SYSTEM FOR MONITORING PHYSIOLOGICAL PARAMETERS

Abstract

The present invention relates to a system for monitoring physiological parameters to an integrated digital system, which is able to determine several biological parameters, such as from photoplethysmographic (PPG) signals and other connected devices or sensors to give a personalized supplement, nutritional and lifestyle recommendation to improve specifically said parameters. By using new algorithms based on PPG signals the cardiovascular condition of a person can be analyzed by estimating cardiovascular parameters.

Claims

1. A system for monitoring physiological parameters of a user, the system comprising: a human body health monitoring device comprising a sensor adapted to obtain primary physiological signals of the user; a processing system communicatively coupled to the sensor adapted to calculate one or more physiological parameters based on the primary physiological signals and based on individual parameters of the user, compare the calculated physiological parameters with prestored physiological index parameters, and determine a specific deviation between the calculated physiological parameters to the prestored physiological index parameters, compare the specific deviation(s) with a database containing nutrients, nutraceuticals, advanced food ingredients and single nutritional components specifically selected via scientific and clinical studies to have a specific positive/normalizing effect on said physiological parameters, provide a nutritional suggestion to the user for the normalization of the physiological parameters based on the comparison of the specific deviation(s) with the nutritional database; and output means adapted to output the calculated physiological parameters, the deviation from the prestored index parameters and the nutritional suggestion.

2. The system of claim 1, wherein the physiological parameters calculated are cardiovascular health parameters, cognitive health parameters, gut health parameters, metabolic parameters, body mass and body efficiency parameters, stress and sleep parameters or inflammatory parameters or a combination.

3. The system of claim 1, wherein the prestored physiological index parameters are stored in a database, which is communicatively coupled to the processing system and define for each physiological parameter an optimal physiological range and at least one higher physiological range and at least one lower physiological range.

4. The system of claim 1, wherein the sensor is a photoplethysmographic (PPG) sensor and the physiological parameters calculated are cardiovascular health parameters selected from the group consisting of vascular age index AgIx.sub.PPG, blood pressure BP.sub.dia and BP.sub.sys, pulse wave velocity PWV, augmentation index AIx.sub.PPG, and heart rate variability HRV.

5. The system of claim 1, further comprising at least one selected from the group consisting of bioimpedance sensor, pulse oximeter, capacitive sensor, temperature sensor, ultraviolet (UV) sensor, ambient light sensor, 3 axis accelerometer, altimeter, barometer, compass, gyroscope, magnetometer, gesture technology, global positioning system (GPS), and long term evolution (LTE).

6. The system of claim 1, wherein the physiological parameters, the primary physiological signals, individual parameters of the subject and nutritional suggestion to the user for the normalization of the physiological parameters are collected to establish a database for comparison and detection of deviations.

7. The system of claim 1, wherein the system is configured to determine one or more of the following cardiovascular parameters of the user, the user having an age and a body height with the following steps: determining the age (p.sub.age) and body height (p.sub.height) of the user, measuring at least two photoplethysmographic (PPG) signals with at least two PPG sensors at two different positions at the subject, separating the PPG signal into PPG pulses, whereby the start point and the end point of the pulse corresponds the systolic foot of the PPG signal, determining the heart rate of the user (p.sub.HR) and calculating the median heart rate, determining the systolic A.sub.sys and diastolic A.sub.dia peak amplitudes and their times t.sub.s and t.sub.d, calculating the second derivative of the PPG pulse, and determining the characteristic points a, b, c, d, and e from the second derivative of the PPG pulse, wherein a and e are the first and second most prominent maxima in the second derivative, respectively, c is the most prominent peak between the points a and e, b is the most prominent minimum in the second derivative and, d is the most prominent minimum between points c and e, determining: a) the vascular age index AgIx using linear regression based on the characteristic points a, b, c, d, and e, age (p.sub.age), body height (p.sub.height) and median heart rate of the user, b) the pulse wave velocity PWV using linear regression based on the time difference between the two PPG pulses (PTT), age (p.sub.age), body height (p.sub.height) and median heart rate estimation of the user, c) blood pressure BP.sub.dia and BP.sub.sys using linear regression based on time difference between the two PPG pulses (PTT) and median heart rate and d) optionally the augmentation index AIx, based on the systolic A.sub.sys and diastolic A.sub.dia peak amplitudes normalized to 75 heartbeats (AIx@75) and using a linear regression based on the normalized augmentation index AIx.

