CN114073508A

CN114073508A - System for evaluating cardiovascular disease risk

Info

Publication number: CN114073508A
Application number: CN202010836647.3A
Authority: CN
Inventors: 徐璎; 杨凌; 周飞; 顾越; 张陶; 杜小娇; 张威; 袁智镕
Original assignee: Suzhou University
Current assignee: Suzhou University
Priority date: 2020-08-19
Filing date: 2020-08-19
Publication date: 2022-02-22
Also published as: WO2022037607A1

Abstract

A method for assessing a subject's risk of cardiac biorhythm or cardiovascular disease using at least one of heart rate trough phase and nocturnal heart rate variability as markers is disclosed. The invention further discloses a device for evaluating the cardiac biorhythm or cardiovascular disease risk of a subject, which comprises a data acquisition module, a data preprocessing module and a fitting module, wherein the fitting module evaluates the cardiovascular disease risk of the subject by obtaining at least one of the heart rate low trough phase and the night heart rate difference as a marker. The data obtained in the data acquisition module can come from a Holter dynamic electrocardiogram monitoring system or wearable equipment with a heart rate module.

Description

System for evaluating cardiovascular disease risk

Technical Field

The invention relates to a marker for evaluating the biological rhythm of heart or the risk of cardiovascular disease, application thereof, and a device and a system for evaluating the biological rhythm of heart or the risk of cardiovascular disease by using the marker.

Background

Human physiological and pathological parameters are tightly controlled by circadian rhythms. Circadian rhythms are of major importance in the cardiovascular system, while chronic destruction of the circadian system, such as shift work, jet lag, social interactions, feeding patterns or circadian rhythm disorders, is also closely associated with the occurrence and development of cardiovascular disease (CVD). Therefore, the evaluation of the circadian rhythm state of the cardiovascular system of an individual has positive significance for functional evaluation, disease risk prediction, diagnostic analysis and even treatment improvement of the cardiovascular system.

There are many measurement methods for evaluating the circadian state of an individual, such as a Dim-Light Melatonin concentration change (DLMO) under Dim Light, a time-type questionnaire, and the like, and researchers have also developed circadian state analysis methods based on various biological samples. However, neither DLMO nor time-based questionnaire directly reflect the circadian rhythm of the heart system. The heart rate is an important parameter of heart health and is most easily and accurately measured, and diurnal variation of the heart rate can reflect the rhythmic variation of cardiovascular system functions. Previous studies have reported that circadian differences in heart rate (scooping) correlate with cardiovascular disease risk, but are not deeply linked to biorhythms.

Disclosure of Invention

Marker and method for evaluating biological rhythm of subject

The invention discloses a marker for evaluating the cardiac biological rhythm of a subject, which is the low valley phase of the heart rate

And a nocturnal heart rate difference (a).

A method of assessing the biorhythm of the heart of a subject is disclosed in which the heart rate trough phase is used

And night heart rate difference (a) determining the subject's biorhythm.

The invention discloses a use of a marker in the preparation of a device for evaluating the biological rhythm of a subject, wherein the marker is the heart rate trough phase

And a nocturnal heart rate difference (a).

In one embodiment, the heart rate trough phase

Or the night heart rate difference (A) is obtained by fitting a curve to the heart rate of the nighttime resting data of the subject, wherein the nighttime resting data refers to the heart rate data during nighttime sleep after the interference of the sleep cycle, the overnight activity and the like in the nighttime resting data is removed.

In one embodiment, the heart rate trough phase is obtained by pre-processing data and fitting data to obtain a heart rate fit of the subject at rest at night using a device capable of collecting continuous heart rate, such as a Holter dynamic electrocardiograph monitoring system or a wearable device with a heart rate module

Or a night time heart rate difference (a), thereby reflecting the subject's cardiac biorhythm.

In a particular embodiment, the subject presents a cardiac biorhythm.

In one embodiment, the means for assessing a biorhythm includes

1) A data acquisition module;

2) a data preprocessing module;

3) a data fitting module comprising

i) A global fitting module; and optionally

ii) a local fitting module;

4) a reporting module;

the data acquisition module is used for acquiring the heart rate data of the subject, and comprises downloading the heart rate data of the subject and outputting the data to the data preprocessing module;

the data preprocessing module is used for acquiring resting state data, and comprises the steps of dividing the heart rate data of a subject by days to determine a night resting state time period and further removing a fluctuating peak to acquire the resting state data;

the global fitting module is used for obtaining heart rate trough phases and/or night heart rate differences, and comprises the steps of fitting the night resting state data into a curve by using a trigonometric function to obtain the heart rate trough phases and/or calculating the night heart rate differences;

the local fitting module is used for obtaining the information of local peaks and local valleys in the night resting state time period in a fitting curve;

the reporting module is used for evaluating the biological rhythm of the heart or the cardiovascular disease risk of the subject.

