System for non-invasive estimation of intracranial pressure by modeling the cerebral blood flow signal

Abstract

A system for the non-invasive estimation of intracranial pressure, comprising: a housing structure configured to enclose and support a variety of interconnected components; a cerebral blood flow measurement unit positioned within or connected to the housing structure and configured to detect physiological signals representative of blood flow in the intracranial vessels of a subject; an analog signal conditioning unit operatively connected to the cerebral blood flow measurement unit, comprising at least one low-noise amplification circuit, at least one band-limiting filter circuit, and an impedance matching circuit configured to improve signal quality and suppress noise components;An analog-to-digital converter unit operationally linked to the analog signal processing unit and configured to convert processed analog signals into digital signal representations with a predefined sampling rate and resolution; a computing unit comprising at least one processor and at least one dedicated signal processing circuit, the computing unit being configured to receive the digital signal representations and perform preprocessing, feature extraction, and physiological modeling operations; a storage unit operationally linked to the computing unit and configured to store physiological parameters, calibration data, and modeling coefficients; and an output interface unit configured to generate intracranial pressure values ​​based on processed signals and to display or transmit these intracranial pressure values.

Description

Technical field of the invention The present invention relates to a biomedical diagnostic device and, in particular, a structurally integrated system for the non-invasive determination of intracranial pressure by acquiring, processing, and modeling cerebral blood flow signals. The invention lies at the interface of neuromonitoring instrumentation, physiological signal processing, and embedded computer hardware for determining intracranial pressure values ​​without invasive catheterization. Background of the invention Intracranial pressure (ICP) is a critical physiological parameter associated with various neurological conditions, including traumatic brain injury, hydrocephalus, intracranial hemorrhage, and cerebral edema. Conventional ICP measurement methods involve invasive procedures such as intraventricular catheterization or the placement of intraparenchymal probes, which carry risks such as infection, bleeding, and tissue damage. Existing non-invasive approaches suffer from limitations in accuracy, temporal resolution, and robustness due to restrictions in signal acquisition and insufficient modeling of cerebral hemodynamics. Therefore, there is a need for a structurally integrated system capable of accurately determining ICP using non-invasive sensor methods combined with advanced signal modeling via dedicated hardware configurations. Intracranial pressure (ICP) is a fundamental physiological parameter that reflects the pressure dynamics within the skull. This is determined by the interplay of brain tissue, cerebrospinal fluid (CSF), and cerebral blood volume, according to the Monro-Kellie doctrine. Precise monitoring of ICP is essential for the diagnosis, treatment, and prognosis of neurological disorders such as traumatic brain injury, subarachnoid hemorrhage, intracerebral hemorrhage, hydrocephalus, ischemic stroke, and brain tumors. Elevated ICP can lead to decreased cerebral perfusion, ischemia, and ultimately, cerebral herniation. Therefore, continuous monitoring is indispensable in intensive care and neurosurgery. Despite its clinical importance, ICP measurement still relies predominantly on invasive procedures, which carry significant risks and surgical limitations. The current gold standard for ICP monitoring includes invasive methods such as intraventricular catheterization, intraparenchymal fiber optic leads, and subdural or epidural pressure transducers. Intraventricular catheters, typically inserted into the lateral ventricles via a burr hole, allow for direct pressure measurements and therapeutic cerebrospinal fluid (CSF) drainage. However, these systems require precise surgical placement and are associated with risks such as infection, bleeding, catheter occlusion, and damage to delicate brain structures. Intraparenchymal leads, inserted directly into brain tissue, offer easier placement and lower infection rates compared to ventricular catheters, but suffer from signal drift, the inability to recalibrate after insertion, and a lack of therapeutic capabilities.Epidural and subdural sensors are less invasive, but have lower accuracy due to signal attenuation and susceptibility to external artifacts. Taken together, these invasive systems represent a significant clinical burden, as they require sterile operating conditions, qualified personnel, and continuous monitoring for complications, thus limiting their applicability in non-critical or outpatient settings. In response to the limitations of invasive monitoring methods, various non-invasive procedures have been investigated. However, due to insufficient accuracy, reproducibility, or robustness, none of these methods has achieved widespread clinical acceptance. One frequently studied approach is transcranial Doppler sonography (TCD), which measures cerebral blood flow velocity in large intracranial arteries such as the middle cerebral artery. Derived indices such as the pulsatility index and the resistance index correlate with ICP fluctuations. However, these indices are indirect surrogate parameters and are influenced by numerous physiological factors such as arterial blood pressure, vascular compliance, and carbon dioxide levels, leading to significant variations in ICP measurements.Furthermore, TCD measurements are highly user-dependent and require precise positioning and angular alignment of the probe, which affects the consistency of the results in long-term monitoring. Another class of non-invasive methods utilizes the measurement of the optic