A portable system for physiological monitoring and signal processing

Abstract

A portable, cuffless blood pressure monitoring system, consisting of: a first photoplethysmography sensor node configured to be positioned at a first peripheral location on the user's body; a second photoplethysmography sensor node configured to be positioned at a second peripheral site on the user, the second peripheral site being physiologically separate from the first peripheral site to allow for measurable vascular propagation delay, each photoplethysmography sensor node comprising an optical sensor operating in red and infrared modes to detect pulse waveforms; a motion detection unit configured to detect multi-axis motion reference signals, wherein the motion detection unit includes a three-axis accelerometer configured to provide motion reference data for each detection node; a microcontroller unit assigned to each sensor node, configured for time-stamped sampling and wireless transmission, the microcontroller unit further being configured to provide each sampling with a synchronized clock to maintain time accuracy between locations; A processing unit that communicates with each sensor node via a microcontroller unit and is configured to receive and process synchronized sensor data, the processing unit being further configured to: perform bandpass filtering of the photoplethysmography signals to isolate the cardiac pulsations; apply cascaded adaptive noise reduction using the multi-axis motion reference signals to suppress motion artifacts; detect reference points in the pulse waveforms using multiple-lead analysis; calculate the vascular transit time as the difference between the pulse onset times at the first and second peripheral measurement sites; extract waveform morphology features from the pulse waveforms;Estimates for systolic and diastolic blood pressure are calculated using a calibrated regression model that incorporates vascular transit time and morphological features; and estimates for blood pressure per heartbeat are generated using signal quality indicators; a memory module connected to the processing unit to store the output results, including blood pressure values ​​for each heartbeat; and An output visualization module connected to the processing unit and the storage module, configured for real-time visualization of the output data, with the visualized data being displayed to the user via a user interface.

Description

AREA OF INVENTION

[0001] The present disclosure relates to a portable physiological monitoring and signal processing system, in particular a portable cuffless blood pressure measurement system, which utilizes synchronized photoplethysmography (PPG) at two measurement points and vascular transit time (VTT) in combination with PPG morphology features, motion artifact suppression and lightweight calibration models to generate continuous, beat-by-beat estimates of systolic and diastolic blood pressure. BACKGROUND OF THE INVENTION

[0002] Conventional intermittent blood pressure monitors with a cuff are unsuitable for continuous ambulatory blood pressure measurement.

[0003] Cuffless methods based on pulse time measurements such as pulse transit time (PTT) and vascular transit time (VTT), as well as PPG morphology, offer the potential for unobtrusive, continuous blood pressure monitoring. However, this approach must overcome practical challenges, including accurate synchronization between measurement sites, robust heart rate detection during movement, precise VTT estimation with subsample time resolution, and patient-specific calibration that translates time and morphology surrogates into clinically useful SBP and DBP values.

[0004] In light of the foregoing and in order to overcome the aforementioned challenges, the present invention provides a portable physiological monitoring and signal processing system, more precisely a portable cuffless blood pressure measurement system. SUMMARY OF THE INVENTION

[0005] The present disclosure relates to a portable, cuffless blood pressure measurement system based on synchronized photoplethysmography at two measurement points and the measurement of vessel transit time. The system integrates dual optical sensors, precise timing, motion artifact suppression, morphology analysis, and calibrated regression mapping for continuous, pulse-wise determination of systolic and diastolic blood pressure, suitable for ambulatory use. The system is configured for synchronized signal acquisition and validation of blood pressure measurements with quality assurance and recalibration support. In the proposed system, finger and earlobe nodes transmit sensor data packets via BLE to the host application. The host application selects the devices, decodes and validates the incoming packets, and forwards the data to the signal processing pipeline.The system includes a central pipeline that performs bandpass filtering and cascaded adaptive noise reduction using accelerometer data, followed by FFT-based heart rate tracking. The HR correction branch and the derivative-based feature extraction branch converge to mark the onset and indentation of the systolic peak and extract HR-SpO2-IBI and morphological features. The processed features feed the blood pressure calculation block, which controls the UI graphs and CSV logging.

[0006] The present disclosure relates to a portable, cuffless blood pressure measurement system. The system comprises a first photoplethysmography sensor, positioned at a first peripheral site of the user, and a second photoplethysmography sensor, positioned at a second peripheral site of the user. The second peripheral site is physiologically separated from the first peripheral site to allow for a measurable vascular propagation delay. Each photoplethysmography sensor has an optical sensor operating in the red and infrared ranges to detect pulse waveforms. The system further comprises a motion detection unit for acquiring multi-axis motion reference signals. This motion detection unit includes a three-axis accelerometer that provides motion reference data for each sensor.Each sensor is assigned a microcontroller unit responsible for time-stamped sampling and wireless transmission. The microcontroller unit is configured to time-stamp each sample using a synchronized clock to ensure timing accuracy between measurement points. The system also includes a processing unit that communicates with each sensor node and is configured to receive and process synchronized sensor data.The processing unit is further configured to: perform bandpass filtering of the photoplethysmography signals to isolate cardiac pulsations; apply cascaded adaptive noise reduction using the multi-axis motion reference signals to suppress motion artifacts; detect reference points in the pulse waveforms using multiple-lead analysis; calculate the heartbeat-to-heartbeat vascular transit time as the difference between the pulse onset times at the first and second peripheral measurement sites; extract waveform morphology features from the pulse waveforms; calculate estimates for systolic and diastolic blood pressure using a calibrated regression model that maps the vascular transit time and morphology features; and generate heartbeat-to-heartbeat blood pressure estimates with signal quality indicators.The system includes a storage module connected to the processing unit to store the output results, including blood pressure values, beat by beat, with the storage module further connected to an output visualization module configured to visualize the output data in real time, with the visualized data being displayed to the user via a user interface.