In a particular embodiment, the apparatus includes at least one of an input device and a wearable device operatively attached to a computing device.

In a particular embodiment, the apparatus comprises a device capable of collecting continuous heart rate, such as a Holter dynamic electrocardiography monitoring system or a wearable device with a heart rate module.

Markers for assessing cardiovascular disease risk in a subject and methods thereof

The invention discloses a target for evaluating cardiovascular disease risk of a subjectThe marker is the heart rate trough phase

And a night time heart rate difference (a).

The invention discloses a method for evaluating the risk of cardiovascular diseases of a subject, which comprises the following steps:

1) collecting continuous heart rate data of a subject using a device capable of collecting a continuous heart rate, such as a Holter dynamic electrocardiography monitoring system or a wearable device with a heart rate module, wherein the heart rate data density is conventionally 1 data point per minute;

2) obtaining a time period for obtaining the resting state data: preprocessing data, and removing fluctuation peaks caused by interference factors such as sleep cycles, night activities and the like in the resting data at night; fitting by using a trigonometric function (cos function) data with the period of 24 hours to obtain a heart rate fitting curve of the resting state of the subject at night, and obtaining a heart rate valley phase in the curve

Or calculating the night heart rate difference A; small peak-to-valley parameters in the nighttime resting data were obtained using Butterworth Filter (Butterworth Filter) filtering. Wherein the night heart rate difference A is the amplitude of a heart rate curve fitted to the night static data.

3) Trough phase of heart rate

Or the value of the heart rate difference A at night so as to judge the cardiovascular disease risk of the subject.

In one embodiment, the heart rate trough phase

Or the nighttime heart rate difference a value is obtained by fitting the heart rate of the subject to a resting state at night, wherein the resting state at night refers to a nighttime sleeping period after the interference of a sleeping period, an overnight activity and the like in the resting state data at night.

In a particular embodiment, the subject is at low risk for cardiovascular disease when the heart rate trough phase is between 0 and 5 and or the nocturnal heart rate variation is between 2.75 and 26; when the heart rate trough phase is ≦ 0, the subject is at high risk for atrial abnormalities such as atrial fibrillation or atrial flutter; when the heart rate trough phase is ≧ 5, the subject is at high risk of an atrial abnormality such as atrial fibrillation or atrial flutter, or is at high risk of a ventricular abnormality such as ventricular fibrillation and/or ventricular flutter; when the nocturnal heart rate difference is more than or equal to 26, the subject is at high risk of atrial abnormality and conduction block; when the nocturnal heart rate difference is less than or equal to 2.75, the subject is at high risk of sinus tachycardia and QRS. Wherein the subject has a cardiac biorhythm.

In a specific embodiment, the subject is assessed as being at high risk for cardiovascular disease when the subject is cardioactive or night time average heart rate/day time average heart rate ≧ 1.

The invention further discloses the use of a marker in the manufacture of a device for assessing the risk of cardiovascular disease in a subject, wherein the marker is heart rate trough phase

And a night time heart rate difference a.

In a particular embodiment, the means for assessing the risk of cardiovascular disease comprises

1) A data acquisition module;

2) a data preprocessing module;

3) a data fitting module comprising

i) A global fitting module; and optionally

ii) a local fitting module;

4) a reporting module;

reporting module the reporting module is configured to assess the risk of cardiovascular disease in the subject according to the value of the heart rate trough phase or the nocturnal heart rate variability.

Device and system

In another aspect, the present invention discloses an apparatus for assessing the risk of a cardiac biorhythm or cardiovascular disease comprising:

1) a data acquisition module;

2) a data preprocessing module;

3) a data fitting module comprising

i) A global fitting module; and optionally

ii) a local fitting module;

4) a reporting module;

Reporting module in a specific embodiment the apparatus further comprises a data acquisition module for acquiring continuous heart rate data of the subject over 24 hours, preferably the data is heart rate data of about 24 hours, more than 24 hours, e.g. 36 hours, 48 hours, 60 hours, 72 hours, 1 week, 2 weeks, 3 weeks, 4 weeks, 1 month, 2 months, 3 months, 6 months, 9 months, 12 months.

In a particular embodiment, markers are used in the device to assess cardiac biorhythm or cardiovascular disease risk for heart rate trough phase and/or nocturnal heart rate difference assessment.

In one embodiment, the nighttime resting data is globally fitted using a cos function with a period of 24 hours using a least squares method to obtain values for the heart rate trough phase.