nerve sheath diameter (ONSD) using ultrasound or imaging techniques. The optic nerve sheath borders the subarachnoid space, and its diameter expands with increased intracranial pressure. While ONSD measurement allows for rapid bedside assessment, it is limited by variability between examiners, anatomical differences between patients, and the lack of standardized threshold values. Furthermore, the ONSD reflects static structural changes rather than continuous dynamic pressure fluctuations, limiting its suitability for real-time monitoring. Magnetic resonance imaging (MRI) and computed tomography (CT) are also used to determine intracranial pressure (ICP) by analyzing the displacement of brain tissue, ventricular size, and the dynamics of cerebrospinal fluid (CSF) flow. Advanced MRI techniques, such as phase-contrast imaging, allow for the estimation of CSF flow velocity and intracranial compliance. However, these imaging techniques are inherently non-continuous, expensive, and unsuitable for critically ill patients requiring bedside monitoring. The temporal resolution of these techniques is insufficient to capture rapid ICP fluctuations, and their reliance on a large-scale imaging infrastructure further limits their availability. Electrophysiological techniques such as electroencephalography (EEG) and near-infrared spectroscopy (NIRS) have been investigated for the indirect assessment of cerebral hemodynamics and oxygenation. NIRS, in particular, measures changes in hemoglobin oxygenation and has been proposed as a surrogate parameter for cerebral blood volume changes associated with intracranial pressure (ICP) fluctuations. However, NIRS signals are highly susceptible to extracranial interference, such as from the scalp and skull, and have limited penetration depth, which restricts their reliability in accurately mapping intracranial conditions. EEG-based techniques are subject to similar limitations, as electrical activity patterns are influenced by a variety of neurological factors beyond ICP. Mathematical modeling techniques have also been proposed for estimating intracranial pressure (ICP), combining several physiological signals such as arterial blood pressure, cerebral blood flow velocity, and heart rate variability. These models attempt to capture the complex relationships between cerebral hemodynamics and intracranial pressure using analytical or data-driven approaches. While these approaches show promise, their implementation often relies on software-based procedures running on general-purpose computers. This leads to problems such as computational latency, a lack of determinism, and limited integration with real-time sensor hardware. Furthermore, many existing models are based on simplified assumptions that do not fully account for the interindividual variability of cerebrovascular characteristics, resulting in lower accuracy in heterogeneous clinical populations. Another limitation of existing non-invasive systems lies in their fragmented architecture, where sensors, data processing, and output functions are distributed across separate devices. This lack of structural integration leads to problems with signal synchronization, increased susceptibility to noise and interference, and higher power consumption. Furthermore, many systems lack adaptive calibration mechanisms, preventing them from adjusting to patient-specific physiological conditions or long-term changes in vascular dynamics. The absence of hardware-level optimization further limits the performance of these systems, particularly with regard to real-time processing and energy efficiency. Portable monitoring devices have recently gained prominence as potential solutions for continuous ICP monitoring outside of intensive care units. However, to achieve portability, these devices often employ simplified sensor methods and basic techniques, which compromises accuracy. Furthermore, the miniaturization of components presents additional challenges regarding signal quality, thermal management, and electromagnetic interference. The lack of robust validation using invasive gold-standard measurements has also hindered the clinical acceptance of such devices. In summary, existing solutions for intracranial pressure measurement are either invasive, with associated clinical risks, or non-invasive, but with significant limitations in accuracy, reliability, and real-time capability. Among the major challenges are the inadequate capture of dynamic intracranial pressure fluctuations, susceptibility to physiological and environmental disturbances, the lack of structural integration of sensor and processing components, and insufficient adaptability to individual patient characteristics. These shortcomings underscore the need for a technologically advanced system that combines highly accurate cerebral blood flow measurement with hardware-optimized signal processing and physiologically accurate modeling to enable reliable, non-invasive estimation of intracranial pressure. Summary of the invention The present invention describes a device with a closed system for the non-invasive estimation of intracranial pressure (ICP). This system acquires cerebral blood flow signals, performs multi-stage signal processing, extracts physiologically relevant features, and applies hardware-based modeling techniques to derive ICP values. The system comprises a sensor unit for acquiring cerebral blood flow velocity or volumetric flow rate, a signal processing circuit for amplification and noise reduction, a digitization unit for converting analog signals into discrete data streams, and a computing unit with dedicated processors for mathematically modeling cerebrovascular dynamics. Furthermore, the device has memory structures for storing calibration parameters and physiological models, as well as an output interface for displaying or transmitting the estimated ICP values.The entire system is housed in a compact casing and is suitable for use on the body or at the bedside. The main objective of the present invention is to provide a structurally integrated system for the non-invasive estimation of intracranial