[0007] The objective of the present invention is to provide a portable, cuffless blood pressure monitoring system that utilizes synchronized photoplethysmography (PPG) at two sites and vascular transit time (VTT) together with PPG morphology features, motion artifact suppression and lightweight calibration models to generate continuous, beat-by-beat estimates of systolic and diastolic blood pressure.

[0008] Another objective of the present invention is to enable reliable, continuous, cuffless blood pressure measurement in the ambulatory setting by integrating synchronized dual-site acquisition, precise onset interpolation, cascaded adaptive motion suppression, morphology analysis and calibrated VTT mapping.

[0009] Another objective of the present invention is to enable synchronous PPG data acquisition at two locations, preferably on the earlobe and finger or on the chest and finger, in order to obtain two temporally aligned pulse waveforms from proximal and distal vascular beds.

[0010] Another objective of the present invention is to provide a non-invasive and comfortable system for continuous blood pressure measurement that eliminates the need to inflate a cuff and thus enables discreet ambulatory monitoring suitable for wearing over a longer period and for continuous health monitoring without the discomfort, inconvenience and periodic interruptions associated with conventional cuff-based blood pressure monitors.

[0011] Another objective of the present invention is to overcome the challenge of motion artifacts in wearable physiological monitoring by implementing cascaded adaptive noise suppression using triaxial acceleration measurement in conjunction with signal quality evaluation mechanisms, thereby ensuring reliable and accurate blood pressure measurement even under real ambulatory conditions with physical movement and everyday activities.

[0012] A further objective of the present invention is the estimation of blood pressure from heartbeat to heartbeat by synergistically combining the calculation of the vascular transit time from synchronized photoplethysmography signals at two measurement sites with a comprehensive waveform morphology analysis. Precise reference point detection using multiple-lead analysis and undersampling interpolation is employed to capture hemodynamic variations in real time.

[0013] However, another objective of the present disclosure is to enable personalized and adaptive blood pressure monitoring through user-specific calibration mechanisms and automatic recalibration functions that take into account the variability between subjects and temporal physiological changes, while maintaining low computational requirements for real-time implementation in embedded systems and providing validated data logging for clinical verification.

[0014] To further clarify the advantages and features of the present disclosure, the invention is described in more detail with reference to specific embodiments illustrated in the accompanying drawings. It is understood that these drawings merely show typical embodiments of the invention and are therefore not to be understood as limiting its scope of protection. The invention is described and explained in more detail and with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE IMAGES

[0015] These and other features, aspects and advantages of the present disclosure will be better understood when the following detailed description is read with reference to the accompanying drawings, in which identical symbols represent identical parts, wherein: Fig. Figure 1 shows a block diagram of a portable, cuffless blood pressure monitoring system according to an embodiment of the present disclosure; Fig. Figure 2 shows a block diagram of the architecture of the proposed blood pressure monitoring system according to an embodiment of the present disclosure; and Fig. Figure 3 shows a block diagram illustrating the operation of the processing unit according to an embodiment of the present disclosure.

[0016] Furthermore, those skilled in the art will recognize that the elements in the drawings are simplified and not necessarily drawn to scale. For example, the flowcharts illustrate the process by highlighting the main steps to facilitate understanding of this disclosure. With regard to the construction of the device, one or more components may be represented in the drawings by conventional symbols. The drawings may show only those specific details relevant to understanding the embodiments of this disclosure, so as not to clutter the drawings with details that are already apparent to those skilled in the art from the description contained herein. DETAILED DESCRIPTION:

[0017] To facilitate understanding of the principles of the invention, reference is made below to the embodiment illustrated in the drawings, which is described using specific terms. It is understood, however, that this does not limit the scope of protection of the invention. Rather, modifications and further developments of the illustrated system, as well as further applications of the inventive principles depicted therein, are conceivable, insofar as they would typically occur to a person skilled in the art in the field of the invention.

[0018] It will be clear to those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not to be understood as a limitation thereof.

