Monitoring apparatuses, systems and methods using arteriovascular pulse signals to assess blood flow indices

Abstract

According to an aspect, there is provided apparatuses, systems, and methods for monitoring blood flow. The apparatus include a computing device including a processing unit coupled to a memory and a sensor interface. The memory containing non-transitory executable code that, when executed by a processor unit causes the processor unit to receive photoplethysmography (PPG) signals from the signal interface, process the PPG signals to determine shape characteristics of a waveform of the PPG signals, calculate a blood flow index (BFI) by taking a ratio of a first and second area each defined at least in part by the shape characteristics, predict a health attribute based at least in part on the BFI, and output the predicted health attribute.

Description

MONITORING APPARATUSES, SYSTEMS AND METHODS USING ARTERIOVASCULAR PULSE SIGNALS TO ASSESS BLOOD FLOW INDICESCROSS-REFERENCE

[0001] This application claims benefit and priority from Canadian Patent Application No 3212831 , entitled “A NEW HEALTH MONITORING DEVICE USING ARTERIOVASCULAR PULSE SIGNAL TO ASSESS BLOOD FLOW INDICES”, filed on September 15, 2023, the contents of which are incorporated by reference.FIELD

[0002] This disclosure relates generally to health monitoring and more specifically to technologies to monitor a blood flow index.BACKGROUND

[0003] Electronic biofeedback equipment has a wide range of applications from police style lie- detectors to medical level assessors of heart rate and blood pressure. Such equipment generally relies on the application of electrodes to the body to monitor the electrical activity of the cardiovascular system.

[0004] However, these devices tend to be tailored for specific uses in brief testing periods (e.g., in hospital usage or during an interview). Furthermore, the electrodes can be uncomfortable for a wearer for long durations. The electrodes may further come loose or otherwise misalign if the wearer bathes or showers. Consequently, such devices may not be suited for measuring a longterm health indicators.

[0005] Improvements in the field of blood flow monitoring devices is desirable.SUMMARY

[0006] Apparatuses, systems, and methods described herein are directed to monitoring blood flow to identify blood flow indexes (BFIs) and / or blood flow index variability (BFIV) that may be associated with health attributes such as the occurrence of cardiovascular events, health conditions, and / or vascular age. The apparatuses, systems, and methods described herein useRECTI FIED SHEET (RULE 91.1 )a photoplethysmography (PPG) sensor to determines BFIs. These BFIs can be the based on the shape characteristics of the PPG signal and may be associated with health attributes.

[0007] The apparatuses, systems, and methods described herein address the limitations of brief testing-oriented application by providing a more convenient sensor modality. Also included herein are BFIs and BFIVs that may provide new modalities for long-term health monitoring.

[0008] According to an aspect, there is provided an apparatus. The apparatus includes a PPG sensor designed to identify the subject's heartbeat and generate an initial signal corresponding to the identified heartbeat, and a processing unit configured to receive the signals and determine the blood flow index (BFI), and / or blood flow index variability (BFIV), along with the heart rate and / or heart rate variability of the subject, a brain part to calculate, a communication unit to transmit the information, and a notification unit, to express the output. BFI may be related to blood flow dynamics and kinetics, which can be indicative of the volume of blood flowing in the body organ in a unit of time.

[0009] In some embodiments, the apparatus further includes a battery and a charging circuit.

[0010] In some embodiments, the PPG sensor is configured to be any kind of PPG sensor in any surface(s) in any shape, size with or without having extra functionality located in any area in close contact with the skin.

[0011] In some embodiments, the PPG sensor is configured to be any kind of PPG sensor working with any light source in transmission or reflectance mode in surfaces direct or indirect contact with the skin.

[0012] In some embodiments, the PPG sensor is configured to be in a surface in close contact with the skin including but not limited to any kind of jewellery or ornamental piece tying, piercing, clipping, hanging or by any means is in close contact with subject’s body, any kind of medical instrument in close contact with subject’s body, or any other device worn by subject in direct or indirect contact with skin.

[0013] In some embodiments, the processing unit is configured to determine the reliability of the PPG signal based on a determined presence or absence of motion artifacts associated with the PPG signal.

[0014] In some embodiments, the processing unit is further configured to determine the waveform and the PPG signal and its characteristics.

[0015] In some embodiments, the processor is further configured to obtain a base signal (as described above) from the individual and process it to be saved as the baseline, at the start or at any time the reset algorithm is activated.

[0016] In some embodiments, the processor is configured to obtain any other set of signals (as described above) and process them and compare with the saved baseline signal to detect any variation.

[0017] In some embodiments, the processor is configured to convey the baseline blood flow index and the transient (i.e. , time-dependent) variations to the brain unit.

[0018] In some embodiments, a brain part includes a storage medium that is non-transitory and includes executable code that, when executed by a processor unit causes the processor unit to receive a signal comprising an indicia of PPG of a subject, analyze the signals and determine the reliable set of signals for use in determinations of blood flow index and / or blood flow index variability of the subject, determine the blood flow index and / or blood flow index variability of the subject, and compute the health and / or medical attributes of the subject.

[0019] In some embodiments, the storage medium upon being activated by a processor unit, triggers the following actions by the said processor unit: reception of a signal comprising PPG of a subject, analyzing the signals and determining the reliable set of signals for use in determinations of blood flow index and / or blood flow index variability of the subject, determining the blood flow index and / or blood flow index variability of the subject, and compute the health and / or medical attributes of the subject.

[0020] In some embodiments, the executable code, when executed by the processor unit, causes the processor unit to use the filtering criteria for calculating the blood flow index and blood flow index variability and the determination that the signal is reliable.

[0021] In some embodiments, the executable code, when executed by the processor unit, causes the processor unit to use the filtering criteria for calculating the blood flow index and / or blood flow index variability and act to repeat or abort the process and inform if the signal is unreliable.

[0022] In some embodiments, the executable code, when executed by the processor unit, causes the processor unit to compute the health attributes and / or medical attributes.

[0023] In some embodiments, the communication unit transmits the obtained blood flow index information and / or the health attributes and / or medical attributes to the notification unit or an external device.

[0024] In some embodiments, the communication unit transmits the obtained blood flow index information and / or the health attributes and / or medical attributes to the notification unit with a signal sent over a wire, a signal sent over Wl Fl , a signal sent over Bluetooth or other transmission methods.

[0025] In some embodiments, the notification unit further includes a motor to vibrate and / or a circuit to generate an alarming sound and / or visual signal triggered by the communication and processing unit, and / or an external device including but not limited to a phone, a laptop, a watch, or a computer.

[0026] In some embodiments, said PPG sensing surface is configured to be worn or used as a single sensing surface or multiple sensing surface with single brain component or multiple brain combinations to receive and process a single set of PPG or multiple sets of PPG from more than one sensor before or after processing.

[0027] In some embodiments, the brain of the system and the sensing components are decoupled in total or partial (i.e., processing unit, a software in a computer or any other processing unit separated from the sensing device) or coupled.

[0028] In some embodiments, the device and / or the method are used for health and / or medical decision making and / or other purposes including but not limited to biometric assessment and / or lie detection.

[0029] In some embodiments, the device or the method are used to assess “blood flow index” and / or “blood flow index variability” and / or other health information including but not limited to blood pressure, heart rate, heart rate variability, respiratory rate and / or respiratory rate variability.

[0030] In some embodiments, the device or the method are used to assess health or medical conditions that make a variation of pulse waveform different from the normal variation of pulse waveform.

[0031] In some embodiments, the device or the method are used to assess health or medical conditions that make a variation of pulse waveform different from the individual’s baseline variation of pulse waveform.

[0032] In some embodiments, the device or the method are used to assess health or medical conditions that make a variation of stroke volume which affects pulse waveforms to be different from the normal variation of pulse waveform.

[0033] In some embodiments, the device or the method are used to assess health or medical conditions that make a variation of stroke volume which affects pulse waveforms to be different from the individual’s baseline variation of pulse waveform.

[0034] In some embodiments, the device or the method are used to assess health or medical conditions that make a variation of QT interval in ECG which affect pulse waveforms to be different from the normal variation of pulse waveform.

[0035] In some embodiments, the device or the method are used to assess health or medical conditions that make a variation of QT interval in ECG which affect pulse waveforms to be different from the individual’s baseline variation of pulse waveform.

[0036] In some embodiments, the device or the method are used to assess vascular age related but not limited to individual’s age and other health and medical factors which affect pulse waveforms.

[0037] According to an aspect, there is provided a method including receiving a first signal comprising indicia of PPG of a subject, analyzing the signals and determining reliability of the signals for use in determinations of the blood flow index or blood flow index variability of the subject and determining the blood flow index or blood flow index variability of the subject.

[0038] In some embodiments, the method further comprises choosing the reliable set of the signals based on a determined digital or analogue filtering criterion.

[0039] In some embodiments, the method further comprises or the processor is configured to compute one or more occasion of the blood flow index.

[0040] In some embodiments, the method further comprises or the processor is configured to compute the baseline blood flow index at start or upon activation of reset algorithm.

[0041] In some embodiments, the method further comprises or the processor is configured to compute occasions of the blood flow index to assess the blood flow index variability.

[0042] In some embodiments, the method further comprises or the processor is configured to compute occasions to health attributes from the blood flow index and I or the blood flow index variability.

[0043] In some embodiments, the method further comprises or the processor is configured to compute occasions to medical attributes from the blood flow index and I or the blood flow index variability.

[0044] According to an aspect, there is provided an apparatus for monitoring blood flow. The apparatus including a computing device including a processing unit coupled to a memory and a sensor interface. The memory containing non-transitory executable code that, when executed by a processor unit causes the processor unit to receive photoplethysmography (PPG) signals from the signal interface, process the PPG signals to determine shape characteristics of a waveform of the PPG signals, calculate a blood flow index (BFI) by taking a ratio of a first and second area each defined at least in part by the shape characteristics, predict a health attribute based at least in part on the BFI, and output the predicted health attribute.

[0045] In some embodiments, the code is further configured to cause the processor to calculate the blood flow index variability (BFIV) by taking a ratio of the calculated BFI and a baseline BFI and predict the health attribute based at least in part on the BFIV.

[0046] In some embodiments, the code is further configured to cause the processor to receive initial PPG signals, process the initial PPG signals to determine initial shape characteristics of an initial waveform of the initial PPG signals, calculate an initial blood flow index (BFI) by taking a ratio of an initial first and second area each defined at least in part by the initial shape characteristics, and use the initial BFI as the baseline BFI to calculate the BFIV.

[0047] In some embodiments, the code is further configured to cause the processor to analyze the PPG signal and determine a reliable set of PPG signals to use to calculate the BFI based on presence or absence of motion artifacts associated with the PPG signals.