In a specific embodiment, the nocturnal heart rate difference is the amplitude of a heart rate curve fitted to the nocturnal static data.

In a particular embodiment, in the reporting module, the subject is assessed as being at low risk for cardiovascular disease when the value of the heart rate trough phase is between 0 and 5 and or the value of the nocturnal heart rate difference is between 2.75 and 26; when the value of the heart rate trough phase is ≦ 0, the subject is assessed as a high risk of atrial abnormalities such as atrial fibrillation or atrial flutter; when the value of the heart rate trough phase is ≧ 5, the subject is assessed as high risk of an atrial abnormality such as atrial fibrillation or atrial flutter, or a ventricular abnormality such as ventricular fibrillation or ventricular flutter; when the value of the nocturnal heart rate difference was ≧ 26, the subject was assessed as a high risk of atrial abnormality and conduction block; when the value of the nocturnal heart rate difference is ≦ 2.75, the subject is assessed as a high risk for sinus tachycardia and QRS. The subject is in the presence of a cardiac biorhythm.

In a specific embodiment, in the reporting module, the subject is assessed as being at high risk for cardiovascular disease when the subject is without cardiac biorhythm or night time average heart rate/day time average heart rate ≧ 1.

In a particular embodiment, the apparatus includes an input device operatively attached to the computing device.

In a particular embodiment, wherein the apparatus may be a wearable device.

In a particular embodiment, wherein the apparatus further comprises a device capable of collecting continuous heart rate, such as a Holter dynamic electrocardiography monitoring system or a wearable device with a heart rate module.

In yet another aspect, the present invention discloses a system for assessing a cardiac biorhythm or cardiovascular disease risk comprising an apparatus as described in any of the preceding claims, wherein the cardiac biorhythm or cardiovascular disease risk is assessed by fitting at least one of a heart rate trough phase and a night time heart rate difference.

In a particular embodiment, the subject is at low risk for cardiovascular disease when the heart rate trough phase is between 0 and 5 and or the nocturnal heart rate variation is between 2.75 and 26; when the heart rate trough phase is ≦ 0, the subject is at high risk for atrial abnormalities such as atrial fibrillation or atrial flutter; when the heart rate trough phase is ≧ 5, the subject is at high risk of an atrial abnormality such as atrial fibrillation or atrial flutter, or is at high risk of a ventricular abnormality such as ventricular fibrillation and/or ventricular flutter; when the nocturnal heart rate difference is more than or equal to 26, the subject is at high risk of atrial abnormality and conduction block; when the nocturnal heart rate difference is less than or equal to 2.75, the subject is at high risk of sinus tachycardia and QRS.

In another aspect, the invention discloses the use of a device capable of collecting continuous heart rate for assessing the risk of a cardiac biorhythm or cardiovascular disease.

In a particular embodiment, the cardiac biorhythm or cardiovascular disease risk is assessed by fitting at least one of heart rate trough phase and nocturnal heart rate variability.

In a specific embodiment, the device capable of collecting continuous heart rate comprises a Holter dynamic electrocardiograph monitoring system or a wearable device with a heart rate module.

Drawings

Fig. 1 shows a flow chart of a heart rate analysis method.

Fig. 2 shows a schematic diagram of the heart rate analysis results. The thin line represents the original heart rate curve; the continuous thick line represents the filtered curve; the dotted line is a curve obtained by global cos fitting; black hexagram is the lowest point of the global fitting curve; x represents a local valley point; represents a local peak point; the bold line segments on both sides are the heart rate curves (for obtaining the fast rising and falling slopes) when falling asleep and waking up.

Fig. 3 shows a schematic diagram of the analysis results of the heart rate biorhythm parameters. Gray points represent raw heart rate data, and continuous gray curves represent filtered curves; the left light gray arc curve is a heart rate trough curve obtained by cos fitting; the black bold lines indicate heart rate decline and rise curves; the black dots represent the lowest heart rate trough.

Fig. 4 shows the heart rate trough phase contrast time-based questionnaire results.

Fig. 5 shows the results of heart rate trough phase contrast DLMO.

FIG. 6 shows Holter patient heart rate rhythm groupings.

Fig. 7 shows CVD risk for a population of extreme heart rate biorhythm parameters.

Detailed Description

The present invention will be further illustrated by the following detailed description.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

In this application, the singular forms "a", "an" and "the" include plural referents unless the context clearly dictates otherwise.

As used herein, the term "subject" includes any human or non-human animal. The term "non-human animal" includes all vertebrates, such as mammals and non-mammals, such as non-human primates, sheep, dogs, cats, horses, cows, chickens, rats, mice, amphibians, reptiles, and the like. The terms "patient" or "subject" are used interchangeably unless otherwise indicated. In the present invention, a preferred subject is a human.

