Heart rate stability assessment method

Abstract

A heart rate stability evaluation method is suitable for evaluating the heart rate stability of a user according to a measurement signal obtained by measuring the user by an evaluation system, is implemented by the evaluation system, and comprises the following steps: (A) generating heartbeat interval information according to the measurement signal; (B) judging whether the heart rate is unstable or not according to the heartbeat interval information; (C) when it is judged that the heart rate is unstable, whether breathing interference exists or not is judged according to the heartbeat interval information; (D) when it is judged that no breathing interference exists, it is determined that the heart rate is unstable.

Description

Technical Field

[0001] The present invention relates to a heart rate stability evaluation method, in particular to a heart rate stability evaluation method taking respiratory interference into consideration. Background Art

[0002] Existing electronic blood pressure monitors can also detect arrhythmia, external interference, and unstable physiological state when measuring blood pressure, and warn the user that the measured data may be abnormal.

[0003] However, the user's breathing can interfere with the heart rate cycle, making it impossible for the electronic blood pressure monitor to accurately distinguish between true arrhythmia and interference caused by breathing. When the subject takes a deep breath or breathes irregularly during blood pressure measurement, the blood pressure monitor may mistakenly warn of arrhythmia, causing the blood pressure monitor to produce a false positive warning.

[0004] That is, existing electronic blood pressure monitors are unable to correctly distinguish whether detected arrhythmias, external interference, and unstable physiological conditions are real or caused by respiratory-induced heart rhythm cycle interference, which may lead users to misinterpret measurement results or unnecessary concerns. Summary of the Invention

[0005] The object of the present invention is to provide a heart rate stability assessment method that can improve the accuracy of the assessment.

[0006] The heart rate stability assessment method of the present invention is suitable for assessing the heart rate stability of a user based on a measurement signal obtained by measuring the user by an assessment system. The method is implemented by the assessment system and includes a step (A), a step (B), a step (C), and a step (D).

[0007] In the step (A), the evaluation system generates heartbeat interval information according to the measurement signal.

[0008] In step (B), the evaluation system determines whether the heart rate is unstable based on the heartbeat interval information.

[0009] In step (C), when it is determined that the heart rate is unstable, the evaluation system determines whether there is respiratory interference based on the heartbeat interval information.

[0010] In this step (D), when it is judged that there is no respiratory disturbance, the evaluation system confirms that the heart rate is unstable.

[0011] The heart rate stability assessment method of the present invention, step (C) comprises the following steps:

[0012] (C-1) obtaining a plurality of feature points using a feature algorithm based on the heartbeat interval information;

[0013] (C-2) obtaining a plurality of feature values ​​based on the feature points; and

[0014] (C-3) Determine whether there is respiratory interference based on the characteristic value.

[0015] In the heart rate stability assessment method of the present invention, in step (C-3), at least one statistical value is calculated based on the characteristic value, and it is determined whether the at least one statistical value meets the preset conditions to determine whether there is respiratory interference. When it is determined that the statistical value meets the preset conditions, it is determined that there is respiratory interference; when it is determined that the at least one statistical value does not meet the preset conditions, it is determined that there is no respiratory interference.

[0016] In the heart rate stability assessment method of the present invention, in step (C-1), the feature algorithm is a peak and trough feature algorithm, the feature points include multiple peak points and multiple trough points, in step (C-2), the feature values ​​include multiple rising values ​​related to the difference from adjacent troughs to peaks, and in step (C-3), a statistical value is calculated based on the feature value, and the statistical value is the average of the rising values.

[0017] In the heart rate stability assessment method of the present invention, in step (C-3), the preset condition is that the statistical value is greater than a first threshold.

[0018] In the heart rate stability assessment method of the present invention, in step (C-1), the feature algorithm is a peak feature algorithm, the feature points include multiple peak points, in step (C-2), the feature values ​​include multiple peak-to-peak values, and in step (C-3), a statistical value is calculated based on the feature values, and the statistical value is the average of the peak-to-peak values ​​divided by the standard deviation of the peak-to-peak values.

