate variability parameters, eliminating non-stationary time intervals, and utilizing mean heart rate parameters and resting heart rate parameters, the accuracy and comparability of parameter measurements are improved.
It reduces the influence of interfering factors in different time intervals, improves the accuracy and comparability of parameter sets, and can more accurately characterize neurohormonal levels and states.
Smart Images
Figure CN120938385A_ABST
Abstract
Description
Technical Field
[0001] This disclosure relates to the field of medical data processing technology, specifically to parameter determination methods, parameter determination devices, computer-readable storage media, and electronic devices. Background Technology
[0002] As is well known, neurohormones driven by the sympathetic and parasympathetic nervous systems are highly susceptible to many factors such as diurnal variations, disease progression, sleep patterns, and medications, making them difficult to measure accurately.
[0003] Therefore, there is an urgent need for a parameter determination method to identify parameters that can accurately characterize neurohormonal levels and / or states. Summary of the Invention
[0004] To address the aforementioned technical problems, this disclosure is proposed. Embodiments of this disclosure provide a parameter determination method, a parameter determination apparatus, a computer-readable storage medium, and an electronic device.
[0005] In one aspect, embodiments of this disclosure provide a parameter determination method, the method comprising: determining a first parameter set corresponding to a test subject based on a first time interval, 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; and determining a second parameter set corresponding to a test subject based on a second time interval, 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.
[0006] In one embodiment of this disclosure, the first time interval includes multiple first time periods, and the first heart rate characterization parameter includes a first average heart rate parameter and a first resting heart rate parameter. Furthermore, 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 multiple first time periods; and determining the first resting heart rate parameter based on the first average heart rate parameter corresponding to each of the multiple first time periods.
[0007] 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 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; and determining the smallest first average heart rate parameter as the first static heart rate parameter.
[0008] In one embodiment of this disclosure, the first heart rate variability characterization parameter 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.
[0009] 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.
[0010] In one embodiment of this disclosure, the second time interval includes multiple second time periods, and the second heart rate characterization parameters include a second average heart rate parameter and a second resting heart rate parameter. Furthermore, determining the second parameter set corresponding to the test subject based on the second time interval includes: determining the second average heart rate parameter corresponding to each of the multiple second time periods; and determining the second resting heart rate parameter based on the second average heart rate parameter corresponding to each of the multiple second time periods.
[0011] 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.
[0012] In one embodiment of this disclosure, the second heart rate variability characterization parameter 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.
[0013] 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.
[0014] In another aspect, embodiments of this disclosure provide a parameter determination device, the device comprising: 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, the first parameter set including a first heart rate characterization parameter and a first heart rate variability characterization parameter; and a second determination module, configured to determine a second parameter set corresponding to a test subject based on a second time interval, 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.
[0015] In another aspect, embodiments of this disclosure provide a computer-readable storage medium storing a computer program for performing the parameter determination method mentioned in the above embodiments.
[0016] In another aspect, embodiments of this disclosure provide an electronic device including a processor and a memory for storing processor-executable instructions, wherein the processor is used to execute the parameter determination method mentioned in the above embodiments.
[0017] The parameter determination method provided in this disclosure reduces the influence of interference factors during parameter measurement by determining the parameter sets (i.e., the first parameter set and the second parameter set) corresponding to the test subject in a first time interval and a second time interval, respectively. This improves the accuracy of the obtained parameter sets and the comparability at different times. Furthermore, when the first parameter set and the second parameter set determined by the embodiments of this disclosure are used to characterize neurohormonal levels and / or states, the accuracy of characterization can be effectively improved. Attached Figure Description
[0018] 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.
[0019] Figure 1 The diagram shown is a scenario applicable to an embodiment of this disclosure.
[0020] Figure 2 The diagram shown illustrates another scenario applicable to the embodiments of this disclosure.
[0021] Figure 3 The diagram shown is a flowchart illustrating a parameter determination method provided in an exemplary embodiment of this disclosure.
[0022] Figure 4The diagram shown is a schematic flowchart of measuring the first set of parameters corresponding to the test object based on a first time interval, according to an exemplary embodiment of this disclosure.
[0023] Figure 5 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.
[0024] Figure 6 The diagram shown is a flowchart illustrating the measurement of a first set of parameters corresponding to the test object based on a first time interval, according to another exemplary embodiment of this disclosure.