As used herein, the term "resting data," also referred to as "nighttime resting data," refers to heart rate data for a period of nighttime sleep obtained after calibration time, data partitioning, and filtering, and after removal of interfering data such as sleep cycles, overnight activity, and the like. The term "night rest period" refers to a period of night sleep state obtained by calibration time, data division, and filtering. The night resting state data is data subjected to data preprocessing.

As used herein, the term "heart rate trough phase" refers to the time point of the lowest trough of a curve obtained after fitting the curve with a trigonometric function using nocturnal resting data, which time is used to reflect the heart biorhythm.

As used herein, the term "night time heart rate variability" refers to the amplitude of a night time heart rate fit curve obtained after fitting the curve with a trigonometric function using night time resting state data.

As used herein, the term "arrhythmic biorhythm" refers to the rhythm data determined using the Jonckheer-Terpstra-Kendall rhythmicity analysis (JTK _ CYCLE) algorithm.

As used herein, the term "wearable device" refers to a portable device that is worn directly on the body, or is integrated into the clothing or accessories of the user. The wearable device may be a hardware device that may be supported by software for data interaction, cloud interaction, or data analysis purposes.

Examples

The experimental procedures in the following examples are conventional unless otherwise specified. The invention will be further understood with reference to the following non-limiting experimental examples.

Example 1 Heart Rate analysis method

The analysis method uses continuously recorded heart rate data which can be sourced from a Holter dynamic electrocardiogram monitoring system or wearable equipment with a heart rate module, and the density of the heart rate data is 1 data point per minute conventionally.

Specifically, 211 volunteers (72 males, 139 females) were recruited in total. The volunteers were required to complete a morning-night questionnaire (MEQ) and then wear a smart bracelet for at least 1 month for data collection. Wearable devices are purchased from two independent vendors. The heart rate HR data of the 1 minute frequency acquired by the smart wristband is retrieved from the manufacturer cloud server through the application program interface after the user is authorized, and is stored in the local database. The study design was in accordance with the declaration of helsinki and approved by the ethical committee of the university of suzhou (ECSU-201800098).

Because the heart rate data in the daytime is influenced by various subjective factors such as sports, working strength, social contact and the like and external factors, the biological rhythm of an individual in the day cannot be objectively reflected, the heart rate data in a resting state at night is selected as a data source for acquiring rhythm parameters. After the interference of sleep cycle, night activity and the like in the night resting state data is removed through preprocessing, a trigonometric function is used for fitting to obtain the core global parameters. In addition, small peak-to-valley parameters in the nighttime resting data were also obtained using Butterworth Filter (Butterworth Filter) filtering. Referring to fig. 1, the general flow diagram specifically includes the following steps:

1) obtaining a time period of the resting state data:

1-1) calibrating time

The time data is a reference for subsequent processing, and the initial time needs to be calibrated before work starts in consideration of the particularity and difference of different software on time data storage.

1-2) data partitioning

The time series data was divided in units of days with 14 points per day as a division point. And meanwhile, incomplete data is screened.

1-3) Filtering

The daily heart rate data is plotted as a curve formed by the superposition of a macroscopic biological clock curve and a number of small noises (jitters), i.e. consisting of a low frequency component and a high frequency component, respectively. Wherein the curve corresponding to the low frequency component changes slowly, and the curve corresponding to the high frequency component changes violently.

In order to study the biological clock curve of the sleep stage, it is necessary to filter out high frequency noise, so a low frequency filter is used to filter or attenuate the high frequency component of the curve by a large margin, and let the low frequency component pass. Here, a Butterworth low pass filter is used to obtain filtered data.

1-4) obtaining sleep data (resting state)

a. And calculating the average value B _ mean of the heart rate of the whole day and the median B _ prctile of the heart rate of the whole day by using the filtered data smooth _ filter, and taking the minimum value of the average value B _ mean and the median B _ prctile of the heart rate of the whole day as a critical value B _ inf.

b. And (3) obtaining a sleep data starting point, namely, taking the length interval (1) as 240 (unit: min), and calculating the heart rate corresponding to how many points in the time interval [1: interval (1) ] is less than B _ inf, namely, the number of points positioned below B _ inf is recorded as count _ point.

c. If count _ point is 0, the interval is slid to the right by interval (1), and the operation similar to b is performed on the new interval.

If 0< count _ point < interval (1), slide 1 unit to the right, do the similar operation to b for the new time interval.

If the count _ point is equal to interval (1), marking the left end point of the corresponding time interval as the starting point of the sleep data, and ending the cycle.

d. And (3) solving a sleep data termination point:

the principle is the same as a, the judgment is changed from right to left,

e. if count _ point is 0, the time interval is slid to the left by interval (1), and the operation of d is repeated for the new time interval.

