Autonomic Nervous Function Assessment Method Based on Phase Ordered Signal Averaging Technique

Abstract

This application relates to the field of electrocardiogram signal analysis technology and discloses a method for assessing autonomic nervous function based on phase-ordered signal averaging technology. The method includes: selecting multiple deceleration anchors and / or acceleration anchors from multiple sequence values ​​of the intercardiac interval sequence of a first test subject; for each deceleration anchor / acceleration anchor, selecting a corresponding deceleration sequence segment / acceleration sequence segment from the intercardiac interval sequence of the first test subject; averaging the values ​​with the same order among the multiple deceleration sequence segments / acceleration sequence segments to obtain a deceleration signal sequence / acceleration signal sequence of the first test subject; performing power spectral density analysis on the deceleration signal sequence / acceleration signal sequence of the first test subject to obtain a deceleration index / acceleration index of the first test subject; and assessing the autonomic nervous function of the first test subject based on the deceleration index and / or acceleration index. This application can more accurately assess autonomic nervous function.

Description

Technical Field

[0001] This application relates to the field of electrocardiogram signal analysis technology, and in particular to a method for assessing autonomic nervous function based on phase-ordered signal averaging technology. Background Technology

[0002] In the field of electrocardiogram (ECG) signal analysis, autonomic nervous system function assessment is an important research and clinical tool. This field focuses on quantitatively reflecting the regulatory status of the sympathetic and vagus nerves on the heart by analyzing changes in the time intervals between consecutive heartbeats.

[0003] Existing power spectral density (PSD) analysis methods estimate the power spectrum directly from the intercardiac interval sequence mathematically. This usually yields specific frequency bands with physiological significance, such as low and high frequencies, and calculates the sum of power within each frequency band to infer the corresponding autonomic nervous activity.

[0004] However, since the original intercardiac interval sequence is not an ideal stationary signal, it often contains non-stationary trends and non-physiological noise. PSD analysis directly performs global power spectral decomposition on the original sequence, and these non-stationary trends and non-physiological noise are indistinguishably mixed with the rhythmic oscillations truly generated by autonomic nervous system regulation, contributing to the power spectral energy. This leads to severe contamination of the calculated low-frequency or high-frequency power values, potentially resulting in biased or even completely erroneous physiological conclusions, greatly limiting its reliability and accuracy in complex real-world scenarios. Summary of the Invention

[0005] In view of the above-mentioned defects or deficiencies in the existing technology, it is desirable to provide an autonomic nervous function assessment method based on phase-ordered signal averaging technology. This method can first align and average the segments around acceleration or deceleration anchor points in the cardiac interbeat sequence to obtain deceleration signal sequences and acceleration signal sequences, thereby suppressing random interference that is out of sync with anchor point events and prioritizing the physiological rhythms closely related to autonomic nervous regulation. Furthermore, by using the deceleration and acceleration indices obtained from the deceleration and acceleration signal sequences, a more accurate assessment of cardiac autonomic nervous function can be achieved.

[0006] In a first aspect, this application provides a method for assessing autonomic nervous function based on phase-ordered signal averaging technology, the method comprising:

[0007] Determine the intercardia sequence of the first subject within a preset time period;

[0008] Multiple deceleration anchors and / or acceleration anchors are selected from multiple sequence values ​​of the intercardia sequence of the first subject to be tested. The deceleration anchors / acceleration anchors are the current sequence values ​​that are greater than / less than the previous sequence value.

[0009] For each deceleration anchor point / acceleration anchor point, a preset number of sequence values ​​located before and after the deceleration anchor point / acceleration anchor point are selected from the intercardia sequence of the first subject to be tested, so as to obtain the deceleration sequence segment / acceleration sequence segment corresponding to each deceleration anchor point / acceleration anchor point.

[0010] Multiple deceleration sequence segments / acceleration sequence segments are aligned, and after alignment, the values ​​with the same order in the multiple deceleration sequence segments / acceleration sequence segments are averaged to obtain the deceleration signal sequence / acceleration signal sequence of the first test object;

[0011] Power spectral density analysis is performed on the deceleration signal sequence / acceleration signal sequence of the first test object to obtain the deceleration index / acceleration index of the first test object;

[0012] The autonomic nervous function of the first test subject is assessed based on the deceleration index and / or acceleration index of the first test subject.

[0013] In conjunction with the first aspect, in one possible implementation, the autonomic nervous function of the first test subject is evaluated based on the deceleration and / or acceleration indices of the first test subject, specifically as follows:

[0014] The first power spectrum is determined based on the deceleration index of the first test object, and the second power spectrum is determined based on the acceleration index of the first test object.

[0015] The first power spectrum is compared with a preset first standard power spectrum to obtain a first comparison result, and the second power spectrum is compared with a preset second standard power spectrum to obtain a second comparison result.

[0016] The autonomic nervous function of the first subject is assessed based on the first comparison results and / or the second comparison results.

[0017] In conjunction with the first aspect, in one possible implementation, prior to assessing the autonomic nervous function of the first test subject based on its deceleration and / or acceleration indices, the method further includes:

[0018] Multiple subjects with known autonomic nervous system functional states were divided into a healthy group and an abnormal group, and the cardiac interval sequence of each subject was obtained.

[0019] For each second subject in the healthy group and the abnormal group, the corresponding deceleration and acceleration indicators are determined based on the heartbeat interval sequence of the second subject.

[0020] The deceleration index and acceleration index are designated as different types of primary target indicators. For each type of primary target indicator, the effectiveness of the primary target indicator of the first test subject is verified based on multiple primary target indicators of the healthy group and the abnormal group.

[0021] In conjunction with the first aspect, in one possible implementation, for each type of first target indicator, the validity of the first target indicator of the first test subject is verified based on multiple first target indicators of the healthy group and the abnormal group, specifically as follows:

[0022] For each type of primary target indicator, determine the median of multiple primary target indicators for the healthy group and the median of multiple primary target indicators for the abnormal group;

[0023] For each type of primary target indicator, the median of the healthy group and the median of the abnormal group are compared to obtain a third comparison result. The effectiveness of the primary target indicator of the first test subject is verified based on the third comparison result.