[0025] Figure 7 The diagram shown is a flowchart illustrating the measurement of a first set of parameters corresponding to the test object based on a first time interval, according to another exemplary embodiment of this disclosure.
[0026] Figure 8 The diagram shown is a schematic flowchart illustrating the measurement of a second set of parameters corresponding to a test object based on a second time interval, according to an exemplary embodiment of this disclosure.
[0027] Figure 9 The diagram shown is a schematic flowchart of an exemplary embodiment of the present disclosure, which describes a process for determining a second static heart rate parameter based on the second average heart rate parameters corresponding to multiple second time periods.
[0028] Figure 10 The diagram shown is a flowchart illustrating the measurement of a second set of parameters corresponding to a test object based on a second time interval, provided by another exemplary embodiment of this disclosure.
[0029] Figure 11 The diagram shown is a schematic representation of the parameter determination apparatus provided in an exemplary embodiment of this disclosure.
[0030] Figure 12 The diagram shown is a structural schematic of the first determining module provided in an exemplary embodiment of this disclosure.
[0031] Figure 13 The diagram shown is a structural schematic of a first determining module provided in another exemplary embodiment of this disclosure.
[0032] Figure 14 The diagram shown is a structural schematic of a first determining module provided in yet another exemplary embodiment of this disclosure.
[0033] Figure 15 The diagram shown is a structural schematic of the second determining module provided in an exemplary embodiment of this disclosure.
[0034] Figure 16 The diagram shown is a structural schematic of a second determining module provided in another exemplary embodiment of this disclosure.
[0035] Figure 17 The diagram shown is a structural schematic of an electronic device provided in an exemplary embodiment of this disclosure. Detailed Implementation
[0036] 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.
[0037] Application Overview
[0038] 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, 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).
[0039] 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, 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 include data from exercise and emotional excitement phases in the measured daytime heart rate and daytime heart rate variability; however, exercise and emotional excitement phases 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, a parameter determination method is urgently needed to identify parameters that accurately characterize neurohormonal levels and / or state, and to make the determined parameters more comparable.
[0040] Based on the aforementioned technical problems, the basic concept of this 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 in this disclosure includes: determining a first set of parameters corresponding to the test subject based on a first time interval, wherein the first time interval includes a nighttime interval and the first set of parameters includes a first heart rate characterization parameter and a first heart rate variability characterization parameter; and determining a second set of parameters corresponding to the test subject based on a second time interval, wherein the second time interval includes a daytime interval and the second set of parameters includes a second heart rate characterization parameter and a second heart rate variability characterization parameter.
[0042] The parameter determination method provided in this disclosure reduces the influence of interference factors during parameter measurement by determining the parameter sets (i.e., the first parameter set and the second parameter set) corresponding to the test subject in a first time interval and a second time interval, respectively. This achieves the goal of improving the accuracy and comparability of the obtained parameter sets. Furthermore, when using the first parameter set and the second parameter set determined by this disclosure to characterize neurohormonal levels and / or states, it can effectively improve the accuracy of characterization.
[0043] 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.
[0044] Exemplary scenario
[0045] 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.
[0046] 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 a first set of parameters corresponding to the test subject based on a first time interval, and a second set of parameters corresponding to the test subject based on a second time interval. The first time interval includes a nighttime interval, and the first set of parameters includes a first heart rate characterization parameter and a first heart rate variability characterization parameter. The second time interval includes a daytime interval, and the second set of parameters includes a second heart rate characterization parameter and a second heart rate variability characterization parameter. In other words, this scenario implements a parameter determination method.
[0047] For example, server 1 determines the aforementioned first parameter set and / or second parameter set based on the reference parameters collected by medical device 2.
[0048] because Figure 1The above scenario illustrates the parameter determination method implemented using server 1. Therefore, it not only improves the adaptability of the scenario but also effectively reduces the computational load of medical device 2.
[0049] It should be noted that this disclosure also applies to another scenario. Figure 2 The diagram illustrates another scenario applicable to the embodiments of this disclosure. Specifically, this scenario includes a medical device 3, which further 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 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 a first set of parameters corresponding to the test subject based on a first time interval, and a second set of parameters corresponding to the test subject based on a second time interval. The first time interval includes a nighttime interval, and the first set of parameters includes a first heart rate characterization parameter and a first heart rate variability characterization parameter. The second time interval includes a daytime interval, and the second set of parameters includes a second heart rate characterization parameter and a second heart rate variability characterization parameter. That is, this scenario implements a parameter determination method.