[0019] In the heart rate stability assessment method of the present invention, in step (C-3), the preset condition is that the statistical value is less than or equal to a second threshold.

[0020] In the heart rate stability assessment method of the present invention, in step (C-1), the feature algorithm is a peak feature algorithm, the feature points include multiple peak points, in step (C-2), the feature values ​​include multiple peak-to-peak values, and in step (C-3), multiple statistical values ​​are calculated based on the feature values, and the statistical values ​​are the absolute values ​​of the differences between each of the peak-to-peak values ​​and the average of the peak-to-peak values.

[0021] In the heart rate stability assessment method of the present invention, in step (E), the preset condition is that all the statistical values ​​are smaller than a third threshold.

[0022] In the heart rate stability assessment method of the present invention, step (C) comprises the following steps:

[0023] (C-1) performing a time-domain-frequency conversion on the heartbeat interval information to generate heartbeat interval frequency-domain information; and

[0024] (C-2) Determine whether there is respiratory interference based on the heartbeat interval frequency domain information.

[0025] In the heart rate stability assessment method of the present invention, in step (C-2), a feature algorithm is used based on the heartbeat interval frequency domain information to obtain a first main peak, and it is determined whether the first main peak is in a predetermined frequency range to determine whether there is respiratory interference. When it is determined that the first main peak is in the predetermined frequency range, it is determined that there is respiratory interference; when it is determined that the first main peak is not in the predetermined frequency range, it is determined that there is no respiratory interference.

[0026] The heart rate stability assessment method of the present invention further comprises the following steps after step (C):

[0027] (E) When respiratory disturbance is detected, confirm that the heart rate is stable.

[0028] The heart rate stability assessment method of the present invention, in step (E), performs a time-domain-frequency conversion on the heartbeat interval information to generate heartbeat interval frequency domain information, and determines whether there is respiratory interference based on the heartbeat interval frequency domain information. After step (C), the method further comprises the following steps:

[0029] (E) when respiratory interference is determined, filtering out at least one specific frequency of the heartbeat interval frequency domain information that has respiratory interference;

[0030] (F) converting the heartbeat interval frequency domain information into a time domain to generate a processing

[0031] Processed heartbeat interval information;

[0032] (G) determining whether the heart rate is unstable based on the processed heartbeat interval information;

[0033] (H) when the heart rate is determined to be unstable, confirming that the heart rate is unstable; and

[0034] (I) When the heart rate is determined to be stable, confirm that the heart rate is stable.

[0035] The heart rate stability assessment method of the present invention, step (A) comprises the following steps:

[0036] (A-1) obtaining a plurality of original heartbeat intervals according to the measurement signal;

[0037] (A-2) performing an interpolation algorithm on the original heartbeat interval to obtain a plurality of interpolated heartbeat intervals; and

[0038] (A-3) Generate the heartbeat interval information including the original heartbeat interval and the interpolated heartbeat interval.

[0039] The beneficial effect of the present invention is that: when the evaluation system determines that the heart rate is unstable, it further determines whether there is respiratory interference, and when it determines that there is no respiratory interference, it confirms that the heart rate is unstable, thereby improving the accuracy of the evaluation through dual judgment. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Other features and effects of the present invention will be more clearly seen in the following embodiments with reference to the accompanying drawings, in which:

[0041] Figure 1 is a block diagram illustrating an evaluation system for implementing the heart rate stability evaluation method of the present invention;

[0042] Figure 2 is a schematic diagram illustrating a sensing unit and a processing unit of the evaluation system;

[0043] Figure 3 is a flow chart illustrating a first embodiment of a heart rate stability assessment method according to the present invention;

[0044] Figure 4 This is a flow chart to help explain Figure 3 The step 21 comprises the steps of;

[0045] Figure 5 is a schematic diagram illustrating a measurement signal and heartbeat interval information;

[0046] Figure 6 This is a flow chart to help explain Figure 3 A first embodiment of step 23 includes the steps of:

[0047] Figure 7 is a schematic diagram illustrating a first main peak of frequency domain information of a heartbeat interval;

[0048] Figure 8 This is a flow chart to help explain Figure 3 A second embodiment of step 23 includes the steps of:

[0049] Figure 9 is a flow chart illustrating a second embodiment of the heart rate stability evaluation method of the present invention;

[0050] Figure 10 is a schematic diagram illustrating a heartbeat interval information and a breathing signal; and

[0051] Figure 11 It is a schematic diagram illustrating a heartbeat interval information and a processed heartbeat interval information. DETAILED DESCRIPTION

[0052] Before the present invention is described in detail, it should be noted that similar components are denoted by the same reference numerals in the following description.