If 0< count _ point < interval (1), slide 1 unit to the left, repeat d for the new time interval.

If the count _ point is equal to interval (1), marking the right end point of the corresponding time interval as a sleep data termination point, and ending the cycle.

In the case that the night sleep time is too short, the above method may not be able to effectively acquire the required data, and at this time, the interval length is changed from interval (1) to 240 to interval (2) to 120 (unit: min), and the relevant steps are repeated.

f. To obtain more sophisticated sleep data, consider extending both ends slightly.

For the left end (start end), if there is an extension margin, it is considered to shift the start point to the left by continuous _ len 60 (unit: min). If an overflow occurs, that is, the starting point is out of the actual recording range at this time, 0<, which is the movement amount < continue _ len, is taken.

For this extended segment of data, a first order difference is made. The rising number is defined as the number greater than zero in the differential data, and the falling number is defined as the number less than zero in the differential data. And calculating the ascending number and the descending number, and if the descending number > is 2 times of the ascending number, determining that the extension is necessary and changing the coordinates of the starting point, otherwise, the extension is not needed, namely, the coordinates are not needed to be modified.

g. For the right end (terminating end), if there is an extension margin, it is considered to move the terminating point to the right by a constant _ len of 60 (unit: min). If an overflow occurs, that is, when the end point is out of the actual recording range, 0<, which is the amount of movement < continue _ len, is taken.

For the extended data, a first difference is made, the ascending number and the descending number are calculated according to the definition, if the ascending number > is 2 times of the descending number, the extension is considered to be necessary, the coordinates of the starting point are changed, otherwise, the extension is not needed, namely, the coordinates are not needed to be modified.

h. And cutting out sleep data according to the coordinates determined by f and g.

2) In the time period of rest state, removing fluctuating peak (considered as interference factors such as sleep cycle and night activity) to obtain preprocessed night rest state data

For the acquired sleep data, because the whole sleep stage comprises a plurality of small sleep cycles which are reflected as a plurality of small fluctuations on the heart rate curve, in order to acquire macroscopic sleep resting state data, a sliding window is used for traversing and comparing the data, and the starting point/the end point of the local wave peak and the wave trough are defined and deleted.

3) Using a cos function with a period of 24 hours, and globally fitting by a least square method (adaptive parameter selection) to obtain the lowest valley phase and heart rate difference;

the phase is the time corresponding to the lowest point of the cos curve obtained by fitting (black hexagram abscissa shown in the figure)

The heart rate difference calculation method comprises the following steps:

night heart rate difference 2 x (night heart rate mean-heart rate at the lowest position of cos fitting curve)

Day and night heart rate difference 2 (whole day heart rate mean-the heart rate at the lowest position of cos fitting curve)

4) Calculating parameters related to rapid descending and ascending curves of the heart rate when the resting state and the activity state change;

the sleep data are divided into three equal parts, namely left, middle and right parts.

And solving the slope value when the user enters the sleep state by using the left section of data. A time interval having a length interval (3) of 50 is taken, and a statistic "descending score" is defined to determine the quality of the time interval selected at this time (i.e., the effect of obtaining the slope from the interval). Defining: first-order difference is carried out on the interval data, the descending number (the number of the data less than zero in the difference data) is calculated, the descending amount (the sum of the values less than zero in the difference data and then the absolute value is taken), and then the two are summed by taking 0.3 and 0.7 as weights, so that the descending score of the interval is called. Traversing the left segment, searching the segment with the highest score, searching the maximum value max _ info of the left half segment of the segment and the position max _ place of the segment, and searching the minimum value min _ info of the right half segment and the position of the segment.

The slope of going to sleep is the number of heart rate drops per minute (negative) over the period. The falling end time is the time corresponding to the last moment of the sleep time period, and the heart rate to be ended is the heart rate corresponding to the falling end time.

And obtaining the slope value of the wake-up stage by using the right-segment data. The "rise score" is defined as a time interval having a length interval (3) of 50 to determine the quality of the time interval selected at that time (i.e., the effect of obtaining the slope of the interval). Defining: and (3) performing first-order difference on the interval data, calculating the rising number (the number of the difference data larger than zero), the rising amount (the sum of the values of the difference data larger than zero), and summing the rising number and the rising amount by taking 0.3 and 0.7 as weights, wherein the sum is called the rising score of the interval. And traversing the right segment and searching the segment with the highest score. And searching the minimum value min _ info and the position min _ place of the left half section of the interval, and searching the maximum value max _ info and the position max _ place of the right half section of the interval. The value of the slope of the wake-up period is the number of heart rate rises per minute over the period. The rising start time is the time corresponding to the first moment of the wake-up period, and the rising start heart rate is the heart rate corresponding to the lower rising start time.