[0024] In conjunction with the first aspect, in one possible implementation, for each type of first target indicator, the validity of the first target indicator of the first test subject is verified based on multiple first target indicators of the healthy group and the abnormal group, specifically as follows:

[0025] For each type of primary target indicator, the area under the primary target characteristic curve of the primary receiver operating characteristic curve is determined based on multiple primary target indicators of the healthy group and multiple primary target indicators of the abnormal group.

[0026] The effectiveness of the first target index of the corresponding type of the first test object is verified based on the area under the first curve.

[0027] In conjunction with the first aspect, in one possible implementation, the effectiveness of the first target indicator corresponding to the type of the first test object is verified based on the area under the first curve, specifically as follows:

[0028] The second target index corresponding to each cardiac interval sequence in the healthy group was determined using power spectral density analysis, and the second target index corresponding to each cardiac interval sequence in the abnormal group was determined using power spectral density analysis.

[0029] The area under the second receiver operating characteristic curve was determined based on multiple secondary target indicators for the healthy group and multiple secondary target indicators for the abnormal group.

[0030] For each type of first target indicator, the first area under the curve and the second area under the curve of that type are compared to obtain a fourth comparison result, and the effectiveness of the first target indicator of that type is verified based on the fourth comparison result.

[0031] In conjunction with the first aspect, in one possible implementation, the intercardiac interval sequence of the first test subject within a preset time period is determined, specifically as follows:

[0032] The electrocardiogram (ECG) signal of the first subject to be tested was acquired during a preset time period, and the ECG signal was sequentially denoised, baseline drift removed, and QRS wave detected to obtain the heartbeat interval sequence of the first subject to be tested.

[0033] In conjunction with the first aspect, in one possible implementation, after obtaining the intercardiac interval sequence of the first test subject, the method further includes:

[0034] The ectopic heartbeat detection and ectopic heartbeat replacement processes were performed sequentially on the heartbeat interval sequence of the first subject to be tested.

[0035] In conjunction with the first aspect, in one possible implementation, before performing power spectral density analysis on the deceleration / acceleration signal sequence of the first test object, the following is also included:

[0036] Detrending processing is performed on the deceleration / acceleration signal sequence of the first test object.

[0037] In conjunction with the first aspect, in one possible implementation, power spectral density analysis is performed on the deceleration signal sequence / acceleration signal sequence of the first test object to obtain the deceleration index / acceleration index of the first test object, specifically as follows:

[0038] The high-frequency band and low-frequency band are divided, and the power spectral density of the deceleration signal sequence / acceleration signal sequence of the first test object is analyzed according to the autoregressive model method to obtain the deceleration index / acceleration index of the first test object.

[0039] In a second aspect, this application also provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the method of any one of the first aspects described above.

[0040] Thirdly, this application also provides a computer program product containing instructions that, when executed, perform any of the methods described in the first aspect above.

[0041] Fourthly, this application also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, it implements the method of any one of the first aspects above.

[0042] Compared with the prior art, this application has the following beneficial effects:

[0043] This application first aligns and averages segments around acceleration or deceleration anchor points in the cardiac interbeat sequence to obtain deceleration and acceleration signal sequences, thereby suppressing random interference that is out of sync with anchor point events and prioritizing physiological rhythms closely related to autonomic nervous regulation; furthermore, by using deceleration and acceleration indices obtained from the deceleration and acceleration signal sequences, a more accurate assessment of cardiac autonomic nervous function is achieved. Attached Figure Description

[0044] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0045] Figure 1 This is a flowchart of the method described in this application;

[0046] Figure 2 This is another flowchart illustrating the method of this application in one embodiment;

[0047] Figure 3 In one embodiment, the electrocardiogram signal of a person with healthy autonomic nervous system function during a preset time period;

[0048] Figure 4 In one embodiment, the electrocardiogram signal of the first subject to be tested during a preset time period;

[0049] Figure 5 In one embodiment, Figure 3 The first standard power spectrum corresponding to the deceleration index of a healthy person is shown.

[0050] Figure 6 In one embodiment, Figure 3 The second standard power spectrum corresponding to the acceleration index of a healthy person is shown.

[0051] Figure 7 In one embodiment, Figure 4 The first power spectrum corresponding to the deceleration index of the first test object is shown.

[0052] Figure 8 In one embodiment, Figure 4 The second power spectrum corresponding to the acceleration index of the first test object is shown.

[0053] Figure 9 for Figure 3 In the middle, the power spectrum of a person with healthy autonomic nervous system function;

[0054] Figure 10 for Figure 4 In the image, the power spectrum of the first object under test;

[0055] Figure 11As one embodiment, a comparison chart of the median of multiple DC_LFs in the healthy group and the median of multiple DC_LFs in the abnormal group;

[0056] Figure 12 A comparison chart of the median of multiple DC_HF values ​​in the healthy group and the median of multiple DC_HF values ​​in the abnormal group;

[0057] Figure 13 A comparison chart of the median DC_LF / HF of multiple DC_LF / HF values ​​in the healthy group and the median DC_LF / HF values ​​in multiple DC_LF / HF values ​​in the abnormal group;

[0058] Figure 14 As one embodiment, a comparison chart of the median of multiple AC_LFs in the healthy group and the median of multiple AC_LFs in the abnormal group;

[0059] Figure 15 As one embodiment, a comparison chart of the median of multiple AC_HFs in the healthy group and the median of multiple AC_HFs in the abnormal group;

[0060] Figure 16 As one embodiment, a comparison chart of the median AC_LF / HF of multiple AC_LF / HF values ​​in the healthy group and the median AC_LF / HF values ​​in multiple AC_LF / HF values ​​in the abnormal group;

[0061] Figure 17 As one embodiment, a comparison chart of receiver operating characteristic (ROC) curves for deceleration index DC_LF, acceleration index AC_LF, and second target index LF;

[0062] Figure 18 As one embodiment, a comparison chart of ROC curves of deceleration index DC_HF, acceleration index AC_HF and second target index HF;

[0063] Figure 19 In one embodiment, a comparison chart of ROC curves for deceleration index DC_LF / HF, acceleration index AC_LF / HF, and the second target index LF / HF is shown. Detailed Implementation

[0064] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0065] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The present application will now be described in detail with reference to the accompanying drawings and embodiments. Furthermore, the term "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. The terms "first" and "second," etc., in the specification and claims of the embodiments of this application are used to distinguish different objects, not to describe a specific order of objects.