[0051] For example, the calculation module 302 determines the aforementioned first parameter set and / or second parameter set based on the reference parameters collected by the parameter acquisition module 301.
[0052] because Figure 2 The above scenario demonstrates the parameter determination method implemented using medical device 3, which eliminates the need for data transmission with servers or other related devices. Therefore, the above scenario ensures the real-time performance of the parameter determination method.
[0053] 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.
[0054] Exemplary methods
[0055] Figure 3 The diagram shown is a flowchart illustrating a parameter determination method provided in an exemplary embodiment of this disclosure. Figure 3 As shown in the embodiments of this disclosure, the parameter determination method includes the following steps.
[0056] Step S100: Based on the first time interval, determine the first parameter set corresponding to the test object.
[0057] 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 first parameter set. 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 asleep, at rest, and in a "lying" posture.
[0058] For example, the test subject mentioned in step S100 refers to the human body. That is, determining the first parameter set corresponding to the human body. It should be understood that determining the first parameter set corresponding to the test subject mentioned in step S100 can mean calculating the first parameter set corresponding to the test subject.
[0059] In this embodiment of the disclosure, the first parameter set includes a first heart rate characterization parameter and a first heart rate variability characterization parameter. The first heart rate characterization parameter refers to a parameter capable of characterizing the heart rate within a first time interval. The first heart rate variability characterization parameter refers to a parameter capable of characterizing the heart rate variability within the first time interval. For example, the first heart rate characterization parameter is obtained based on a real-time measured heart rate, and the first heart rate variability characterization parameter is obtained based on a real-time measured heart rate variability.
[0060] Step S200: Based on the second time interval, determine the second parameter set corresponding to the test subject.
[0061] 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 essentially inactive state to reduce interference and thus improve the accuracy and comparability of the final second parameter set. More preferably, during the second time interval, the test subject is in a resting or inactive state in a non-standing position. For example, the aforementioned inactive 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 if the test subject's real-time heart rate does not exceed a preset exercise heart rate threshold, then the test subject can be determined to be in an inactive state.
[0062] In this embodiment of the disclosure, the second parameter set includes a second heart rate characterization parameter and a second heart rate variability characterization parameter. The second heart rate characterization parameter refers to a parameter capable of characterizing the heart rate within a second time interval. The second heart rate variability characterization parameter refers to a parameter capable of characterizing the heart rate variability within the second time interval. For example, the second heart rate characterization parameter is obtained based on a real-time measured heart rate, and the second heart rate variability characterization parameter is obtained based on a real-time measured heart rate variability.
[0063] In practical applications, a first set of parameters corresponding to the test object is determined based on a first time interval, and a second set of parameters corresponding to the test object is determined based on a second time interval.
[0064] The parameter determination method provided in this disclosure reduces the influence of interference factors during parameter measurement by determining the parameter sets (i.e., the first parameter set and the second parameter set) corresponding to the test subject in a first time interval and a second time interval, respectively. This achieves the goal of improving the accuracy and comparability of the obtained parameter sets. Furthermore, when the first parameter set and the second parameter set determined in this disclosure are used to characterize neurohormonal levels and / or states, the accuracy of characterization can be effectively improved.
[0065] Figure 4 The diagram shown is a schematic flowchart illustrating the measurement of a first set of parameters corresponding to a test object based on a first time interval, 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.
[0066] like Figure 4 As shown, in the parameter determination method provided in this embodiment, the first time interval includes multiple first time periods, and the first heart rate characterization parameter includes a first average heart rate parameter and a first resting heart rate parameter. Furthermore, 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 multiple first time periods, determine the first average heart rate parameter corresponding to each of the multiple first time periods.
[0068] For example, the first time period is a time window with a duration 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 first time interval.
[0069] Optionally, the first mean heart rate (MHR) parameter for each time window refers to the average heart rate measured within that time window.
[0070] Step S120: Determine the first static heart rate parameter based on the first average heart rate parameters corresponding to each of the multiple first time periods.
[0071] In one embodiment of this disclosure, the first static heart rate parameter is the sleep rest heart rate (Sleep RHR) parameter.
[0072] 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.