pressure by modeling the cerebral blood flow signal. The system is configured to accurately determine intracranial pressure values ​​without requiring invasive catheterization or penetration of the skull tissue. A further objective of the invention is to provide a device with a sensor array for acquiring highly accurate cerebral blood flow signals from a subject. This minimizes operator dependence and ensures reproducible signal acquisition under various physiological conditions.Another objective of the invention is to provide an analog signal processing arrangement with amplification, filter and impedance matching circuits to improve signal integrity by suppressing noise components and preserving physiologically relevant features of the acquired signals. A further objective of the invention is to provide a digitization unit for converting processed analog cerebral blood flow signals into high-resolution digital representations suitable for real-time computer analysis, thus enabling precise temporal characterization of hemodynamic patterns. Another objective of the invention is to provide a computing unit with dedicated hardware processors for performing multi-stage signal preprocessing, feature extraction, and physiological modeling operations with deterministic latency and reduced computational effort. The invention further aims to provide a system in which the computing unit is configured to implement a mathematical model of cerebrovascular dynamics, wherein the model establishes a quantitative relationship between the properties of cerebral blood flow and intracranial pressure. A further objective of the invention is to provide a system for extracting hemodynamic parameters, including pulsatility indices, waveform morphology descriptors, and spectral components, from cerebral blood flow signals and using them in conjunction with stored physiological models for a more accurate and robust estimation of intracranial pressure. A further objective of the invention is to provide a storage unit for storing calibration parameters, patient-specific coefficients, and model data, thereby enabling adaptive estimation of intracranial pressure based on individual physiological variability. The invention also aims to provide a calibration device that adjusts system parameters depending on baseline signal acquisition, thus ensuring consistent performance across different patients and operating conditions. A further objective of the invention is to provide an output interface for displaying intracranial pressure values ​​and associated trends in real time, as well as for transmitting the estimated data to external monitoring systems via communication interfaces. Another objective of the invention is to provide a compact housing for accommodating and mechanically supporting the sensor, processing, and output components, while simultaneously ensuring electromagnetic shielding, thermal stability, and portability for clinical and outpatient use. The invention further aims to provide an energy management system that regulates the energy distribution between the system components to enable continuous operation with optimized energy consumption. A further objective of the invention is to provide a system for the continuous monitoring of intracranial pressure with high temporal resolution, thereby enabling the early detection of pathological changes and timely clinical intervention. Another objective is to provide a device that reduces the risk of infection, bleeding, and other complications associated with conventional invasive monitoring methods, thereby increasing patient safety and expanding the applicability of intracranial pressure monitoring to a wider range of clinical and non-clinical applications. The invention also aims to provide a system with increased reliability through integrated self-diagnostic circuits that monitor sensor functionality, signal integrity, and processing performance. A further objective of the invention is to provide a system that, through robust signal modeling techniques implemented in dedicated hardware structures, enables precise measurements even under physiological and environmental fluctuations. The invention further aims to provide a scalable architecture that allows the integration of additional physiological sensor modalities, thus enabling the monitoring of multiple parameters and a comprehensive assessment of cerebral hemodynamics. Overall, the invention is intended to overcome the limitations of existing methods for intracranial pressure measurement by providing a non-invasive, precise, real-time, and structurally integrated system for estimating intracranial pressure based on the modeling of the cerebral blood flow signal. BRIEF DESCRIPTION OF THE IMAGE These and other features, aspects and advantages of the present invention will be better understood if the following detailed description is read with reference to the accompanying drawing, in which the same symbols represent the same parts: Fig. 1 shows a block diagram of a system for non-invasive estimation of intracranial pressure. Furthermore, those skilled in the art will recognize that the elements in the drawing are simplified and not necessarily drawn to scale. For example, the flowcharts illustrate the process by highlighting the main steps to facilitate understanding of the present disclosure. With regard to the construction of the device, one or more components may be represented in the drawing by conventional symbols. The drawing may show only those specific details relevant to understanding the embodiments of the present disclosure, so as not to clutter the drawing with details that are already apparent to those skilled in the art from the description contained herein. Detailed description of the invention To facilitate understanding of the principles of the invention, reference is made below to the embodiment shown in the drawing, 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 depicted system, as well as further applications of the inventive principles shown therein, are conceivable, insofar as they would normally occur to a person skilled in the art in the field of the invention. 