[0019] References to “an aspect”, “another aspect”, or similar phrases in this description mean that a particular feature, structure, or property described in connection with the embodiment is included in at least one embodiment of the present disclosure. Therefore, phrases such as “in one embodiment”, “in another embodiment”, and similar expressions in this description may, but do not necessarily, all refer to the same embodiment.

[0020] The terms "includes," "comprehensive," or similar expressions denote non-exclusive inclusion. Thus, a procedure or method containing a list of steps does not only include those steps but may also include further steps not explicitly listed or inherent in the procedure or method. Likewise, the statement "includes..." for one or more devices, subsystems, elements, structures, or components, without further limitations, does not preclude the existence of other devices, subsystems, elements, structures, or components.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meanings generally known to those skilled in the art in the field to which this invention belongs. The systems, methods, and examples described herein serve only for illustration and are not to be understood as limiting.

[0022] Embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

[0023] The functional units described in this specification are referred to as devices. A device may be implemented in programmable hardware such as processors, digital signal processors, central processing units, FPGAs, PALs, PLDs, cloud processing systems, or similar. Devices may also be implemented in software for execution by various processor types. An identified device may contain executable code and, for example, comprise one or more physical or logical blocks of computer instructions, which may be organized as an object, procedure, function, or other construct. However, the executable files of an identified device need not be physically related; they may consist of different instructions stored in different locations that, when logically combined, constitute the device and fulfill its purpose.

[0024] The executable code of a device or module can consist of a single instruction or multiple instructions and can even extend across different code sections, applications, and storage media. Similarly, operational data within the device can be identified and represented, and can exist in any suitable form and be organized in any data structure. The operational data can be captured as a single data record or distributed across various storage media and may exist, at least partially, as electronic signals within a system or network.

[0025] References to “a selected embodiment”, “an embodiment”, or “an embodiment” in this description mean that a particular feature, structure, or property described in connection with the embodiment is included in at least one embodiment of the disclosed subject matter. Therefore, the phrases “a selected embodiment”, “in an embodiment”, or “in an embodiment” appearing at different points in this description do not necessarily refer to the same embodiment.

[0026] Furthermore, the described features, structures, or properties can be combined in one or more embodiments in any suitable manner. The following description contains numerous specific details to enable a comprehensive understanding of the embodiments of the disclosed subject matter. However, a person skilled in the art will recognize that the disclosed subject matter can also be realized without one or more of the specific details or with other methods, components, materials, etc. In other cases, known structures, materials, or processes are not presented or described in detail so as not to obscure aspects of the disclosed subject matter.

[0027] According to the exemplary embodiments, the disclosed computer programs or modules can be executed in a variety of ways, for example, as an application running in the memory of a device or as a hosted application running on a server and communicating with the device application or browser via various standard protocols such as TCP / IP, HTTP, XML, SOAP, REST, JSON, and other suitable protocols. The disclosed computer programs can be written in programming languages ​​that run either in the device's memory or on a hosted server, such as BASIC, COBOL, C, C++, Java, Pascal, or scripting languages ​​such as JavaScript, Python, Ruby, PHP, Perl, or other suitable programming languages.

[0028] Some of the described embodiments involve data transmission over a network, such as the transmission of various inputs or files. The network may include, for example, the internet, wide area networks (WANs), local area networks (LANs), analog or digital wired and wireless telephone networks (e.g., PSTN, ISDN, cellular networks, and xDSL), radio, television, cable, satellite, and / or other transmission or tunneling mechanisms for data. It may include multiple networks or subnetworks, each of which may, for example, have a wired or wireless data path. The network may include a circuit-switched voice network, a packet-switched data network, or another network for transmitting electronic data. For example, it may be based on the Internet Protocol (IP) or Asynchronous Transfer Mode (ATM) and support voice communication using VoIP, Voice over ATM, or similar protocols.In one embodiment, the network comprises a mobile network configured for the exchange of text or SMS messages.

[0029] Examples of networks include Personal Area Networks (PAN), Storage Area Networks (SAN), Home Area Networks (HAN), Campus Area Networks (CAN), Local Area Networks (LAN), Wide Area Networks (WAN), Metropolitan Area Networks (MAN), Virtual Private Networks (VPN), Enterprise Private Networks (EPN), the Internet, Global Area Networks (GAN), and so on.

[0030] Fig. Figure 1 shows a block diagram of a portable cuffless blood pressure monitoring system (100) according to an embodiment of the present disclosure.