[0048] In some embodiments, at least one additional BFI is calculated based on at least one additional first and second area each defined at least in part by the shape characteristics; and the health attribute is predicted based on a weighted sum of the BFI and the at least one additional BFI.

[0049] In some embodiments, the health attribute is an occurrence of a cardiovascular event.

[0050] In some embodiments, the health attribute is an occurrence of premature ventricular contractions.

[0051] In some embodiments, the health attribute is the presence or absence of a health condition.

[0052] In some embodiments, the health attribute is a vascular age of the wearer.

[0053] According to an aspect, there is provided a system for monitoring blood flow. The system including the apparatus described in the paragraphs above and a photoplethysmography sensor configured to measure the PPG signals.

[0054] According to an aspect, there is provided a method for monitoring blood flow. The method includes receiving photoplethysmography (PPG) signals, processing the PPG signals to determine shape characteristics of a waveform of the PPG signals, calculating a blood flow index (BFI) by taking a ratio of a first and second area each defined at least in part by the shape characteristics, predicting a health attribute based at least in part on the BFI, and outputting the predicted health attribute.

[0055] In some embodiments, the method further includes calculating the blood flow index variability (BFIV) by taking a ratio of the calculated BFI and a baseline BFI and predicting the health attribute based at least in part on the BFIV.

[0056] In some embodiments, the method further includes receiving initial PPG signals, processing the initial PPG signals to determine initial shape characteristics of an initial waveform of the initial PPG signals, calculating an initial blood flow index (BFI) by taking a ratio of an initial first and second area each defined at least in part by the initial shape characteristics, and using the initial BFI as the baseline BFI to calculate the BFIV.

[0057] In some embodiments, the method further includes analyzing the PPG signal and determining a reliable set of PPG signals to use to calculate the BFI based on presence or absence of motion artifacts associated with the PPG signals.

[0058] In some embodiments, the method further includes calculating at least one additional BFI based on at least one additional first and second area each defined at least in part by the shape characteristics and the health attribute is predicted based on a weighted sum of the BFI and the at least one additional BFI.

[0059] In some embodiments, the health attribute is an occurrence of a cardiovascular event.

[0060] In some embodiments, the health attribute is an occurrence of premature ventricular contractions.

[0061] In some embodiments, the health attribute is the presence or absence of a health condition.

[0062] In some embodiments, the health attribute is a vascular age of the wearer.

[0063] In some embodiments, the method further includes measuring the PPG signals using a photoplethysmography sensor.

[0064] Many further features and combinations thereof concerning embodiments described herein will appear to those skilled in the art following a reading of the instant disclosure.DESCRIPTION OF THE FIGURES

[0065] In the figures, embodiments are illustrated by way of example. It is to be expressly understood that the description and figures are only for the purpose of illustration and as an aid to understanding.

[0066] Embodiments will now be described, by way of example only, with reference to the attached figures, wherein in the figures:

[0067] FIG. 1 illustrates an example sensing apparatus, according to some embodiments.

[0068] FIG. 2A is a flowchart illustrating a method for detecting and measuring BFI, according to some embodiments.

[0069] FIG. 2B is a flowchart illustrating another method for detecting and measuring BFI, according to some embodiments.

[0070] FIG. 3 shows a PPG waveform with pulsatile and non-pulsatile components, according to some embodiments.

[0071] FIG. 4 shows a PPG waveform divided into areas, according to some embodiments.

[0072] FIG. 5 illustrates flowcharts of signal indexing examples, according to some embodiments.

[0073] FIG. 6 illustrates an example process for detecting and measuring BFIV, according to some embodiments.

[0074] FIG. 7 illustrates sample of filtered signals from sensors in different health conditions, according to some embodiments.

[0075] FIG. 8 illustrates example user interfaces along with example of sensor(s), according to some embodiments.

[0076] FIG. 9 is a violin plot for presenting the APS as a BFI in each cohort of the study, according to some embodiments.

[0077] FIG. 10 shows a schematic diagram of computing device, according to some embodiments.DETAILED DESCRIPTION

[0078] With every heartbeat, the heart propels a surge of blood into the aorta, instigating a transient but crucial expansion of this pivotal artery. The arterial pulse is generally an abrupt artery dilation caused by the rapid ejection of blood into the aorta, followed by its propagation across the entire arterial network. Medical professionals can use this palpable blood surge to capture a snapshot of the forceful ejection of blood from the left ventricle. The characteristic of this pulse can reveal valuable information about individual’s health status. Pulse fluctuations can commonly be encountered in various scenarios, ranging from ordinary conditions such as exercise and fever to more serious health concerns like hyperthyroidism and heart disorders. Furthermore, psychological factors like anxiety can also influence pulse rates. Photoplethysmography (PPG) can serve as a non-invasive circulatory signal that reflects this pulsatile volume of blood withintissue. The PPG signal can commonly be visualized on pulse oximeters and bedside monitors, accompanied by the calculated arterial oxygen saturation levels. Its visual pattern can bear resemblance to an arterial blood pressure waveform which can be an invasive measurement of the arterial blood pressure waveform. So, PPG can be a non-invasive method to measure the underlying circulation. However, the optical, biomechanical, and physiologic covariates affect the appearance of the PPG signals. Accordingly, relation of PPG with health conditions can be complicated.

[0079] While traditionally employed for detecting pulse and oxygen saturation, the versatility of PPG in capturing subtle physiological variations is described herein for a health monitoring system based on blood flow indices. The present disclosure provides various embodiments of apparatuses, systems, and methods that can measure the PPG signals as a continuous health index.

[0080] Apparatuses, systems, and methods described herein are directed to monitoring blood flow to identify blood flow indexes (BFIs) and / or blood flow index variability (BFIV) that may be associated with health attributes such as the occurrence of cardiovascular events, health conditions, and / or vascular age. The apparatuses, systems, and methods described herein use a photoplethysmography (PPG) sensor to determines BFIs. These BFIs can be the based on the shape characteristics of the PPG signal and may be associated with health attributes.

[0081] The apparatuses, systems, and methods described herein address the limitations of brief testing-oriented application by providing a more convenient sensor modality. Also included herein are BFIs and BFIVs that may provide new modalities for long-term health monitoring.

[0082] The system may use a wearable PPG sensor to identify the subject’s heartbeat. The system can use the PPG sensor to measure the waveform of the blood flow.

[0083] The apparatuses and methods outlined in the current disclosure can be used in a portable or wearable apparatus sensing the BFI and / or BFIV in a subject. The apparatus may also be affixed to a body part of an individual, such as the wrist. The apparatus may encompass one or multiple sensors to sense the BFI or the BFIV. These sensors may operate within certain ranges that are identical, partially overlapping, or exclusive to one another. For example, the deployment of one or more PPG sensors could serve to sense the BFI during varying activity levels or timeframes.

[0084] The BFI and / or BFIV may be able to reveal cardiovascular events currently visible through other, more invasive sensor modalities, (e.g., electrocardiograms (ECG)). Furthermore, the cardiovascular events shown in other sensor modalities may not provide indicators of cardiovascular flow dynamics. Accordingly, there may be further utility in measuring the BFI and BFIV using, for example, PPG sensor modalities. In some embodiments, BFI and / or BFIV may show premature ventricular contractions (PVC) which can represent cardiovascular performance. In some embodiments, BFI and / or BFIV may be used to identify any event that changes the blood flow dynamic can be may by these indices (e.g., other events in the cardiovascular system). In some embodiments, the BFI and / or BFIV may represent conditions (e.g., cardiovascular conditions that change the blood flow dynamic). In some embodiments, the BFI and / or BFIV can be used for age recognition / prediction (chronological and biological age).

[0085] The apparatuses, systems, and methods described herein may be used for hospital triage, diagnosis of, for example, congestive heart failure decompensation, medical shock index, and monitoring exercise for geriatrics, or people with underlying medical conditions. Further still applications are also envisioned.

[0086] Hospital triage is a process in emergency departments, to prioritize patients based on the severity of their condition. The goal is to ensure that those who need urgent medical care receive it as quickly as possible. During triage, healthcare professionals assess each patient's symptoms, vital signs, and medical history to determine the level of urgency. Integrating BFI / BFIV into triage may enhance the accuracy of patient assessments by providing real-time, objective data on blood flow conditions. This may improve prioritization, leading to more effective use of resources and better patient outcomes.

[0087] In congestive heart failure, decompensation refers to the worsening of symptoms due to the heart's inability to maintain adequate blood flow and perfusion to meet the body's needs. BFI / BFIV may be capable of monitoring blood flow and detecting critical variations, which may be crucial for the early identification of decompensated heart failure, enabling timely medical intervention and potentially preventing severe complications. Its real-time alerts may provide healthcare providers with valuable insights into a patient's cardiovascular status, facilitating proactive and personalized care.

[0088] Variations in blood flow in vessels during different types of medical shock may be critical for diagnosis and management, as they reflect underlying pathophysiological mechanisms anddetermine the adequacy of tissue perfusion and oxygenation. BFI / BFIV may show these variations and provide a guide in selecting appropriate treatments to restore hemodynamic stability and prevent organ failure.

[0089] For older adults or individuals with underlying cardiovascular conditions, it is important to tailor exercise intensity and duration to their specific health needs. Walking can be a beneficial and low-impact exercise, but adjustments may be necessary based on individual health conditions. Consulting with a healthcare provider can help determine an appropriate walking regimen that balances exercise benefits with safety. BFI / BFIV may provide real-time feedback on cardiovascular status during walking, allowing users to monitor changes in heart condition and adjust their activity level as needed. This can enable safer exercise by ensuring that individuals with heart conditions can receive immediate alerts if their cardiovascular health fluctuates.

[0090] EXEMPLARY APPARATUS CONFIGURATION

[0091] FIG. 1 illustrates an example sensing apparatus 10, according to some embodiments.

[0092] The contents presented herein pertain to apparatuses and methods for sensing the blood flow index (BFI) and / or blood flow index variability (BFIV) in a subject. Various sensors or sensing methods may be employed for the purpose of detecting and measuring these parameters. These sensors could potentially be incorporated into wearable or portable devices. The operation of these sensors may rely on power sources, including batteries, a solar power sources, a portable or wearable power source or any other power sources. These sensors and sensing techniques may operate based on diverse principles and exhibit varying power profiles. It is plausible the sensors and / or sensing techniques to be used at the same time or at different times with possible partial or complete overlap, or even in isolation from each other.

[0093] The sensing apparatus 10 can be used for the detection of BFI and / or BFIV. This apparatus 10 includes a main sensor 11 , extra sensor(s) 12, processor 13, a brain unit 14, storage 15, a communication unit 16, a notification unit 17, a user interface 18, and software 19. The sensing apparatus 10 can be compact enough to allow for portability or wearable integration by an individual.