[0073] The parameter determination method provided in this disclosure achieves the goal of determining a first set of parameters corresponding to a test subject based on a first time interval by 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. Compared with real-time heart rate, the first average heart rate parameter and the first resting heart rate parameter can more accurately characterize the heart rate status 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 average heart rate parameter and the first resting heart rate parameter obtained in this disclosure embodiment, more accurate neurohormone prediction results can be obtained.
[0074] In another embodiment of this disclosure, the process of determining the first average heart rate parameter is as follows: based on multiple first time periods, determine the heart rate variability parameters corresponding to each of the multiple first time periods; determine the first average heart rate parameter based on the heart rate variability parameters corresponding to each of the multiple first time periods. For example, firstly, determine the first time period corresponding to the highest heart rate variability parameter among the heart rate variability parameters corresponding to each of the multiple first time periods, and then determine the average heart rate parameter corresponding to that first time period as the first average heart rate parameter. Other statistical descriptions can also be used for the heart rate variability parameters, such as the 75% value. Furthermore, it should be noted that the average heart rate parameter can also be other statistical descriptions, such as the 25% value.
[0075] Similarly, in embodiments of this disclosure, non-stable time intervals in the first time interval can also be removed first. The specific meaning of non-stable time intervals can be found in [reference needed]. Figure 7 The embodiments shown are not described in detail in this disclosure.
[0076] Figure 5 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 4 Extending from the illustrated embodiment Figure 5 The illustrated embodiment will be described in detail below. Figure 5 The illustrated embodiments and Figure 4 The differences between the embodiments shown are not repeated here, and the similarities are not repeated here.
[0077] like Figure 5 As shown, in the parameter determination method provided in this embodiment, the step of determining the first static heart rate parameter based on the first average heart rate parameters corresponding to multiple first time periods includes the following steps.
[0078] Step S121: Determine the smallest first average heart rate parameter among the first average heart rate parameters corresponding to each of the multiple first time periods.
[0079] Step S122: The smallest first average heart rate parameter is determined as the first static heart rate parameter.
[0080] For example, the first time interval includes five first time periods: 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, for first time period b it is 85 beats / minute, for first time period c it is 90 beats / minute, for first time period d it is 75 beats / minute, and for first time period e it 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 S122, the first average heart rate parameter for first time period d (75 beats / minute) can be determined as the first resting heart rate parameter.
[0081] The parameter determination method provided in this disclosure achieves the purpose of determining a 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 average heart rate parameters corresponding to each first time period, and then 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 multiple first average heart rate parameters corresponding to each first time period can characterize the heart rate characteristics of the test subject in the first time interval to a certain extent, this disclosure embodiment can further enrich the effective information contained in the first parameter set, thereby improving the accuracy and comparability of subsequent predictions of neurohormonal levels and / or states.
[0082] exist Figure 5 This disclosure extends the illustrated embodiment to include another embodiment. In this embodiment, before determining the smallest first average heart rate parameter among the first average heart rate parameters corresponding to each of the multiple first time periods, the method further includes: selecting a preset time interval within the first time interval, wherein the length of the preset time interval is less than the length of the first time interval. Furthermore, in this embodiment, the step of determining the smallest first average heart rate parameter among 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 the first time periods included within the preset time interval. This configuration effectively removes average heart rate parameters corresponding to some atypical time intervals (e.g., [specific time intervals]) within the first time interval, thereby further improving the accuracy of the determined smallest first average heart rate parameter.
[0083] Figure 6 The diagram shown is a schematic representation of a process for measuring a first set of parameters corresponding to a test object based on a first time interval, provided by another exemplary embodiment of this disclosure. Figure 4 Extending from the illustrated embodiment Figure 6 The illustrated embodiment will be described in detail below. Figure 6 The illustrated embodiments and Figure 4 The differences between the embodiments shown are not repeated here, and the similarities are not repeated here.
[0084] like Figure 6 As shown, in the parameter 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 plurality of first time periods, the following steps are also included.
[0085] Step S130: Determine the first time period corresponding to the first static heart rate parameter as the first static time period.
[0086] Step S140: Determine the heart rate variability parameter corresponding to the first static time period as the first static heart rate variability parameter.
[0087] In one embodiment of this disclosure, the first static heart rate variability parameter is the sleep-rest heart rate variability (Sleep RHRV) parameter.
[0088] In practical applications, based on multiple first time periods, the first average heart rate parameter corresponding to each of the multiple first time periods is determined, and the first static heart rate parameter is determined based on the first average heart rate parameter corresponding to each of the multiple first time periods. 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, the second parameter set corresponding to the test subject is determined.