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. 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. 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. 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. Embodiments of the present disclosure are described in detail below with reference to the attached drawing. Fig. 1 shows a block diagram of a system for the non-invasive measurement of intracranial pressure. The system 100 comprises: a cerebral blood flow measurement unit (102) positioned in or connected to the housing, which acquires physiological signals representing the blood flow in the intracranial vessels of a subject; an analog signal conditioning unit (104) connected to the cerebral blood flow measurement unit, comprising at least one low-noise amplification circuit, at least one band-limiting filter, and an impedance matching circuit for improving signal quality and suppressing noise components; an analog-to-digital converter unit (106) connected to the analog signal conditioning unit, which converts the processed analog signals into digital signals with a predefined sampling rate and resolution;a computing unit (108) comprising at least one processor and at least one dedicated signal processing circuit that receives the digital signals and performs preprocessing, feature extraction, and physiological modeling; a storage unit (110) that is operationally connected to the computing unit and configured to store physiological parameters, calibration data, and modeling coefficients; and an output interface unit (112) configured to generate intracranial pressure values ​​based on processed signals and to display or transmit the intracranial pressure values. In one embodiment, the cerebral blood flow measurement unit (102) comprises an ultrasound transducer array configured to emit ultrasound waves through a skull region and receive reflected signals corresponding to the blood flow velocity in a cerebral artery, wherein the array is configured with phase-aligned transducer elements to enable directional sensitivity and depth-resolved signal detection. In one embodiment, the cerebral blood flow sensor unit (102) comprises an optical emitter and photodetector pair configured for operation in the near-infrared wavelength range, wherein the optical emitter is configured to illuminate the brain tissue and the photodetector is configured to detect intensity variations corresponding to changes in cerebral perfusion. In one embodiment, the analog signal processing unit (106) further comprises a programmable amplifier circuit configured to dynamically adjust the gain levels based on changes in signal amplitude, and a notch filter circuit configured to attenuate components of mains disturbances. In one embodiment, the analog-to-digital converter unit comprises a multi-channel sampling circuit configured to simultaneously digitize signals from multiple sensor elements, wherein the sampling circuit is configured to operate with a resolution of at least twelve bits to maintain waveform fidelity. In one embodiment, the computing unit (108) is configured to perform preprocessing operations, including baseline deviation correction by means of high-pass filtering, signal amplitude normalization, and segmentation of the digital signal representations into intervals matched to the cardiac cycle based on peak detection. In one embodiment, the computing unit (108) is configured to extract hemodynamic features such as pulsatility characteristics, systolic rise time, diastolic decay profile and harmonic frequency components derived from the spectral transformation of the digital signal representations. In one embodiment, the computing unit (108) is further configured to perform a physiological modeling process that establishes a relationship between the properties of cerebral blood flow and intracranial pressure. The physiological modeling process includes parameters representing vascular resistance, vascular compliance, and intracranial volume constraints, which are stored in the memory unit. In one embodiment, the computing unit (108) comprises a hardware-based iterative estimation circuit configured to adjust physiological parameters in real time by minimizing the deviation between measured signal features and modeled signal characteristics through recursive computation. In one embodiment, the storage unit (110) comprises a non-volatile memory for storing patient-specific calibration coefficients and a volatile memory for storing intermediate results generated during real-time signal processing. The system is implemented entirely through physical, hardware-based components housed within a chassis. This chassis mechanically secures the subsystems and connects them electrically. The cerebral blood flow measurement unit is implemented as physical sensor hardware and comprises ultrasound transducer arrays and / or optical near-infrared emitter detector units. These are manufactured as discrete electromechanical or optoelectronic elements and configured to directly capture physiological signals from brain tissue. The analog signal processing unit consists of integrated analog circuitry with low-noise amplifier stages, band-limiting filters, impedance matching networks, programmable amplifiers, and notch filters. These are implemented as fixed electronic components to process the incoming sensor signals in real time.The analog-to-digital conversion unit is implemented as a dedicated multi-channel converter circuit with a defined sampling rate and resolution. It enables the direct digitization of the processed analog signal waveforms without program-controlled execution layers. The processing unit is implemented as dedicated signal processing hardware, for example, as an application-specific integrated circuit (ASIC) and / or reconfigurable logic hardware. It is designed for preprocessing, feature extraction, and physiological modeling using fixed digital logic operations. The storage unit comprises physically implemented non-volatile and volatile semiconductor memories for storing calibration