5) A plurality of local wavelet dynamic parameters (possibly related to sleep cycles) within a period of rest are calculated based on a butterworth filter.

Calculating local peaks and troughs by using the filtered sleep data, trisecting a time interval, and calculating the heart rate mean value of the left part, the middle part and the right part of the time interval and the maximum or small value of the heart rate of the left part and the right part. And if the heart rate average value in the middle of the interval is simultaneously larger than the average values of the left part and the right part, and the difference between the maximum value of the middle part and the minimum value of the left half part and the difference between the maximum value of the middle part and the minimum value of the right half part are both larger than the threshold value, taking the maximum value of the heart rate in the time interval as a local peak value point. And if the heart rate average value in the middle of the interval is smaller than the average values of the left part and the right part at the same time, and the difference between the maximum value of the left half part and the maximum value of the right half part and the minimum value of the middle part is larger than a threshold value, taking the minimum value of the heart rate in the time interval as a local valley point.

The condition that the amplitude at night is lower than the lowest 5 percent is classified as arrhythmic, and the condition that the phase at night is different from the average phase of the crowd by plus or minus 10-14 hours is classified as reverse phase. Both cases do not continue to judge other parameters. If there are no two cases, 6 of the output parameters whose rhythmic significance is significant: night phase place, the amplitude of heart rate at night, the mean value of heart rate at night, the slope that the heart rate at night descends fast, the slope that the heart rate at night rises fast, the symmetry index of sleeping, with a result output customer.

The fitting of the heart rate curve is shown in fig. 2, the analysis result of the biorhythm parameters is shown in fig. 3, the analyzed parameter list is shown in table 1, wherein the most key parameters are as follows:

1) heart rate trough phase: the time point corresponding to the highest peak of the rhythmical data is conventionally defined as a phase (peak phase), but because daytime heart rate data is greatly interfered by activities, the time point corresponding to the lowest valley of a heart rate fitting curve in the night sleep period is selected by the algorithm to reflect the heart rate rhythm phase, namely the heart rate low valley phase.

2) Night heart rate difference: amplitude is also an important parameter in biological rhythms, and is conventionally the difference between the highest peak and the lowest valley of a fitted curve, representing the oscillation intensity of the heart rate over a 24 hour period. Since we mainly perform the analysis on the nighttime data, the nighttime heart rate variation, i.e. the nighttime heart rate difference, is defined.

TABLE 1 Heart Rate biorhythm parameter List

Example 2 heart Rate biorhythm analysis result accuracy

To demonstrate that heart rate trough phase can be used as a marker for biorhythms, the inventors compared using well-established tools commonly used in biorhythm analysis in the prior art (early-late type questionnaire MED and melatonin concentration change DLMO in dark light). The analytical procedures and results were as follows:

1. heart rate trough phase and time type questionnaire result comparison

The Morning and Evening Questionnaire (MEQ) is a mature tool for analyzing the human type, and the volunteer experiments are carried out to compare the heart rate trough phase and the MEQ type obtained by analyzing the heart rate biorhythm. According to the results obtained in 211 volunteers, the heart rate trough phase and EMQ-time pattern showed a good correlation (fig. 4A). Correlation analysis was further performed on the heart rate trough phase values and the time-type questionnaire scores (higher score, later time-type), and the results showed a significant negative correlation between the two (fig. 4B, significance P < 0.0001).

2. Comparison of heart rate trough phase and DLMO results

The melatonin concentration change (DLMO) in dim light is considered to be a gold standard for determining the circadian rhythm of individuals. We also performed experiments comparing heart rate trough phase and DLMO obtained from heart rate biorhythm analysis.

12 volunteers were grouped in two photophobic chambers from 18:00 to 24: 00. Saliva samples were taken every 30 minutes, stored in a refrigerator and tested with an ELISA kit (IBL International, Switzerland). The melatonin onset time was determined for each individual by The hockey-stick method in MATLAB (Danilenko KV, Verevkin EG, Anteufeeve VS, Wirz-Justic A, Cajohen C: The hockey-stick method to estimate The elevation of Dim Light Melatonin Onset (DLMO) in humans. Chonobol Int 2014,31: 349-355.).

From the results obtained in 9 volunteers, correlation analysis of heart rate trough phase and DLMO demonstrated a linear correlation between the two (fig. 5, correlation coefficient r 0.89, significance analysis p < 0.05).

The comparative analysis of the two aspects proves that the heart rate low trough phase can accurately reflect the biorhythm state of the individual and is a good marker.