[0089] 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 and comparability of the determined first static heart rate variability parameter to characterize the neurohormonal level and / or state of the test subject in a first time interval compared with the prior art.
[0090] 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 heart rate variability parameters can also be described using other statistical methods, such as the 25% value.
[0091] Figure 7 The diagram illustrates a flowchart of a method for determining a first set of parameters corresponding to a test object based on a first time interval, according to yet another exemplary embodiment of this disclosure. Figure 4 Extending from the illustrated embodiment Figure 7 The illustrated embodiment will be described in detail below. Figure 7 The illustrated embodiments and Figure 4 The differences between the embodiments shown are not repeated here, and the similarities are not repeated here.
[0092] like Figure 7 As shown, in the parameter determination method provided 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 following steps are also included.
[0093] Step S105: Remove the unstable time intervals corresponding to each of the multiple first time periods.
[0094] For example, the unstable time interval mentioned in step S105 refers to, for example, the time interval of snoring and / or the time interval of unstable respiratory rate (e.g., sleep apnea).
[0095] In one embodiment of this disclosure, the non-steady time interval also includes the time interval 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, where heart rate changes significantly. Furthermore, the non-steady time interval also includes the time interval corresponding to events such as premature atrial contractions (PAC), premature ventricular contractions (PVC), atrial tachycardia or atrial arrhythmias (such as atrial fibrillation), and ventricular tachycardia (VT).
[0096] Furthermore, in the parameter determination method provided in this embodiment, the step of determining the first average heart rate parameter corresponding to each of the multiple first time periods based on multiple first time periods includes the following steps.
[0097] Step S115: Based on multiple first time periods after removing non-stable time intervals, determine the first average heart rate parameter corresponding to each of the multiple first time periods.
[0098] 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 variability parameter by removing the non-stationary time intervals.
[0099] Figure 8 The diagram shown is a schematic representation of an exemplary embodiment of this disclosure, illustrating the process of measuring a second set of parameters corresponding to a test object based on a second 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.
[0100] like Figure 8 As shown, in the parameter determination method provided in this embodiment, the second time interval includes multiple second time periods, and the second heart rate characterization parameters include a second average heart rate parameter and a second resting heart rate parameter. Furthermore, 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: Based on multiple second time periods, determine the second average heart rate parameter corresponding to each of the multiple second time periods.
[0102] For example, the second time period is a time window with a duration 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 second time interval.
[0103] Optionally, the second mean heart rate (MHR) parameter for 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 parameter corresponding to each of the multiple second time periods.
[0105] In one embodiment of this 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 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.
[0107] The parameter determination method provided in this disclosure achieves the goal of determining a second set of parameters corresponding to a test subject based on a second time interval by determining a second average heart rate parameter corresponding to each of the multiple second time intervals, and determining a second resting heart rate parameter based on the second average heart rate parameter corresponding to each of the multiple second time intervals. Compared with real-time heart rate, the second average heart rate parameter and the second resting heart rate parameter can more accurately characterize the heart rate status 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 resting heart rate parameter obtained in this disclosure embodiment, more accurate neurohormone prediction results can be obtained.
[0108] Figure 9 The diagram illustrates a flowchart of an exemplary embodiment of this disclosure, illustrating the process of determining a second static heart rate parameter based on second average heart rate parameters corresponding to multiple second 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.
[0109] like Figure 9 As shown, in the parameter determination method provided in this embodiment, 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 includes the following steps.
[0110] Step S221: Determine the smallest second average heart rate parameter among the second average heart rate parameters corresponding to each of the multiple second time periods.
[0111] Step S222: The smallest second average heart rate parameter is determined as the second static heart rate parameter.
[0112] For example, the second time interval includes 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 (80 beats / minute) for second time period a is the smallest. Thus, as described in step S222, the second average heart rate parameter (80 beats / minute) for second time period a can be determined as the second resting heart rate parameter. It should be noted that other statistical descriptions, such as the 25% value, can also be used for the second resting 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 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 second parameter set, thereby improving the prediction accuracy and comparability of subsequent neurohormonal levels and / or states.
[0114] exist Figure 8 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 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 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.