parameters, physiological constants, and intermediate calculation results. The output interface consists of a hardware-based display, communication, or transmission circuit for displaying or forwarding intracranial pressure values. The system for non-invasive intracranial pressure measurement using cerebral blood flow signal modeling employs a tightly coupled sequence of hardware-based signal processing, signal conditioning, transformation, feature derivation, and physiological measurement processes. Each stage is implemented in the computing unit with dedicated circuitry to ensure deterministic timing and high accuracy. The cerebral blood flow measurement unit acquires raw physiological signals corresponding to blood flow velocity or perfusion characteristics in the intracranial vessels. These signals are immediately passed to the analog signal conditioning unit, where low-noise amplifier circuits increase the signal amplitude while preserving the waveform morphology.Band limiter filters dampen unwanted frequency components outside the physiologically relevant frequency band, and impedance matching circuits ensure optimal energy transfer between the measuring unit and the subsequent stages, thus minimizing signal distortion. The processed analog signals are digitized by the analog-to-digital converter unit at a predefined sampling frequency, chosen to capture the pulsating components associated with the cardiac cycles as well as the harmonics present in the cerebral blood flow signal. The resulting digital signal representations are transferred to the processing unit, where an initial preprocessing sequence is executed. This preprocessing sequence includes baseline removal using a high-pass filter implemented with a hardware-configured finite impulse response (FIR) filter structure, signal amplitude normalization to compensate for interindividual variability, and signal segmentation into discrete time windows corresponding to individual cardiac cycles.Segmentation is achieved through a peak detection logic configured to identify systolic maxima within the signal, followed by the temporal alignment of successive cycles to enable consistent feature extraction. After preprocessing, the processing unit extracts features using specialized arithmetic circuits configured for analysis in the time and frequency domains. In the time domain, the system calculates parameters such as the systolic rise rate, the diastolic decay rate, the pulse amplitude, and the time intervals between characteristic waveform points. In the frequency domain, the system applies a spectral transformation, performed using a hardware-implemented discrete transformation circuit, to decompose the signal into its frequency components. This allows for the extraction of harmonic amplitudes and phase relationships that indicate vascular compliance and resistance. Additionally, the system calculates composite indices derived from ratios and differences between the extracted features.This increases sensitivity to subtle physiological changes associated with intracranial pressure fluctuations. The extracted features are then fed into a physiological modeling process within the processing unit. The model represents the dynamic relationship between cerebral blood flow and intracranial pressure based on the principles of cerebrovascular hemodynamics. It incorporates parameters for vascular resistance, arterial compliance, and intracranial volume constraints, which are retrieved from memory. The processing unit uses a hardware-based iterative estimation circuit to adjust these parameters in real time, ensuring that the modeled output waveform approximates the measured cerebral blood flow signal as closely as possible. This adjustment is achieved through a recursive calculation process in which an error signal—defined as the deviation between measured and modeled waveform features—is minimized through successive parameter updates.The recursive process is implemented with fixed arithmetic paths to ensure consistent execution time and avoid computational variability. Intracranial pressure estimation is based on converged model parameters, where the relationship between pressure and flow characteristics is encoded in the physiological model. The processing unit transforms these parameters into a quantitative intracranial pressure value using predefined mapping relationships stored in memory. To increase robustness, the system averages successive pressure measurements over time and can simultaneously capture rapid transient changes through a parallel, high-resolution estimation path. This dual-track approach ensures both stability and responsiveness of the result. The system also includes an adaptive calibration process in which cerebral blood flow signals are acquired as baseline values ​​under controlled conditions and used to refine patient-specific model coefficients. The calibration unit updates the stored parameters in memory, allowing the physiological model to account for individual differences in vascular properties and cranial compliance. The processing unit continuously refines these parameters by monitoring long-term trends and incorporating newly acquired data, resulting in steadily improving estimation accuracy. The output interface receives the calculated intracranial pressure values ​​and formats them for display and transmission. The display unit generates real-time visual representations of the pressure values ​​and their temporal evolution, allowing clinicians to assess both momentary and long-term changes. The communication interface transmits data to external systems for further analysis or integration into clinical monitoring networks. Simultaneously, the self-diagnostic unit monitors signal integrity, sensor performance, and processing accuracy by evaluating consistency metrics and detecting anomalies such as signal saturation, noise spikes, or calculation errors. Diagnostic alerts are generated upon detection of irregularities to ensure operational safety. The entire technology is realized through a sequence of hardware-based transformations, beginning with