[0066] The following is an explanation of some of the terms used in this application:

[0067] Electrocardiogram (ECG) signal: A waveform curve showing the changes in cardiac electrical activity over time, recorded by electrodes at specific sites on the body surface. In clinical practice and research, ECG signals are the core basis for diagnosing cardiac diseases such as arrhythmias, myocardial ischemia, and electrolyte imbalances. They are also the primary data source for non-invasive assessments of autonomic nervous system function (such as heart rate variability analysis).

[0068] Heart rate interval sequence: Also known as the RR interval sequence, this sequence is a one-dimensional time series formed by detecting and locating the R-wave peak of each heartbeat in continuously recorded electrocardiogram (ECG) signals, and then calculating the time interval between two adjacent heartbeats, arranged in chronological order. This sequence is essentially a non-uniformly sampled time series; its minute fluctuations directly reflect changes in heart rate, making it the most fundamental and direct raw data for analyzing heart rate variability.

[0069] ROC curve: A graphical tool used to evaluate the discriminative ability of a testing method or diagnostic indicator. Its core idea is to depict the dynamic relationship between the probability of correctly identifying a positive result and the probability of incorrectly classifying a negative result as positive under different judgment criteria. Simply put, it shows how the outcomes of "identifying the real patient" and "falsely identifying healthy individuals" inversely increase as diagnostic criteria continuously change from strict to lenient. The area under the curve (AUC) is a key quantitative indicator; a larger area (maximum 1) indicates higher overall accuracy of the diagnostic method in distinguishing between "disease-positive" and "disease-free" individuals; if the area is only 0.5, it means that the method's judgment ability is no different from random guessing.

[0070] Power spectrum density (PSD) analysis is a non-invasive analytical technique for assessing autonomic nervous function by quantifying the energy distribution of RR interval sequences across different frequency bands. This technique typically sets the low-frequency band to 0.04 Hz–0.15 Hz and the high-frequency band to 0.15 Hz–0.4 Hz, using power spectrum estimation methods to derive the low-frequency power, high-frequency power, and their ratio LF / HF.

[0071] Low-frequency power (LF): Traditionally known as the "baroreflex zone," it primarily reflects baroreflex activity. It is currently widely believed that LF is influenced by both the sympathetic and vagus nerves (parasympathetic nerves), and is a product of their combined action. It should more accurately be considered an indicator of sympathetic modulation rather than a measure of its absolute activity.

[0072] High-frequency power (HF): Highly synchronized with the respiratory cycle, hence called the "respiratory zone," it arises from the periodic changes in heart rate caused by respiratory activity (respiratory sinus arrhythmia). HF is a very pure and effective indicator of vagal tone. During respiration, the efferent activity of the vagus nerve undergoes periodic changes, causing the heart rate to accelerate and decelerate accordingly. A high HF indicates strong inhibitory regulation of the heart by the vagus nerve, good heart rate flexibility, and is generally associated with good health and recovery ability.

[0073] The low-frequency to high-frequency power ratio (LF / HF) aims to quantify the sympathetic-vagus nerve balance, providing quantitative information on their relative activity. Numerous studies have confirmed its significant correlation with cardiovascular risk and stress levels, demonstrating clear clinical and research value. An elevated LF / HF ratio is generally interpreted as a state of sympathetic dominance or vagus nerve insufficiency. This is commonly seen in certain autonomic neuropathy conditions, physiological or psychological stress, tension, and anxiety. A decreased LF / HF ratio is generally interpreted as a state of vagus nerve dominance, commonly seen during rest, relaxation, and sleep. It is important to note that because LF power is not a purely sympathetic indicator, the LF / HF ratio should not be simply interpreted as an absolute balance between the sympathetic and vagus nerves. Instead, it should be considered a relative indicator reflecting the overall "regulatory pattern" bias of the autonomic nervous system (whether it leans towards excitation or inhibition).

[0074] The following explains the application process of PSD analysis in autonomic nervous system function assessment:

[0075] The acquired ECG signals were preprocessed, including denoising and baseline drift removal; then the QRS complex was detected and the RR interval sequence was extracted.

[0076] Ectopic heartbeats were detected, replaced, and detrended in the RR interval sequence; power spectral density was calculated using power spectral estimation methods such as autoregressive models.

[0077] The obtained power spectrum is divided into a low-frequency band of 0.04Hz to 0.15Hz and a high-frequency band of 0.15Hz to 0.4Hz, and the low-frequency power, high-frequency power, and the ratio of low-frequency power to high-frequency power are calculated.

[0078] By repeating the above steps on healthy individuals and diseased individuals respectively and statistically analyzing the significant differences in the average levels of indicators in each group, we can infer the state of autonomic nervous function under disease conditions.

[0079] However, the PSD analysis method has the following drawbacks:

[0080] (1) The physiological significance of low-frequency power, a core indicator, is controversial. It is not a purely sympathetic nerve activity indicator. It is simultaneously modulated by the sympathetic nerve and the vagus nerve, and is also affected by a variety of non-neural factors, which makes the interpretation of its physiological meaning often unclear.

[0081] (2) The high-frequency power, which is a key indicator of vagal tone, is easily affected by changes in individual respiratory rate and depth, which impairs its stability and reliability.

[0082] (3) The ratio of low-frequency power to high-frequency power (LF / HF) is easily affected by specific analysis conditions such as power spectrum estimation method and recording time. When the recording time is short or there are confounding factors such as arrhythmia and irregular breathing, the explanatory power of this ratio will be significantly weakened. Furthermore, in complex situations such as "heart rate fragmentation", the analysis of LF / HF may even produce paradoxical erroneous results, that is, misjudging pathological autonomic dysfunction as a healthy regulatory state.

[0083] (4) PSD analysis is a linear analysis method that cannot capture and extract the nonlinear dynamic characteristics contained in the electrocardiogram signal. This fundamentally limits the ability to more comprehensively and deeply characterize the complex nonlinear system of the heart's autonomic nervous system.

