WO2023108177A2 - Pulsewave velocity detection device and the hemodynamic determination of hba1c, arterial age and calcium score - Google Patents

Abstract

A pulsewave velocity detection device provides a photodiode capable of detecting light transmission between 600-700nm and 880-940 nm and a light emitting diode capable of transmitting the same. A optical isolation spacer separates the photodiode and the LED. A main control chip provides processing of collected pulsewave velocity signal detection by measuring with the photodetector. A microcontroller thereby calculates a volumetric blood flow pulsation signal consisting of a wave and a retrowave are superimposed together to create a pulse wave curve and then mapped to correlate characteristics against a standard for measurement based on a normal model. Variations of the mapped pulse wave curve are then gauged to physiological parameters predictive of cholesterol levels, hemoglobin A1 C level, and peripheral artery disease indicators.

Description

PULSEWAVE VELOCITY DETECTION DEVICE AND THE HEMODYNAMIC DETERMINATION OF HBA1C, ARTERIAL AGE AND CALCIUM SCORE

BACKGROUND OF THE INVENTION

1. Field of the Invention

[0001] The present invention relates generally to systems for the measurement of glycated hemoglobin (HbA1 C), arterial age or calcium score and, more particularly, to a novel non-invasive pulsewave velocity detection device for use therein.

2. Description of the Related Art

[0002] The human body coordinates various physiological systems such as the cardiovascular neuromuscular, and excretory systems to communicate internally with each other and also with an external environment. However, there are cases where there is a malfunction within one of these systems that cause general, adverse health problems. Cardiovascular health problems caused by issues related directly or indirectly to the cardiovascular system have been one of the major, global, health concerns contributing majorly to the number of human deaths annually.

[0003] The basic function of arterial circulation is to supply body tissue with blood and to transfer pulse flow from the heart to stable flow in peripheral areas. Arterial blood pressure creates shaped waveforms when interacting between the stroke volume and the compliance of the large arteries and resistance of smaller arteries, also known as the “Windkessel effect”. Large arteries with a prevalence of elastic fibers in the arterial wall have the ability to adapt to the large pulse wave from the heart during the systolic period, in order to widen and store blood which may be supplied to tissues and organs during diastolic phase, creating compliance of the arterial system. The opposite is defined as arterial stiffness, which characterizes the degree of ageing of the arterial wall due to mechanical wear.

[0004] Pulse wave velocity increases with propagation to peripheral areas as it travels through vessels with an increased amount of collagen fibers in the arterial wall. This gradual increase of pulse wave towards the periphery is called pulse pressure amplification. Pulse wave ejected from the heart during the systolic phase is indicated as the primary pulse wave. In areas of increased resistance (structural or functional), many reverberations of the primary pulse wave occur. This results in a secondary pulse wave; this secondary wave moves through the arterial system in an opposite direction and interferes with the primary pulse wave. The resulting pulse wave and its shape in any segment of the arterial trunk are the result of summation of the primary and secondary waves.

[0005] The arterial stiffness index (AS I) is an overall measure of the health of a patient's cardiovascular system and has been identified as one predictor of cardiovascular disease morbidity and mortality. As the vessels become diseased or age with time, they become stiffer, increasing the pulse wave velocity and thus the arterial stiffness index. As such, the arterial stiffness index is a direct measure of the overall health of the cardiovascular system. Aortic stiffness is associated with atherosclerosis which may further impair ventricular perfusion, possibly leading to catastrophic reductions in ventricular function during ischemia. Aortic stiffening may increase the risk for the development of atherosclerosis. Arteriosclerosis is the hardening of the arteries, which can affect the heart, brain, kidneys, and extremities and constitute a main cause of morbidity and mortality.

[0006] Overall, cardiovascular disease accounts for 1 of every 3 deaths in the US and is the leading global cause of death. Cardiovascular disease also carries an astronomical toll to the healthcare system worldwide, and in the United States, including billions of dollars in healthcare costs annually, tremendous use of healthcare resources, and overall morbidity to the general population.

However, there are no effective ways currently in the market that can accurately diagnose the early stages of coronary artery disease. Current technology in cardiovascular disease has not been able to prevent coronary artery disease, but rather is simply able to detect and monitor the damage comorbidities have on the cardiovascular system when they become severe and symptomatic.

[0007] Prediction of cardiovascular disease in early stages would thereby be greatly beneficial. As such it has the potential to be a great screening tool for coronary artery and vascular disease. Pulse wave propagation can be described as an arterial wall disturbance caused by the ejection of the blood from the heart that propagates mainly toward the periphery. Pulsewave Velocity (“PWV”) and Arterial Stiffness Measurements are methods to measure regional arterial stiffness of the arterial territory between two measurement sites. This parameter is related not only to the elastic modulus of the arterial wall (which represents the intrinsic stiffness of the wall), but also to the arterial geometry (thickness and radius) and also blood density.

[0008] Some methods and devices are known that incorporate various mechanisms for measuring pulse wave propagation. However, in spite of these many methods and devices for identifying PWV or measure arterial stiffness, none of these current technologies have resulted in an affordable and accessible diagnostic solution for detecting and monitoring the damage comorbidities have on the cardiovascular system in their early stages. What are currently available are expensive diagnostic tests that are poorly executed for daily practice.

Carotid ultrasonography is an ultrasound-based diagnostic imaging technique to evaluate structural details of the two major blood vessels in the neck - the “carotid arteries”. Pulsewave Technologies in Austin, Texas provides a very large and expensive device that uses ankle brachial index and pulsewave velocity methodology. Non-contrast enhanced computed tomography (CT), also called calcium score heart scan, is currently used to find calcium deposits in arterial plaque of people with heart disease but has no role in screening and is mostly used to diagnose when a blockage is already 80-90%. Additionally, such testing imparts high dosages of radiation to the patient and personnel. Coronary angiography is an invasive procedure that inserts a small tube into an arm vessel and threaded through to the aorta and into heart in order to perform tests.

[0009] Consequently, there are many unmet needs for practicing physicians to overcome high cost, equipment scarcity, the need for skilled personal or specialist, or lack of portability, improvements are needed for affordable detection and monitoring of cardiovascular diseases and the state of diabetes management in order to provide early intervention for disease regression.

SUMMARY OF THE INVENTION

[0010] It is thus a general object of the present invention to provide a device for the rapid screening and monitoring of cardiovascular disease and diabetes.

[0011] It is another object of the present invention to allow for accurate screening of elasticity of small and large arteries in order to provide healthcare practitioners with a comprehensive analysis and critical information regarding arterial aging to determine the optimal treatment pathway. [0012] It is a feature of the present to provide a novel pulsewave velocity detection device having a robust and optimal form factor for use therein. More specifically, it is a feature of the present to identify the graduation of mild, moderate, and severe vascular disease in a manner that can be linked to or identified with abnormalities of the arterial stiffness index.