[0053] See Figure 1 , illustrating an evaluation system 1 for implementing a first embodiment and a second embodiment of the heart rate stability evaluation method of the present invention, the evaluation system 1 includes a sensing unit 11 and a processing unit 12 signal-connected to the sensing unit 11.

[0054] See Figure 2 It is worth noting that in this embodiment, the sensing unit 11 is a wearable device, such as a blood pressure monitor, exercise watch, heart rate monitor, or blood oximeter. The processing unit 12 is a device with greater computing and storage capabilities than the sensing unit 11, capable of handling more complex data analysis and signal processing operations, such as a cloud server, computer, or smart device. The sensing unit 11 is connected to the processing unit 12 via a communication network 100, such as Bluetooth or Wi-Fi. In other embodiments, the assessment system 1 can also be a wearable device with powerful computing and storage capabilities.

[0055] See Figure 1 、 2 3. This first embodiment of the heart rate stability evaluation method of the present invention will now be described with reference to the steps included in this embodiment.

[0056] In step 21 , the processing unit 12 generates heartbeat interval information according to a measurement signal obtained by the sensing unit 11 from measuring a user.

[0057] Matching reference Figure 4 , step 21 includes steps 211 to 213.

[0058] In step 211 , the processing unit 12 obtains a plurality of original heartbeat intervals according to the measurement signal.

[0059] In step 212 , the processing unit 12 performs an interpolation algorithm on the original heartbeat interval to obtain a plurality of interpolated heartbeat intervals.

[0060] In step 213, the processing unit 12 generates the heartbeat interval information including the original heartbeat interval and the interpolated heartbeat interval. Figure 5It is worth noting that in this embodiment, the sphygmomanometer uses an oscillometric method to observe the measurement signal that is gradually amplified and reduced due to the change in cuff pressure during the decompression (or pressurization) process. Each pulse of the measurement signal is synchronized with the heartbeat, so the pulse interval is the original heartbeat interval. After the processing unit 12 performs the interpolation algorithm on the original heartbeat interval, the original heartbeat interval and the interpolated heartbeat interval are combined to generate the heartbeat interval information.

[0061] It is worth noting that in this embodiment, the original heartbeat interval is a single value for each heartbeat interval. However, due to the consistency of the corresponding units of the frequency domain conversion, it is necessary to interpolate the value to the original signal resolution. The interpolation algorithm is, for example, a monotone cubic interpolation algorithm. In other embodiments, the interpolation algorithm can be omitted and the heartbeat interval information can be directly generated using the original heartbeat interval.

[0062] In step 22, the processing unit 12 determines whether the heart rate is unstable based on the heartbeat interval information. If the heart rate is unstable, the process proceeds to step 23; if the heart rate is stable, the process proceeds to step 25.

[0063] It is worth noting that step 22 is to determine whether the heart rhythm is unstable in a conventional manner, such as by using heart rate variability (HRV). How to determine whether the heart rhythm is unstable is not the focus of the present invention and will not be described in detail here.

[0064] It should be noted that since step 22 is to determine the heart rhythm in the existing manner, if the heart rate is determined to be unstable, it may be a false positive result caused by respiratory interference. Therefore, further analysis is required to determine whether it is a false positive result caused by respiratory interference.

[0065] In step 23, the processing unit 12 determines whether there is respiratory interference based on the heartbeat interval information. If it is determined that there is no respiratory interference, the process proceeds to step 24; otherwise, if it is determined that there is respiratory interference, the process proceeds to step 25.