[0084] Based on this, this application provides an autonomic nervous system function assessment method based on phase-ordered signal averaging technology. This method can effectively suppress non-stationary trends and noise interference in RR interval sequences, enhance specific physiologically relevant oscillation patterns, and overcome the assessment paradox of traditional methods under pathological conditions, significantly improving the accuracy and reliability of autonomic nervous system function assessment. In autonomic nervous system function assessment and monitoring, it can provide clinicians with purer and more physiologically meaningful indicators. It is easy to operate, requires no additional hardware costs, and has good clinical application value and social benefits.

[0085] like Figure 1 As shown, this application provides a method for assessing autonomic neural function based on phase-ordered signal averaging technology, the method comprising:

[0086] S1. Determine the intercardia sequence of the first subject to be tested within a preset time period;

[0087] In one possible implementation, such as Figure 2 As shown, S1 is specifically:

[0088] The electrocardiogram (ECG) signal of the first subject to be tested was acquired during a preset time period, and the ECG signal was sequentially denoised, baseline drift removed, and QRS wave detected to obtain the heartbeat interval sequence of the first subject to be tested.

[0089] In one possible implementation, after QRS wave detection, such as Figure 2 As shown, it also includes:

[0090] The ectopic heartbeat detection and ectopic heartbeat replacement processes were performed sequentially on the heartbeat interval sequence of the first subject to be tested.

[0091] S2. Select multiple deceleration anchors and / or acceleration anchors from multiple sequence values ​​of the intercardia sequence of the first subject to be tested. The deceleration anchors / acceleration anchors are the current sequence values ​​that are greater than / less than the previous sequence value.

[0092] Specifically, such as Figure 2 As shown, each sequence value in the heartbeat interval sequence represents the interval between two adjacent heartbeats. If the current heartbeat interval is... RR i Compared to the previous heartbeat interval RR i-1 Longer, that is RR i > RR i-1 ,but RR i This serves as a deceleration anchor point. If RR i < RR i-1 ,but RR i This is an acceleration anchor point. Traverse the entire RR interval sequence to obtain all acceleration and deceleration anchor points.

[0093] S3. For each deceleration anchor point / acceleration anchor point, select a preset number of sequence values ​​located before and after the deceleration anchor point / acceleration anchor point from the intercardia sequence of the first test subject to obtain the deceleration sequence segment / acceleration sequence segment corresponding to each deceleration anchor point / acceleration anchor point.

[0094] Specifically, taking deceleration anchors as an example, let's explain S3. Centered on each deceleration anchor, we obtain the length of each deceleration anchor from the sequence values ​​before and after that anchor in the RR interval sequence. L The deceleration sequence segments. It is worth mentioning that in each deceleration sequence segment, the number of sequence values ​​before and after the corresponding deceleration anchor point differs by 1; in other embodiments, when the length of the deceleration sequence segment is odd, the number of sequence values ​​before and after the deceleration anchor point is the same.

[0095] It is worth noting that for deceleration anchors near the start or end of the RR interval sequence, if a complete length of 2 cannot be obtained around them... L The deceleration sequence segments are discarded.

[0096] The principle for determining multiple acceleration sequence segments is the same as that for determining multiple deceleration sequence segments, and will not be elaborated upon here.

[0097] S4. Align multiple deceleration sequence segments / acceleration sequence segments, and after alignment, average the values ​​with the same order in the multiple deceleration sequence segments / acceleration sequence segments to obtain the deceleration signal sequence / acceleration signal sequence of the first test object.

[0098] Taking the deceleration sequence segment corresponding to the deceleration anchor point as an example, the specific process of S4 is explained. Specifically, with the deceleration anchor point as the center, all deceleration sequence segments are aligned to obtain multiple aligned sequence segments. The average value of the sequence values ​​at the corresponding positions of all aligned sequence segments is calculated to obtain a deceleration signal sequence DC_PRSA.

[0099] In the same way, an acceleration signal sequence AC_PRSA is obtained.

[0100] Phase-rectified signal averaging (PRSA) is used to identify subtle, short-term repetitive patterns to characterize complex, nonlinear, nonstationary, and quasi-periodic signals. In this invention, PRSA acts as a pre-processing "biofilter," not simply smoothing data but selectively amplifying physiological information relevant to autonomic nervous function while suppressing irrelevant noise and interference. To avoid redundancy, both accelerated and decelerated signal sequences are collectively referred to as PRSA signal sequences. PRSA signal sequences are quasi-stationary time series with extremely high signal-to-noise ratios. Subsequent PSD analysis of PRSA signal sequences yields purer and more reliable indicators, resulting in more accurate assessments of autonomic nervous function.

[0101] The PRSA signal sequence extracted in this application exhibits excellent noise and non-stationarity resistance. Specifically, the original RR interval sequence is highly non-stationary (e.g., trend, drift, heteroscedasticity) and contains a large amount of non-physiological noise (e.g., ectopic pulsations, signal loss, measurement errors). These factors can lead to erroneous low-frequency or high-frequency power estimates in PSD analysis, making the assessment results of autonomic nervous function difficult to interpret or even misleading. In contrast, the PRSA technique effectively suppresses random noise that is out of sync with anchor events and eliminates the influence of non-stationary trends through "phase rectification" (aligning all acceleration or deceleration points) and "averaging" processes.

[0102] Furthermore, PRSA signal sequences enhance specific physiologically relevant oscillation patterns. Specifically, traditional PSD analysis displays the sum of all oscillatory components in the signal but cannot distinguish which oscillations are physiologically significant and regulated by the autonomic nervous system, and which are random or meaningless fluctuations. PRSA technology, by selecting deceleration or acceleration anchors, produces DC_PRSA and AC_PRSA signal sequences that preferentially amplify and display physiological oscillations closely related to autonomic regulation (especially low-frequency oscillations mediated by the baroreflex), while downplaying fluctuations unrelated to autonomic control, thus making the functional assessment of the autonomic nervous system more accurate.

[0103] S5. Perform power spectral density analysis on the deceleration signal sequence / acceleration signal sequence of the first test object to obtain the deceleration index / acceleration index of the first test object;

[0104] In one possible implementation, prior to S5, it also includes:

[0105] Detrending processing is performed on the deceleration signal sequence and acceleration signal sequence of the first test object.