[0013] Briefly described according to the present invention a new pulse detection module is provided that comprises a circuit board with an integrated pulse detection component. The pulse detection component that comprises a main control chip and a pulse acquisition unit for acquiring human pulse information. Having a smaller form factor and being more precise is pulse detection, the module is user-friendly and includes start and shutdown operations are very responsive and fast. The pulse wave data detected may be used, inter alia, in the methods for non-invasively measuring glycated hemoglobin (HbA1 C), arterial age or calcium score of cardiovascular, cholesterol, and diabetes-related parameters. Based on the different cycle methods used in combination with modeled Al networks, various PPG features are extracted and further developed or analyzed. Based on preset cardiovascular parameters function, corresponding cardiovascular parameters of each cardiac cycle are obtained. The device provided, along with its operation software, allow for monitoring the status of a blood vessel by using several differential functions of the pulse wave and classifying types of the blood vessel flow. The optical finger-PPG sensing device with a biomedical sensing function measures various bio-information, pulse waves of blood vessels, and various indices to estimate the risks of arteriosclerosis. The light intensity signal of a finger as measured is used with time dynamics and analytics to evaluate the PPG signal changes for other related, biomedical applications.

[0013] According to another aspect of the present invention, the development of such non-invasive sensor may be further adapted for the measurement of glucose level directly through the measurement of oxygen saturation deduced through metabolic heat.

[0014] Advantages of the present invention allows for the acquisition of pulse waveforms, heart rate, and blood pressures, blood oxygen saturation, and vascular microcirculation parameters that can be directly outputted for direct monitoring or further analysis.

[0015] It is another advantage of the present invention to provide an integrated red infrared dual-LED for use in blood oxygen measurement through either in-office or remote patient monitoring.

[0016] It is yet another advantage of the present invention to provide a light sensor with high sensitivity over a wide spectrum.

[0017] Further, the present invention provides an ultra-small form factor (i.e. , 11 ,8mm x 5mm), has an ultra-low power consumption, and utilizes a flexible and easy-to-use LIART interface output. [0018] Further objects, features, elements and advantages of the invention will become apparent in the course of the following description.

BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The advantages and features of the present invention will become better understood with reference to the following more detailed description and claims taken in conjunction with the accompanying drawings, in which like elements are identified with like symbols, and in which:

[0020] FIG. 1 depicts a QuanCardio™ finger-tip PPG (optical) device structure according to the preferred embodiment of the present invention;

[0021] FIG. 2A, 2B and 2C are schematic representations of a top view, bottom view and side view thereof, respectively;

[0022] FIG. 3 is a schematic of the shell of the device of FIG. 1 and FIG.

2A-2C;

[0023] FIG. 4 is system configuration node diagram according to the preferred embodiment of the present invention;

[0024] FIG. 5 is a schematic block diagram depicting the operation of the QuanCardio™ finger-tip PPG (optical) device structure according to the preferred embodiment of the present invention;

[0025] FIG. 6 is a communication flowchart for a typical and exemplary operation of the preferred embodiment of the present invention; and [0026] FIG. 7 is a device-software outline for a typical and exemplary operation of the preferred embodiment of the present invention;

[0027] FIG. 8 is a Schematic Representation of Sensor Modes in a fingertype PPG sensor;

[0028] FIG. 9 depicts a PPG signal and its characteristic points;

[0029] FIG. 10 is a pictograph indicating preferred focus areas for use with the present invention;

[0030] FIG. 11 is a schematic of a Wave and Retro-wave for Pulse Wave;

[0031] FIG. 12 is a graphical representation of a Pulse Wave Structure according to the preferred embodiment of the present invention;

[0032] FIG. 13 is a graphical representation of a QuanCardio Pulse Wave Velocity Determination;

[0033] FIG. 14 is a bar graph showing Average Reference PWV Values;

[0034] FIG. 15 is a chart depicting Pulse Wave Velocity Trends;

[0035] FIG. 16 are graphical representation of QuanCardio PPG Signal Wave and its Derivatives;

[0036] FIG. 17 are graphical representations of Original Wave and Derivatives with denoted points;

[0037] FIG. 18 is a graphical representation of a PPG signal denoting the PPG peaks;

[0038] FIG. 19 is a graphical representation of a Reflection Index Trends; [0039] FIG. 20 is a graphical representation of Arterial Stiffness Trends;

[0040] FIG. 21 is a graphical representation of CAC score trends with

Aortic P W;

[0041] FIG. 22 is a graphical representation of Calcium Score Trends;

[0042] FIG. 23 is a graphical representation of a Second Derivative of a

PPG signal with notations;

[0043] FIG. 24 is a graphical representation of a Segmented Second Derivative of a PPG wave;

[0044] FIG. 25 is a bar chart showing Preliminary Accuracy Results of QuanCardio Testing;

[0045] FIG. 26 is a pictograph showing Complications Related to Diabetes;

[0046] FIG. 27 us a Schematic Description of Glucose on Light

Propagation;

[0047] FIG. 28 is a graphical representation of a Data Fitting Process according to the preferred embodiment of the present invention;

[0048] FIG. 29 is a graphical representation of a Fitted Curve for use therewith;

[0049] FIG. 30 is a graphical representation of a Cut-Off Low Values Points for use therewith;

[0050] FIG. 31 is a schematic showing a Calculation method of AGI and other parameters for use therewith; [0051] FIG. 32 is a schematic depicting a Sloping Method for AGI calculation for use therewith;

[0052] FIG. 33 is a flow diagram depicting a Verified Algorithm paired with Al Quintuple Layers of Analysis according to the preferred embodiment of the present invention;

[0053] FIG. 34 is a block diagram of the QuanCardio Algorithm for use therewith;

[0054] FIG. 35 is a block diagram of the QuanCardio Parameters Analysis for use therewith;

[0055] FIG. 36 is a block diagram of an alternate function of the present invention outlining the method for measuring metabolic heat to determine a glucose level; and

[0056] FIG. 37 is a block diagram for a method for measuring metabolic heat.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0057] The best mode for carrying out the invention is presented in terms of its preferred embodiment, herein depicted within the Figures and Tables.

1 . Detailed Description of the Figures

[0058] The present invention provides a system for use in various methods of disease screening which identifies volumetric blood flow pulsation signals. The preferred embodiment of the present invention, generally referred to herein as the “QuanCardio™” finger-type PPG (optical) sensor device, generally denoted as 10, is shown in FIG. 1 through FIG. 3. The sensor device 10 forms a pulse detection module which is highly integrated in size and operability. The sensor device 10 has a product housing 12 having a compact and integrated form factor. As shown best in conjunction with FIG. 2A through FIG. 2C, the housing 12 is formed of dimensions approximated by Table 1 below.