[0031] The portable, cuffless blood pressure monitoring system (100) according to Fig. The system (100) comprises a first photoplethysmography sensor (102) positioned at a first peripheral location of the user. The system (100) further comprises a second photoplethysmography sensor (104) positioned at a second peripheral location of the user. This second location is physiologically separated from the first to allow for measurable vascular propagation delay. Each photoplethysmography sensor (102 and 104) has an optical sensor (106) operating in the red and infrared range to detect pulse waveforms. The system (100) also includes a motion detection unit (108) for acquiring multi-axis motion reference signals. This motion detection unit (108) includes a three-axis accelerometer (108a) that provides motion reference data for each sensor.The system (100) further comprises a microcontroller unit (110) assigned to each sensor node (102 and 104) and configured for time-stamped sampling and wireless transmission. The microcontroller unit (110) is also configured to time-stamp each sampling using a synchronized clock to ensure temporal accuracy between locations. The system (100) further comprises a processing unit (112) that communicates with each sensor node (102 and 104) via the microcontroller unit (110) and is configured to receive and process synchronized sensor data.The processing unit is further configured to: perform bandpass filtering of the photoplethysmography signals to isolate cardiac pulsations; apply cascaded adaptive noise reduction using the multi-axis motion reference signals to suppress motion artifacts; identify reference points in the pulse waveforms using multi-lead analysis; and calculate the vascular transit time from heartbeat to heartbeat as the difference between the pulse onset times at the first and second peripheral measurement points. The system (100) extracts morphological features from the pulse waveforms, calculates systolic and diastolic blood pressure using a calibrated regression model that maps the vascular transit time and morphological features, and generates blood pressure values ​​for each heartbeat, including signal quality indicators.It also includes a storage module (114) connected to the processing unit (112) that stores the output results, including blood pressure values ​​for each heartbeat, and a visualization module (116) connected to the processing unit (112) and the storage module (114) that visualizes the output data in real time. The visualized data is displayed to the user via a user interface.

[0032] In one embodiment, the microcontroller unit (110) is configured to establish a Bluetooth Low Energy (BLE) connection with the processing unit (112) to transmit the timestamped data. The processing unit (112) receives the BLE data packets via a user-defined dashboard, which analyzes and validates the packets, compares the timestamp of the first sensor node (102) with the time axis of the second sensor node (104) to synchronize the nodes, and then implements a filtering and peak detection block on the synchronized measurements.

[0033] In one embodiment, the processing unit (112) is further configured to apply a bandpass filter to each photoplethysmography channel. Subsequently, zero-phase filtering is performed to avoid phase distortion. The processing unit (112) also removes slowly changing DC components using a moving average window to obtain AC signals that represent pulsatile blood volume changes.

[0034] In one embodiment, the processing unit (112) performs cascaded adaptive noise reduction by sequentially processing the accelerometer axes as noise references in a cascaded adaptive filter configuration. Subsequently, an LMS-Newton filter is applied, with each accelerometer axis being used sequentially to estimate and subtract motion-related components from the photoplethysmography signals. The processing unit (112) further performs heart rate tracking using short-window FFT to verify physiological components and restore waveform integrity.

[0035] In one embodiment, the processing unit (112) is further configured to calculate a signal quality index for each detected heartbeat. This index is based on an assessment of the consistency of the lead morphology, the plausibility of the interval between heartbeats, the accelerometer energy, and the amplitude stability. The processing unit (112) only allows heartbeats that exceed predefined quality thresholds to be processed in order to calculate the vessel transit time and estimate the blood pressure.

[0036] In one embodiment, the processing unit (112) performs reference point detection by conducting multiple-derivative analysis and subsample interpolation. The processing unit is configured to perform the multiple-derivative analysis as follows: calculating the first, second, third, and fourth derivatives of the cleaned photoplethysmography waveform; subsequently identifying the pulse foot or onset as the minimum before the systolic rise or at the zero crossing of the first derivative with amplitude validation; subsequently identifying the systolic peak; and finally, identifying the dicrotic notch. The subsample interpolation is performed by the processing unit (112) by applying cubic interpolation around the detected onset regions to generate subsample resolution timestamps for each pulse onset.

[0037] In one embodiment, the processing unit (112) is further configured to perform heartbeat matching between different measurement sites. The processing unit (112) matches heartbeat pairs between the first and second peripheral measurement sites by searching for corresponding start times within a physiologically valid time window. The matched heartbeat pairs must exhibit valid signal quality, consistency of the interval between heartbeats, and a vessel transit time within physiological limits at both measurement sites. The processing unit (112) also calculates the vessel transit time for a heartbeat.

[0038] In one embodiment, the processing unit (112) extracts waveform morphology features, including: pulse width at half amplitude; rise edge; time to systolic peak; systolic area under the curve; ejection time, defined as time from onset to dicrotic notch; and heart rate.

[0039] In one embodiment, the processing unit (112) is further configured as follows: The body surface area is calculated based on the user's demographic parameters, including age, weight, and height; the stroke volume is estimated using a regression that takes into account ejection time, heart rate, body surface area, and age; the pulse pressure is derived from the stroke volume; and the diastolic blood pressure is calculated by subtracting the pulse pressure from the calculated systolic blood pressure.

