Classifications

• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/02—Detecting, measuring or recording for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
  • A61B5/021—Measuring pressure in heart or blood vessels
  • A61B5/02108—Measuring pressure in heart or blood vessels from analysis of pulse wave characteristics
  • A61B5/02125—Measuring pressure in heart or blood vessels from analysis of pulse wave characteristics of pulse wave propagation time
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/02—Detecting, measuring or recording for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
  • A61B5/024—Measuring pulse rate or heart rate
  • A61B5/02416—Measuring pulse rate or heart rate using photoplethysmograph signals, e.g. generated by infrared radiation
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value ; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid or cerebral tissue
  • A61B5/14532—Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value ; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid or cerebral tissue for measuring glucose, e.g. by tissue impedance measurement

Definitions

• 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.
• HbA1 C glycated hemoglobin
• 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.
• cardiovascular neuromuscular, and excretory systems to communicate internally with each other and also with an external environment.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• a new pulse detection module 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.
• 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.
• 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.
• 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.
• FIG. 1 depicts a QuanCardioTM finger-tip PPG (optical) device structure according to the preferred embodiment of the present invention
• FIG. 2A, 2B and 2C are schematic representations of a top view, bottom view and side view thereof, respectively;
• FIG. 3 is a schematic of the shell of the device of FIG. 1 and FIG.
• FIG. 4 is system configuration node diagram according to the preferred embodiment of the present invention.
• FIG. 5 is a schematic block diagram depicting the operation of the QuanCardioTM finger-tip PPG (optical) device structure according to the preferred embodiment of the present invention
• FIG. 6 is a communication flowchart for a typical and exemplary operation of the preferred embodiment of the present invention.
• FIG. 7 is a device-software outline for a typical and exemplary operation of the preferred embodiment of the present invention.
• FIG. 8 is a Schematic Representation of Sensor Modes in a fingertype PPG sensor
• FIG. 9 depicts a PPG signal and its characteristic points
• FIG. 10 is a pictograph indicating preferred focus areas for use with the present invention.
• FIG. 11 is a schematic of a Wave and Retro-wave for Pulse Wave
• FIG. 12 is a graphical representation of a Pulse Wave Structure according to the preferred embodiment of the present invention.
• FIG. 13 is a graphical representation of a QuanCardio Pulse Wave Velocity Determination
• FIG. 14 is a bar graph showing Average Reference PWV Values
• FIG. 15 is a chart depicting Pulse Wave Velocity Trends
• FIG. 16 are graphical representation of QuanCardio PPG Signal Wave and its Derivatives
• FIG. 17 are graphical representations of Original Wave and Derivatives with denoted points
• FIG. 18 is a graphical representation of a PPG signal denoting the PPG peaks
• FIG. 19 is a graphical representation of a Reflection Index Trends
• FIG. 20 is a graphical representation of Arterial Stiffness Trends
• FIG. 21 is a graphical representation of CAC score trends with
• FIG. 22 is a graphical representation of Calcium Score Trends
• FIG. 23 is a graphical representation of a Second Derivative of a
• FIG. 24 is a graphical representation of a Segmented Second Derivative of a PPG wave
• FIG. 25 is a bar chart showing Preliminary Accuracy Results of QuanCardio Testing
• FIG. 26 is a pictograph showing Complications Related to Diabetes
• FIG. 27 us a Schematic Description of Glucose on Light
• FIG. 28 is a graphical representation of a Data Fitting Process according to the preferred embodiment of the present invention.
• FIG. 29 is a graphical representation of a Fitted Curve for use therewith.
• FIG. 30 is a graphical representation of a Cut-Off Low Values Points for use therewith;
• FIG. 31 is a schematic showing a Calculation method of AGI and other parameters for use therewith;
• FIG. 32 is a schematic depicting a Sloping Method for AGI calculation for use therewith;
• 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.
• FIG. 34 is a block diagram of the QuanCardio Algorithm for use therewith;
• FIG. 35 is a block diagram of the QuanCardio Parameters Analysis for use therewith;
• 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
• FIG. 37 is a block diagram for a method for measuring metabolic heat.
• 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 “QuanCardioTM” 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.
• 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.
• 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.
• 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.
• FIG. 5 a schematic block diagram is shown depicting the operation of the QuanCardioTM 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.
• 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.
• UART universal asynchronous receiver transmitter
• the QuanCardioTM 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.
• 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).
• 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.
• a photoplethysmographic acquisition system may be used in reflection or transmission modes.
• reflection mode 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.
• 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.
• 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.
• 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.
• PPG photoplethysmography
• 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.
• the present invention uses 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.
• the PPG wave signal may be processed to reveal the condition of the cardiovascular circulatory system, especially the arterial system.
• an arterial stiffness index (“SI”) is determined by identifying the conduction time difference between the incident wave and the reflected wave (Ax Index).
• 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.
• a signal processing circuitry which contains amplifying and filtering steps, a microcontroller, and an analog to digital converter, for display and further analysis.
• 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).
• 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.
• 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.
• pulse wave velocities 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.
• 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.
• 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.
• 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:
• the distance between any point defined on one part of the wave to the same point recurring on the wave is considered the wavelength.
• 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.
• 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.
• 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.
• 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.
• the segmenting method 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.
• 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.
• 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).
• average reference PWV values as shown in FIG. 14 compared with QuanCardio PWV produced the results as shown in FIG. 15.
• 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.
• Point c the second extreme point of the PPG cycle and used for PTT calculations and Rl calculations.
• 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.
• 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.
• DPPG Derivative PPG
• SDPPG Second derivative PPG
• 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.
• SDPPG is auxiliary tools used to analyze PPG images. It is a tool used to visually analyze the properties of PPG images.
• 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.
• 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).
• 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.
• 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).
• PPI Pulse Pressure Index
• 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.
• 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.
• 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.
• 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).
• AGI Ageing Index
• ACI Ageing Character Index
• CAC score build-up of mineral of fat deposits causing the blockage in the arteries
• 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.
• AGI (b-c-d-c)/ a (4)
• AGI (b-e)/a (5).
• 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.
• PPD Peripheral Artery Disease
• Diabetic patients are most prone to stroke and heart attacks. In most cases, high blood pressure is a pre-indicator condition for diabetes.
• 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.
• 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.
• 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).
• HbA1 c Glycated hemoglobin
• the vascular health condition is calculated by the ratio of peak to the second derivative of blood volume pulse wave.
• 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.
• 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.
• 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.
• 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.
• the data fitting method preferably obtains a plurality of time spaced pulse points to form a line.
• 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.
• 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.
• 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.
• 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 .
• 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.
• 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.
• the slope method 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.
• 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.
• 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.
• 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.
• a non-invasive glucose measurement technology may be provided using metabolic heat conformation and optical methodology.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.

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• 2023
  • 2023-01-13 EP EP23729644.7A patent/EP4444166A4/de active Pending
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