8. The system of claim 1, further comprising the determination of Crest Time (CT), Stiffness Index (SI) and Pulse Area (PA) of the PPG signal and wherein the cardiovascular parameters are estimated with the following equations: a) vascular age index AgIx: AgIx=d.sub.0+d.sub.1+d.sub.2p.sub.age+d.sub.3p.sub.height+d.sub.4, wherein is estimated based on characteristic points a, b, c, d, and e: $=45.4*\frac{b-c-d-e}{a}+65.9;$ b) pulse wave velocity PWV:
PWV=g.sub.0+g.sub.1+g.sub.2p.sub.age+g.sub.3p.sub.height+g.sub.4; c) blood pressure BPdia and BPsys:
BP.sub.dia=l.sub.0d+l.sub.1d+l.sub.2d+l.sub.3dCT.sub.p+l.sub.4dSI.sub.p+l.sub.5dPA.sub.p
BP.sub.sys=k.sub.0s+k.sub.1s+k.sub.2s; d) normalized augmentation index AIx@75: =(x−y)/y by the sum of two exponential, and AIx@75=b.sub.0+b.sub.1, wherein AIx@75 is the augmentation index (AIx) normalized to 75 heartbeats; wherein, p.sub.age is the age and p.sub.height is the body height of the subject, median (HR) is the median heart rate, PTT is the time difference between the PPG pulses, A.sub.sys and A.sub.dia are magnitudes of the systolic and diastolic peak, respectively, CT is the Crest Time, ST is the Stiffness Index and PA is the Pulse Area of the PPG signal, d.sub.0 to d.sub.4, g.sub.0 to g.sub.4, l.sub.0d to l.sub.kd, k.sub.0s to k.sub.2s, and b.sub.0 to b.sub.1 represent the coefficients of the respective linear regression equation.

9. The system of claim 7, wherein the characteristic points a, b, c, d, and e are automatically derived from the second derivative of the PPG pulse, wherein a and e are the first and second most prominent maxima in the second derivative, respectively, c is the most prominent peak between the points a and e, b is the most prominent minimum in the second derivative and, d is the most prominent minimum between points c and e.

10. The system of claim 7, wherein the systolic A.sub.sys and diastolic A.sub.dia peak amplitudes and their times t.sub.s and t.sub.d are determined by one of the following methods: modeling the PPG waveform as a sum of two pulse waves through exponential functions and applying nonlinear regression to fit the model to the PPG waveform and receive estimates of t.sub.s and t.sub.d to find A.sub.sys and A.sub.dia, respectively, or modeling the first wave with known position at the systolic peak A.sub.sys, and subtracting its exponential model from the PPG signal and thereby yielding the remaining reflected wave, whose maximal value Â.sub.dia and {circumflex over (t)}.sub.d is the corresponding diastolic time index estimate.

11. The system of claim 1, further comprising an online marketing platform, wherein the processing system is linked to the online marketing platform configured to visualize improvements and to directly order nutritional or nutraceutical products according to the suggestion provided.

12. The system of claim 1, further comprising an application, wherein the processing system is linked to the application configured to visualize improvements and to directly order nutritional or nutraceutical products according to the suggestion provided.

13. A method for monitoring physiological parameters of a user, the method comprising: receiving input from at least one sensor and an interface of a human body health monitoring device of the user; calculate one or more physiological parameters based on the primary physiological signals and based on individual parameters of the user, comparing the calculated physiological parameters with prestored physiological index parameters, and determining a specific deviation between the calculated physiological parameters to the prestored physiological index parameters, comparing the specific deviation(s) with a database containing nutrients, nutraceuticals, advanced food ingredients and single nutritional components specifically selected via scientific and clinical studies to have a specific positive/normalizing effect on said physiological parameters, providing a nutritional suggestion to the user for the normalization of the physiological parameters based on the comparison of the specific deviation(s) with the nutritional database; and outputting the calculated physiological parameters and the nutritional suggestion.

Description

WORKING EXAMPLE

[0269] Nutrition and lifestyle behaviors have a significant influence on the wellbeing of on individual. This wellbeing can be verified by estimating the individual vital parameters. An exemplary but not limiting list of such vital parameters are cardiovascular parameters (heart rate, blood pressure, pulse wave velocity), stress level, and sleep indicators like sleep quality and latency. Exemplary but not limiting correlations between nutrition and their influence on such vital parameters are shown in Table 1. The following concept explains the determination of individual nutrition/lifestyle recommendations to an individual (FIG. 7).