high-precision signal acquisition and culminating in physiologically sound estimation of intracranial pressure. The integration of signal processing, feature extraction, and model-based estimation within a unified framework enables highly accurate and reproducible real-time, non-invasive monitoring. The use of dedicated processing circuits ensures that each computational step is executed with minimal latency and predictable performance. This overcomes the limitations of conventional software-based approaches and allows for deployment in continuous monitoring environments. The present invention comprises a device in a housing made of biocompatible material, which accommodates several interconnected hardware components for non-invasive intracranial pressure measurement. The device has a sensor interface that can be positioned near a region of the skull and contains at least one sensor element for measuring cerebral blood flow. This sensor element is designed to detect hemodynamic activity in the intracranial vessels. In one embodiment, the sensor element consists of an ultrasound transducer array that emits and receives ultrasound waves for transcranial Doppler measurement of cerebral blood flow velocity. In another embodiment, the sensor element consists of an optical emitter-receiver pair for near-infrared spectroscopy-based detection of cerebral perfusion characteristics. The signals generated by the sensor element are transmitted to an analog signal processing unit consisting of a cascade of low-noise amplifiers, bandpass filters, and impedance matching circuits. These circuits are configured to improve signal quality and suppress physiological and environmental interference. The processed analog signals are then passed to an analog-to-digital converter unit containing a high-resolution sampling circuit. This generates discrete digital representations of the cerebral blood flow signals at predefined sampling frequencies. The digitized signals are transmitted to a processing unit comprising at least one microprocessor and at least one digital signal processor. This unit performs a sequence of operations for signal preprocessing, feature extraction, and physiological modeling. Preprocessing includes baseline deviation removal, normalization, and temporal segmentation of the signal into cardiac cycle-corrected segments. Feature extraction involves calculating hemodynamic parameters such as pulsatility index, resistance index, systolic rise time, diastolic decay constants, and harmonic spectral components, which are obtained via Fourier or wavelet decomposition by dedicated hardware arithmetic units. The processing unit is configured to execute a cerebral hemodynamic model stored in memory. This model establishes a mathematical relationship between the properties of cerebral blood flow and intracranial pressure. It incorporates parameters such as cerebrovascular resistance, arterial compliance, and the elasticity of intracranial compartments. The processing unit iteratively adjusts the model parameters using real-time signals and hardware-accelerated optimization routines designed to minimize the error between observed and modeled signal properties. In one embodiment, the optimization is performed using a recursive estimation method implemented by hard-wired arithmetic circuits to ensure deterministic execution latency. The measured intracranial pressure values ​​are transmitted to an output interface, which includes a display unit for real-time ICP readings and trend graphs. Additionally, the device includes a communication interface for transmitting the estimated ICP data to external monitoring systems via wired or wireless communication protocols. The system also includes a power management unit for regulating the power supply to the various components, including battery-powered or external power sources. The device's structural design ensures that the sensor element, signal conditioning circuitry, digitization unit, and processing unit are interconnected via dedicated conductor tracks on a printed circuit board substrate. This minimizes signal delays and electromagnetic interference. The housing is designed to provide mechanical stability, heat dissipation, and electromagnetic shielding, thus ensuring reliable operation in clinical environments. The invention further comprises a calibration circuit that adjusts system parameters based on patient-specific physiological characteristics. The calibration process includes the acquisition of cerebral blood flow signals within the reference range under controlled conditions and the updating of the model coefficients stored in memory. The device is also configured to perform continuous self-diagnostics by means of integrated test circuits that monitor signal integrity, sensor functionality, and the performance of the processing unit. The system thus enables non-invasive, precise, real-time measurement of intracranial pressure through the use of structurally integrated hardware components that model the dynamics of cerebral blood flow. The invention eliminates the need for invasive procedures while ensuring clinical reliability and precision, thereby offering significant advantages for neurological monitoring applications. The present invention relates to a biomedical diagnostic device and, in particular, a structurally integrated system for the non-invasive determination of intracranial pressure by modeling the cerebral blood flow signal. The invention relates to the field of neuromonitoring instrumentation and comprises the acquisition of physiological signals, hardware-based signal processing, and cerebrovascular modeling using special processing circuits to determine intracranial pressure values ​​without invasive intervention. The drawing 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. 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 system for non-invasive measurement of intracranial pressure. 102 Unit for measuring cerebral blood flow. 104 Analog signal processing unit. 106 Analog-to-digital converter unit. 108 Processing unit. 110 Storage unit. 112 Output interface unit.