Table 1

[0059] The housing 12 comprises a photo diode sensor (“sensor”) 14 that is operatively controlling a light emitting diode (“LED”) 16, with a spacer 18 separating the LED 16 from the sensor 1 . The spacer 18 may be black or opaque, and preferably would provide for non-transmission between the sensor 14 and the LED 16 in order to improve signal quality. The sensor 14 may comprise an integrated circuit. Within the housing 12 the integrated circuit and LED 16 may be encased with a glass sheet 22. The glass sheet 22 protect the sensor and the sensor projection area needs to be well-connected to red and infrared light transmission materials. As such, the housing 12 and sensor 14 should preferably fit closely and, if necessary, filled with resin.

[0060] As best shown in conjunction with FIG. 4, a schematic of the integrated circuit shows a main control chip 30 in operational connection with pulse acquisition unit 32, with the additional components described in conjunction with Table 2, with the electrical performance thereof best described in conjunction with Table 3. Table 2

Table 3

[0061] Referring in conjunction with FIG. 5, a schematic block diagram is shown depicting the operation of the QuanCardio™ finger-tip PPG (optical) device structure according to the preferred embodiment of the present invention.

The device 10 includes an analog side 40 in communication with a digital side 50. The analog side 40 incorporates the optical module to act as scanner that detects the data and generates the pulsewave at a certain baud rate. The digital side 50 controls the power and manages the device 10. Analog data generated is communication 42 to a computer 44 where data may be upload and sent to the algorithm software on the computer 44 that does the analysis. The computer 44 may also contains USB and driver software.

[0062] The pulse acquisition 32 unit further comprises a photosensitive assembly, the photosensitive assembly comprises a photodiode 14 and a resistor 34. The resistor 34 is connected with the photodiode 14 in parallel, and two ends of the photodiode 14 are connected with the main control chip. The working principle involves the acquisition and processing of collected signals, calculating physiological indexes such as heart rate, blood oxygen saturation, microcirculation, and blood pressures, and the transmission of data waveforms through the universal asynchronous receiver transmitter (“UART”) interface. [0063] The QuanCardio™ device 10 of the present invention is thereby adapted for use as a piece of medical testing equipment, used mainly in cardiovascular chronic disease management and the monitoring of health abnormalities. One such preferred use includes providing a system and method for disease screening by identifying volumetric blood flow pulsation signals and to correlate the same to monitor to detect the early stages of vascular disease. The device has been developed to function much more than a pulse oximeter that measures cardiovascular conditions and used in conjunction with its related software is designed as a preventive, pre-cursive indicator of cardiovascular health and events. While various uses may be identified by those having ordinary skill in the relevant art in light of the present teachings, the device is intended to be used in particular focus on basic cardiovascular flow parameters (heart rate, mean arterial pressures), pulse analyses (pulse wave velocity, pulse pressure indices) diabetes-related factors (glucose measurements and glycated hemoglobin, peripheral artery diseases), stress indicators, arterial age estimation (arterial stiffness indices), and vascular conditions (large and small arteries circulation and blockage).

[0064] The present invention allows preventative diagnosis to be obtained by utilizing data acquired from a dual-data collection micro biosensor that provides a platform that couples photoplethysmography PPG technology with artificial intelligence (Al). Sensors embedded in a finger probe capture pulse wave velocity to measure the status of heart and blood vessels, arterial wall stiffness and biological age of arteries, among other factors. The present non- invasive medical device rapidly detects the different stages of cardiovascular disease and diabetes management by analyzing the subtle characteristics of the volumetric blood flow pulsation signals of the human body. Additionally, a non- invasive and rapid measurement of HbA1 C (average level of blood sugar over the last 3 months), arterial age or calcium score can also be obtained.

[0065] Depending on the relative position between the LED and the PD, a photoplethysmographic acquisition system may be used in reflection or transmission modes. As shown in conjunction with FIG. 8, when both optical elements are placed on the same surface and applied onto the measurement site (for example, upon the wrist), this is called reflection mode. The reflection mode as shown is where the light source and photodetector are placed in parallel, thereby allowing for the measurement of backscattered light from any skin surface. The intensity of light reaching the photodetector is measured and the variations are amplified, filtered, and recorded as a voltage signal.

[0066] In the transmission mode, also shown in FIG. 8 and as employed in the QuanCardio device, optical elements are placed from either side of the measurement site (for example, across the finger). When using photoplethysmography for heart rate acquisition, motion artifacts are one of the main issues to be addressed, particularly in ambulatory conditions. For example, if a user is still and no motion is induced at the measurement site, photoplethysmographic signal frequency are more representative of cardiac activity and heart rate. However, if the user engages physical activity, and especially ambulatory activity such as walking, running, or any other activity that can impart or imply motion on the measurement site, an unwanted noise on the PPG signal may be induced. Unfortunately, such noise has a spectral content that overlaps with the cardiac band and thus, cannot be removed using conventional linear filtering techniques.

[0067] The amplitude of the volume pulsations with each heartbeat is correlated with the flow. In PPG, the volume under study, depending on the probe design, can be of the order of 1 cm³ for transmission mode systems. The Beer-Lambert law for light transmission in an absorbing medium, as shown in Equation (1 ), is the primary basis for the functioning of the photo- plethysmograph: l = lo e - ) (1) where I is the transmitted intensity, Io is the input light intensity, p_a is the absorption coefficient, and d is the distance between source and detector.

[0068] Since blood is a highly scattering medium, the Beer-Lambert's law must be modified to include an additive term G due to scattering losses and a multiplier B to account for the increased optical path length due to scattering and absorption. The modified Beer-Lambert's law which incorporates these two additions is shown in Equation (2): l = l o e - ^B+G) (2).

This approach helps to develop an understanding of the absorbencies of light as it passes through living tissues and the mechanism of PPG, where G is a factor dependent upon the measurement geometry and the scattering coefficient of the tissue. The wavelength of the source used is of significant importance in PPG. Light sources that operate in the near red (600-700nm) and near-infrared (880- 940 nm) of the spectrum are most preferred as effective because the whole blood has a relatively small absorption at wavelengths greater than 620nm. [0069] PPG signals usually exhibit an early systolic peak and a later peak or point of inflection that occurs a short time (t) after the first peak in early diastole. The first peak is formed mainly by pressure transmitted along a direct path from the left ventricle to the finger or measuring site (where it generates a change in blood volume). The second peak is formed in part by pressure transmitted along the aorta and large arteries to sites of impedance mismatch in the lower body, where it is reflected up the aorta. It can thus be used to infer the transit time taken for pressure to propagate along the aorta and large arteries to the major sites of reflection in the lower body and back to the root of the subclavian artery.