[0094] The main sensor 11 can be employed to capture blood flow related wave signals. It can be an optical sensor employed to capture PPG data, which can be used to infer BFI information. The state of PPG technology can be enhanced by modifying the light source in terms ofwavelength, intensity, and polarity. Instead of using a single light source, multiple light sources can be provided for different applications. Infrared (e.g., 800 to 960 nm) can be used which can work well for oximetry. A blue light window with adjustable intensity can also be used for other applications such as imaging. In some embodiments, the sensor can operate as a PPG sensor. In some embodiments, the sensor can operate as a light-conditioned PPG (LCPPG), where the light conditions (e.g., intensity, polarity, and wavelength) can be adjusted.

[0095] The extra sensor(s) 12 can be used to measure the BFI in other body location(s) or with other condition(s) often used for health and medical interpretations.

[0096] The main sensor 11 and extra sensor(s) 12 may comprise multiple individual sensors, each contributing to their respective sensing capabilities, directly or indirectly related to the BFI / BFIV measurement and interpretation, including but not limited to PPG sensors, or motion sensors.

[0097] The processor 13 may be responsible for executing computational tasks and managing the overall operation of the device. The processor 13 may facilitate communication and control operations for the main sensor 11 and extra sensor(s) 12 and sensing techniques. The processor 13 can oversee the operations of main sensor 11 , extra sensor(s) 12, brain unit 14, communication unit 16, and / or notification unit 17. The processor 13 can manage bi-directional communication, both receiving and transmitting signals to / from these components. The processor13 may be, for example, any type of general-purpose microprocessor or microcontroller, a digital signal processing (DSP) processor, an integrated circuit, a field programmable gate array (FPGA), a reconfigurable processor, a programmable read-only memory (PROM), or any combination thereof.

[0098] The brain unit 14 can introduce advanced processing capabilities and incorporate storage for data management. The brain unit 14 may interpret the stored signals and check it with health and medical indicators. The brain unit 14 can serve as the core analysis hub, utilizing the calculated BFIs and the affixed database indexes to calculate the BFI variability. The brain unit14 can then utilize the health attribute detection to interpret the calculated BFI and BFIV. As a result, the brain unit 14 can transform measured BFI into actionable insights, facilitating informed decision-making which may be conveyed by the communication unit 16 and the notification unit 17.

[0099] In some embodiments, the processor 13 may carry out the operations of the brain unit 14. The processor 13 may retrieve instructions from a storage 15 and execute them to perform the designated communication and control tasks. The processor 13 can possess analog or digital attributes and encompass various elements including storage capacity, input / output regulation, one or more central processing units, and / or arithmetic processing units.

[0100] The storage 15 can accommodate various types of media suitable for apparatus 10. This storage may house software 19. The storage 15 may include a suitable combination of any type of computer memory that is located either internally or externally such as, for example, random-access memory (RAM), read-only memory (ROM), compact disc read-only memory (CDROM), electro-optical memory, magneto-optical memory, erasable programmable read-only memory (EPROM), and electrically-erasable programmable read-only memory (EEPROM), Ferroelectric RAM (FRAM) or the like.

[0101] The software 19 can include a collection of instructions executable by processor 13. These instructions facilitate the execution of BFI and / or BFIV sensing operations, along with other potential functions.

[0102] The communication unit 16 can facilitate seamless interaction with external systems, enabling data exchange and communication. The communication unit 16 can enable the apparatus 10 to communicate with other components, to exchange data with other components, to access and connect to network resources, to serve applications, and perform other computing applications by connecting to a network (or multiple networks) capable of carrying data including the Internet, Ethernet, plain old telephone service (POTS) line, public switch telephone network (PSTN), integrated services digital network (ISDN), digital subscriber line (DSL), coaxial cable, fiber optics, satellite, mobile, wireless (e.g. Wi-Fi, WiMAX), SS7 signaling network, fixed line, local area network, wide area network, and others, including any combination of these.

[0103] The notification unit 17 can include the user interface 18, mechanisms for delivering alerts and updates, and ports and protocols linking the device externally.

[0104] The user interface 18 can provide a mode for users to interact with the apparatus 10, potentially through graphical or tactile elements. The user interface 18 can enable the apparatus 10 to interconnect with one or more input devices, such as a keyboard, mouse, camera, touch screen and a microphone, or with one or more output devices such as a display screen and a speaker.

[0105] In some embodiments, the processor 13 can manage the sensor(s) 11 and 12 to identify BFI and / or BFIV across different conditions like over different range of physical activity or conditions experienced by the monitored subject. For example, the processor 13 might activate or deactivate specific sensor(s) 11 and 12 as needed. Additionally, the processor 13 may send signals to the sensor(s) 11 and 12 to alter their operational ranges. This may involve modifying sensor sensitivity to specific factors that could influence BFI and / or BFIV detection.

[0106] Data from the sensor(s) 11 and 12 can be used by the processor 13 to calculate BFI and / or BFIV, often entailing the selective use of data from one or multiple sensor(s) 11 and 12. This data selection process can encompass diverse actions, such as activating or deactivating sensor(s) 11 and 12, blocking, or receiving sensor data, transitioning sensor(s) 11 and 12 into lower or higher power modes, applying specific algorithms to process or analyze sensor data, or any other method that orchestrates the selective acquisition of data from individual or multiple sensor(s) 11 and 12. This data selection can be based on signal quality from the sensor(s) 11 and 12, or inputs from external sources, including accelerometers, time of day, data from user interface, predefined reference data or algorithmic application on sensor signals with particular attributes. These diverse sources may indicate a preference for specific sensing algorithm or choice of sensor(s) 11 and 12 for BFI and / or BFIV detection. In some embodiments, the apparatus 10 can include an accelerometer, and / or the accelerometer's functionality may be toggled on or off. The processor 13 can control the sensor activity through the execution of software instructions aligned with a particular algorithm.

[0107] For example, the processor 13 may evaluate the signal quality emanating from the sensor(s) 11 and 12 and prioritize one sensor 11 over other sensor(s) 12 for BFI detection. The processor 13 may control the sensor(s) 11 and 12 activity based on the power consumption. The processor 13 can affix data management indexes to each set of signals, enhancing data organization and enabling efficient retrieval based on factors like time, location, and order. These indexes can optimize data utilization by providing structured reference points for analysis and comparison.

[0108] In operation, the processor 13 can receive an array of signals, such as raw data from main sensor 11 or extra sensor(s) 12. The processor 13 may also or alternatively obtain processed or filtered data representations from these sensor(s) 11 and 12. The data received from these sensor(s) 11 and 12 may be sent to, for example, the brain unit 14 for storage, analysis and / or future reference. The processor 13 can use software 19 to execute these functions,accessing and applying it to control the main sensor 11 and / or extra sensor(s) 12. This software 19 may also help process signals and data, possibly in conjunction with brain unit 14 and notification unit 17.

[0109] Through its control over main sensor 11 and extra sensor(s) 12, the processor 13 can enact various actions. These include data acquisition, activating or deactivating specific sensor functionalities, and toggling the sensors on or off as needed. The processor 13 may not only oversee sensor operations but also handling data flow, interaction with brain unit 14, and interaction with communication unit 16 and notification unit 17, leading to a cohesive and controlled functioning of the entire apparatus 10.

[0110] Executing software 19, the processor 13 can change in operation of apparatus 10 based on signals received from main sensor 11 and / or extra sensor(s) 12. The following include a few non-limiting scenarios to exemplify the dynamic adaptability of the processor 13.

[0111] Scenario 1 - Indexing the signals received from main sensor 11 or extra sensor(s) 12: The processor 13 indexing can involve creating indexes that can allow quick retrieval of data without having to scan through the entire dataset.

[0112] Scenario 2 - Motion Artifact Detection: Processor 13 can analyze signals from main sensor 11 and / or extra sensor(s) 12 to identify the presence of motion artifacts or the loss of a reliable signal for BFI and / or BFIV determination. Upon making this determination, the processor 13 can choose to include or exclude particular sensor(s) 11 and 12.

[0113] Scenario 3 - Adaptive Response: In response to motion artifacts, low quality signal or signal loss in main sensor 11, the processor 13 can execute software 19 to adapt the operation of the apparatus 10. For instance, it might activate the notification process and / or deactivate the related sensor.

[0114] Scenario 4 - Transition to Alternative Source: In cases where main sensor 11 becomes unreliable, the processor 13 can initiate a switch. It might turn on extra sensor(s) 12 and activate a notification process.

[0115] This adaptability may help provide accurate readings and efficient power management, ultimately enhancing the overall performance of apparatus 10.

[0116] Executing software 19, the processor 13 can control the operation of main sensor 11 and extra sensor(s) 12. The control mechanism can involves selecting which sensor, signal source, or sensing technique to employ for the purpose of detecting and / or measuring BFI. In practice, the processor 13 might decide to prioritize the signals from a sensor (e.g., main sensor 11) over other sensor(s) (e.g., extra sensor(s) 12) by any of the following means.

[0117] Turning sensors On / Off: The processor 13 may activate or deactivate main sensor 11 and / or extra sensor(s) 12 based on the analysis of signals and / or the presence of artifacts.

[0118] Power Mode Adjustment: The processor 13 can shift sensor(s) 11 and 12 into low-power modes when not actively needed (e.g., to optimize energy usage).

[0119] Sensor Selection: Depending on data reliability, the processor 13 may select the most suitable sensor 11 and 12, sensor signal, or sensing technique to carry out BFI detection and measurement.

[0120] While the apparatus 10 is illustrated with the sensor(s) 11 and 12 included therein, the sensor(s) 11 and 12 could equally be provided by another separate component in communication with the apparatus 10. For example, the sensor(s) 11 and 12 may be a separate PPG sensing component in communication with a user’s mobile device that is running an app configured to carry out the processing steps described above. Alternatively, the sensor(s) 11 and 12 or the user’s mobile device may send the PPG signals to an external server for processing and the results may be communicated by the display on the user’s mobile device. Still further system architectures are envisioned without deviating from the teachings herein.

[0121] EXAMPLE PROCESSING

[0122] FIG. 2A is a flowchart illustrating a method 200 for detecting and measuring BFI, according to some embodiments.

[0123] The method 200 can initiate by retrieving an output from a sensor (e.g., sensor(s) 11 and 12) (block 210). The obtained output from the sensor can undergo enhancement procedures, such as filtering or compensation mechanisms, including steps to detect motion artifacts (block 211). The resulted signal can go through the decision block 220. In the event that resulted signal is not acceptable, the communication unit 16 and / or notification unit 17 can be employed to notify user(s) (block 270). If the quality is accepted, the waveform can be recorded (block 225), and the process advances to detect and measure the BFI (block 230; described in greater detail below),the BFI can be recorded (block 240), and the signals can be indexed (block 250). Finally, the acquired BFI can be processed by, for example, the brain unit 14 (block 260; described in greater detail below). The order of the steps can be changed without deviating from the teachings herein.