Claims

A system for the non-invasive estimation of intracranial pressure, comprising: a housing structure configured to enclose and support a variety of interconnected components; a cerebral blood flow measurement unit positioned within or connected to the housing structure and configured to detect physiological signals representative of blood flow in the intracranial vessels of a subject; an analog signal conditioning unit operatively connected to the cerebral blood flow measurement unit, comprising at least one low-noise amplification circuit, at least one band-limiting filter circuit, and an impedance matching circuit configured to improve signal quality and suppress noise components;An analog-to-digital converter unit operationally linked to the analog signal processing unit and configured to convert processed analog signals into digital signal representations with a predefined sampling rate and resolution; a computing unit comprising at least one processor and at least one dedicated signal processing circuit, the computing unit being configured to receive the digital signal representations and perform preprocessing, feature extraction, and physiological modeling operations; a storage unit operationally linked to the computing unit and configured to store physiological parameters, calibration data, and modeling coefficients; and an output interface unit configured to generate intracranial pressure values ​​based on processed signals and to display or transmit these intracranial pressure values. System according to claim 1, wherein the cerebral blood flow measurement unit comprises an ultrasound transducer array configured to emit ultrasound waves through a skull region and receive reflected signals corresponding to the blood flow velocity in a cerebral artery, and wherein the array is configured with phase-matched transducer elements to enable directional sensitivity and depth-resolved signal detection. System according to claim 1, wherein the unit for measuring cerebral blood flow comprises a pair of optical emitters and a photodetector configured for operation in the near-infrared wavelength range, wherein the optical emitter is configured to illuminate the brain tissue and the photodetector is configured to detect intensity variations corresponding to changes in cerebral blood flow. System according to claim 1, wherein the analog signal processing unit further comprises a programmable amplifier circuit configured to dynamically adjust the gain levels based on changes in signal amplitude, and a notch filter circuit configured to attenuate components of mains interference. System according to claim 1, wherein the analog-to-digital converter unit comprises a multi-channel sampling circuit configured to simultaneously digitize signals from multiple sensor elements, and wherein the sampling circuit is configured to operate with a resolution of at least twelve bits to maintain waveform fidelity. System according to claim 1, wherein the computing unit is configured to perform preprocessing operations, including baseline deviation correction by means of high-pass filtering, signal amplitude normalization, and segmentation of the digital signal representations into intervals matched to the cardiac cycle based on peak detection. System according to claim 1, wherein the computing unit is configured to extract hemodynamic features such as pulsatility characteristics, systolic rise time, diastolic decay profile and harmonic frequency components from the spectral transformation of the digital signal representations. System according to claim 1, wherein the computing unit is further configured to perform a physiological modeling process that establishes a relationship between the properties of cerebral blood flow and intracranial pressure, wherein the physiological modeling process includes parameters representing vascular resistance, vascular compliance and intracranial volume constraints, which are stored in the storage unit. System according to claim 8, wherein the computing unit comprises a hardware-based iterative estimation circuit configured to adjust physiological parameters in real time by minimizing the deviation between measured signal features and modeled signal characteristics through recursive computation. System according to claim 1, wherein the storage unit comprises a non-volatile memory for storing patient-specific calibration coefficients and a volatile memory for storing intermediate results generated during real-time signal processing.

Images