[0070] The finger-type PPG signal reflects the blood movement in the vessel, which goes from the center (heart) to the end (fingertips) in a wave-like motion as shown in FIG. 9, wherein M is the Systolic peak, S is the rise point, P is the Systolic notch and Q is the Diastolic peak. The signal may be affected by the heartbeat, hemodynamics, and the physiological condition caused by the change in the properties of an arteriole. These effects can be observed as distortions in the wave profiles. It is preferred that analyzing the PPG waveform may be used in circulatory and respiratory monitoring.

[0071] Two phases of the signal may be distinguished in an AC-PPG pulse: the anacrotic phase between points S and M, corresponding to the rising edge of the pulse and mainly related to systole; and, the catacrotic phase between points M, P and Q, corresponding to the falling edge of the pulse and related to the diastole and wave reflections from the periphery. A dicrotic notch is usually observed in the catacrotic phase.

[0072] It is further intended that similar modification and improvement in the simplified model of light behavior developed based on Beer-Lambert law may provide new possibilities in interpreting the PPG wave and may include identification of many smaller components superimposed into the PPG signal and its resulting wave. It is intended that identification of such smaller components may be identified as indicative with the cooperation of circulatory, respiratory, and autonomic systems and their influence on the flow of both arterial and venous blood.

[0073] A multitude of factors affecting the blood flow and factors variable individually (such as the different thickness of the finger, skin color, content of subcutaneous fat) can cause many difficulties in reading and interpretation of the PPG wave. An example is a fact that until today there is no known method of PPG calibration, which means that one cannot compare the absolute numbers received in different people and it is impossible to set one reference point.

[0074] The present improved technologies of photoplethysmography (PPG) may thereby be used in various fields of medicine and clinical studies such as the monitoring of physiological parameters, levels of oxygen saturation in the blood, cardiac output, heart rate, and its variability, blood and pulse pressures, respiration, lung capacity, vascular assessment, arterial diseases, arterial compliance and aging, venous assessment, endothelial functions, microvascular blood flow or microcirculation, vasospastic conditions, autonomic function monitoring, vasomotor function and thermoregulation, orthostasis and other cardiovascular variability assessments. However, with the main focus on the cardiovascular system, the present invention focuses primarily on those areas as shown in Figure, with particular focus on the indicators and measurement of parameters to aid in the detection of atherosclerosis by measuring cholesterol levels, and stiffness index. Further focuses on hypertension may also be achieved by measuring basic cardiovascular parameters such as the heartrate, blood pressures and pulse pressures. Further still, the severity of glucose related diseases and adverse effects on peripheral blood vessels may also be measured with HbA1 C and PAD indicators being identified.

[0075] Using the finger type (clip) PPG technology, the present invention provides the platform to detect arterial stiffness, heart rate, measure blood oxygen, microcirculation, HbA1c and CAC using cloud-computing and an artificial intelligence to aid the doctor to make very informed analysis. In order to accomplish such various early indicators, the PPG wave signal may be processed to reveal the condition of the cardiovascular circulatory system, especially the arterial system. By analyzing the subtle characteristics of the volumetric blood flow pulsation signals of the human body, an arterial stiffness index (“SI”) is determined by identifying the conduction time difference between the incident wave and the reflected wave (Ax Index).

[0076] To accomplish this, a software interface is provided forming a computer-based PPG analyzer. The signal is first obtained by infrared light through the finger. This signal is then converted into the digital domain by a signal processing circuitry, which contains amplifying and filtering steps, a microcontroller, and an analog to digital converter, for display and further analysis. [0077] While other measurements may be obtained, three main forms of measurements may be obtained to estimate a depiction of current health conditions and as an indicator for predicting future cardiovascular risks and events. Blood flow analysis records and measures the flood of blow in the blood vessels from the changes in the direct relation of light absorption or blood flow changes. Blood volume pulse sensing deals with the PPG signal and the basic parameters deduced from the PPG wave. Digital Pulse Wave Analysis estimates and assesses how the walls of the arteries expand and relax when the heart beats and the blood travels through the arteries, and other derivative information from the original PPG wave. Signal analysis techniques may be used to estimate cardiovascular parameters such as: Basic Cardio-related Parameters; Pulse Wave Velocity; Pulse Wave Analysis; Pulse Transit Time; Pulse Pressure Index; Arterial Conditions, Vascular Health and Stiffness Index; Estimated Arterial Age; Diabetes (i.e., A1 C).

[0078] Basic Cardio-Related Parameters. Use of basic heart-related parameters may be used to indicate general cardio-health, risk of arteriosclerosis, and other heart-related diseases. Examples of such parameters include; heart rate, pulse signal, blood pressures (systolic and diastolic), blood oxygen saturation, mean arterial pressure and microcirculation within small vessels.

[0079] Pulse Wave Velocity. Pulse wave velocity is the speed at which the blood pressure pulse propagates an artery or a combined length of arteries.

The pulse wave velocity may be obtained by segmenting the PPG signal from the device with noise-signal cancellation methods for accuracy and reproducibility, into parts with various problems of capped data (wave distortion) solved with the averaging technique, a model fitting to optimize. Further methods and development of the Pulse Wave Velocity will be to model and predict a general equation of a PPG signal wave based on an age group, race, and gender types for a normal person used as a standard for measurement. To derive the Pulse Wave Velocity, both the Wavelength and the Pulse Transit Time are needed. Several types of pulse wave velocities currently exist such as; normal or arterial, the pressure, the aortic, the branchial, the carotid, the ankle, the toe, the femoral, and the blood pulse wave. These different types evolve as there is contraction and retraction of the heart muscles to various parts of the body. However, for different devices, either one or a combination of these pulse waves are used.

[0080] As shown best in conjunction with FIG. 11 , according to one aspect of the present invention a double system of a wave and retro-wave are incorporate to create a super-imposed pulse wave that may be referred to as a heart-finger and heart-toe-finger paths, or a carotid-femoral pulse wave structure. For determination of the Wavelength of a Pulse Wave, several approaches may be used such as the Peak, Delay, Notch, and Maximum Slope methods. As shown best in conjunction with FIG. 12 and FIG. 13, these methods may incorporate the following datapoints:

• A and A' = Starting points of the Pulse Waves (Foot of the wave);

• B and B' = Pre-defined point of the Pulse Waves;

• C and C = Highest peaks of the Pulse Waves (Systolic Peaks);

• D and D' = Second rise (peak) of the Pulse Waves (Diastolic Peaks);

• E and E' = Systolic Notches; and

• F and F' = Dicrotic Notches.

In general, the distance between any point defined on one part of the wave to the same point recurring on the wave is considered the wavelength. For the Peak Method, either the distances between the systolic peaks (C and C) as shown in FIG. 12 or between the diastolic peaks (D and D') are considered as the Pulse Wavelength. Signal conditioning algorithms may be incorporated using such a method, specifically the Systolic peak-to-peak intervals. For the Delay Method, the distance from the starting points (A and A') as shown in FIG. 12, is considered the wavelength of the pulse wave, with the dotted lines sectioning a complete, single pulse wave. This method is called the delay method because there is usually a time delay in the recurrence of the start point between successive intervals of the wave. The compensated time delay is usually used in pulse-time calculations.