[0115] For example, the test subject may be emotionally agitated (due to arguments or other reasons). Furthermore, non-steady time intervals also include the time intervals corresponding to events such as premature atrial contraction (PAC), premature ventricular contraction (PVC), atrial tachycardia or atrial arrhythmias (such as atrial fibrillation), and ventricular tachycardia (VT). Therefore, the embodiments of this disclosure can further improve the accuracy and comparability of predicting subsequent neurohormonal levels and / or states. The specific meaning of non-steady time intervals can be found in [reference needed]. Figure 7 The embodiments shown are not described in detail in this disclosure.
[0116] Figure 10 The diagram shown is a schematic representation of a process for measuring a second set of parameters corresponding to a test object based on a second time interval, 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.
[0117] like Figure 10 As shown, in the parameter determination method provided in this embodiment, the second heart rate variability characterization parameter 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 the following steps.
[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: The heart rate variability parameter corresponding to the third static time period is determined as the second static heart rate variability parameter.
[0120] In one embodiment of this disclosure, the second static heart rate variability parameter is the day-resting heart rate variability (Day RHRV) parameter.
[0121] In practical applications, based on the first time interval, the first set of parameters corresponding to the test subject is determined. Then, based on multiple second time intervals, the second average heart rate parameters corresponding to each of the multiple second time intervals are determined. Based on the second average heart rate parameters corresponding to each of the multiple second time intervals, the second static heart rate parameters are determined. Subsequently, the second time interval corresponding to the second static heart rate parameters is determined as the third static time interval, and the heart rate variability parameters corresponding to the third static time interval are determined as the second static heart rate variability parameters.
[0122] Since the determined second static heart rate variability parameter is based on the second static heart rate parameter, the embodiments of this disclosure are able to improve the ability of the determined second static heart rate variability parameter to characterize the neurohormonal level and / or state of the test subject in a second time interval compared with the prior art.
[0123] In one embodiment of this disclosure, the first average heart rate parameter and / or the second average heart rate parameter mentioned in the above embodiments can be replaced with other statistical descriptions, such as the corresponding median and / or quartiles of heart rate (e.g., 75% or 25%). Alternatively, based on the above embodiments, the first parameter set and / or the second parameter set can be supplemented with corresponding median and / or quartiles of heart rate (e.g., 75% or 25%). This configuration further improves the flexibility of the parameter determination method and further enriches the effective information contained in the corresponding parameter set.
[0124] In one embodiment of this disclosure, the first heart rate variability characterization parameter mentioned in the above embodiments includes a corresponding heart rate variability parameter, and / or, the second heart rate variability characterization parameter mentioned in the above embodiments includes, but is not limited to, heart rate variability parameters commonly used in the literature. Furthermore, the included heart rate variability parameters are obtained using the standard deviation information of heart rate (or RR or PP interval).
[0125] exist Figure 3Based on the illustrated embodiment, another embodiment of this disclosure is extended. In this embodiment, after determining the second parameter set corresponding to the test subject based on the second time interval (i.e., step S200), the method further includes: determining the neurohormonal level and / or state corresponding to the test subject based on the first parameter set and the second parameter set.
[0126] The embodiments disclosed herein can improve the accuracy of characterization of neurohormonal levels and / or states by using a first set of parameters and a second set of parameters, thereby providing a prerequisite for better assisting doctors in disease diagnosis.
[0127] Exemplary device
[0128] Figure 11 The diagram shown is a structural schematic of a parameter determination device provided in an exemplary embodiment of this disclosure. Figure 11 As shown, the parameter determination device provided in this embodiment includes:
[0129] The first determining module 100 is used to determine the first parameter set corresponding to the test object based on the first time interval;
[0130] The second determining module 200 is used to determine the second parameter set corresponding to the test object based on the second time interval.
[0131] Figure 12 The diagram shown is a structural schematic of a first determining module provided in an 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.
[0132] like Figure 12 As shown, in the parameter determination device provided in this embodiment, the first determination module 100 includes:
[0133] The first average heart rate parameter determination unit 110 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.
[0134] The first static heart rate parameter determination unit 120 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.
[0135] In one embodiment of this 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 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.
[0136] Figure 13 The diagram shown is a structural schematic of a first determining module provided in another exemplary embodiment of this disclosure. Figure 12 Extending from the illustrated embodiment Figure 13 The illustrated embodiment will be described in detail below. Figure 13 The illustrated embodiments and Figure 12 The differences between the embodiments shown are not repeated here, and the similarities are not repeated here.