[0124] FIG. 3 shows a PPG waveform 300 with pulsatile 302 and non-pulsatile 304 components, according to some embodiments.

[0125] The PPG waveform 300 includes two primary components: a pulsatile component 302 and a non-pulsative component 304. The pulsatile component 302, recognized as the alternating current (AC) component is generally in synchrony with the cardiac cycle.

[0126] FIG. 4 shows a PPG waveform 400 divided into areas 402a-402e, according to some embodiments.

[0127] The waveform of the blood flow 400 can then be divided into areas 402a-402e (e.g., the 5 areas 402a-402e defined by the shape of the waveform 400). These areas 402a-402e are then used to determine a Blood Flow Index (BFI) by dividing one of the areas 402a-402e by another area 402a-402e to produce an index (the BFI). These BFIs can be used to determine the presence and / or severity of a medical condition. For example, the BFI can be used to identify premature ventricular contractions.

[0128] In some embodiments, the system can measure the PPG of a patient to form a baseline BFI (i.e., prior to any particular cardiac event or condition). Afterwards, the moment-to-moment BFI can be compared to the baseline BFI to determine the Blood Flow Index Variability (BFIV). This BFIV may be useful to identify abnormal changes in an individual’s PPG signals that may otherwise present as normal BFIs without the comparison to the individual’s baseline BFI.

[0129] In another embodiment, the slopes based on the shape of the waveforms 400 (i.e., the slopes of lines 404a and 404b) can be used as a further BFI. Line 404a is defined by connecting the maxima of the cardiac cycles. Line 404b is defined by connecting the minima of the cardiac cycles. For example, these may be used to identify sharp increases or decreases in the peak height of the waveform. These lines 404a and 404b can go on to define lines 406a and 406b which are straight lines corresponding to the highest maxima and lowest minima of each segment, respectively. The areas between these lines (and the ratios therebetween) may be used as a BFI.

[0130] Referring to block 230 of method 200, a segment of a pre-defined time interval (e.g., 6- second segments) can be taken of the PPG waveform 400. Within the segments, in someembodiments, the first maximum and last maximum may be used to define the cardiac cycles included in any subsequent calculations (e.g., there may be an omitted portion of the first and last cardiac cycles). In some embodiments, the omitted portions might be used. The BFIs can be derived from distinct areas 402a-402e of these segments, which can be calculated by dividing respective areas 402a-402e within the waveform 400. The area 402a can be the area between line 404a and line 406a. The area 402b may be associated with the arterial blood area complement in the pulsatile area and can be the area between the waveform 400 and line 404a. The area 402c may be associated with the arterial blood area and can be the area between the waveform 400 and the line 404b. The area 402d can be the area between the line 404b and 406b. The area 402e can be the area between the line 406b and 0.

[0131] From the waveform 400, a variety of different BFIs may be calculated such as the Arterial Blood Ratio (ABR), Arterial Blood Proportion (ABP), Exact Pulsatile Ratio (EPR), Waveform Ratio (WFR), and Average Peak Slope (APS).

[0132] The Arterial Blood Ratio (ABR) can be calculated by dividing the arterial blood area by its complement within the pulsatile area (i.e., area 402c I area 402b).

[0133] The Arterial Blood Proportion (ABP) can be calculated by dividing the arterial blood segment by the sum of the arterial blood area and its complement (i.e., area 402c I [area 402b + area 402c]).

[0134] The Waveform Ratio (WFR) can be calculated to address low-frequency fluctuations in the PPG waveform 400. This can involve calculating the ratio of the entire area under the waveform 400, with a lower limit defined by the baseline created from the lowest minimum of the waveform 400 (line 406b) to the area above the waveform 400, with an upper limit established by the tip of the highest peak (line 406a) (i.e., [area 402c + area 402d] I [area 402a + area 402b]).

[0135] The Exact Pulsatile Ratio (EPR) can be calculated by dividing the pulsatile area, with the exclusion of the impact of low-frequency variations, by the complement area positioned between the waveform 400 and the lower (line 406b) and upper (line 406a) limits established by the low-frequency fluctuations (i.e., [area 402b + area 402c] / [area 402a + area 402d]).

[0136] The Average Peak Slope (APS) may be the average absolute value of the periodical slope of the PPG waveform 400 (e.g., the slope of line 404a between the maxima). There maybe, for example, 5 maximum peaks (as shown in FIG. 4). This may define 4 segments of the line 404a. The average absolute value of the line 404a can be calculate in these 4 segments.

[0137] These BFIs can be calculated at block 230 of method 200. From these BFI, the variability can be calculated (e.g., against an initial BFI as a baseline or against acceptable ranges of BFIs, for example, given the subject’s health / medical condition).

[0138] FIG. 5 illustrates flowcharts of signal indexing examples, according to some embodiments.

[0139] The processor 13 can employ a detailed indexing strategy to organize and retrieve data originating from main sensor 11 and / or extra sensor(s) 12 across various locations and timeframes. Through one or more of, including but not limited to, timestamp 501 , sensor identifier 502, and signal sequence indexing 503, or a combination thereof, each data point can be uniquely tagged with temporal and spatial attributes. For instance, when a signal is recorded from main sensor 11 , the system can assign a sequential index to maintain the order of signals. The brain unit 14 can use these indexes to measure BFIV and interpret the BFI and / or BFIV considering various health and medical conditions. For instance, by efficiently indexing the sensor identifiers and timestamps, the approach can facilitate swift retrieval of data specific to a sensor and within desired time ranges by the brain unit 14. The sequential indexing 503, combined with a defined initial baseline signal, can empower the brain unit 14 to perform accurate and insightful comparisons. Future signals can be effortlessly contrasted against the baseline or among themselves, providing valuable insights into changes, trends, and / or anomalies within the measured BFI. The brain unit 14 can analyze data from a particular sensor and / or across multiple sensors based on different indexes to measure BFIV and / or interpret the BFI and / or BFIV.

[0140] With this indexing synergy, the brain unit 14 can explore trends over time and analyze measured BFI from different sensors and apply them in the health and medical decision-making process.

[0141] The processor unit 13 can measure BFI and add the indexes to each datapoint.

[0142] FIG. 6 illustrates an exemplary process for detecting and measuring BFIV 600 by the brain unit 14 (e.g., carrying out block 260 of FIG. 2), according to some embodiments.

[0143] As described above, BFIV can be calculated (e.g., against an initial BFI as a baseline or against acceptable ranges of BFIs, for example, given the subject’s health / medical condition).

[0144] The process 600 can begin with obtaining a measured and indexed BFI (block 410). The brain unit 14 can apply the various indices (for example, as described above) in interpreting the BFI and / or measuring BFIV.

[0145] The brain unit 14 can detect the presence of multiple sensors within the dataset (block 420). By iterating through the dataset, the brain unit 14 can capture the unique sensor IDs encountered in a set. As an example, if the count of unique sensor IDs surpasses one, it signifies the presence of data from multiple sensors. Conversely, if only one unique sensor ID is identified, it indicates data from a single sensor. This straightforward method can offer a preliminary means of ascertaining whether the dataset encompasses contributions from multiple sensors, facilitating efficient data analysis and interpretation.

[0146] The brain unit 14, can then calculate the BFIV (block 430). In one example, the brain unit 14 can identify variability in BFI, with the initial BFI as the baseline, which can offer a versatile means of assessing different types of changes. By comparing subsequent BFIs to the baseline BFI, the brain unit 14 can detect variations including but not limited to sharp increases, sharp decreases, and / or general changes. Upon identifying any of these types of variability, the brain unit 14 can log the specific type and the corresponding timestamp and can include that information in health detection and / or transmission. If no significant variability is detected, the brain unit 14 can confirm the absence of noteworthy changes. This adaptable process can provide a foundational tool for gauging the health events related to BFI and / or BFIV.

[0147] In another example, the brain unit 14 can calculate the BFIV (block 430) based on different indexes, such as comparing data against baseline values of a single sensor or between data from two different sensors. In an example, the BFIV calculation (block 430) can be tailored to accommodate diverse indexing scenarios, enabling comparisons based on various factors. Whether analyzing the baseline data of a single sensor or the divergence between data from two distinct sensors, the brain unit 14 can remain adaptable. By systematically evaluating data entries against chosen references, the brain unit 14 can effectively detect variations including but not limited to sharp increases, sharp decreases, and / or general changes. This approach empowers this system to explore the BFIV patterns across different indexes, which can be used in the health attribution detection.

[0148] The brain unit 14 can detect health attributes (block 460). Health attributes (which may also be referred to as medical attributes) can refer to the characteristics or qualities that describean individual's overall state of well-being. Health attributes can also refer to the characteristics or features that are relevant within the context of medical conditions, diagnoses, and treatments (e.g., those focused on the clinical and objective aspects of health). Health attributes can include factors like symptoms, signs, laboratory results, diagnostic findings, treatment responses, and the physiological aspects of a person's health. Health attributes can be used by healthcare professionals to diagnose, treat, and monitor health conditions and to make informed medical decisions. These attributes can encompass various dimensions of health, including but not limited to physical, mental, emotional, social, and even spiritual well-being. In some embodiments, the health attribute is an occurrence of a cardiovascular event. In some embodiments, the health attribute is an occurrence of premature ventricular contractions. In some embodiments, the health attribute is the presence or absence of a health condition. In some embodiments, the health attribute is a vascular age of the wearer. Health attributes can also provide a holistic view of a person's state of health and can include factors like but not limited to fitness level, energy levels, emotional resilience, social interactions, and more. In some embodiments, these attributes may be subjective and can vary from person to person based on their perceptions and experiences.

[0149] The brain unit 14 can transmit the results (block 450). The communication unit 16, tasked with concluding data dissemination, can navigate various communication routes for adaptive decision-making. In an example, transmission may be underpinned by destination type distinctions including, but not limited to, "computer," "user interface," or "device," and can include a determination of the optimal method of communication for each case. For instance, when communicating with a computer, a wired connection like Ethernet might be chosen, while wireless connections like Bluetooth or Wi-Fi are favored for user interfaces and devices, respectively. In this example, the communication unit 16 can tailor the communication approach, dynamically selecting the most suitable means based on the destination's unique characteristics. This ensures efficient and context-sensitive data transfer, aligning with the specific needs of diverse endpoints and facilitating seamless interactions within the system.