[0081] The Notch method for wavelength determination utilizes either the distances between two successive systolic notches (E and E') or dicrotic notches (F and F'). The presence or absence of especially the dicrotic notch makes this method not very comprehensive and inaccurate. The maximum slope method is usually used for pre-defined points on the pulse wave where the second derivative of the wave function is equal to zero. In the preferred embodiment of the present invention, some techniques may be used to measure and ensure accuracy, stability, and precision, as well as error estimation in the reading of Pulse Wave Velocity values using Averaging and Segmenting Methods. The Averaging method used in the beginning stages of development considered the final reading of the PWV value as the average of all systolic peaks. Due to noise, this method was further advanced into the Segmenting method. The segmenting method, however, incorporates other methods such as Time-Filtering Stabilization, Fitting and Modelling, and the averaging method. The time stabilization methods allow a few seconds where the data recorded is filtered out due to fluctuations caused by noise, and to stabilize the range of reading. This method also brings problems as data seem to be capped and very high values distort the readings. To solve the data capping problems, the model was redesigned and fitted with the pulse wave, segmented into single parts, and then wavelengths and pulse times determined. The individual pulse wave velocities were then averaged.

[0082] Algorithms for Wavelength may thereby be used to determine the distance between point C to C and for Pulse Transit Time, the time that the wave takes to travel from the Systolic to the Diastolic peaks (from C to D). After preliminary tests, average reference PWV values as shown in FIG. 14 compared with QuanCardio PWV produced the results as shown in FIG. 15.

[0083] Pulse Wave Analysis. The fluctuations observed in the original PPG as shown in FIG. 16 are influenced by arterial and venous blood flow, as well as the autonomic and respiratory systems of the peripheral circulation. Such information could be used more comprehensively for phenotyping cardiovascular health. Due to increasing health care costs, a single sensor from which multiple clinical data points can be derived has become very attractive from a financial perspective.

[0084] Various feature points of PPG images are particularly useful. Four (4) key feature points include:

• Point a, the first extreme point of a PPG cycle, which is used to divide the whole signal into small cycles and PTT calculations;

• Point b, the first minimal point of the PPG cycle and used to calculate the AGI;

Point c, the second extreme point of the PPG cycle and used for PTT calculations and Rl calculations; and

• Point d, the second minimal point of the PPG cycle and is used for the AGI calculation.

A wave-point algorithm may focus on the points on the wave, as well as its derivatives for the determination of AGI and other parameters.

[0085] In many pulse wave applications, the path length of the wave is unknown but can be assumed to be proportional to subject height (h). The diastolic peak or inflection point is defined as the point at which the first time derivative of the wave (dPPG/dt) is closest to zero. The diastolic peak occurs when dPPG/dt is zero, whereas an inflection point occurs when dPPG/dt approaches zero.

[0086] As shown in conjunction with FIG. 16 and FIG. 17, various feature points of a Derivative PPG (DPPG) and Second derivative PPG (SDPPG) are also particularly useful. The analysis of PPG images is used to extract the mathematical information of the four points, a, b, c and d. The first and second derivatives method was used to calculate further information from the PPG wave. DPPG is the first order derivative of the PPG image, and a zero value of DPPG means that there are extreme points in the PPG image. SDPPG is the second order derivative of the PPG image, and also is the first order derivative of the DPPG image. If the value of DPPG is zero and the value of SDPPG is negative, then the PPG image has extreme value points. If the value of DPPG is zero and the value of SDPPG is positive, then the PPG image has a minimal value point. Both SDPPG and DPPG are auxiliary tools used to analyze PPG images. It is a tool used to visually analyze the properties of PPG images.

[0087] With the second derivative as shown in FIG. 17, four separate systole waves (named a - d) and a diastole wave (named e) may be obtained. The a and b waves on the second derivative of PPG are included in the early systolic phase of the PPG whereas the c and d waves are included in the late systolic phase. The height of each wave from the baseline was measured and their ratios b/a, c/a, d/a, and e/a were calculated.

[0088] Pulse Transit Time. Pulse Transit Time is a measurement of the time it takes for the heart pulse wave to travel throughout your body (heart to the left index finger, and heart to the legs, and then to the left finger in a retro-wave, for the present invention). Out of the various methods of pulse transit time determination, the foot-to-foot algorithm was used, where the time it takes for the PPG signal to rise (or the foot of the wave) is used as the reference point of measurement. Pulse Transit Time in terms of accuracy was well-calibrated based on the various algorithms used in QuanCardio for pulse wavelength determination, hence, there is no need to incorporate delay factors that may occur in other measuring methods such as the brachial-ankle method of Pulse Wave Velocity determination. With a consistency of ten-time points for each segmented pulse, with the time for one point on the PPG image being 0.02s, the normal average PTT value is 0.02 seconds.

[0089] Pulse Pressure Index. Despite the standard measurement of the blood pressures (systolic and diastolic blood pressures), a more accurate and reliable index of the mean arterial pressure is used. The pulse pressure is also further optimized to a Pulse Pressure Index (PPI). A Reflection Index indicates peripheral arterial stiffness and vascular tone of small arteries. The Rl index is calculated as the ratio of PPG's second wave peak to its first wave peak amplitudes. The Reflection Index (Rl) is derived as a ratio of pulse inflection peak amplitude (second peak) over the pulse max amplitude, as shown in FIG.

18 and Equation (3):

Rl = b / a (3); where Rl can provide a window to vascular age and arterial compliance. Rl mainly depends on the detection of PPG second peak which tends to be less pronounced with aging. The systole, diastole, and dicrotic notch points over the PPG contour may also be located and calculated by an optimized algorithm.

[0090] The measure of the vascular tone of the small arteries is known as the Reflection Index or Rl. Influences that cause variations in Rl can be as simple as the effect of caffeine or exercise. Reflection Index trends measured with preliminary testing of the QuanCardio device is shown in FIG. 19.

[0091] Arterial Conditions, Vascular Health and Stiffness Index. The present invention may estimate various conditions of the arteries such as Stiffness index, and other related indices for vascular such as Ageing Index (AGI), Ageing Character Index (ACI), and build-up of mineral of fat deposits causing the blockage in the arteries (CAC score).

[0092] The coronary artery calcium score is an atherosclerosis test used to screen individuals at risk for coronary heart disease who do not yet have symptoms. This is a direct assessment of the disease itself and allows for a more granular and objective assessment of heart attack and stroke risk.

CAC score have shown to have various relationships with PWV as shown in FIG. 21 , and as well as other cardiovascular parameters which were optimized and calculate the score in as shown in FIG. 22 during preliminary testing.