[0040] In one embodiment, the system (100) further comprises: a calibration module (118) configured to: perform an initial calibration using synchronous cuff measurements; calculate regression coefficients that map vascular transit time and morphological features to systolic and diastolic blood pressure; and calculate systolic and diastolic blood pressure beat by beat using stored regression coefficients, thereby generating final systolic and diastolic pressure outputs; a drift monitoring module (120) configured to monitor long-term deviation from the calibration baseline and request recalibration when the sustained deviation exceeds threshold values; and a post-processing module (122) configured to smooth heartbeat-to-heartbeat blood pressure values ​​using exponential moving average.and a data logging module (124) connected to the storage module (114) and configured to log raw and processed signals including start timestamps, vessel transit time values, morphological features and blood pressure estimates.

[0041] In one embodiment, the first sensor node (102), the second sensor node (104), the motion detection unit (108), the microcontroller unit (110), the processing unit (112), the memory module (114), the visualization module (116), the calibration module (118), the drift monitoring module (120), the post-processing module (122) and the data logging module (124) can be implemented in programmable hardware devices such as processors, digital signal processors, central processing units, field-programmable gate arrays, programmable array logic, programmable logic devices, cloud processing systems or the like.

[0042] The present invention relates to a portable system for estimating arterial blood pressure without a cuff, wherein in this invention the integration of synchronized dual-site acquisition, precise onset interpolation, cascaded adaptive motion suppression, morphology analysis and calibrated VTT mapping enables reliable continuous cuffless blood pressure measurement in the ambulatory setting.

[0043] Fig. Figure 2 shows a block diagram illustrating the architecture of the proposed blood pressure monitoring system according to an embodiment of the present disclosure.

[0044] For data acquisition at two sites, the system uses two photoplethysmography sensors positioned at physiologically separate peripheral sites, preferably on the finger and earlobe, as shown in Fig. 2 shown. The spatial separation between these points enables the measurable vascular propagation delay required for calculating the vascular transit time.

[0045] In one embodiment, each sensor node comprises a PPG MAX30102 optical sensor operating in red and infrared modes, a three-axis accelerometer for capturing motion reference data, and an ESP32 microcontroller for time-stamped sampling and wireless transmission. Signals are sampled at 100–125 Hz to provide sufficient temporal resolution for subsample-level time estimation. Each sample is time-stamped using a synchronized clock reference to maintain timing accuracy between locations, as detailed in the firmware flow. Accurate synchronization is critical because VTT estimation depends directly on the precise temporal alignment of the proximal and distal pulse waveforms.The firmware and host integration workflow documents all the embedded and host-side processing required to generate time-stamped, dual-site streams suitable for VTT estimation. On the embedded side, the ESP32 establishes a BLE connection and transmits the real-time clock (RTC) to both nodes. Finger and earlobe setup routines initiate the MAX30102 measurements and generate packets containing the elapsed time and sensor channels (IR and red-light accelerometer / gyroscope, if present). BLE data packets are received by the custom dashboard, which analyzes and validates them. The first earlobe's timestamp is then compared to the finger timeline to synchronize the nodes. The synchronized measurements are then passed to filtering and peak detection blocks.Based on matching onset values, the VTT (Velocity Time Time) is calculated for each heartbeat. The process emphasizes periodic offset correction of the RTC handshake and FIFO buffering to minimize BLE jitter.

[0046] Fig. Figure 3 shows a block diagram illustrating the operation of the processing unit according to an embodiment of the present disclosure.

[0047] The raw PPG signals contain high-frequency noise and motion artifacts. Therefore, preprocessing is performed before time analysis (see Fig. 3) Each PPG channel is subjected to a 4th-order Butterworth bandpass filter in the 0.5–4.0 Hz range to isolate cardiac pulsations while removing respiratory baseline variations and electronic noise (see signal processing pipeline in Fig. 3) To avoid phase distortions that could affect timing accuracy, a zero-phase filter is applied. Slowly varying DC components are removed using a moving average window. The resulting AC waveform represents the pulsating blood volume changes and is used for feature extraction.

[0048] For body-worn monitoring, robust handling of motion artifacts is required. The system uses cascaded adaptive noise reduction controlled by accelerometer signals, as in Fig. 2 and Fig. 3 shown in detail. Each accelerometer axis XY and Z is used sequentially as a noise reference in a cascaded adaptive LMS-Newton filter configuration ( Fig. 3).

[0049] Fig. Figure 3 shows the detailed signal processing pipeline with the individual processing steps for each channel. PPG and accelerometer signals are first bandpass filtered to isolate the heart rate band. The accelerometer axes feed a cascaded, adaptive noise reduction chain, which is processed sequentially (X, then Y, then Z) to generate a motion-suppressed PPG signal. Fast Fourier transforms of the PPG and accelerometer spectra enable robust heart rate tracking. Where heart rate energy is lost due to motion or filtering, the pipeline uses adaptive notch filters on the tracked heart rate and its harmonics to recover the lost energy and reconstruct a clean PPG signal for derivative analysis.