[0106] In one possible implementation, S5 is specifically:

[0107] The high-frequency band and low-frequency band are divided, and the power spectral density of the deceleration signal sequence / acceleration signal sequence of the first test object is analyzed according to the autoregressive model method to obtain the deceleration index / acceleration index of the first test object.

[0108] It is worth mentioning that the indicators obtained from power spectral density analysis include at least one of low-frequency power (LF), high-frequency power (HF), and low-frequency to high-frequency power ratio (LF / HF).

[0109] In one possible implementation, the deceleration index obtained by this application includes: the low-frequency power (DC_LF), the high-frequency power HF (DC_HF) corresponding to the deceleration signal sequence DC_PRSA, and the low-frequency power ratio LF / HF (DC_LF / HF).

[0110] The acceleration metrics include: the low-frequency power LF (AC_LF), the high-frequency power HF (AC_HF), and the low-to-high-frequency power LF / HF (AC_LF / HF) corresponding to the accelerated signal sequence AC_PRSA.

[0111] It is worth noting that in this application, the subsequent processing and analysis steps are the same for any specific indicator under both the deceleration and acceleration indicators. For the sake of simplicity and ease of understanding, the terms "deceleration indicator" and "acceleration indicator" will be used interchangeably in the following description to represent any specific indicator under their respective categories, and the relevant steps will be described.

[0112] S6. Evaluate the autonomic nervous function of the first test subject based on the deceleration index and / or acceleration index of the first test subject.

[0113] Compared to existing technologies, the acceleration and deceleration indices of this application provide a more direct and interference-resistant physiological interpretation of autonomic nervous function. Specifically, as mentioned above, the origin of LF in traditional PSD analysis has always been a focus of debate, while the periodic fluctuations characterized by the deceleration signal sequence in this application mainly reflect changes in vasoconstriction and vasodilation regulated by sympathetic nervous system activity and baroreflex mechanisms. Since this fluctuation is mainly reflected as LF power in traditional heart rate variability power spectrum analysis, the deceleration indices obtained based on the method of this application can provide a clearer and purer physiological explanation for the traditionally ambiguous "low-frequency components." At the same time, HF power in traditional heart rate variability analysis is easily affected by respiratory rhythm. However, in the process of signal alignment and averaging, the random fluctuations caused by respiration are significantly weakened because there is no fixed time correspondence between respiratory actions and heart rate acceleration / deceleration events. This allows the analysis results to effectively reduce the influence of "respiratory sinus arrhythmia" on high-frequency components, thus allowing for a greater focus on extracting and evaluating low-frequency autonomic nervous regulation functions with clear physiological significance related to baroreflexes.

[0114] Furthermore, this application aligns and averages the cardiac interbeat sequence based on anchor points of regular deceleration or acceleration of the heartbeat. This effectively filters out truly physiologically rhythmic oscillation signals controlled by the autonomic nervous system, while filtering out non-periodic, chaotic fluctuations. Therefore, this application can distinguish between truly healthy, vagus nerve-mediated coordinated oscillations and pathological "pseudo-complex" fluctuations, thereby avoiding the aforementioned problems and achieving a more realistic and reliable assessment of autonomic nervous function.

[0115] In one possible implementation, S6 is specifically:

[0116] S61. Determine the first power spectrum based on the deceleration index of the first test object, and determine the second power spectrum based on the acceleration index of the first test object;

[0117] S62. Compare the first power spectrum with the preset first standard power spectrum to obtain the first comparison result, and compare the second power spectrum with the preset second standard power spectrum to obtain the second comparison result;

[0118] Specifically, the power spectrum of a person with healthy autonomic nervous system function is used as the standard power spectrum. In the power spectrum, the horizontal axis represents frequency, with 0.04Hz to 0.15Hz divided into the low-frequency band and 0.15Hz to 0.4Hz divided into the high-frequency band. The vertical axis represents the power at each frequency. Figure 5 and Figure 6 The figures shown are the first standard power spectrum corresponding to the deceleration index and the second standard power spectrum corresponding to the acceleration index of a person with healthy autonomic nervous system function.

[0119] like Figure 7 and Figure 8 The figures shown are the first power spectrum corresponding to the deceleration index of the first test object and the second power spectrum corresponding to the acceleration index, respectively.

[0120] like Figure 3 As shown, Figure 5 , Figure 6 The image shows the electrocardiogram (ECG) signal of a person within a preset time period; such as... Figure 4 As shown, Figure 7 , Figure 8 The image shows the electrocardiogram (ECG) signal of a person over a preset time period.

[0121] Will Figure 5 and Figure 7 A comparison reveals that the power distribution of the first power spectrum of the first test object in the low-frequency range (0.04 Hz~0.15 Hz) exhibits a flattened peak and a discrete distribution compared to the first standard power spectrum. Simultaneously, the power peak value in the high-frequency range (0.15 Hz~0.4 Hz) also significantly deviates from the healthy standard, showing an overall increase in power and a discrete distribution. Figure 6 and Figure 8 A comparison reveals similar conclusions. This indicates that the low-frequency oscillatory activity characterized by the deceleration and acceleration indices determined in this application, which is related to baroreflex and vasomotor regulation, is abnormal, and the high-frequency regulatory components that may be coupled with respiratory rhythm have also been altered.

[0122] In this application, both comparison results showed significant differences from the health standard in both the low-frequency and high-frequency ranges. Therefore, it can be comprehensively judged that the autonomic nervous function assessment of the first subject was abnormal. Further diagnosis confirmed this. Figure 4The second subject shown is a patient with end-stage renal disease.

[0123] S63. Evaluate the autonomic nervous function of the first subject based on the first comparison results and / or the second comparison results.

[0124] It is worth noting that the first comparative result reflects the functional state dominated by the parasympathetic and baroreflex pathways; the second comparative result reflects the functional activity modulated by the sympathetic nervous system. Both can be used independently as the basis for assessing the autonomic nervous function of the first subject. When the two results are consistent, the reliability of the assessment conclusion is enhanced; when the two show abnormalities with different characteristics, they can provide a basis for further differentiation of specific autonomic nervous dysfunction patterns (such as sympathetic hyperexcitability, vagal insufficiency, or mismatch between the two).