[0093] Estimated Arterial Age. Based on various parameters optimized, estimates of the age of the arteries were deducted, and the information from the derivatives of the original PPG wave was further used to analyze the age conditions of the arteries and other blood vessels. Aging Index Value, one of the parameters used, represents the age of the arteries and other cardiovascular vessels. This parameter was calculated with the Equations (4) and (5), and the process of measuring shown in FIG. 23 and FIG. 24:

AGI = (b-c-d-c)/ a (4); and

AGI = (b-e)/a (5).

Preliminary testing of the QuanCardio device and software gave the results as summarized in FIG. 25.

[0094] Diabetes. The determination of A1 C value may be provided based on the shape of the PPG signal, with an emphasis of the dicrotic notch, and the b/a index is used in the estimation of a patient's diabetic conditions or Peripheral Artery Disease (PAD) conditions, and also the impact of other cardio-vascular diseases such as COVID-19 and recovery, sugar and fat build-up (cholesterol) on general health.

[0095] Diabetic patients are most prone to stroke and heart attacks. In most cases, high blood pressure is a pre-indicator condition for diabetes.

Diabetes or an upsurge of blood sugar levels hinders the balance between vasodilation and vasoconstriction, by reducing levels of Nitrogen oxide as well as other agents in the blood and this causes different elasticities within the blood vessel. The endothelium, or lining of the arteries, gets destroyed eventually with time and the plaque buildup within the arteries causes a hardening of the arteries (arteriosclerosis).

[0096] Blood pressure is strongly related to PVW, as shown in FIG. 26. Harder arteries will exhibit a different PPG morphology than normal or high elastic arteries, hence the arterial stiffness indices, as well as PWV of diabetic patients can be used as a parameter to measure diabetic-related diseases and applied to Peripheral Artery Disease. Further, the use of PPG may have additional applications in the assessment of blood circulation in patients with diabetes. Diabetes is a disease that can cause complications for virtually any system. Particularly destructive change in macro-and microcirculation, which significantly reduces the quality of life of patients and increases their mortality Many studies have confirmed the utility of PPG as a simple test to assess peripheral circulation in patients without systemic diseases, but the use of this method in patients with diabetes and vascular complications may be significantly impeded.

[0097] In addition, researchers indicate that PPG may be a reliable tool for the assessment of disorders in microcirculation in patients with type 2 diabetes.

PPG pulse amplitudes can be used to assess microcirculation in people with type 2 diabetes. The b/a index represents the ratio of PPG's second derivative b value to PPG's second derivative a value. (SDPPG) as shown in Equation (6).

This index indicates the risk of occurrence of atherosclerosis and other cardiovascular diseases. b/a_index = b / a (6).

Glycated hemoglobin (HbA1 c) is the average blood glucose (sugar) level usually within a period between the last three months. It gives the probability of the development of diabetes complications, with the eyes and feet.

2. Operation of the Present Invention

[0098] In the method of predicting the type of blood vessel from the acceleration pulse wave, the vascular health condition is calculated by the ratio of peak to the second derivative of blood volume pulse wave. However, to accurately diagnose the condition of blood vessels by the ratio of peak to the second derivative. Difficult cases may arise, and the results may vary with each measurement, resulting in poor reliability as a diagnostic device. Thus, there is a need for a method for more accurately and reliably diagnosing vascular conditions using acceleration pulse waves. In addition, there is a need to detect various health indices such as vascular age, vascular health index, and arteriosclerosis by detecting the state of blood vessels. Accordingly, as best shown in conjunction with FIG. 6 and FIG. 7. the present invention provides a method for detecting an accurate vascular health condition by detecting a waveform of a PPG signal and converting the waveform into a derivative function of various orders to quantify the degree of blood circulation.

[0099] In operation the improved pulse wave velocity detection device of the present invention may be uses as part of a wide variety of noninvasive diagnostic testing in a number of areas. These may include the following.

[0100] Various algorithms were used to optimize the accuracy of readings of the QuanCardio finger-type PPG (optical) sensor device. The analyses of the SI and Ax index within the present invention consists essentially of five layers of processing: data fitting method; image analysis; data clustering; cycle acquisition method; and artificial intelligence. [0101] The data fitting method preferably obtains a plurality of time spaced pulse points to form a line. In a more preferred embodiment, initial data collection involved the capture of the individual patient's pulse information connected to form the curve or wave form. The device collects sixty-four (64) separate pulse points every 1 .28 seconds and connected them into a line to form the PPG Image. Data filtering was done to remove extreme outlier points, and the remaining good fit data used for further calculation. This method however, posed challenges as the device captured one pulse point every 0.02 seconds, making any subtle movement while the patient is being measured captured by the device. This causes the data collected by the device to be susceptible to interference, and irrelevant noise. Various statistical filtering tools such as averaging was used in the calculation process to reduce interference, but that was not enough for high accuracy readings.

[0102] An improved solution may fit the data collected through a modelled fitting process as shown in FIG. 28. The data fitting process uses each pulse point in an image and find a curve that best represents all the points instead of the original pulse points. The fitted curve as shown in FIG. 23 is able to analyze the image shape as a whole, which is more effective than the original way of directly connecting and then filtering.

[0103] Another challenge noticed after the data fitting process was some peak values were cut-off and represented as very low digits as can be seen in FIG. 30. This issue was resolved by recalibrating the peaks of the device and adding 256 to the values of the very-low recorded data. This fixed all cut-off points to their supposed locations on the curve. It was still noticed that the accuracies of the readings were not optimum, hence the need for another algorithm to collect the data in periodic cycles.

[0104] The original acquisition method was based on a linear acquisition method, which is susceptible to fluctuations in patient posture changes. Any slight change in the patient's posture caused a dramatic change in the data.

[0105] The use of cycle acquisition method excluded abnormal cycle data and ensure correct data acquisition. The periodic cycle acquisition method is to decompose the PPG images into small cycles, analyze the images of each small cycle individually, and finally average all the cycle images for calculation., as shown in FIG. 31 . In the periodic cycle method, firstly, the PPG image is decomposed into smaller images according to the cycle. The PWV, PTT, CAC, Aging Index (AGI) and other parameters are individually for each small image.

The values calculated for each cycle are then averaged and distributed to obtain the average.

[0106] The initial AGI method called the slope method which was further advanced to the wave-point method, would be further explored with the Al techniques, however, at the beginning stages, this method was not helpful. By comparing newly obtain images with historical records of other patients, different patterns and their association with other general health data can be correlated to arrive at a diagnosis. Using differential diagnostics, the Al comparison may become more accurate and robust over time. With the slope method, each wave was to be considered based on the shape of the wave. As shown in FIG. 32, the slope of the first minimum and the second minimum of each cycle determined the health of the patient's blood vessels. The larger the slope, the healthier the blood vessel; the smaller the slope, the more senile the vessel.