[0137] like Figure 13 As shown, in the parameter determination device provided in this embodiment, the first determination module 100 further includes:
[0138] The first static time period determination unit 130 is used to determine the first time period corresponding to the first static heart rate parameter as the first static time period.
[0139] The first static heart rate variability parameter determination unit 140 is used 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 diagram shown is a structural schematic of a first determining module provided in yet another exemplary embodiment of this disclosure. Figure 12 Extending from the illustrated embodiment Figure 14 The illustrated embodiment will be described in detail below. Figure 14 The illustrated embodiments and Figure 12 The differences between the embodiments shown are not repeated here, and the similarities are not repeated here.
[0141] like Figure 14 As shown, the parameter determination device provided in this embodiment further includes:
[0142] The unstable time interval removal unit 105 is used to remove the unstable time intervals corresponding to each of the multiple first time periods.
[0143] Furthermore, in the parameter determination device provided in this embodiment, the first average heart rate parameter determination unit 110 includes:
[0144] The first average heart rate parameter determination subunit 115 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 after removing the non-stable time intervals.
[0145] Figure 15 The diagram shown is a structural schematic of the second determining module provided in an exemplary embodiment of this disclosure. Figure 12 Extending from the illustrated embodiment Figure 15 The illustrated embodiment will be described in detail below. Figure 15 The illustrated embodiments and Figure 12The differences between the embodiments shown are not repeated here, and the similarities are not repeated here.
[0146] like Figure 15 As shown, in the parameter determination device provided in this embodiment, the second determination module 200 includes:
[0147] The second average heart rate parameter determination unit 210 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.
[0148] The second static heart rate parameter determination unit 220 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.
[0149] In one embodiment of this 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 each of the plurality of second time periods, and determine the smallest second average heart rate parameter as the second static heart rate parameter.
[0150] Figure 16 The diagram shown is a structural schematic of a second determining module provided in another exemplary embodiment of this disclosure. Figure 15 Extending from the illustrated embodiment Figure 16 The illustrated embodiment will be described in detail below. Figure 16 The illustrated embodiments and Figure 15 The differences between the embodiments shown are not repeated here, and the similarities are not repeated here.
[0151] like Figure 16 As shown, in the parameter determination device provided in this embodiment, the second determination module 200 further includes:
[0152] The third static time period determination unit 230 is used to determine the second time period corresponding to the second static heart rate parameter as the third static time period.
[0153] The second static heart rate variability parameter determination unit 240 is used 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 operation and function of the first determining module 100 and the second determining module 200 in the provided parameter determining device, as well as the first average heart rate parameter determining unit 110, the first static heart rate parameter determining unit 120, the first static time period determining unit 130, the second static time period determining unit 135, and the first static heart rate variability parameter determining unit 140 included in the first determining module 100, and the second average heart rate parameter determining unit 210, the second static heart rate parameter determining unit 220, the third static time period determining unit 230, and the second static heart rate variability parameter determining unit 240 included in the second determining module 200, and the first static heart rate variability parameter determining subunit 141 included in the first static heart rate variability parameter determining unit 140, can be referred to the above. Figures 3 to 10 The method for determining the provided parameters will not be repeated here to avoid duplication.
[0155] Furthermore, it should be noted that the parameter determination device mentioned in the above embodiments may have its own parameter determination method mentioned in the above embodiments or be combined with existing medical devices / devices. It can realize the parameter determination method mentioned in the above embodiments by using the benchmark parameters collected by the data acquisition function of existing medical devices / devices and / or the judgment function of existing medical devices / devices. Then, it can use the first parameter set and the second parameter set determined by the parameter determination method 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)).
[0156] Below, for reference Figure 17 To describe an electronic device according to embodiments of the present disclosure. Figure 17 The diagram shown is a structural schematic of an electronic device provided in an exemplary embodiment of this disclosure.
[0157] like Figure 17 As shown, the electronic device 1700 includes one or more processors 1701 and memory 1702.
[0158] The processor 1701 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 1700 to perform desired functions.
[0159] The memory 1702 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 1701 may execute the program instructions to implement the parameter determination methods of the various embodiments of this disclosure described above and / or other desired functions. Various contents such as heart rate information may also be stored in the computer-readable storage medium.
[0160] In one example, the electronic device 1700 may also include an input device 1703 and an output device 1704, which are interconnected via a bus system and / or other forms of connection mechanism (not shown).