[0150] The notification unit 17 may be responsible for delivering alerts and information. It can work together with different endpoints, including user interfaces, to effectively share the information. The notification unit 17 can determine the best way to notify for each situation, using methods including, but not limited to, updating external database, push notifications, alarms, emails, or SMS alerts. This focused notification system 17 may enhance user involvement, provide prompt responses, and assist with well-informed choices.

[0151] While the process 600 has been illustrated with one particular order, the order of the steps can be changed without deviating from the teachings herein.

[0152] FIG. 7 illustrates sample of filtered signals 710 and 720 from sensors in different health conditions, according to some embodiments.

[0153] The waveforms labeled as trace 710 and 720 depict the filtered signals from a sensor in different health conditions. The processor unit 13 can apply software 19 and calculate the BFI for each of those signals 710 and 720. The BFI for the waveform 710 may be, for example, 8.49, and the BFI for the waveform 720 may be, for example, 4.25. The brain unit 14 can detect health attributes (block 640 of FIG. 6) and measure and / or interpret the health conditions related to the reported BFI. Calculated BFIV can also be applied in the health attribute detection and the related health condition can be measured and / or interpreted. Waveforms 710 and 720 exemplify blood pressure as the health attribute. The waveform 710 may relate to a blood pressures of 91 / 61 mmHg attributed to the BFI of 8.49. The waveform 720 may relate to a blood pressures of 119 / 80 mmHg attributed to the BFI of 4.25. In this example the BFI, the BFIV, the attributed variation in the blood pressure, and / or the related medical effects can be reported to the notification unit 17.

[0154] FIG. 8 illustrates some examples user interfaces 18 along with example of sensor(s) 11 and 12, according to some embodiments.

[0155] The user interface 18 can employ a range of diverse notification methods. These methods can cater to a variety of user needs and circumstances. These methods include, but are not limited to, using mobile phones for quick and direct alerts, wearable devices for convenient on-the-go notifications, addition to a piece of jewellery or wearable, and remote computers in distant collection centers to facilitate widespread engagement of health or medical related counterparts. Additionally, the system can employ specialized mobile devices designed specifically for these applications. These methods are complemented by custom-designed interfaces that provide a personalized touch. This combination of approaches may ensure that notifications, whether delivered through text messages, push notifications, or customized interfaces, are tailored to suit user preferences and enhance communication effectiveness.

[0156] In some embodiments, these interfaces 18 can facilitate a two-way interaction, enabling users to provide feedback that is subsequently processed by the processor unit 13. In such embodiments, users may not only receive notifications but also have the ability to respond and share input through these interfaces 18. Once feedback is provided, the processor unit 13 mayincorporate this information within the storage unit 15, allowing for a continuous and adaptive loop of communication and decision-making. This bi-directional communication mechanism can enhance the available data for the brain unit 14’s ability to detect health attributes, contributing to an informed and responsive operational framework.

[0157] EXAMPLE IMPLEMENTATIONS

[0158] According to an aspect, there is provided an apparatus 10. The apparatus 10 includes a PPG sensor 11 designed to identify the subject's heartbeat and generate an initial signal corresponding to the identified heartbeat, and a processing unit 13 configured to receive the signals and determine the blood flow index (BFI), and / or blood flow index variability (BFIV), along with the heart rate and / or heart rate variability of the subject, a brain part 14 to calculate, a communication unit 16 to transmit the information, and a notification unit 17 to express the output. BFI may be related to blood flow dynamics and kinetics, which can be indicative of the volume of blood flowing in the body organ in a unit of time.

[0159] In some embodiments, the apparatus 10 further includes a battery and a charging circuit.

[0160] In some embodiments, the PPG sensor 11 is configured to be any kind of PPG sensor in any surface(s) in any shape, size with or without having extra functionality located in any area in close contact with the skin.

[0161] In some embodiments, the PPG sensor 11 is configured to be any kind of PPG sensor working with any light source in transmission or reflectance mode in surfaces direct or indirect contact with the skin.

[0162] In some embodiments, the PPG sensor 11 is configured to be in a surface in close contact with the skin including but not limited to any kind of jewellery or ornamental piece tying, piercing, clipping, hanging or by any means is in close contact with subject’s body, any kind of medical instrument in close contact with subject’s body, or any other device worn by subject in direct or indirect contact with skin.

[0163] In some embodiments, the processing unit 13 is configured to determine the reliability of the PPG signal based on a determined presence or absence of motion artifacts associated with the PPG signal.

[0164] In some embodiments, the processing unit 13 is further configured to determine the waveform and the PPG signal and its characteristics.

[0165] In some embodiments, the processor 13 is further configured to obtain a base signal (as described above) from the individual and process it to be saved as the baseline, at the start or at any time the reset algorithm is activated.

[0166] In some embodiments, the processor 13 is configured to obtain any other set of signals (as described above) and process them and compare with the saved baseline signal to detect any variation.

[0167] In some embodiments, the processor 13 is configured to convey the baseline blood flow index and the transient (i.e. , time-dependent) variations to the brain unit.

[0168] In some embodiments, a brain part 14 includes a storage medium 15 that is non- transitory and includes executable code 19 that, when executed by a processor unit causes the processor unit 13 to receive a signal comprising an indicia of PPG of a subject, analyze the signals and determine the reliable set of signals for use in determinations of blood flow index and / or blood flow index variability of the subject, determine the blood flow index and / or blood flow index variability of the subject, and compute the health and / or medical attributes of the subject.

[0169] In some embodiments, the storage medium 15 upon being activated by a processor unit 13, triggers the following actions by the said processor unit: reception of a signal comprising PPG of a subject, analyzing the signals and determining the reliable set of signals for use in determinations of blood flow index and / or blood flow index variability of the subject, determining the blood flow index and / or blood flow index variability of the subject, and compute the health and / or medical attributes of the subject.

[0170] In some embodiments, the executable code 19, when executed by the processor unit 13, causes the processor unit 13 to use the filtering criteria for calculating the blood flow index and blood flow index variability and the determination that the signal is reliable.

[0171] In some embodiments, the executable code 19, when executed by the processor unit 13, causes the processor unit 13 to use the filtering criteria for calculating the blood flow index and / or blood flow index variability and act to repeat or abort the process and inform if the signal is unreliable.

[0172] In some embodiments, the executable code 19, when executed by the processor unit 13, causes the processor unit to compute the health attributes and / or medical attributes.

[0173] In some embodiments, the communication unit 16 transmits the obtained blood flow index information and / or the health attributes and / or medical attributes to the notification unit or an external device.

[0174] In some embodiments, the communication unit 16 transmits the obtained blood flow index information and / or the health attributes and / or medical attributes to the notification unit 17 with a signal sent over a wire, a signal sent over WIFI, a signal sent over Bluetooth or other transmission methods.

[0175] In some embodiments, the notification unit 17 further includes a motor to vibrate and / or a circuit to generate an alarming sound and / or visual signal triggered by the communication and processing unit, and / or an external device including but not limited to a phone, a laptop, a watch, or a computer.

[0176] In some embodiments, said PPG sensing surface is configured to be worn or used as a single sensing surface or multiple sensing surface with single brain component or multiple brain combinations to receive and process a single set of PPG or multiple sets of PPG from more than one sensor before or after processing.

[0177] In some embodiments, the brain of the system 14 and the sensing components 11 are decoupled in total or partial (i.e., processing unit 13, a software 19 in a computer or any other processing unit separated from the sensing device 11) or coupled.

[0178] According to an aspect, there is provided a method 200 including receiving a first signal comprising indicia of PPG of a subject (block 210), analyzing the signals and determining reliability of the signals for use in determinations of the blood flow index or blood flow index variability of the subject (block 220) and determining the blood flow index (block 230) or blood flow index variability of the subject (block 260).

[0179] In some embodiments, the method 200 further comprises choosing the reliable set of the signals based on a determined digital or analogue filtering criterion.

[0180] In some embodiments, the method 200 further comprises or the processor 13 is configured to compute one or more occasion of the blood flow index.

[0181] In some embodiments, the method 200 further comprises or the processor 13 is configured to compute the baseline blood flow index at start or upon activation of reset algorithm.

[0182] In some embodiments, the method 200 further comprises or the processor 13 is configured to compute occasions of the blood flow index to assess the blood flow index variability.

[0183] In some embodiments, the method 200 further comprises or the processor 13 is configured to compute occasions to health attributes from the blood flow index and / or the blood flow index variability.

[0184] In some embodiments, the method 200 further comprises or the processor 13 is configured to compute occasions to medical attributes from the blood flow index and I or the blood flow index variability.

[0185] In some embodiments, the device 10 and / or the method 200 are used for health and / or medical decision making and / or other purposes including but not limited to biometric assessment and / or lie detection.

[0186] In some embodiments, the device 10 or the method 200 are used to assess “blood flow index” and / or “blood flow index variability” and / or other health information including but not limited to blood pressure, heart rate, heart rate variability, respiratory rate and / or respiratory rate variability.

[0187] In some embodiments, the device 10 or the method 200 are used to assess health or medical conditions that make a variation of pulse waveform different from the normal variation of pulse waveform.

[0188] In some embodiments, the device 10 or the method 200 are used to assess health or medical conditions that make a variation of pulse waveform different from the individual’s baseline variation of pulse waveform.

[0189] In some embodiments, the device 10 or the method 200 are used to assess health or medical conditions that make a variation of stroke volume which affects pulse waveforms to be different from the normal variation of pulse waveform.

[0190] In some embodiments, the device 10 or the method 200 are used to assess health or medical conditions that make a variation of stroke volume which affects pulse waveforms to be different from the individual’s baseline variation of pulse waveform.

[0191] In some embodiments, the device 10 or the method 200 are used to assess health or medical conditions that make a variation of QT interval in ECG which affect pulse waveforms to be different from the normal variation of pulse waveform.

[0192] In some embodiments, the device 10 or the method 200 are used to assess health or medical conditions that make a variation of QT interval in ECG which affect pulse waveforms to be different from the individual’s baseline variation of pulse waveform.

[0193] In some embodiments, the device 10 or the method 200 are used to assess vascular age related but not limited to individual’s age and other health and medical factors which affect pulse waveforms.

[0194] According to an aspect, there is provided an apparatus 10 for monitoring blood flow. The apparatus 10 including a computing device including a processing unit 13 coupled to a memory 15 and a sensor interface. The memory 15 containing non-transitory executable code 19 that, when executed by a processor unit 13 causes the processor unit 13 to receive photoplethysmography (PPG) signals from the signal interface, process the PPG signals to determine shape characteristics of a waveform of the PPG signals, calculate a blood flow index (BFI) by taking a ratio of a first and second area each defined at least in part by the shape characteristics, predict a health attribute based at least in part on the BFI, and output the predicted health attribute.