TABLE-US-00002 TABLE 1 Overview on integrated vital parameter with nutrition recommendation for an improvement. Vital parameter Nutrition Recommendation Sleep quality/latency Vitamins D, Amino acids, Food supplements based on magnesium or zinc Stress Omega-3 fatty acids Heart Rate Omega-3 fatty acids Blood pressure Anthocyanins, Omega-3 fatty acids Pulse wave velocity Anthocyanins, Omega-3 fatty acids

[0270] For an individual recommendation, a measurement of vital parameters of the individual must be conducted. This can be done in a continuous manner (continuous session) over a certain time period. An example of such a continuous session is a photoplethysmography (PPG) based measurement (with PPG sensors integrated in a fitness tracker) of a population. The obtained PPG signal are then used to calculate specific cardiovascular physiological parameters, via the algorithm according to the specific embodiments of the present invention.

[0271] Pilot Study (Continuous PPG Measurement to Monitor Cardiovascular Parameters)

[0272] A pilot study was conducted to analyze the functionality of the present invention. 22 healthy individuals (age: 29-59 years, gender: 82% male, 18% female) continuously measured their physiological parameters with a human body health monitoring device (fitness tracker), comprising two PPG sensors. In general, per day, two PPG-measurements for each user were performed and thereby primary physiological signals were obtained for each individual. The physiological parameters of the individuals were collected for 14 days, during which over 1800 cardiovascular parameters were calculated in total and 60 personal suggestions were given, based on deviations of calculated cardiovascular parameters from reference values. The cardiovascular parameters and the suggestions were displayed to each individual via a mobile application on a mobile device.

[0273] Based on the measured PPG signals and the specific parameters of the user: age, gender, height and weight of the user, the physiological parameters vascular age index (AgIx), pulse wave velocity (PWV), blood pressure (BP.sub.dia and BP.sub.sys) and were calculated using the algorithms: [0274] a) vascular age index AgIx: [0275] AgIx=d.sub.0+d.sub.1+d.sub.2p.sub.age+d.sub.3p.sub.height+d.sub.4, wherein is estimated based on characteristic points a, b, c, d, and e:

[00008]$=45.4*\frac{b-c-d-e}{a}+65.9;$ [0276] b) pulse wave velocity PWV:

PWV=g.sub.0+g.sub.1+g.sub.2p.sub.age+g.sub.3p.sub.height+g.sub.4; [0277] c) blood pressure BP.sub.dia and BP.sub.sys:

BP.sub.dia=l.sub.0d+l.sub.1d+l.sub.2d+l.sub.3dCT.sub.p+l.sub.4dSI.sub.p+l.sub.5dPA.sub.p

BP.sub.sys=k.sub.0s+k.sub.1s+k.sub.2s; [0278] wherein, p.sub.age is the age and p.sub.height is the body height of the subject, median (HR) is the median heart rate, PTT is the time difference between the PPG pulses, A.sub.sys and A.sub.dia are magnitudes of the systolic and diastolic peak, respectively, CT is the Crest Time, ST is the Stiffness Index and PA is the Pulse Area of the PPG signal, d.sub.0 to d.sub.4, g.sub.0 to g.sub.4, l.sub.0d to l.sub.kd, k.sub.0s to k.sub.2s, and b.sub.0 to b.sub.1 represent the coefficients of the respective linear regression equation.

[0279] The median heart rate was determined from the PPG signal and the Heart Rate Variability (HRV) was determined based on the median heart rate and the Root Mean Square of Successive Difference between normal heartbeats (RMSSD). The RMSSD was obtained by first calculating each successive time difference between heartbeats and then, each of the values is squared and the result is averaged before the square root of the total.

[0280] The calculated values for the physiological parameters were compared with pre-stored reference values (prestored physiological index parameters) relating to age, gender, height and weight of the user. Those reference values were summarized from the European Society of Hypertension (ESH) and of the European Society of Cardiology (ESC) and Bel Marra Health, and the deviation between the calculated physiological parameter and physiological index parameter was determined for each calculation.

[0281] A database was prepared, based on scientific publications indicating beneficial effects of single nutritional elements on said physiological parameters.

[0282] When a deviation from the reference values was determined, a nutritional suggestion was displayed (biofeedback/recommendation to the user), in order to achieve an improvement of said physiological parameter and overall cardiovascular health of the user.

[0283] The nutritional suggestion was outputted in a mobile application on a mobile device (output means). The user could then also provide feedback on health status via the mobile application run on a mobile phone.