[0050] In one embodiment, the following filter parameters are implemented in the system: Filter order M = 33; step size µ ≈ 9×10 -5; and forgetting factor α ≈ 2×10 -4 The adaptive filter estimates motion-related components and subtracts them from the PPG signal. Sequential axis processing (ACC_X → ANC_X → ANC_Y → ANC_Z, Fig. 3) preserves the physiological waveform morphology while significantly suppressing movement. Additionally, heart rate is monitored using short-window FFT to verify the physiological component and restore waveform integrity if necessary, as described in the HR tracking block of [reference]. Fig. Figure 3 is shown. The HR estimate generated here is used for subsequent smoothing and feature extraction.

[0051] After suppressing motion artifacts, a signal quality index is calculated for each heartbeat, as described in Fig. 2 shown.

[0052] In one embodiment, the signal index evaluates: consistency of the lead morphology, plausibility of the interval between heartbeats, accelerometer energy, and amplitude stability. Only heartbeats exceeding predefined quality thresholds are permitted for VTT calculation and blood pressure estimation. This ensures robustness during walking movements.

[0053] Accurate detection of pulse onset is essential for measuring vascular transit time, as emphasized in the lead-based reference point detection phase ( Fig. 2).

[0054] In one embodiment, the cleaned PPG waveform is differentiated for multi-lead analysis to obtain first, second, third, and fourth leads. These leads aid in identifying the pulse foot or onset, systolic peak, and dicrotic notch. The pulse onset is detected as a minimum before the systolic rise or at the zero crossing of the first lead with amplitude validation and is incorporated into the peak and onset detection. To increase timing accuracy beyond the sampling interval, cubic interpolation is also performed around the detected onset region. This generates subsample-resolution timestamps for each pulse, significantly reducing quantization error and improving VTT accuracy.

[0055] For each detected finger pulse onset, the system searches for the corresponding earlobe pulse onset within a physiologically meaningful time window. Matching pulse pairs must meet the following criteria: valid signal quality at both measurement points, consistency of the interval between pulse beats, and a ventricular transit time (VTT) within physiological limits. Non-matching or ambiguous pulse beats are discarded.

[0056] For each heartbeat, the vascular transit time (VTT) is calculated based on interpolated times of excitation onset at the earlobe and fingertips. Outliers are eliminated using median filtering and statistical deviation thresholds. A moving average can be used to ensure temporal stability. The VTT is inversely proportional to arterial stiffness. As blood pressure increases, arterial stiffness also increases, leading to a shortened transit time. Therefore, the VTT serves as the primary surrogate marker for cuffless blood pressure measurement in this system.

[0057] To improve the robustness of the estimation, waveform morphology features are extracted. These features include: pulse width at half amplitude, rise time, time to systolic peak, systolic area under the curve, ejection time, and heart rate. Demographic parameters such as age, weight, and height are included to account for interindividual variability.

[0058] The processing unit is configured to convert PPG waveforms from two measurement points into systolic and diastolic blood pressure. This process includes preprocessing peak and onset detection, as well as motion artifact removal. From the cleaned heartbeats, the algorithm calculates heart rate and ejection time (time from onset to dicrotic notch) and determines body surface area using the subject's anthropometric data. Ejection time and body surface area are incorporated into a stroke volume regression, which generates a pulse pressure surrogate. In parallel, matching onset values ​​from the earlobe and finger provide the vascular transit time, which is used by the systolic estimator. Diastolic blood pressure is determined by subtracting the pulse pressure from the calculated systolic blood pressure. Beat quality gating, median filtering, and outlier detection are applied before the final mapping.A surrogate value for body surface area and stroke volume is calculated. From the stroke volume surrogate value and the vascular compliance approximation, a pulse pressure is derived, and finally the diastolic blood pressure is calculated.

[0059] The system is also configured for calibration. Initial calibration is performed using synchronous cuff measurements at rest. Regression coefficients are calculated by mapping ventricular tachycardia (VTT) and morphological features onto systolic (SBP) and diastolic (DBP) blood pressure. After calibration, SBP and DBP are calculated beat by beat using the stored regression coefficients, resulting in the final systolic and diastolic blood pressure values. For real-time implementation, resource-efficient linear or ridge regression is preferred. The system is further configured for post-processing and drift monitoring. Blood pressure values ​​are smoothed beat by beat using exponential moving average to reduce jitter. The system monitors the long-term deviation from the calibration baseline.If the persistent deviation exceeds threshold values, recalibration is requested. The system outputs are displayed in the visualization phase. Fig. Figure 2 shows SBP and DBP beat by beat, VTT values, heart rate, and signal quality index. All raw and processed signals, including signal onset times, VTT morphology features, and BP estimates, are logged for validation and regulatory purposes, as specified in the host data flow.