[0195] In some embodiments, the code 19 is further configured to cause the processor 13 to calculate the blood flow index variability (BFIV) by taking a ratio of the calculated BFI and a baseline BFI and predict the health attribute based at least in part on the BFIV.

[0196] In some embodiments, the code 19 is further configured to cause the processor 13 to receive initial PPG signals, process the initial PPG signals to determine initial shape characteristics of an initial waveform of the initial PPG signals, calculate an initial blood flow index (BFI) by taking a ratio of an initial first and second area each defined at least in part by the initial shape characteristics, and use the initial BFI as the baseline BFI to calculate the BFIV.

[0197] In some embodiments, the code 19 is further configured to cause the processor 13 to analyze the PPG signal and determine a reliable set of PPG signals to use to calculate the BFI based on presence or absence of motion artifacts associated with the PPG signals.

[0198] In some embodiments, at least one additional BFI is calculated based on at least one additional first and second area each defined at least in part by the shape characteristics; and the health attribute is predicted based on a weighted sum of the BFI and the at least one additional BFI.

[0199] In some embodiments, the health attribute is an occurrence of a cardiovascular event.

[0200] In some embodiments, the health attribute is an occurrence of premature ventricular contractions.

[0201] In some embodiments, the health attribute is the presence or absence of a health condition.

[0202] In some embodiments, the health attribute is a vascular age of the wearer.

[0203] According to an aspect, there is provided a system for monitoring blood flow. The system including the apparatus described in the paragraphs above and a PPG sensor 11 configured to measure the PPG signals.

[0204] FIG. 2B is a flowchart illustrating another method 280 for detecting and measuring BFI, according to some embodiments.

[0205] According to an aspect, there is provided a method 280 for monitoring blood flow. The method includes receiving photoplethysmography (PPG) signals (block 282), processing the PPG signals to determine shape characteristics of a waveform of the PPG signals (block 284), calculating a blood flow index (BFI) by taking a ratio of a first and second area each defined at least in part by the shape characteristics (block 286), predicting a health attribute based at least in part on the BFI (block 288), and outputting the predicted health attribute (block 290).

[0206] In some embodiments, the method 280 further includes calculating the blood flow index variability (BFIV) by taking a ratio of the calculated BFI and a baseline BFI and predicting the health attribute based at least in part on the BFIV.

[0207] In some embodiments, the method 280 further includes receiving initial PPG signals, processing the initial PPG signals to determine initial shape characteristics of an initial waveform of the initial PPG signals, calculating an initial blood flow index (BFI) by taking a ratio of an initial first and second area each defined at least in part by the initial shape characteristics, and using the initial BFI as the baseline BFI to calculate the BFIV.

[0208] In some embodiments, the method 280 further includes analyzing the PPG signal and determining a reliable set of PPG signals to use to calculate the BFI based on presence or absence of motion artifacts associated with the PPG signals.

[0209] In some embodiments, the method 280 further includes calculating at least one additional BFI based on at least one additional first and second area each defined at least in part by the shape characteristics and the health attribute is predicted based on a weighted sum of the BFI and the at least one additional BFI.

[0210] In some embodiments, the health attribute is an occurrence of a cardiovascular event.

[0211] In some embodiments, the health attribute is an occurrence of premature ventricular contractions.

[0212] In some embodiments, the health attribute is the presence or absence of a health condition.

[0213] In some embodiments, the health attribute is a vascular age of the wearer.

[0214] In some embodiments, the method 280 further includes measuring the PPG signals using a photoplethysmography sensor.

[0215] EXAMPLE STUDY 1 - CORRELATION BETWEEN BFIs AND PVCs

[0216] The following provides a description of an exemplary, non-limiting study that correlates some BFIs (as described in the present application) with a cardiovascular event (premature ventricular contractions (PVCs)). This is provided by means of example only and does not limit the scope of the teachings provided herein.

[0217] While traditionally employed for detecting pulse and oxygen saturation, the versatility of PPG in capturing subtle physiological variations is described herein for a health monitoring systembased on BFIs. The following retrospective cohort study is aimed at exploring the correlation between BFIs and the occurrence of PVCs.

[0218] Electrocardiograms (ECG) and corresponding PPG waveforms were studied from a randomly selected individual from the Medical Information Mart for Intensive Care III (MIMIC-111) database to develop the indices and analyze the approach. The generated BFIs include the Arterial Blood Ratio (ABR), Arterial Blood Proportion (ABP), Exact Pulsatile Ratio (EPR), Waveform Ratio (WFR), and the Average Peak Slope (APS). Statistical methods, including Mann- Whitney II tests, were employed to assess the correlations. The analyses revealed a significant correlation between each of the BFIs and the occurrence of PVCs on ECG (p-values < 0.0001). The findings suggest that the BFIs may serve as a marker for PVCs occurrence on ECG.

[0219] Introduction

[0220] In the realm of cardiovascular health, the ECG stands as an indispensable tool, providing clinicians with invaluable insights into the electrical activity of the heart. Among the various patterns and rhythms that ECG records, the presence of PVCs emerges as a phenomenon deserving attention. PVCs are characterized by premature depolarization originating in the heart's ventricles and have long been acknowledged as a prevalent arrhythmia. A higher frequency of PVCs is generally predicted by advancing age, increased height, elevated blood pressure, a history of heart disease, reduced physical activity, and a smoking habit. While frequently deemed benign, PVCs carry the potential to escalate into more severe conditions, acting as precursors to non-sustained or sustained ventricular tachycardia. Studies show that PVCs can be related to atrial fibrillation, lethal arrhythmias, stroke, all-cause and cardiac mortality and can increase the risk of atrial fibrillation and heart failure among middle-aged individuals without cardiovascular diseases.

[0221] PPG is an optical technique, that can offer a cost-effective and straightforward means to non-invasively detect changes in microvascular blood volume at the skin surface. The PPG waveform can include a pulsatile physiological component associated with cardiac-related blood volume changes synchronizing with each heartbeat alongside a slowly varying baseline featuring lower frequency elements related to respiration, sympathetic nervous system activity, and thermoregulation. PPG can provide insights into cardiovascular dynamics. PPG can be applied in medical devices for measuring oxygen saturation, pulse rate, and blood pressure. Data extracted from arteriovascular pulse signals can hold a significantly broader range of applications. Thisstudy investigates how BFIs are correlated to the occurrence of PVCs detected on the ECG of an individual. Such correlation emphasize PPG's possible adaptability and relevance across a spectrum of clinical physiological measurements.

[0222] Materials and Methods

[0223] Data from the Matched Subset database within the Multiparameter Intelligent Monitoring in Intensive Care III (MIMIC III) dataset was used. This database is matched with de-identified demographic data in the MIMIC III Clinical database. The Matched Subset database waveforms include ECG, respiration, continuous blood pressure, and PPG signals, all sampled at 125 Hz. A fingertip sensor measured the PPG data. A patient was randomly selected from the matched subset database who had both PPG and ECG records. The primary exposure is whether there is a PVC occurrence recorded on the ECG or not. The 8-minute ECG waveform was examined and every instance of PVCs was documented. Subsequently, 6-second segments of the PPG waveform were identified corresponding to each PVC occurrence to generate the cohort of segments with PVC occurrences. These segments started at 2.4 seconds before the PVC peak and extended for 6 seconds, as delineated in Table 1. The remaining parts of the PPG waveform were partitioned into 6-second segments to measure the outcome for the cohort of segments without PVC occurrences. The PPG waveform includes two primary components, a pulsatile and a non-pulsative component. The pulsatile component, recognized as the alternating current (AC) component, is in synchrony with the cardiac cycle. In this study, BFIs derived from distinct segments of the pulsatile component were calculated by dividing respective areas within the waveform or from the periodical slope of the PPG waveform (see, for example, FIG. 7).

[0224] Table 1 : Start and end points of 6-second segments of the PPG waveform corresponding to each PVC occurrence, according to some embodiments.

[0225] Due to the non-normal distribution of the data, non-parametric statistics were employed for analysis. Continuous variables were assessed using the Mann-Whitney II test, and results were presented in medians and interquartile ranges. Fisher's exact test was applied for the analysis of categorical variables, with presentation in percentages and 95% confidence intervals(Cis). Statistical significance was determined with a threshold of p-value <0.05.

[0226] Results

[0227] The entire cohort consisted of 77 PPG waveform segments. It was divided into two groups based on having PVCs on the corresponding segment of the ECG waveform. The result was 18 data segments with PVCs and 59 data segments without PVCs in their corresponding ECG waveforms. The mean number of PPG peaks in data segments was 8.32 (SD: 0.77). In this study, BFIs were incorporated to comprehensively analyze the pulsatile component of the PPG waveform. These BFIs include the ABR, ABP, EPR, WFR, and APS each calculated as described above this study.

[0228] FIG. 9 shows a violin plot for presenting the APS as a BFI in each cohort of the study, according to some embodiments.

[0229] In Table 2, the central tendencies of all BFIs are presented. The median APS for the entire cohort was 0.082 (IQR 0.063 - 0.109). The cohort containing the PPG segments with PVCsin the corresponding ECG waveform (n=18) had a median of 0.122 (IQR 0.100- 0.144), and the median APS for the cohort of PPG segments without PVCs was 0.075 (IQR 0.060- 0.088) (see FIG. 9). The entire cohort's BFIs presented median values as follows: Arterial Blood Ratio (ABR) 0.47 (0.43-0.49), Waveform Ratio (WFR) 0.56 (0.52-0.62), Exact Pulsatile Ratio (EPR) 2.42 (1 .89- 2.86), and Arterial Blood Proportion (ABP) 0.32 (0.30-0.33).

[0230] Table 2: Central tendencies of BFIs in total PPG segments and in each cohort, according to some embodiments.Average peak slope; *: Two-sample Wilcoxon rank-sum (Mann-Whitney) test.

[0231] The Mann-Whitney II test revealed a statistically significant difference in APS between the cohort with PVCs and the cohort without PVCs (z = -4.45, p< 0.0001). Notably, the cohort with PVCs exhibited a higher APS compared to the cohort without PVCs.

[0232] Moreover, the test results indicated significant differences between the two cohorts in other BFIs. The cohort with PVCs displayed lower values in ABR (z = 4.65, p< 0.0001), WFR (z = 4.35, p< 0.0001), EPR (z = 3.41 , p= 0.0006), and ABP (z = 4.65, p< 0.0001), compared to the cohort without PVCs.