[0107] Data Clustering is performed using a decision tree to match the PPG images read by the device to one of the seven classes of arterial stiffness. Period cycling method excludes abnormal cycle data and ensure correct data acquisition. Artificial Intelligence Image comparison and data analytics helps the software get smarter with every patient. Comparing images with images of other patients and general health data to identify patterns. Using differential diagnostics to arrive at a correct diagnosis. These algorithms can be summarized as shown in FIG. 33.

[0108] The present invention, in terms of parameters measured, may also be classified as shown in FIG. 34. Further analysis on parameters can also be summarized as shown in FIG. 35.

[0109] It should be understood that the legal scope of the description is defined by the words of the claims set forth at the end of this patent and that the detailed description is to be construed as exemplary only and does not describe every possible embodiment since describing every possible embodiment would be impractical, if not impossible. Numerous alternative embodiments could be implemented, using either current technology or technology developed after the filing date of this patent, which would still fall within the scope of the claims. By way of example, and not meant as a limitation, according to another aspect of the present invention the development of such non-invasive sensor may be further adapted for the measurement of glucose level directly through the measurement of oxygen saturation deduced through metabolic heat. Such an adaptation would result in the first non-invasive glucose meter for continuous monitoring and analyzing of blood glucose levels. By adapting the described non-invasive hardware, a non-invasive glucose measurement technology may be provided using metabolic heat conformation and optical methodology. Such a non- invasive portable glucose meter may integrate various sensors for signal collection, a processing circuit for signal conversion and amplification, a microprocessor to calculate the values of the parameters (such as blood glucose concentration, oxygen saturation, hemoglobin concentration, blood flow volume, pulse, ambient temperature and humidity, and shell temperature and humidity), and to display the measurement results.

[0110] The principle of operation for such a device adaptation provides two parameters, oxygen saturation and Hb concentration, which are measured using photoelectric methods. Infrared lights of specific wavelengths irradiate the skin surface of fingers at a fixed time series. Subsequently, the luminous intensity through the finger is measured and used to calculate the required parameter information. The heat dissipation cannot represent local metabolic heat; thus, core temperature, resistance of clothes, and blood flow volume are used to correct the local metabolic heat. The blood glucose value of the metabolic heat integration method is calculated by using local metabolic rate, blood flow volume, Hb concentration, oxygen saturation, and corrected blood glucose. Photoelectric signals and Hb concentration are used to obtain infrared-based blood glucose value.

[0111] The metabolic oxidation of glucose in the human body, also known as cellular respiration, provides most of the energy necessary for cellular activities. Non-invasive measurements of glucose concentration in the blood takes into account the body heat generated by glucose oxidation and local oxygen supply. This concept was developed by observing that the circadian rhythm of the human body conforms to the subtle balance seen among metabolic heat, local oxygen supply, and glucose concentration. As best shown in conjunction with FIG. 36 and FIG. 37, the system may be adapted to provide three temperature sensors, two humidity sensors, an infrared sensor and an optical measurement device. The glucose level can be deduced from the quantity of heat dissipation, blood flow rate of local tissue and degree of blood oxygen saturation. [0112] The two parameters, oxygen saturation and Hb concentration, may thereby be measured using photoelectric methods. Infrared lights of specific wavelengths irradiate the skin surface of fingers at a fixed time series.

Subsequently, the luminous intensity through the finger is measured and used to calculate the required parameter information. The heat dissipation cannot represent local metabolic heat; thus, core temperature, resistance of clothes, and blood flow volume are used to correct the local metabolic heat. The blood glucose value of the metabolic heat integration method is calculated by using local metabolic rate, blood flow volume, Hb concentration, oxygen saturation, and corrected blood glucose. Photoelectric signals and Hb concentration are used to obtain infrared-based blood glucose value.

[0113] The foregoing descriptions of specific embodiments of the present invention are presented for purposes of illustration and description. The Title, Background, Summary, Brief Description of the Drawings and Abstract of the disclosure are hereby incorporated into the disclosure and are provided as illustrative examples of the disclosure, not as restrictive descriptions. It is submitted with the understanding that they will not be used to limit the scope or meaning of the claims. In addition, in the Detailed Description, it can be seen that the description provides illustrative examples, and the various features are grouped together in various embodiments for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claimed subject matter requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive subject matter lies in less than all features of a single disclosed configuration or operation. The following claims are hereby incorporated into the Detailed Description, with each claim standing on its own as a separately claimed subject matter.

Claims

- 42 - CLAIMS What is claimed is:

1 . A pulsewave velocity detection device comprising: a pulse acquisition unit comprising: a photosensitive assembly comprising: a photodiode capable of detecting red light transmission between 600nm and 700nm and near-infrared light transmission between 880nm and 940 nm; a resistor in electrical communication with the photodiode in parallel; a light emitting diode capable of transmitting the red light and the near-infrared light; a spacer separating the LED from the photodiode, said spacer providing optical isolation between the photodiode and the LED; a main circuit board having a main control chip in operational connection with the pulse acquisition unit; said main control chip adapted for processing of collected pulsewave velocity signals and transmitting of data waveforms through a universal asynchronous receiver transmitter interface; and a housing configured as a finger mounted sensor device containing the - 43 - pulse acquisition unit, the spacer and the main circuit board, at least a portion of said housing be encased with a material optically transmissive at least to the red light and the near-infrared light, said transmission material in close optical connection with at least the photodetector and the LED.

2. The pulsewave velocity detection device of claim 1 , wherein said data waveforms transmitted through the universal asynchronous receiver transmitter interface are adapted for the identification of at least one physiological parameter selected from a group consisting of: heart rate; pulse signal; systolic blood pressure; diastolic blood pressure; blood oxygen saturation; mean arterial pressure; microcirculation within small vessels; levels of oxygen saturation in the blood; cardiac output; heart rate; heart rate variability; blood pressures; pulse pressures; respiration; lung capacity; vascular assessment; arterial diseases; arterial compliance; arterial aging; venous assessment; endothelial functions; microvascular blood flow or microcirculation; vasospastic conditions; autonomic function monitoring; vasomotor function and thermoregulation; orthostasis; other cardiovascular variability assessments.

3. The pulsewave velocity detection device of claim 3, wherein said data waveforms are filtered for motion artifacts prior to transmission. - 44 -

4. The pulsewave velocity detection device of claim 1 , wherein said data waveforms transmitted through the universal asynchronous receiver transmitter interface are adapted for the identification of at least one predicted physiological parameters are used to estimate cardiovascular parameters selected from a group consisting of: Basic Cardio-related Parameters; Pulse Wave Velocity; Pulse Wave Analysis; Pulse Transit Time; Pulse Pressure Index; Arterial Conditions, Vascular Health and Stiffness Index; Estimated Arterial Age; and HbA1 C.