[0060] The invention provides a portable system for estimating arterial blood pressure without a cuff. This system combines: synchronized PPG acquisition at two sites, preferably the earlobe and finger or chest and finger, to obtain two temporally aligned pulse waveforms from proximal and distal vascular beds; high-precision heartbeat annotation using lead-based reference point detection, including the systolic baseline or onset and the dicrotic notch, as well as subsample interpolation to increase temporal accuracy; and calculation of the vascular transit time (VTT). n ) for each heartbeat, defined as VTT n= t_proximal onset n - t_distal onset n, where t denotes the detected pulse onset time for the same heartbeat at each measurement site; extraction of morphological features, including rise time, pulse width at half amplitude, systolic area, normalized amplitude, and time to peak, to supplement the temporal information; cascaded suppression of motion artifacts by triaxial acceleration measurement utilizes adaptive noise reduction and signal quality indices, excluding unreliable heartbeats from further processing. Lightweight calibration models, such as subject-specific linear regression, regularized linear regression, or compact nonlinear models, map VTT and morphological features to systolic and diastolic blood pressure. Mechanisms for initial calibration and periodic recalibration using cuff reference values ​​are integrated.Real-time operation and logging include confidence indicators for each measurement, as well as the ability to export raw data and processed data streams for clinical validation. The integration of synchronized dual-site data acquisition, precise onset interpolation, cascaded adaptive motion suppression, morphological analysis, and calibrated VTT mapping enables reliable, continuous, cuffless blood pressure measurement in the ambulatory setting.

[0061] The drawings and the preceding description illustrate embodiments. Those skilled in the art will recognize that one or more of the described elements can be combined to form a single functional element. Alternatively, certain elements can be divided into several functional elements. Elements of one embodiment can be added to another. For example, the process flows described here can be modified and are not limited to the manner described herein. Furthermore, the actions of a flowchart need not be performed in the sequence shown; nor do all actions necessarily need to be carried out. Actions that do not depend on other actions can be performed in parallel with the other actions. The scope of protection of the embodiments is in no way limited by these specific examples. Numerous variations, whether explicitly stated in the description or not, such as...Differences in structure, dimensions, and materials are possible. The scope of protection of the embodiments is at least as comprehensive as described by the following claims.

[0062] The advantages, other benefits, and problem solutions have been described above with reference to specific embodiments. However, the advantages, benefits, problem solutions, and any components that can effect or enhance an advantage, benefit, or solution are not to be construed as critical, necessary, or essential features or components of the claims. REFERENCES 100 A portable blood pressure measuring system without a cuff. 102 First PPG sensor node 104 Second PPG Sensor Node 106 Optical Sensor 108 Motion sensor unit 108a Triaxial accelerometer 110 microcontroller unit 112 processing units 114 memory module 116 Output Visualization Module 118 Calibration module 120 Drift monitoring module 122 Post-processing module 124 Data logging module 202 Data collection 202a Data Collection From Finger 202b Data Collection From the Earlobe 202c Data transmission via BLE 202d Selection of Custom Application Device and Connections 202e Data Decoding and Validation 204 Signal Filtering and Noise Reduction 204a Introduction to Signal Filtering 204b PPG data 204c Accelerometer data 204d Butterworth - 4th order filter 0.5-5 Hz 204e Cascaded Adaptive Noise Cancellation 204f FFT and heart rate tracking 206 HR Correction and Feature Extraction 206a Correction of Heart Rate 206b 4. Derivation of the PPG signal 206c Notch Filtering and Signal Recovery 206d Detection of Reference Points in Derivative Signals 206e PPG Cleaned 206f Marking of the Systolic Peak Value, Onset, Diastolic Peak and Notch 206g DMM tip detection algorithm 206h heart rate, SpO2, IBI detection 208 Output Visualization and Storage 208a Blood pressure measurement with 2 devices 208b Creation of User Interface, Diagram and Visualization 208c Saving Data and Events to a CSV File 302 Bandpass filtering (0.4-4.0 Hz) 302a PPG Entrance 302b BPF 302c PPG_Avg 302d ACC input 302e BPF 302f ACC_Avg 304 Cascaded Adaptive Noise Cancellation 304a Adaptive Noise Cancellation X 304b Adaptive Noise Cancellation Y 304c Adaptive Noise Cancellation Z 304d PPG_ANC 306 Heart Rate Tracking and Correction 306a Fast Fourier Method Transform 306b Heart Rate Tracking 306c Heart Rate Correction 306d Fast Fourier Method Transform 308 Noise Filtering and Output 308a Notch filter I BW = 0.4 Hz Fn1 = Fhr 308b Notch filter I BW = 0.4 Hz Fn2 = 2fhr 308c PPG_Rec 308d Heart Rate Calculation 308e Hr_Est