[0233] Discussion

[0234] The outcomes of this study, as revealed through the Mann-Whitney II test, provide valuable insights into the unique PPG characteristics exhibited by the two cohorts. The observed differences in the BFIs, serve as indicators of potential variations in hemodynamic profiles between these two distinct cohorts. The higher APS observed in the cohort with PVCs may suggest an increased variability in blood flow waveform compared to the non-PVCs cohort. This differential BFI may hint at altered hemodynamic patterns related to the occurrence of the PVCs.The manifestation of PVCs in the ECG not only signifies an electrical aberration but may also exert a discernible mechanical effect on cardiac function. Beyond the typical depiction of irregular electrical impulses, PVCs may disrupt the synchronized contraction and relaxation of the heart's chambers, affecting its mechanical efficiency. This change in blood flow may be seen in the PPG waveform.

[0235] The relationship observed between BFIs and the occurrence of the PVCs in the study may underscore the capacity of the BFIs to capture physiological variations in cardiovascular function. The association between the BFIs and PVCs occurrence may suggest the feasibility of incorporating these metrics into health monitoring frameworks, demonstrating their potential as valuable additions to the field of health care.

[0236] EXAMPLE STUDY 2 - CORRELATION BETWEEN BFIs AND AGE

[0237] The following provides a description of an exemplary, non-limiting study that correlates some BFIs (as described in the present application) age. This is provided by means of example only and does not limit the scope of the teachings provided herein.

[0238] Aging is a multifaceted biological process characterized by a gradual decline in physiological function, particularly within the cardiovascular system. Chronological age (the number of years since birth) and biological age (the state of physiological functioning relative to chronological age) both influence an individual's health status and disease risk. Blood flow dynamics, a critical marker of cardiovascular health, can undergo substantial changes with age, reflecting both the structural and functional alterations in the vascular system. This study aims to investigate the relationship between BFIs and age, with a specific focus on the potential of these indices to predict chronological age.

[0239] In this study, five BFIs (Arterial Blood Ratio (ABR), Waveform Ratio (WFR), Exact Pulsatile Ratio (EPR), Arterial Blood Proportion (ABP), and Average Peak Slope (APS)) were derived from PPG waveforms and analyzed. The BFIs were each calculated as described above this study. A total of 53 patients' PPG waveforms were segmented into 6-second intervals, resulting in the analysis of 4,240 waveform segments.

[0240] The results demonstrate that the APS index (which reflects the average absolute value of the periodic slope of the PPG waveform) may be associated with age. A Pearson correlation test and multiple regression analyses confirmed that APS was negatively correlated with age, witha coefficient of -8.75 (p = 0.002), indicating that lower APS values are associated with older age.The remaining indices may show no statistically significant association with age.

[0241] These findings suggest that APS, as a marker of cardiovascular aging, may also predict chronological age.

[0242] Introduction

[0243] With advancing age, several vascular changes can occur, including stiffening of arteries, reduced elasticity, and endothelial dysfunction. These changes affect blood flow dynamics by altering vascular resistance, compliance, and the amplitude of blood pressure and flow waves. Chronological aging can be associated with an increase in arterial stiffness, which can be observed through elevated pulse wave velocity and reduced distensibility of the arterial wall. As arteries stiffen, the ability to cushion the pulsatile flow generated by the heart diminishes, resulting in higher systolic blood pressure and increased pulse pressure. Furthermore, aging can contribute to reduced endothelial function, decreased nitric oxide availability, and impaired vasodilatory response, all of which can alter blood flow patterns and overall cardiovascular health.

[0244] Biological age, however, may differ from chronological age due to individual variability in the rate of aging influenced by genetics, lifestyle, and environmental factors. Biological age can provide a more accurate measure of an individual's physiological state and potential risk for age- related diseases. In the context of blood flow dynamics, biological age can manifest through varying degrees of arterial stiffness, vascular inflammation, and microcirculatory function, which are not always aligned with chronological age. Therefore, the ability to predict age based on blood flow characteristics may offer potential advantages in early detection of cardiovascular risk and personalized health management.

[0245] PPG is a non-invasive optical method that can assess blood volume changes in the microvascular bed of tissue. BFIs can provide additional insights into vascular aging. By analyzing these indices, a model for predicting chronological age based on the dynamic characteristics of blood flow can be developed. This study aims to explore the application of BFIs in predicting chronological age, offering a new perspective on age-related cardiovascular changes.

[0246] Biological age

[0247] Vascular aging, a process involving both structural and functional alterations of the vascular wall, is intricately linked to biological aging. Biological age refers to the physiologicalstate of an individual, which may differ from their chronological age (the actual number of years lived). These vascular changes include endothelial dysfunction, reduced arterial compliance, and thickening of the vascular wall, which contribute to the development of cardiovascular diseases and other age-related pathologies.

[0248] Several mechanisms underlie vascular aging, such as oxidative stress, inflammation, changes in the extracellular matrix (including the activity of metalloproteinases), telomere shortening, and cellular senescence. For example, oxidative stress and vascular inflammation can lead to the damage of endothelial cells, which impairs the vessel's ability to relax and contract efficiently. Over time, this contributes to arterial stiffness, a hallmark of vascular aging. Telomere shortening, another critical aspect of biological aging, affects the replication capacity of vascular cells, contributing to their dysfunction and aging phenotype.

[0249] Importantly, vascular aging is not uniform and may begin early in life, influenced by factors like birth weight and in utero conditions. Early alterations in the vascular structure can set the stage for accelerated aging and increased risk of cardiovascular events in later life. This variability underscores the concept that biological age, particularly in the vascular context, does not always align with chronological age.

[0250] Therefore, developing indices based on blood flow dynamics, such as those derived from photoplethysmography (PPG), may provide valuable insights into both the chronological and biological aspects of aging. These indices might help to assess the rate of vascular aging and its impact on overall health, thereby serving as potential markers for predicting biological age and identifying individuals at higher risk for age-related vascular diseases.

[0251] Chronical age

[0252] Blood flow indices, derived from measurements such as PPG, may reflect age-related changes in vascular function. As individuals age, structural and functional changes occur in the vasculature, including increased arterial stiffness, reduced endothelial function, and alterations in microvascular circulation. These changes may manifest in measurable ways through blood flow dynamics, as captured by various indices such as pulse wave amplitude, arterial blood ratio, and waveform variability. For example, with advancing chronological age, the arterial walls become stiffer due to the accumulation of collagen and the breakdown of elastin, leading to increased pulse wave velocity and decreased pulse wave amplitude. BFIs can provide quantitative insights into these changes, reflecting the declining ability of blood vessels to expand and contracteffectively with each heartbeat. Moreover, arterial stiffness and pulse pressure, correlate strongly with chronological age and can predict cardiovascular risks in older adults.

[0253] The BFIs presented in this study, may provide a non-invasive and readily accessible method for assessing vascular aging, offering a more comprehensive understanding of how blood flow dynamics change over time.

[0254] Materials and Methods

[0255] The BIDMC PPG and Respiration dataset was employed, sourced from the Matched Subset database within the Multiparameter Intelligent Monitoring in Intensive Care III (MIMIC-111) dataset. This database integrates de-identified demographic data from the MIMIC-III Clinical database. The Matched Subset database provides a comprehensive set of waveforms, including ECG, respiration, continuous blood pressure, and PPG signals, all sampled at 125 Hz. The PPG data were acquired using a fingertip sensor.

[0256] The dataset includes PPG waveforms from 53 patients, with each waveform recorded for 8 minutes. To facilitate analysis, each 8-minute PPG waveform was segmented into 6-second intervals, resulting in 80 segments per patient. Thus, the analysis encompassed a total of 4,240 waveform segments (80 segments per patient across 53 patients). The PPG waveform consists of two main components: the pulsatile component, aligned with the alternating current (AC) component and synchronized with the cardiac cycle, and the non-pulsatile component. In this study, BFIs were calculated by analyzing specific areas within the waveform or the periodic slope of the PPG signal (see FIG. 7).

[0257] For each 6-second segment, the BFIs were calculated and then the average of these indices per patient was determined. These average BFIs were utilized for subsequent analysis. The primary aim of this study was to explore the relationship between BFIs and patient age, assessing whether these indices could serve as potential indicators of age.

[0258] Results

[0259] A Pearson correlation test revealed that only APS showed a statistically significant correlation with age, according to this study. Specifically, there was a significant negative association between APS and age, indicating that lower APS values are associated with older age. The results for the other indices (ABR, WFR, EPR, and ABP) were not statistically significant, as shown in Table 3.

[0260] Table 3: Correlation coefficient between the BFIs and age, according to some embodiments.*p value < 0.05 in Pearson correlation test a: Average peak slope; b: Arterial blood proportion; c: Exact pulsatile ratio; d: Waveform ratio; e: Arterial blood ratio.

[0261] In addition to the correlation analysis, various methods, including multiple linear regression, principal component analysis, support vector regression, and Bayesian ridge regression, were considered to estimate age as a continuous variable. Multiple regression models were employed to predict age using any of the five indices: APS, ABP, EPR, WFR, and ABR. The crude model revealed that only the APS index was statistically significant, with a coefficient of -8.75 (p = 0.002), indicating that for every unit decrease in APS, age increases by approximately8.75 years. None of the other indices (ABP, EPR, WFR, or ABR) showed statistically significant associations with age, according to this study.

[0262] After adjusting the model for gender, APS remained the only statistically significant predictor of age, with a slightly changed coefficient of -8.89 (p = 0.002), while the other indices still did not reach statistical significance. These findings further support the significant relationship between APS and age, while the other indices do not appear to contribute meaningfully to age prediction in the models. The results of the regression analyses are summarized in Table 4.

[0263] Table 4: Coefficient for effect of Blood Flow Indices (BFIs) on estimating age, according to some embodiments.# Estimates adjusted for gender.*p value < 0.05 of Multiple regression test. a: Average peak slope; b: Arterial blood proportion; c: Exact pulsatile ratio; d: Waveform ratio; e: Arterial blood ratio.

[0264] Discussion

[0265] APS may be a statistically significant predictor of age, offering important insights into its potential role as a biomarker of cardiovascular aging.

[0266] Some BFIs may capture subtle aspects of blood flow dynamics that are influenced by changes in the vascular system with aging. The significance of APS, in particular, may highlight the importance of developing new methods for non-invasive assessment of vascular health. APS, which represents the average absolute value of the periodic slope of the PPG waveform, may reflect the ability of the cardiovascular system to respond to pulsatile blood flow. As vascular structures stiffen with age, the flexibility of arteries decreases, leading to less pronounced changes in the slope of the PPG waveform. This relationship between APS and age mayunderscore the potential of this index to provide insights into cardiovascular function that go beyond traditional measures like pulse wave velocity or blood pressure.

[0267] Importantly, the use of these BFIs may offer a fresh perspective on age-related changes in the cardiovascular system. Other measures of cardiovascular aging, such as pulse wave velocity and arterial stiffness, while effective, often require specialized equipment or invasive procedures. In contrast, PPG is a non-invasive, easily accessible method. The identification of APS as a significant marker of age suggests that it may be developed into a robust tool for predicting cardiovascular aging, offering potential advantages for early detection of age-related cardiovascular dysfunction.