[0284] One example of such a continuous session is continuous blood pressure measurement, with a total of 660 data points, which is displayed in FIG. 8. The figure shows the calculated blood pressures of a population (22 individuals) and the frequency of count of each blood pressure value inside the population. The results show a clear distinction between a diastolic and systolic blood pressure of the population. Furthermore, a normal distribution in the counts of blood pressure values can be observed (visibly shown by Gaussian function). In a control session, the used technology, was also compared to a simultaneous reference technology (sphygmomanometer). As an example of a control session, PPG measurements and vital calculations via the mentioned algorithm are compared to a simultaneous reference technology via a sphygmomanometer. An example of such a control session, with 48 data points, can be found in FIG. 9 (heart rate), FIG. 10 (vascular age index), FIG. 11 (systolic blood pressure) and FIG. 12 (diastolic blood pressure). The figures are showing the frequency of variations between calculated values using PPG devices and a simultaneous reference measurement.

[0285] After calculation of the physiological parameters, a comparison to a pre-stored reference value was conducted. Examples of such a comparison for four individuals (named A, B, C, D) in a population is summarized in table 2 and table 3. After comparison of the calculated physiological parameter with prestored physiological index parameters, the measured blood pressure (shown in table 2) and/or heart rate (shown in table 3) of each individual was classified in one of five prevention classes. Such prevention class can be for example “optimal”, “slightly higher than optimal” or “higher than optimal”. For each classified prevention class, a specific recommendation (Rec.) was outputted (summarized in table 4), e.g. user A had optimal values for blood pressure and the recommendation “0” was outputted via the mobile application, which means that no change of behavior is required.

TABLE-US-00003 TABLE 2 Individual recommendations (Rec.) for blood pressure improvement bases on continuous PPG measurement; with classification in prevention class. Blood Pressure (Average ± Deviation) [mmHg] Range Example (Systolic) (Diastolic) [Systolic/Diastolic] * Rec. A 116.92 ± 0.93 82.25 ± 1.79 Optimal/optimal 0 B 122.14 ± 1.76 88.43 ± 1.29 Optimal/slightly 1 higher than optimal C 123.75 ± 0.69 93.25 ± 2.19 Optimal/higher 2 than optimal * Blood pressure prevention class according to the European Society of Hypertension (ESH) and of the European Society of Cardiology (ESC)

TABLE-US-00004 TABLE 3 Individual recommendations (Rec.) for heart rate improvement bases on continuous PPG measurement; with classification in prevention class. Heart Rate (Average ± Deviation) Example Age/Gender [Beats per minute] Range .sup.# Rec. A 59/male 61.65 ± 6.49 Optimal 0 B .sup. 32/female 80.73 ± 1.76 Higher than 3 optimal D 32/male 70.92 ± 1.6  Slightly higher 3 than optimal .sup.# Heart rate prevention class according to Bel Marra Health considering age and gender influence

[0286] According to the prevention class for each physiological parameter, an individual recommendation for each user was generated and outputted via the mobile application on a mobile phone. As an example, the four individual recommendations from tables 2 and 3 are summarized in table 4. In case of the optimal values for physiological parameters, a biofeedback can include the information that the nutrition/lifestyle behavior is optimal, and no modification is needed “recommendation: 0” (table 4). In the case of a non-optimal physiological parameter (e.g. a blood pressure and heart rate higher than optimal for user B), biofeedback can give a recommendation on nutrition/lifestyle variation to the individual. In this example, information is given on lowering blood pressure and/or heart rate by a quantitative daily intake of specific substances “recommendation: 1+3” (table 4). Those recommendations are based on published literature (table 4). The influence of such nutrition/lifestyle variation on the improvement of the vital parameters can be measurable through continuous measurement.

TABLE-US-00005 TABLE 4 Individual recommendations to lower blood pressure and heart rate values, with quantitative daily intake information, and references to literature. Recommendation Daily intake Reference-DOI Reference-Article 0 No change of behavior needed 1 1.5 g 10.1016/j.jpeds.2010.04.001 2010, The Journal of pediatrics, Fish oil Vol. 157, No. 3, pp. 395-400 2 300 mg 10.1177/2156587213482942 2013, Journal of Evidence-Based Anthocyanin Complementary & Alternative Medicine, 18, 4, 237-242 3 0.85-3.4 g 10.1016/j.atherosclerosis.2013.10.014 2014, Atherosclerosis, 232, 1, 10- Omega-3 fatty acids 16