5. A method for the noninvasive measurement of blood glucose levels comprising: irradiating a skin surface of a finger with infrared light at an operative wavelength and at a fixed time series using a pulsewave velocity detection device of claim 1 ; measuring a luminous intensity through the finger using the photodetector; correcting the measurement of luminous intensity for resistance of clothes; calculating local metabolic rate, blood flow volume, Hb concentration, oxygen saturation, and corrected blood glucose; and correlating a corrected blood glucose value to the calculated local metabolic rate, blood flow volume, Hb concentration, and oxygen saturation.

6. A method for the noninvasive measurement of blood glucose levels comprising: irradiating a skin surface of a finger with infrared light at an operative wavelength and at a fixed time series using a pulsewave velocity detection device of claim 2; measuring a luminous intensity through the finger using the photodetector; correcting the measurement of luminous intensity for resistance of clothes; calculating local metabolic rate, blood flow volume, Hb concentration, oxygen saturation, and corrected blood glucose; and correlating a corrected blood glucose value to the calculated local metabolic rate, blood flow volume, Hb concentration, and oxygen saturation.

7. A method for identifying precursor indicators of cardiovascular disease and diabetes comprising: identifying a volumetric blood flow pulsation signal consisting of a wave and a retrowave superimposed together to create a pulse wave curve; mapping the pulse wave curve to correlate characteristics of the mapped pulse wave curve to characteristics of the volumetric blood flow pulsation signals, identify a conduction time difference between the incident wave and the reflected wave, and to identify systolic peaks, systolic notches, diastolic peaks, dicrotic notches, and curve maximum slopes within the mapped pulse wave curve; comparing the mapped pulse wave curve against a standard for measurement based on a normal model; predicting, based upon variations of the mapped pulse wave curve from the normal model, at least one physiological parameter selected from a group consisting of: levels of oxygen saturation in the blood; cardiac output; heart rate; heart rate variability; blood pressures; pulse pressures; respiration; lung capacity; vascular assessment; arterial diseases; arterial compliance; arterial aging; venous assessment; endothelial functions; microvascular blood flow or microcirculation; vasospastic conditions; autonomic function monitoring; vasomotor function and thermoregulation; orthostasis; other cardiovascular variability assessments.

8. The method of claim 7, wherein the volumetric blood flow pulsation signal is filtered for motion artifacts prior to create the pulse wave curve.

9. The method of claim 8, wherein the normal model is modified based on physiologic characteristics selected from a group consisting of: age; race; and gender.

10. The method of claim 9, wherein said cardiovascular flow parameters are selected from a first group - 47 - consisting of: heart rate, pulse signal; systolic blood pressure; diastolic blood pressure; blood oxygen saturation; mean arterial pressure; and microcirculation within small vessels; said pulse analyses are selected from a second group consisting of: pulse wave velocity; pulse pressure indices; and pulse wave velocity (PWV) values; said diabetes-related factors are selected from a third group consisting of: glucose measurements; and glycated hemoglobin; and said vascular conditions are selected from a fourth group consisting of: large arteries circulation; small arteries circulation; and blockages or calcium scores.

11 . The method of claim 10, wherein the pulse wave velocity (PWV) values are further correlated to a vascular indicator selected from a group consisting of: an arterial stiffness index (“SI”), an arterial age estimation; an Ageing Index (AGI); Ageing Character Index (ACI); and build-up of mineral of fat deposits causing the blockage in the arteries (CAC score).

12. A system for performing the method of claim 7, wherein the volumetric blood flow pulsation signal is obtained using photoplethysmography

13. The system of claim 12, wherein the photoplethysmography comprises a - 48 - finger-type optical sensor device.

1 . The system of claim 13, wherein the optical sensor device operates at a spectrum with the near red (600-700nm) or the near-infrared (880-940 nm).

15. The method of claim 7, wherein the mapped pulse wave curve to characteristics of the volumetric blood flow pulsation signals is further calibrated to variable physiological traits.

16. The method of claim 15, wherein the variable physiologic traits are selected from a group consisting of: different thickness of the finger; skin color; and content of subcutaneous fat.

17. The method of claim 7, wherein the predicted physiological parameter is further gauged to a cholesterol levels.

18. The method of claim 7, wherein at least one of the predicted physiological parameters is further gauged to a hemoglobin A1 C level based on a shape including the dicrotic notch.

19. The method of claim 7, wherein at least one of the predicted physiological - 49 - parameters is further gauged to a peripheral artery disease indicator.

20. The method of claim 7, wherein at least one of the predicted physiological parameters are used to estimate cardiovascular parameters selected from a group consisting of: Basic Cardio-related Parameters; Pulse Wave Velocity;

Pulse Wave Analysis; Pulse Transit Time; Pulse Pressure Index; Arterial Conditions, Vascular Health and Stiffness Index; Estimated Arterial Age; and HbA1 C.

21 . The system of claim 12, wherein the method of mapping of the pulse wave curve is obtained with a data fitting method comprising obtaining a plurality of time spaced pulse points to form a line.

22. The system of claim 15, wherein the plurality of time spaced pulse points comprises a collection of sixty-four (64) separate pulse points every 1 .28 seconds and connected to a line to form the PPG Image.

23. The method of claim 7, further wherein the standard for measurement based on a normal model is iteratively improved by comparing with historical records of other patients and different patterns and their association with other general health data and diagnoses. - 50 -

24. A method for identifying precursor indicators of disease comprising: identifying a volumetric blood flow pulsation signal consisting of a wave and a retrowave superimposed together to create a pulse wave curve; mapping the pulse wave curve to correlate characteristics of the mapped pulse wave curve to characteristics of the volumetric blood flow pulsation signals, identify a conduction time difference between the incident wave and the reflected wave, and to identify systolic peaks, systolic notches, diastolic peaks, dicrotic notches, and curve maximum slopes within the mapped pulse wave curve; comparing the mapped pulse wave curve against a standard for measurement based on a normal model; predicting, based upon variations of the mapped pulse wave curve from the normal model, at least one physiological parameter that is predictive of the disease selected from a group consisting of: cardiac stress; physical stress; clinical depression; mental stress; cerebrovascular disease; and anxiety.

25. The method of claim 24, wherein said precursor indicators of disease is correlated to a measured index selected from a group consisting of: vascular index (VI); Systemic Vascular Resistance Index (SVRI); Pulse Pressure Index (PPI); Heart Rate Variability; cortisol level; dopamine level; norepinephrine; and serotonin level. - 51 -

26. A method for the noninvasive measurement of blood glucose levels comprising: irradiating a skin surface of a finger with infrared light at an operative wavelength and at a fixed time series; measuring a luminous intensity through the finger using a photodetector; correcting the measurement of luminous intensity for resistance of clothes; calculating local metabolic rate, blood flow volume, Hb concentration, oxygen saturation, and corrected blood glucose; and correlating a corrected blood glucose value to the calculated local metabolic rate, blood flow volume, Hb concentration, and oxygen saturation.