[0066] It should be noted that in this embodiment, there are two implementation methods for determining whether there is breathing interference. A first implementation method is to determine whether there is breathing interference in the frequency domain, and a second implementation method is to determine whether there is breathing interference in the time domain.

[0067] Matching reference Figure 6 In the first embodiment, step 23 includes steps A231 and A232.

[0068] In step A231 , the processing unit 12 performs time-domain-frequency domain conversion on the heartbeat interval information to generate heartbeat interval frequency domain information.

[0069] For example, the heartbeat interval information is converted from the time domain to the frequency domain using Fast Fourier Transform (FFT).

[0070] In step A232 , the processing unit 12 determines whether there is respiratory interference based on the heartbeat interval frequency domain information.

[0071] In detail, the processing unit 12 uses a characteristic algorithm to obtain a first main peak based on the heartbeat interval frequency domain information, and determines whether the first main peak is in a predetermined frequency range to determine whether there is respiratory interference. When it is determined that the first main peak is in the predetermined frequency range, it is determined that there is respiratory interference. When it is determined that the first main peak is not in the predetermined frequency range, it is determined that there is no respiratory interference. The characteristic algorithm is a peak and valley detection algorithm, and the predetermined frequency range is, for example, 0.2 to 0.3 Hz. Figure 7 As shown, the first main peak of the heartbeat interval frequency domain information is in the predetermined frequency range, so it is determined that there is respiratory interference.

[0072] Matching reference Figure 8 In the second embodiment, step 23 includes steps B231 to B233.

[0073] In step B231 , the processing unit 12 obtains a plurality of feature points using a feature algorithm according to the heartbeat interval information.

[0074] The feature points include multiple peak points and multiple trough points. In other implementations, the feature algorithm may also be a peak feature algorithm or a trough feature algorithm, and the feature points only include multiple peak points or only include multiple trough points.

[0075] In step B232 , the processing unit 12 calculates a plurality of feature values ​​according to the feature points.

[0076] It is important to note that in this embodiment, the characteristic value includes a plurality of rise values ​​associated with the difference between adjacent troughs and peaks. In other embodiments, the characteristic value may include a plurality of fall values ​​associated with the difference between adjacent peaks and troughs, or a plurality of peak-to-peak interval values, or a plurality of valley-to-valley interval values.

[0077] In step B233, the processing unit 12 determines whether the heartbeat interval information has respiratory interference according to the characteristic value.

[0078] In detail, the processing unit 12 calculates at least one statistical value based on the characteristic value and determines whether the at least one statistical value meets a preset condition to determine whether the heartbeat interval information has respiratory interference. When it is determined that the statistical value meets the preset condition, it is determined that the heartbeat interval information has respiratory interference; when it is determined that the at least one statistical value does not meet the preset condition, it is determined that the heartbeat interval information does not have respiratory interference.

[0079] It should be noted that, in this embodiment, the processing unit 12 calculates a statistical value based on the characteristic value, where the statistical value is an average of the rising values, and the default condition is that the statistical value is greater than a first threshold.

[0080] In other implementations where the characteristic value includes the peak-to-peak value, the processing unit 12 calculates a statistical value based on the characteristic value, where the statistical value is the average of the peak-to-peak values ​​divided by the standard deviation of the peak-to-peak values, and the preset condition is that the statistical value is less than or equal to a second threshold.

[0081] In other embodiments where the characteristic values ​​include the peak-to-peak values, the processing unit 12 calculates a plurality of statistical values ​​based on the characteristic values, wherein the statistical values ​​are the absolute values ​​of the differences between each of the peak-to-peak values ​​and the average of the peak-to-peak values, and the preset condition is that the statistical values ​​are all less than a third threshold. If the statistical values ​​are all less than the third threshold, it indicates that the heartbeat interval information fluctuates with a substantially fixed period, and respiratory interference is determined to be present.

[0082] It should be noted that the implementation method in which the characteristic value includes the decreasing value corresponds to the implementation method in which the characteristic value includes the increasing decreasing value, and the implementation method in which the characteristic value includes the valley-to-valley value corresponds to the implementation method in which the characteristic value includes the peak-to-peak value, so they are not elaborated here.