Claims

A portable, cuffless blood pressure monitoring system comprising: a first photoplethysmography sensor node configured to be positioned at a first peripheral site on the user's body; a second photoplethysmography sensor node configured to be positioned at a second peripheral site on the user, the second peripheral site being physiologically separate from the first peripheral site to allow for measurable vascular propagation delay, each photoplethysmography sensor node comprising an optical sensor operating in red and infrared modes to detect pulse waveforms;a motion detection unit configured to detect multi-axis motion reference signals, wherein the motion detection unit includes a three-axis accelerometer configured to provide motion reference data for each detection node; a microcontroller unit associated with each sensor node, configured for time-stamped sampling and wireless transmission, wherein the microcontroller unit is further configured to provide each sampling with a synchronized clock to maintain time accuracy between locations;A processing unit that communicates with each sensor node via a microcontroller unit and is configured to receive and process synchronized sensor data, the processing unit being further configured to: perform bandpass filtering of the photoplethysmography signals to isolate the cardiac pulsations; apply cascaded adaptive noise reduction using the multi-axis motion reference signals to suppress motion artifacts; detect reference points in the pulse waveforms using multiple-lead analysis; calculate the vascular transit time as the difference between the pulse onset times at the first and second peripheral measurement sites; extract waveform morphology features from the pulse waveforms;Estimates for systolic and diastolic blood pressure are calculated using a calibrated regression model that maps vascular transit time and morphological features; and estimates for blood pressure per heartbeat are generated using signal quality indicators; a memory module is connected to the processing unit to store the output results, including the blood pressure values ​​for each heartbeat; and an output visualization module is connected to the processing unit and the memory module and is configured for real-time visualization of the output data, with the visualized data being displayed to the user via a user interface. System according to claim 1, wherein the microcontroller unit is configured to establish a Bluetooth Low Energy (BLE) connection with the processing unit to transmit the timestamped data, wherein the processing unit receives the BLE data packets via a user-defined dashboard that analyzes and validates the packets, compares the timestamp of the first sensor node with the time axis of the second sensor node to synchronize the nodes, and subsequently implements a filter and peak detection block on the synchronized measurements. System according to claim 1, wherein the processing unit is further configured to: apply a bandpass filter to each photoplethysmography channel; apply zero-phase filtering to avoid phase distortion; and remove slowly changing DC components using a sliding mean window to obtain AC waveforms that represent pulsatile blood volume changes. System according to claim 1, wherein the processing unit performs cascaded adaptive noise reduction by: sequential processing of the accelerometer axes as noise references in a cascaded adaptive filter configuration; application of an adaptive LMS-Newton filter using each accelerometer axis sequentially to estimate and subtract motion-related components from the photoplethysmography signals; and heart rate frequency tracking by means of short-window fast Fourier transform to verify physiological components and restore waveform integrity. System according to claim 1, wherein the processing unit is further configured to calculate a signal quality index for each detected heartbeat by evaluating the consistency of the lead morphology, the plausibility of the interval between heartbeats, the accelerometer energy and the amplitude stability; and only allows heartbeats that exceed predefined quality thresholds for the calculation of the vascular transit time and the blood pressure estimation. System according to claim 1, wherein the processing unit performs reference point detection by performing multiple derivative analysis and subsample interpolation, wherein the processing unit is configured to perform the multiple derivative analysis by: calculating the first, second, third, and fourth derivatives of the cleaned photoplethysmography waveform; identifying the pulse foot or onset as a minimum before the systolic rise or at the zero crossing of the first derivative with amplitude validation; identifying the systolic peak; and identifying the dicrotic notch, and wherein the processing unit performs subsample interpolation by performing cubic interpolation around the detected onset regions to generate subsample resolution timestamps for each pulse onset. System according to claim 1, wherein the processing unit is further configured to perform a heartbeat matching between different measuring sites, wherein the processing unit matches heartbeat pairs between the first peripheral measuring site and the second peripheral measuring site by searching for corresponding start times within a physiologically valid time window, wherein the matched heartbeat pairs at both measuring sites have a valid signal quality, consistency of the intervals between the heartbeats and a vessel transit time within physiological limits, and wherein the processing unit further performs a vessel transit time calculation by calculating the vessel transit time for a heartbeat. System according to claim 1, wherein the processing unit extracts waveform morphology features comprising: pulse width at half amplitude; rise edge; time to systolic peak; systolic area under the curve; ejection time, defined as time from onset to dicrotic notch; and heart rate. System according to claim 1, wherein the processing unit is further configured as follows: The body surface area is calculated based on demographic parameters of the user, including age, weight and height; the stroke volume is estimated using a regression that takes into account ejection time, heart rate, body surface area and age; the pulse pressure is derived from the stroke volume; and the diastolic blood pressure is calculated by subtracting the pulse pressure from the calculated systolic blood pressure. System according to claim 1, wherein the system further comprises: a calibration module configured to: perform an initial calibration using synchronous cuff measurements; calculate regression coefficients that map vascular transit time and morphological features to systolic and diastolic blood pressure; and calculate systolic and diastolic blood pressure beat by beat using stored regression coefficients, thereby generating final systolic and diastolic pressure outputs; a drift monitoring module configured to monitor long-term deviation from the calibration baseline and request recalibration when the sustained deviation exceeds threshold values; a post-processing module configured to smooth the blood pressure values ​​beat by beat using exponential moving average;and a data logging module connected to the storage module and configured to log raw and processed signals, including start timestamps, vessel transit time values, morphological features, and blood pressure estimates.

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