Example 3 correlation of Heart Rate biorhythms with cardiovascular disease

To further analyze the relationship of heart rate biorhythm parameters to incidence of cardiovascular disease, 11074 Holter historical data sets were collected covering relevant patients during 9 months 2010 to 7 months 2014 of cardiology department, the first subsidiary hospital of the university of suzhou. The Holter data set is then screened to ensure data integrity and patients using artificial cardiac pacemakers are excluded for unbiased analysis of the temporal pattern. There were 10,095 data sets that were further analyzed. The simplified HR data at 1 minute frequency was derived using a custom module of the Holter software ecllab (shenzhen biomedical instruments, inc.) along with diagnostic conclusions. The retrospective study design was approved by the ethical review board of the hospital (application No.: 2019025). Of the Holter heart rate data for 10,095 clinical patients, patients were 8 to 97 years old, with the majority of the population (25% -75% percentile) between 48 and 68 years old. The Holter data contained a complete electrocardiogram from which we derived simple heart rate data (1 data point per minute) and the biorhythmic parameters of the heart rate of these patients were analyzed using the method described in example 1. Furthermore, the Holter data contains diagnostic conclusions, as well as the Holter data set containing diagnostic conclusions given by experienced cardiologists according to clinical guidelines (recommendations for standardization and interpretation of the electrocardiogram by AHA/ACCF/HRS). We also extracted clinical indices associated with cardiovascular disease from them and classified them into 7 major categories and 13 minor categories of CVD indices (table 2).

TABLE 2 CVD index Classification

We first filter and denoise the data with a Butterworth filter. A sliding window is then used to automatically distinguish between rest and active periods, replacing the fixed day and night by marking the slope of HR decline and rise. The first time point of the sliding window, i.e. the time point at which all HR values are below the threshold value, is considered as the start of the night period by comparison with a threshold value calculated from the filtered HR data. Similar processing is performed to determine the end of the night. Then, a cos function fit is performed on the nighttime HR data using a least squares method. The lowest fit HR value and trough time are automatically determined. Furthermore, nighttime and diurnal variations in HR for the subject over nighttime and the entire circadian cycle can be calculated using the daily average HR value, the average resting HR value, and the lowest fit HR value. And automatically searching the start and the opportunity of the transition from the low stable state to the high state by adopting a K-means clustering algorithm. The first activity point t tonset1 and the first time point t tonset2, where the time point at which the difference between the data point of tonset2 and the average of the first three points reached a certain threshold was two candidate times of onset after 4 am. The earlier of the two candidate points is selected as the onset time. Again, all parameters are retrieved from the wristband-based HR data.

We firstly analyze the relationship between the existence and the absence of rhythm and CVD, judge whether the rhythm exists in the heart rate data by using a Jonckheer-Terpsra-Kendall rhythmicity analysis (JTK _ CYCLE) algorithm, and divide Holter patients into two main categories of the existence and the absence of rhythm (32.5%) and the existence and the absence of rhythm (67.5%) according to the result, wherein the existence and the absence of rhythm are further divided into a phase reversal group (the average heart rate at night/the average heart rate at day is more than or equal to 1) and a phase reversal group (figure 6). Linear regression was used to evaluate the correlation using the Bonferroni test of one-way analysis of variance in GraphPad Prism 8, by inputting the baseline characteristics and the time-profile of the CVD index in the electrocardiographic data: arrhythmic, inverted and rhythmic types, and the association between disease occurrence and chronotype is evaluated. In all statistical analyses, a two-sided P value of <0.05 was considered statistically significant. Using Bonferroni analysis, we found: the risk of atrial events, ventricular events, sinus tachycardia, conduction block and QRS is significantly increased in the anti-phase population compared to the normal rhythm population; while the risk of atrial events, sinus bradycardia, conduction block and QRS is significantly increased in arrhythmic populations (table 3).

In order to evaluate the correlation between the heart rate biorhythm parameters and the CVD indicators, we performed a progressive partial correlation analysis to determine the critical points of the two parameters, heart rate Trough Phase (HR Trough Phase) and night time Variation (noctual Variation) of the population with rhythm group (including positive Phase and negative Phase). We have found that: the risk of CVD in people with heart rate trough phase between 0 and 5 (89.7% of rhythmic people) and nocturnal heart rate variability between 2.75 and 26 (91.9% of rhythmic people) is low. After a normal range is determined according to the critical point, correlation between the heart rate biorhythm parameters of the crowd in the abnormal range and the CVD risk index is further researched by using Pearson correlation analysis. The results show that:

TABLE 3 comparison of CVD Risk in different rhythm population

Statistical significance of difference

*:P<0.05；**:P<0.01；***:P<0.001；****:P<0.0001

Extreme heart rate trough phase in the rhythmic population (1:)

Or

) Closely related to atrial abnormal events (atrial fibrillation, atrial flutter) (fig. 7A), while ventricular abnormal events (ventricular fibrillation and ventricular flutter) were only severely delayed from the heart rate trough phase

Correlation (fig. 7A).