[0083] It should be noted that if the second embodiment is used to determine whether there is respiratory interference, the interpolation algorithm may not be performed in step 21 .

[0084] In step 24 , the processing unit 12 determines that the heart rate is unstable.

[0085] In step 25 , the processing unit 12 confirms that the heart rate is stable.

[0086] See Figure 1 、 9The second embodiment of the heart rate stability assessment method of the present invention is similar to the first embodiment. The second embodiment includes steps 31-40, of which steps 31-35 are the same as steps 21-25 of the first embodiment. Step 33 has only one implementation method for determining whether there is respiratory interference, namely, determining whether there is respiratory interference in the frequency domain. The determination method is the same as steps A231 and A232 of the first embodiment. The differences are described below.

[0087] When step 33 determines that there is breathing interference, the process proceeds to step 36 .

[0088] In step 36 , the processing unit 12 filters out a plurality of specific frequencies with respiratory interference from the heartbeat interval frequency domain information.

[0089] It is worth noting that Figure 10 As shown, the heartbeat interval information at a frequency of 0.1 to 0.4 Hz will be affected by the respiratory signal. Therefore, in this embodiment, the heartbeat interval information at a frequency of 0.1 to 0.4 Hz is filtered out. In other implementations, multiple or single frequencies may also be filtered out.

[0090] In step 37 , the processing unit 12 performs frequency domain-time domain conversion on the heartbeat interval frequency domain information to generate processed heartbeat interval information.

[0091] It is important to note that if Figure 11 As shown, after removing the specific frequency with respiratory interference, the processed heartbeat interval information has a smaller interval variation.

[0092] In step 38, the processing unit 12 determines whether the heart rate is unstable based on the processed heartbeat interval information. If the heart rate is unstable, the process proceeds to step 34; if the heart rate is stable, the process proceeds to step 35.

[0093] In this embodiment, when step 33 determines that there is respiratory interference, the processing unit 12 generates the heartbeat interval frequency domain information, and then removes the specific frequency with respiratory interference to obtain the processed heartbeat interval information, and judges again whether the heart rate is unstable based on the processed heartbeat interval information. Compared with the first embodiment, when step 23 determines that there is respiratory interference, it confirms that the heart rate is stable. The judgment result of this embodiment has higher accuracy.

[0094] In summary, the heart rate stability assessment method of the present invention uses the assessment system to further determine whether respiratory interference exists after determining that the heart rate is unstable. When it is determined that there is no respiratory interference, the heart rate is confirmed to be unstable. When it is determined that there is respiratory interference, the heart rate is confirmed to be stable. Alternatively, the specific frequency containing respiratory interference in the heartbeat interval frequency domain information is filtered out, and then the heart rate is determined to be unstable based on the processed heartbeat interval information. In this way, the accuracy of the assessment is improved by double judgment or further filtering out respiratory interference, thereby effectively achieving the purpose of the present invention.

[0095] The above descriptions are merely embodiments of the present invention and should not be used to limit the scope of the present invention. In other words, any simple equivalent changes and modifications made according to the claims and description of the present invention still fall within the scope of the present invention.

Claims

1. A heart rate stability evaluation method, adapted to evaluate a user's heart rate stability based on a measurement signal obtained by an evaluation system, and implemented by the evaluation system, characterized in that: The heart rate stability assessment method comprises the following steps: (A) generating heartbeat interval information according to the measurement signal; (B) determining whether the heart rate is unstable based on the heartbeat interval information; (C) when it is determined that the heart rate is unstable, determining whether there is respiratory interference based on the heartbeat interval information; (D) When it is determined that there is no respiratory disturbance, confirm that the heart rate is unstable.

2. The heart rate stability assessment method according to claim 1, wherein: Step (C) comprises the following steps: (C-1) obtaining a plurality of feature points using a feature algorithm based on the heartbeat interval information; (C-2) obtaining a plurality of feature values ​​based on the feature points; and (C-3) Determine whether there is respiratory interference based on the characteristic value.