[0125] In one possible implementation, after S6, this application also utilizes existing PSD analysis methods to... Figure 5 , Figure 6 The individuals shown have healthy autonomic nervous system function (hereinafter referred to as healthy individuals) and Figure 7 , Figure 8 An analysis was conducted on individuals with autonomic nervous system dysfunction (hereinafter referred to as abnormal individuals). Figure 9 The power spectrum of a healthy person. Figure 10 The power spectrum of an abnormal individual. Figure 9 respectively with Figure 5 , Figure 6 In comparison, it can be observed that the spectra obtained using the PSD analysis method show flattened peaks in both the low and high frequency ranges, especially in the high frequency range where the peaks are low and flat. In contrast, the spectra obtained using the method of this application show sharp peaks in both the low and high frequency ranges, with a frequency shift between the low and high frequencies, and the power is more concentrated near the low and high frequency peak frequencies. Figure 10 respectively with Figure 7 , Figure 8 A similar conclusion can be found through comparison. This shows that, in both healthy and diseased individuals, this application, by using the PRSA method to perform phase ordering and averaging of electrocardiogram signals based on physiological events, effectively filters out pathological disordered fluctuations, thereby more realistically revealing the state of impaired autonomic nervous coordination function and avoiding potential misinterpretations that may result from the traditional PSD method.

[0126] In one possible implementation, prior to S6, it also includes:

[0127] Multiple subjects with known autonomic nervous system functional states were divided into a healthy group and an abnormal group, and the cardiac interval sequence of each subject was obtained.

[0128] Specifically, the experimental data for the second group of subjects came from the THEW database (Telemetric and Holter ECG Warehouse), which contains 24-hour Holter electrocardiogram (ECG) signals from various populations. Two sub-databases were selected: one was the Normal sub-database (E-HOL-03-0202-003), which included ECG data from 202 healthy individuals; the other was the ESRD sub-database (E-HOL-12-0051-016), which included 51 patients with end-stage renal disease (ESRD) undergoing hemodialysis treatment. These patients also had hypertension or diabetes and were considered a high-risk group for autonomic dysfunction.

[0129] For each second subject in the healthy group and the abnormal group, the corresponding deceleration and acceleration indicators are determined based on the heartbeat interval sequence of the second subject.

[0130] Specifically, the Normal sub-database was selected as the healthy group, and the ESRD sub-database represented the abnormal group of autonomic neuropathy. After removing records with incomplete diurnal data, 191 records from the healthy group and 51 records from the abnormal group were selected. A 2-hour cardiac interval sequence was extracted from each record during the daytime (8:00 AM to 5:00 PM) without exercise or napping. All electrocardiogram (ECG) signals were selected from the same time period to reduce the confounding effects of diurnal rhythms and physical activity. For ease of description, the autonomic nervous function assessment method disclosed in this application is referred to as the PRSA-PSD analysis method. Based on the PRSA-PSD analysis method in S1-S5, acceleration and deceleration indices were calculated for each second subject in the 2-hour ECG recordings.

[0131] The deceleration index and acceleration index are designated as different types of primary target indicators. For each type of primary target indicator, the effectiveness of the primary target indicator of the first test subject is verified based on multiple primary target indicators of the healthy group and the abnormal group.

[0132] In other embodiments, the deceleration index and acceleration index may also be collectively referred to as PRSA-

[0133] PSD metrics.

[0134] Since the validity verification principles of acceleration and deceleration indicators are the same, this application refers to both as the first target indicator and does not describe their respective verification processes separately.

[0135] In one possible implementation, for each type of first target indicator, the validity of the first target indicator of the first test subject is verified based on multiple first target indicators of the healthy group and the abnormal group, specifically as follows:

[0136] For each type of primary target indicator, determine the median of multiple primary target indicators for the healthy group and the median of multiple primary target indicators for the abnormal group;

[0137] For each type of first target indicator, the median of the healthy group and the median of the abnormal group are compared to obtain the fourth comparison result. The effectiveness of the fourth target indicator of the first test subject is verified based on the first comparison result.

[0138] like Figures 11 to 16 As shown, this application uses the median to represent the overall level of the healthy group and the abnormal group on each type of first target indicator, and systematically conducts inter-group comparisons. Both the deceleration and acceleration indicators are further subdivided into three identical sub-indicator types: low-frequency power, high-frequency power, and the ratio of low-frequency to high-frequency power. Therefore, this application compares the medians of a total of six sub-indicators:

[0139] Specifically, regarding the deceleration indicator among the two categories of primary target indicators, Figure 11 A comparison chart of the median DC_LF of multiple DC_LF values ​​in the healthy group and the median DC_LF values ​​in multiple DC_LF values ​​in the abnormal group. Figure 12 A comparison chart of the median DC_HF of multiple DC_HF values ​​in the healthy group and the median DC_HF values ​​in multiple DC_HF values ​​in the abnormal group. Figure 13 A comparison chart of the median DC_LF / HF of multiple DC_LF / HF values ​​in the healthy group and the median DC_LF / HF values ​​in multiple DC_LF / HF values ​​in the abnormal group;

[0140] Regarding the acceleration indicators in the two categories of primary target indicators Figure 14 A comparison chart of the medians of multiple AC_LF values ​​in the healthy group and the abnormal group. Figure 15 A comparison chart of the medians of multiple AC_HF values ​​in the healthy group and the abnormal group. Figure 16 A comparison chart of the median AC_LF / HF values ​​for multiple AC_LF / HF values ​​in the healthy group and the median AC_LF / HF values ​​for multiple AC_LF / HF values ​​in the abnormal group.

[0141] Figures 11-16Statistical results showed that each index (LF, HF, and LF / HF) under both deceleration and acceleration indices differed significantly between the two groups (all P values ​​were 0.00). Compared with the healthy group, the abnormal group showed significantly decreased DC_LF (0.44 vs. 0.85), DC_LF / HF (0.78 vs. 5.64), AC_LF (0.39 vs. 0.85), and AC_LF / HF (0.63 vs. 5.60), and significantly increased DC_HF (0.56 vs. 0.15) and AC_HF (0.61 vs. 0.15). These results demonstrate that autonomic dysfunction leads to abnormal changes in PRSA-PSD indices, reflecting the potential of PRSA-PSD in detecting autonomic neuropathy.