In the anti-phase population, nocturnal heart rate variability is most strongly correlated with atrial abnormalities and sinus bradycardia (fig. 7B).

In the rhythmic population, too large a change in the nocturnal heart rate (A.gtoreq.26) correlates strongly with atrial events and conduction blocks (FIG. 7C), while too small a change in the nocturnal heart rate (A.gtoreq.2.75) correlates strongly with sinus tachycardia and QRS (FIG. 7C).

Claims

1. An apparatus for assessing a subject's risk of cardiac biorhythm or cardiovascular disease, comprising:

1) the data acquisition module is used for acquiring the heart rate data of the testee, and comprises downloading the heart rate data of the testee and outputting the data to the data preprocessing module;

2) the data preprocessing module is used for acquiring resting state data, and comprises the steps of dividing the heart rate data of the subject by days to determine a night resting state time period and further removing a fluctuating peak to acquire the resting state data;

3) a data fitting module comprising:

i) a global fitting module for obtaining heart rate trough phases and/or night heart rate differences, including fitting the night resting data to a curve using a trigonometric function to obtain heart rate trough phases and/or calculate night heart rate differences; and optionally

ii) a local fitting module for obtaining local peak points and local valley points in the nighttime rest time period in the fitted curve;

4) a reporting module for assessing a subject's cardiac biorhythm or cardiovascular disease risk.

2. The apparatus of claim 1, further comprising a data acquisition module to acquire heart rate data for a subject over 24 hours.

3. The apparatus of claim 1, wherein the global fitting module fits the nighttime resting data using a cos function with a period of 24 hours, least squares.

4. The apparatus of claim 1, wherein the nocturnal heart rate difference is an amplitude of a heart rate curve fitted to the nocturnal static data.

5. The apparatus of claim 1, wherein in the local fitting module, local peak points and local valley points are calculated based on a butterworth filter.

6. The apparatus of claim 1, wherein in the reporting module, the subject is assessed as being at low risk for cardiovascular disease when the value of the heart rate trough phase is between 0 and 5 and or the value of the nocturnal heart rate difference is between 2.75 and 26; when the value of the heart rate trough phase is ≦ 0, the subject is assessed as a high risk of atrial abnormalities such as atrial fibrillation or atrial flutter; when the value of the heart rate trough phase is ≧ 5, the subject is assessed as high risk of an atrial abnormality such as atrial fibrillation or atrial flutter, or a ventricular abnormality such as ventricular fibrillation or ventricular flutter; when the value of the nocturnal heart rate difference was ≧ 26, the subject was assessed as a high risk of atrial abnormality and conduction block; when the value of the nocturnal heart rate difference is ≦ 2.75, the subject is assessed as a high risk for sinus tachycardia and QRS.

7. The device of claim 6, wherein the subject has a cardiac biorhythm.

8. The apparatus of claim 1, wherein the subject is assessed as being at high risk for cardiovascular disease when the subject is cardioactive in the reporting module or the night time average heart rate/day time average heart rate is ≧ 1.

9. The apparatus of claim 1, wherein the apparatus comprises an input device operably attached to a computing device.

10. The apparatus of claim 1, wherein the apparatus is a wearable device.

11. The apparatus of claim 1, wherein the apparatus further comprises a device capable of collecting continuous heart rate such as a Holter dynamic electrocardiography monitoring system or a wearable device with heart rate module.

12. A system for assessing the risk of a cardiac biorhythm or cardiovascular disease comprising:

comprising the apparatus of any one of claims 1 to 11, wherein the cardiac biorhythm or cardiovascular disease risk is assessed by fitting at least one of a lowest trough phase of heart rate and a nighttime heart rate difference.

13. Use of a device capable of collecting a continuous heart rate for assessing a subject's cardiac biorhythm or cardiovascular disease risk, wherein the cardiac biorhythm or cardiovascular disease risk is assessed by fitting at least one of a heart rate trough phase and a nocturnal heart rate difference.

14. The use of claim 13, wherein the device capable of collecting continuous heart rate comprises a Holter dynamic electrocardiography monitoring system or a wearable device with a heart rate module.

15. Use of a heart rate trough minimum phase or a nocturnal heart rate difference in the manufacture of a system or device for assessing the risk of a cardiac biorhythm or cardiovascular disease in a subject.