3. The heart rate stability assessment method according to claim 2, wherein: In step (C-3), at least one statistical value is calculated based on the characteristic value, and it is determined whether the at least one statistical value meets the preset conditions to determine whether there is respiratory interference. When it is determined that the statistical value meets the preset conditions, it is determined that there is respiratory interference. When it is determined that the at least one statistical value does not meet the preset conditions, it is determined that there is no respiratory interference.

4. The heart rate stability assessment method according to claim 3, wherein: In step (C-1), the feature algorithm is a peak and trough feature algorithm, and the feature points include multiple peak points and multiple trough points. In step (C-2), the feature values ​​include multiple rising values ​​related to the difference from adjacent troughs to peaks. In step (C-3), a statistical value is calculated based on the feature value, and the statistical value is the average of the rising values.

5. The heart rate stability assessment method according to claim 4, wherein: In step (C-3), the preset condition is that the statistical value is greater than a first threshold.

6. The heart rate stability assessment method according to claim 3, wherein: In step (C-1), the feature algorithm is a peak feature algorithm, the feature points include multiple peak points, in step (C-2), the feature values ​​include multiple peak-to-peak values, and in step (C-3), a statistical value is calculated based on the feature values, and the statistical value is the average of the peak-to-peak values ​​divided by the standard deviation of the peak-to-peak values.

7. The heart rate stability assessment method according to claim 6, wherein: In step (C-3), the preset condition is that the statistical value is less than or equal to a second threshold.

8. The heart rate stability assessment method according to claim 3, wherein: In step (C-1), the feature algorithm is a peak feature algorithm, the feature points include multiple peak points, in step (C-2), the feature values ​​include multiple peak-to-peak values, and in step (C-3), multiple statistical values ​​are calculated based on the feature values, and the statistical values ​​are the absolute values ​​of the differences between each of the peak-to-peak values ​​and the average of the peak-to-peak values.

9. The heart rate stability assessment method according to claim 8, wherein: In step (E), the preset condition is that all the statistical values ​​are smaller than a third threshold.

10. The heart rate stability assessment method according to claim 1, wherein: Step (C) comprises the following steps: (C-1) performing a time-domain-frequency conversion on the heartbeat interval information to generate heartbeat interval frequency-domain information; and (C-2) Determine whether there is respiratory interference based on the heartbeat interval frequency domain information.

11. The heart rate stability assessment method according to claim 10, wherein: In step (C-2), based on the heartbeat interval frequency domain information, a feature algorithm is used to obtain the first main peak, and it is determined whether the first main peak is in a predetermined frequency range to determine whether there is respiratory interference. When it is determined that the first main peak is in the predetermined frequency range, it is determined that there is respiratory interference. When it is determined that the first main peak is not in the predetermined frequency range, it is determined that there is no respiratory interference.

12. The heart rate stability assessment method according to claim 1, wherein: After step (C), the method further comprises the following steps: (E) When respiratory disturbance is detected, confirm that the heart rate is stable.

13. The heart rate stability assessment method according to claim 1, wherein: In step (E), the heartbeat interval information is converted from the time domain to the frequency domain to generate heartbeat interval frequency domain information, and whether there is respiratory interference is determined based on the heartbeat interval frequency domain information. After step (C), the method further includes the following steps: (E) when respiratory interference is determined, filtering out at least one specific frequency of the heartbeat interval frequency domain information that has respiratory interference; (F) performing frequency domain-time domain conversion on the heartbeat interval frequency domain information to generate processed heartbeat interval information; (G) determining whether the heart rate is unstable based on the processed heartbeat interval information; (H) when the heart rate is determined to be unstable, confirming that the heart rate is unstable; and (I) When the heart rate is determined to be stable, confirm that the heart rate is stable.

14. The heart rate stability assessment method according to claim 1, wherein: Step (A) comprises the following steps: (A-1) obtaining a plurality of original heartbeat intervals according to the measurement signal; (A-2) performing an interpolation algorithm on the original heartbeat interval to obtain a plurality of interpolated heartbeat intervals; and (A-3) Generate the heartbeat interval information including the original heartbeat interval and the interpolated heartbeat interval.

Images