[0161] The input device 1703 may include, for example, a keyboard, a mouse, etc.
[0162] The output device 1704 can output various information to the outside, including a determined first parameter set and a 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, etc.
[0163] Of course, for the sake of simplicity, Figure 17 Only some of the components of the electronic device 1700 relevant to this disclosure are shown, omitting components such as buses, input / output interfaces, etc. In addition, the electronic device 1700 may include any other suitable components depending on the specific application.
[0164] In addition to the methods and apparatus described above, embodiments of this disclosure may also be computer program products comprising computer program instructions that, when executed by a processor, cause the processor to perform the steps in the parameter determination methods according to various embodiments of this disclosure described above.
[0165] 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.
[0166] Furthermore, embodiments of this disclosure may also be computer-readable storage media storing computer program instructions that, when executed by a processor, cause the processor to perform the steps in the parameter determination methods according to various embodiments of this disclosure described above.
[0167] 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.
[0168] 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.
[0169] 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.
[0170] 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.
[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 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.
[0172] 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 parameters, characterized in that, include: Based on a first time interval, a first set of parameters corresponding to the test subject is determined, wherein the first time interval includes multiple first time periods, the first set of parameters includes a first heart rate characterization parameter and a first heart rate variability characterization parameter, 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.
2. The method according to claim 1, characterized in that, The step of determining the parameter set corresponding to the test subject based on the first time interval includes: Based on the multiple first time periods, determine the first average heart rate parameter corresponding to each of the multiple first time periods; The first static heart rate parameter is determined based on the first average heart rate parameter corresponding to each of the plurality of first time periods; The first time period corresponding to the first static heart rate parameter is defined 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.
3. The method according to claim 2, characterized in that, The first time interval includes the nighttime interval, the first static heart rate parameter is the sleep resting heart rate parameter, and the first static heart rate variability parameter is the sleep resting heart rate variability parameter. The step of determining the first static heart rate parameter based on the first average heart rate parameters corresponding to each of the plurality of first time periods includes: 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; Furthermore, the minimum first average heart rate parameter is determined as the first static heart rate parameter.
4. The method according to claim 2, characterized in that, Before 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, the method further includes: Remove the unstable time intervals corresponding to each of the multiple first time periods; The step of 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 includes: Based on multiple first time periods after removing non-stable time intervals, the first average heart rate parameter corresponding to each of the multiple first time periods is determined.
5. The method according to claim 1, characterized in that, Also includes: Based on a second time interval, a second parameter set corresponding to the test subject is determined. The second parameter set includes a second heart rate characterization parameter and a second heart rate variability characterization parameter. The second time interval includes multiple second time periods. The second heart rate characterization parameter includes a second average heart rate parameter and a second resting heart rate parameter. The second heart rate variability characterization parameter includes a second resting heart rate variability parameter. The step of determining the second parameter set corresponding to the test subject based on the second time interval includes: 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; The second time period corresponding to the second static heart rate parameter is determined as the third static time period; and 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 5, characterized in that, The second time interval includes the daytime interval, the second resting heart rate parameter is the daytime resting heart rate parameter, and the second resting heart rate variability parameter is the daytime resting heart rate variability parameter; 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.
7. The method according to claim 1, characterized in that, The step of determining the parameter set corresponding to the test subject based on the first time interval includes: Based on the plurality of first time periods, determine the first heart rate variability parameter corresponding to each of the plurality of 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; and The first average heart rate parameter corresponding to the first static time period is determined as the first static heart rate parameter.
8. The method according to claim 7, characterized in that, The first time interval includes the nighttime interval, the first resting heart rate parameter is the sleep-resting heart rate parameter, and the first resting heart rate variability parameter is the sleep-resting heart rate variability parameter. The step of determining the first resting heart rate variability parameter based on the first heart rate variability parameters corresponding to each of the plurality of first time intervals 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; and determine the highest first heart rate variability parameter as the first static heart rate variability parameter.
9. The method according to any one of claims 1 to 7, characterized in that, Also includes: The neurohormonal levels and / or states of the test subject are determined based on the first set of parameters.
10. A computer-readable storage medium storing a computer program for performing the parameter determination method according to any one of claims 1 to 9.
11. An electronic device, the electronic device comprising: processor; Memory used to store the processor's executable instructions; The processor is configured to execute the parameter determination method according to any one of claims 1 to 9.