[0142] In other embodiments, this application further employs a statistical verification method based on intra-group dispersion: for each type of first target indicator to be evaluated, the interquartile range of the indicator values ​​in the healthy control group and the abnormal group is calculated respectively; by comparing the difference in the interquartile range between the healthy group and the abnormal group, the effectiveness of each type of indicator is verified. If the difference in the interquartile range of an indicator between the two groups is significant, it indicates that the data distribution concentration and dispersion characteristics of the indicator under different states have good discriminative power, thereby verifying its effectiveness and stability as an evaluation basis.

[0143] In one possible implementation, for each type of first target indicator, the validity of the first target indicator of the first test subject is verified based on multiple first target indicators of the healthy group and the abnormal group, specifically as follows:

[0144] For each type of primary target indicator, the area under the primary target characteristic curve of the primary receiver operating characteristic curve is determined based on multiple primary target indicators of the healthy group and multiple primary target indicators of the abnormal group.

[0145] In other words, the two first target indicators of this application—acceleration indicator and deceleration indicator—have their own corresponding first subject operating characteristic curves, and this application also calculates the first area under the curve for each first subject operating characteristic curve.

[0146] The effectiveness of the first target index of the corresponding type of the first test object is verified based on the area under the first curve.

[0147] In one possible implementation, the validity of the first target indicator of the corresponding type of the first test object is verified based on the area under the first curve. This can be done by determining whether the area under the first curve of the first target indicator of that type is greater than a preset threshold. If it is greater than the threshold, it indicates that the first target indicator of that type has good discriminative efficacy in distinguishing between normal and abnormal states of autonomic nervous function, and the verification result is valid. If it is less than or equal to the threshold, it suggests that the discriminative ability of the first target indicator of that type may be insufficient, and the verification result is invalid.

[0148] In another possible implementation, the validity of the first target index of the corresponding type of the first test object is verified based on the area under the first curve, specifically as follows:

[0149] The second target index corresponding to each cardiac interval sequence in the healthy group was determined using power spectral density analysis, and the second target index corresponding to each cardiac interval sequence in the abnormal group was determined using power spectral density analysis.

[0150] The second target indicator is at least one of LF, HF, and LF / HF obtained by PSD analysis.

[0151] The area under the second receiver operating characteristic curve was determined based on multiple secondary target indicators for the healthy group and multiple secondary target indicators for the abnormal group.

[0152] For each type of primary target indicator, the area under the first curve and the area under the second curve for that type are compared, and the effectiveness of the primary target indicator for that type is verified based on the comparison results.

[0153] Since there are many categories of indicators in this application, for ease of understanding, the content described above will be introduced again. The two types of first target indicators in this application include deceleration indicators and acceleration indicators. Furthermore, the deceleration indicators include at least one of DC_LF, DC_HF, and DC_LF / HF; the acceleration indicators include at least one of AC_LF, AC_HF, and AC_LF / HF.

[0154] In one possible implementation, this application compares the ROC curves of the deceleration index, acceleration index, and the sub-indices of the same type included in the second target index one by one:

[0155] Figure 17 A comparison chart of the ROC curves for the deceleration index DC_LF, the acceleration index AC_LF, and the second target index LF, as shown below. Figure 17The three broken lines shown are the ROC curves obtained based on multiple deceleration indicators DC_LF for the healthy group and the patient group, the ROC curves obtained based on multiple acceleration indicators AC_LF for the healthy group and the patient group, and the ROC curves obtained based on multiple secondary target indicators LF for the healthy group and the patient group, respectively.

[0156] Figure 18 A comparison chart of the ROC curves for the deceleration index DC_HF, the acceleration index AC_HF, and the second target index HF, as shown below. Figure 18 The three broken lines shown are the ROC curves obtained based on multiple deceleration indicators DC_HF for the healthy group and the patient group, the ROC curves obtained based on multiple acceleration indicators AC_HF for the healthy group and the patient group, and the ROC curves obtained based on multiple secondary target indicators HF for the healthy group and the patient group, respectively.

[0157] Figure 19 A comparison chart of the ROC curves for the deceleration index DC_LF / HF, the acceleration index AC_LF / HF, and the second target index LF / HF, as shown below. Figure 19 The three broken lines shown are the ROC curves obtained based on multiple deceleration indicators DC_LF / HF for the healthy group and the patient group, the ROC curves obtained based on multiple acceleration indicators AC_LF / HF for the healthy group and the patient group, and the ROC curves obtained based on multiple secondary target indicators LF / HF for the healthy group and the patient group.

[0158] right Figures 17-19 The area under the curve (AUC) of each ROC curve obtained from the analysis can be used to assess the ability of the PRSA-PSD index to distinguish between abnormal and healthy groups, and to compare its performance with that of the traditional PSD index. The optimal diagnostic threshold is obtained by obtaining the maximum Youden index.

[0159] Specifically, ROC analysis shows (see...) Figures 17-19 The AUCs for DC_LF, DC_HF, and DC_LF / HF in distinguishing between abnormal and healthy groups were all 0.809, while the AUCs for AC_LF, AC_HF, and AC_LF / HF in distinguishing between abnormal and healthy groups were all 0.805. In contrast, the AUCs for the traditional PSD indices LF, HF, and LF / HF in distinguishing between abnormal and healthy groups were all 0.767. Therefore, the PRSA-PSD index demonstrates a significantly better ability to distinguish between abnormal and healthy groups than the traditional PSD index, illustrating the effectiveness and superiority of the two types of primary target indices in this application. Furthermore, Figures 17-19In the illustrated embodiment, the optimal diagnostic threshold for DC_LF is 0.6085 (sensitivity of 0.838), the optimal diagnostic threshold for DC_HF is 0.3915 (specificity of 0.838), and the optimal diagnostic threshold for DC_LF / HF is 1.5525 (sensitivity of 0.838).

[0160] It is worth mentioning that ROC curve validation can be performed after the median validation mentioned above has been passed, or it can be performed independently of the median validation process. Then, a person skilled in the art can comprehensively judge the effectiveness of the PRSA-PSD index based on the validation results of each validation method.