[0268] The negative correlation of APS with age (indicating that lower APS values are associated with older individuals) may reflect underlying age-related changes in vascular elasticity and responsiveness, making it a potential marker for non-invasive cardiovascular health monitoring.

[0269] COMPUTER IMPLEMENTATION DETAILS

[0270] Throughout the foregoing discussion, numerous references were made regarding servers, services, interfaces, portals, platforms, or other systems formed from computing devices. It should be appreciated that the use of such terms is deemed to represent one or more computing devices having at least one processor configured to execute software instructions stored on a computer readable tangible, non-transitory medium. For example, a server can include one or more computers operating as a web server, database server, or other type of computer server in a manner to fulfill described roles, responsibilities, or functions.

[0271] The embodiments of the devices, systems and methods described herein may be implemented in a combination of both hardware and software. These embodiments may be implemented on programmable computers, each computer including at least one processor, a data storage system (including volatile memory or non-volatile memory or other data storage elements or a combination thereof), and at least one communication interface. The embodiments of the devices, systems and methods described herein can be implemented using, for example, cloud computing, services, and / or edge computing.

[0272] Program code is applied to input data to perform the functions described herein and to generate output information. The output information is applied to one or more output devices. Insome embodiments, the communication interface may be a network communication interface. In embodiments in which elements may be combined, the communication interface may be a software communication interface, such as those for inter-process communication. In still other embodiments, there may be a combination of communication interfaces implemented as hardware, software, and combination thereof.

[0273] The technical solution of embodiments may be in the form of a software product. The software product may be stored in a non-volatile or non-transitory storage medium, which can be a compact disk read-only memory (CD-ROM), a USB flash disk, or a removable hard disk. The software product includes a number of instructions that enable a computer device (personal computer, server, or network device) to execute the methods provided by the embodiments.

[0274] The embodiments described herein are implemented by physical computer hardware, including computing devices, servers, receivers, transmitters, processors, memory, displays, and networks. The embodiments described herein provide useful physical machines and particularly configured computer hardware arrangements. The embodiments described herein are directed to electronic machines and methods implemented by electronic machines adapted for processing and transforming electromagnetic signals which represent various types of information. The embodiments described herein pervasively and integrally relate to machines, and their uses; and the embodiments described herein have no meaning or practical applicability outside their use with computer hardware, machines, and various hardware components. Substituting the physical hardware particularly configured to implement various acts for non-physical hardware, using mental steps for example, may substantially affect the way the embodiments work. Such computer hardware limitations are clearly essential elements of the embodiments described herein, and they cannot be omitted or substituted for mental means without having a material effect on the operation and structure of the embodiments described herein. The computer hardware is essential to implement the various embodiments described herein and is not merely used to perform steps expeditiously and in an efficient manner.

[0275] FIG. 10 shows a schematic diagram of computing device 1000, according to some embodiments.

[0276] As depicted, computing device 1000 includes at least one processor 1002, memory 1004, at least one I / O interface 1006, and at least one network interface 1008. Computing device 1000 may be implemented as apparatus 10.

[0277] For simplicity only one computing device 1000 is shown but system may include more computing devices 1000 operable by users to access remote network resources and exchange data. The computing devices 1000 may be the same or different types of devices. The computing device 1000 at least one processor, a data storage device (including volatile memory or nonvolatile memory or other data storage elements or a combination thereof), and at least one communication interface. The computing device components may be connected in various ways including directly coupled, indirectly coupled via a network, and distributed over a wide geographic area and connected via a network (which may be referred to as “cloud computing”).

[0278] For example, and without limitation, the computing device 1000 may be a server, network appliance, set-top box, embedded device, computer expansion module, personal computer, laptop, personal data assistant, cellular telephone, smartphone device, LIMPC tablets, video display terminal, gaming console, electronic reading device, and wireless hypermedia device or any other computing device capable of being configured to carry out the methods described herein

[0279] Each processor 1002 may be, for example, any type of general-purpose microprocessor or microcontroller, a digital signal processing (DSP) processor, an integrated circuit, a field programmable gate array (FPGA), a reconfigurable processor, a programmable read-only memory (PROM), or any combination thereof.

[0280] Memory 1004 may include a suitable combination of any type of computer memory that is located either internally or externally such as, for example, random-access memory (RAM), read-only memory (ROM), compact disc read-only memory (CDROM), electro-optical memory, magneto-optical memory, erasable programmable read-only memory (EPROM), and electrically- erasable programmable read-only memory (EEPROM), Ferroelectric RAM (FRAM) or the like.

[0281] Each I / O interface 1006 enables computing device 1000 to interconnect with one or more input devices, such as a keyboard, mouse, camera, touch screen and a microphone, or with one or more output devices such as a display screen and a speaker.

[0282] Each network interface 1008 enables computing device 1000 to communicate with other components, to exchange data with other components, to access and connect to network resources, to serve applications, and perform other computing applications by connecting to a network (or multiple networks) capable of carrying data including the Internet, Ethernet, plain old telephone service (POTS) line, public switch telephone network (PSTN), integrated servicesdigital network (ISDN), digital subscriber line (DSL), coaxial cable, fiber optics, satellite, mobile, wireless (e.g. Wi-Fi, WiMAX), SS7 signaling network, fixed line, local area network, wide area network, and others, including any combination of these.

[0283] Computing device 1000 is operable to register and authenticate users (using a login, unique identifier, and password for example) prior to providing access to applications, a local network, network resources, other networks and network security devices. Computing devices 1000 may serve one user or multiple users.

[0284] IMPLEMENTATION DETAILS

[0285] Applicant notes that the described embodiments and examples are illustrative and nonlimiting. Practical implementation of the features may incorporate a combination of some or all of the aspects, and features described herein should not be taken as indications of future or existing product plans. Applicant partakes in both foundational and applied research, and in some cases, the features described are developed on an exploratory basis.

[0286] The term “connected” or "coupled to" may include both direct coupling (in which two elements that are coupled to each other contact each other) and indirect coupling (in which at least one additional element is located between the two elements).

[0287] Although the embodiments have been described in detail, it should be understood that various changes, substitutions and alterations can be made herein without departing from the scope. Moreover, the scope of the present application is not intended to be limited to the particular embodiments of the process, machine, manufacture, composition of matter, means, methods and steps described in the specification.

[0288] As one of ordinary skill in the art will readily appreciate from the disclosure, processes, machines, manufacture, compositions of matter, means, methods, or steps, presently existing or later to be developed, that perform substantially the same function or achieve substantially the same result as the corresponding embodiments described herein may be utilized. Accordingly, the appended claims are intended to include within their scope such processes, machines, manufacture, compositions of matter, means, methods, or steps.

[0289] As can be understood, the examples described above and illustrated are intended to be exemplary only.

Claims

WHAT IS CLAIMED IS:

1. An apparatus for monitoring blood flow, comprising: a computing device comprising a processing unit coupled to a memory and a sensor interface, the memory containing non-transitory executable code that, when executed by a processor unit causes the processor unit to: receive photoplethysmogram (PPG) signals from the signal interface; process the PPG signals to determine shape characteristics of a waveform of the PPG signals; calculate a blood flow index (BFI) by taking a ratio of a first and second area each defined at least in part by the shape characteristics; predict a health attribute based at least in part on the BFI; and output the predicted health attribute.

2. The apparatus of claim 1 , wherein the code is further configured to cause the processor to: calculate the blood flow index variability (BFIV) by taking a ratio of the calculated BFI and a baseline BFI; and predict the health attribute based at least in part on the BFIV.

3. The apparatus of claim 2, wherein the code is further configured to cause the processor to: receive initial PPG signals; process the initial PPG signals to determine initial shape characteristics of an initial waveform of the initial PPG signals; calculate an initial blood flow index (BFI) by taking a ratio of an initial first and second area each defined at least in part by the initial shape characteristics; and use the initial BFI as the baseline BFI to calculate the BFIV.

4. The apparatus of claim 1 , wherein the code is further configured to cause the processor to analyze the PPG signal and determine a reliable set of PPG signals to use to calculate the BFI based on presence or absence of motion artifacts associated with the PPG signals.

5. The apparatus of claim 1 , wherein at least one additional BFI is calculated based on at least one additional first and second area each defined at least in part by the shape characteristics; and the health attribute is predicted based on a weighted sum of the BFI and the at least one additional BFI.

6. The apparatus of claim 1 , wherein the health attribute is an occurrence of a cardiovascular event.

7. The apparatus of claim 6, wherein the health attribute is an occurrence of premature ventricular contractions.

8. The apparatus of claim 1 , wherein the health attribute is the presence or absence of a health condition.

9. The apparatus of claim 1 , wherein the health attribute is a vascular age of the wearer.

10. A system for monitoring blood flow, the system comprising: the apparatus of claim 1 ; and a photoplethysmography sensor configured to measure the PPG signals.

11. A method for monitoring blood flow, the method comprising: receiving photoplethysmography (PPG) signals; processing the PPG signals to determine shape characteristics of a waveform of the PPG signals; calculating a blood flow index (BFI) by taking a ratio of a first and second area each defined at least in part by the shape characteristics; predicting a health attribute based at least in part on the BFI; and outputting the predicted health attribute.

12. The method of claim 11 , further comprising: calculating the blood flow index variability (BFIV) by taking a ratio of the calculated BFI and a baseline BFI; and predicting the health attribute based at least in part on the BFIV.

13. The method of claim 12, further comprising: receiving initial PPG signals; processing the initial PPG signals to determine initial shape characteristics of an initial waveform of the initial PPG signals; calculating an initial blood flow index (BFI) by taking a ratio of an initial first and second area each defined at least in part by the initial shape characteristics; and using the initial BFI as the baseline BFI to calculate the BFIV.

14. The method of claim 11 , further comprising analyzing the PPG signal and determining a reliable set of PPG signals to use to calculate the BFI based on presence or absence of motion artifacts associated with the PPG signals.

15. The method of claim 11 , further comprising: calculating at least one additional BFI based on at least one additional first and second area each defined at least in part by the shape characteristics; and the health attribute is predicted based on a weighted sum of the BFI and the at least one additional BFI.

16. The method of claim 11 , wherein the health attribute is an occurrence of a cardiovascular event.

17. The method of claim 16, wherein the health attribute is an occurrence of premature ventricular contractions.

18. The method of claim 11 , wherein the health attribute is the presence or absence of a health condition.

19. The method of claim 11 , wherein the health attribute is a vascular age of the wearer.

20. The method of claim 11 , further comprising measuring the PPG signals using a photoplethysmography sensor.

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