[0161] The sequential validation method, employing median validation and ROC curve validation, helps ensure that the primary target indicator being evaluated first possesses basic inter-group discriminative power, and then further validates the accuracy of the primary target indicator's classification. Independent validation provides complementary and corroborating evidence from different statistical dimensions (central tendency and classification performance). Ultimately, those skilled in the art can synthesize the validation results of the two methods. If the results are consistent, it significantly enhances the confidence in the effectiveness of the PRSA-PSD indicator; if the results differ, a deeper analysis of the indicator characteristics or data distribution can be conducted to make a more comprehensive judgment.

[0162] It should be noted that although the operations of the method of the present invention are described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in that specific order, or that all of the operations shown must be performed to achieve the desired result. On the contrary, the steps depicted in the flowchart may be performed in a different order. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0163] In another aspect, this application also provides a computer-readable storage medium. The aforementioned computer-readable storage medium stores one or more programs that, when used by one or more processors, execute the methods described in this application. For example, it can execute... Figure 1 The steps of the method shown are as follows.

[0164] This application provides a computer program product including instructions that, when executed, cause the method described in this application to be performed. For example, it can execute... Figure 1 The steps of the method shown are as follows.

[0165] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0166] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the inventive concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A method for assessing autonomic nervous function based on phase-ordered signal averaging technology, characterized in that, The method includes: Determine the intercardia sequence of the first subject within a preset time period; Multiple deceleration anchors and / or acceleration anchors are selected from multiple sequence values ​​of the intercardia sequence of the first test subject, wherein the deceleration anchor / acceleration anchor is the current sequence value that is greater than / less than the previous sequence value; For each deceleration anchor point / acceleration anchor point, a preset number of sequence values ​​located before and after the deceleration anchor point / acceleration anchor point are selected from the intercardia sequence of the first test subject to obtain the deceleration sequence segment / acceleration sequence segment corresponding to each deceleration anchor point / acceleration anchor point. Multiple deceleration sequence segments / acceleration sequence segments are aligned, and after alignment, the values ​​with the same order in the multiple deceleration sequence segments / acceleration sequence segments are averaged to obtain the deceleration signal sequence / acceleration signal sequence of the first test object. Power spectral density analysis is performed on the deceleration signal sequence / acceleration signal sequence of the first test object to obtain the deceleration index / acceleration index of the first test object; The autonomic nervous function of the first test subject is evaluated based on the deceleration index and / or acceleration index of the first test subject.

2. The method according to claim 1, characterized in that, The autonomic nervous function of the first test subject is evaluated based on the deceleration index and / or acceleration index of the first test subject, specifically as follows: A first power spectrum is determined based on the deceleration index of the first test object, and a second power spectrum is determined based on the acceleration index of the first test object. The first power spectrum is compared with a preset first standard power spectrum to obtain a first comparison result, and the second power spectrum is compared with a preset second standard power spectrum to obtain a second comparison result. The autonomic nervous function of the first test subject is evaluated based on the first comparison result and / or the second comparison result.

3. The method according to claim 1, characterized in that, Before assessing the autonomic nervous function of the first test subject based on the deceleration and / or acceleration indices, the method further includes: Multiple subjects with known autonomic nervous system functional states were divided into a healthy group and an abnormal group, and the cardiac interval sequence of each subject was obtained. For each second subject in the healthy group and the abnormal group, the corresponding deceleration index and acceleration index are determined based on the heartbeat interval sequence of the second subject. The deceleration index and the acceleration index are designated as different types of first target indices. For each type of first target indices, the validity of the first target indices of the first test object is verified based on multiple first target indices of the healthy group and the abnormal group.

4. The method according to claim 3, characterized in that, For each type of first target indicator, the validity of the first target indicator of the first test subject is verified based on multiple first target indicators of the healthy group and the abnormal group, specifically as follows: For each type of first target indicator, determine the median of multiple first target indicators for the healthy group and the median of multiple first target indicators for the abnormal group. For each type of first target indicator, the median of the healthy group and the median of the abnormal group are compared to obtain a third comparison result, and the effectiveness of the first target indicator of the first test subject is verified based on the third comparison result.

5. The method according to claim 3, characterized in that, For each type of first target indicator, the validity of the first target indicator of the first test subject is verified based on multiple first target indicators of the healthy group and the abnormal group, specifically as follows: For each type of first target indicator, a first area under the curve of the first subject operating characteristic curve is determined based on multiple first target indicators of the healthy group and multiple first target indicators of the abnormal group; The effectiveness of the first target index of the corresponding type of the first test object is verified based on the area under the first curve.

6. The method according to claim 5, characterized in that, The effectiveness of the first target indicator of the corresponding type of the first test object is verified based on the area under the first curve, specifically as follows: The second target index corresponding to each cardiac interval sequence in the healthy group was determined using power spectral density analysis, and the second target index corresponding to each cardiac interval sequence in the abnormal group was determined using power spectral density analysis. The area under the second subject operating characteristic curve is determined based on multiple second target indicators of the healthy group and multiple second target indicators of the abnormal group; For each type of first target indicator, the area under the first curve and the area under the second curve of that type are compared to obtain a fourth comparison result, and the effectiveness of the first target indicator of that type is verified based on the fourth comparison result.

7. The method according to claim 1, characterized in that, The heartbeat interval sequence of the first subject to be tested within a preset time period was determined as follows: The electrocardiogram (ECG) signal of the first subject to be tested is acquired within a preset time period, and the ECG signal is sequentially denoised, baseline drift removed, and QRS wave detected to obtain the heartbeat interval sequence of the first subject to be tested.

8. The method according to claim 7, characterized in that, After obtaining the intercardiac interval sequence of the first test subject, the process also includes: The heartbeat interval sequence of the first test subject was sequentially processed for ectopic heartbeat detection and ectopic heartbeat replacement.

9. The method according to any one of claims 1 to 8, characterized in that, Before performing power spectral density analysis on the deceleration signal sequence / acceleration signal sequence of the first test object, the method further includes: Detrending processing is performed on the deceleration signal sequence / acceleration signal sequence of the first test object.

10. The method according to claim 1, characterized in that, Power spectral density analysis is performed on the deceleration signal sequence / acceleration signal sequence of the first test object to obtain the deceleration index / acceleration index of the first test object, specifically: The high-frequency band and low-frequency band are divided, and the power spectral density of the deceleration signal sequence / acceleration signal sequence of the first test object is analyzed according to the autoregressive model method to obtain the deceleration index / acceleration index of the first test object.

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