System for non-invasive clinical diagnosis using sensors synchronised on a common time axis

Abstract

The invention relates to a non-invasive system for the clinical diagnosis of diseases, which uses a Doppler ultrasound, an infrared (IR) reflectometer, and an electrocardiograph (ECG) synchronised on a common time axis. The system, called PDS, uses artificial intelligence to analyse the physiological data collected, enabling rapid and accurate diagnosis of various vascular and cardiac diseases. The data can be transmitted to remote servers for analysis, and the system is applicable in both human and veterinary medicine.

Description

[0001] Title of the Invention

[0002] System for Non-lnvasive Clinical Diagnosis Using Sensors Synchronised on a Common Time Axis

[0003] The present invention relates to the medical diagnostics field, in particular to a system and method for the non-invasive detection of clinical pathologies, including vascular and cardiac pathologies, through the use of synchronised sensors and advanced data processing based on artificial intelligence. The system analyses the characteristics of the arterial system using a mathematical model to determine critical parameters of vascular and cardiac health. The invention is applicable in both human and veterinary medicine, with the necessary modifications.

[0004] State of the Art

[0005] Current techniques for diagnosing vascular and cardiac diseases often require invasive procedures, such as tissue or blood sampling, which expose patients to contamination risks and require lengthy analysis times. For example, a biopsy or cardiac catheterisation, which are necessary to diagnose certain diseases, are invasive procedures that involve risks and require time for clinical analysis. Current non-invasive techniques are often limited in their ability to provide accurate, real-time diagnoses of vascular and cardiac conditions. The invention described here addresses these limitations by offering a solution for the early and accurate diagnosis of vascular and cardiac diseases through the evaluation of mathematical modelling of arteries and the analysis of physiological data collected non-invasively.

[0006] Invention Summary

[0007] The present invention proposes a system for non-invasive clinical diagnosis, called the PDS (Pathology Detection System), which uses a combination of an electrocardiograph (ECG), an infrared reflectometer (IR), a Doppler ultrasound and a data acquisition system synchronised on a common time axis. The data collected by sensors positioned on the wrists, ankles, and chest are transmitted to a cloud server for post-processing and analysis using artificial intelligence, providing a rapid and accurate diagnosis of conditions such as arteriosclerosis, peripheral venous disorders, and other arterial and cardiac conditions.

[0008] Detailed Description of the Invention

[0009] As shown in Figure 1 , the PDS system comprises:

[0010] • Electrocardiograph (ECG) (2): Positioned near the chest area to detect the heartbeat and provide an initial impulse that acts as a ‘trigger’ for the vascular system.

[0011] • Infrared (IR) reflectometer (4, 4a, 4b, 4c) and Doppler ultrasound (5, 5a, 5b, 5c): Sensors positioned at the wrists (6) and ankles (7, 8) to detect changes in blood flow and other physiological parameters along the vascular pathway.

[0012] • Electronic Control Unit (ECU) (3): Configured to synchronise the data collected by the various sensors on a common time axis, ensuring accurate and consistent readings.

[0013] • WiFi Connection System and Cloud Server: Enables data transmission to a remote server where it is analysed by artificial intelligence algorithms to identify potential pathologies based on the data acquired and additional parameters provided by the user, such as age, gender, ethnicity, lifestyle habits, and medical history.

[0014] The system involves an initial set-up phase to calibrate the sensors and provide the artificial intelligence machine with the initial references necessary for analysis. The typical set-up includes sequential ECG, reflectometry and Doppler recordings at each application point, synchronised to obtain consistent data on the time axis.

[0015] The invention relates to an innovative system for the non-invasive diagnosis of vascular and cardiac pathologies, based on the analysis of arterial pressure propagation in blood vessels. The key to this technology lies in the precise measurement of delay times and the shape of arterial pressure, using the ECG signal as a temporal reference for the trigger. The ECG provides a precise reference point for the start of the pressure pulse, allowing accurate synchronisation of measurements taken with other sensors placed at the wrists and ankles. Operating mechanism:

[0016] The system uses ECG sensors to detect the heartbeat, which acts as a trigger signal for monitoring the propagation of blood pressure through the vascular system. The blood pressure generated by the heartbeat propagates along the arteries and blood vessels, and this propagation can be monitored using infrared reflectometers and Doppler sensors positioned at the wrists and ankles.

[0017] The information collected includes:

[0018] 1 . Delay times: The time between the heartbeat (detected by the ECG) and the arrival of the arterial pressure wave at the peripheral points (wrists and ankles). These delay times are indicative of the condition of the blood vessels, as a slowdown or change in propagation may indicate obstructions, stiffness or other vascular pathologies.

[0019] 2. Blood pressure waveform: The specific shape of the pressure wave over time provides additional information about the condition of the blood vessels. The morphology of the wave, including peaks, troughs, and rise and fall rates, can indicate various pathological conditions.

[0020] Mathematical modelling:

[0021] The human arterial system can be modelled using differential equations that describe the behaviour of fluid (blood) in an elastic medium (vessel walls). The mathematical model mainly uses modified Navier-Stokes equations to take into account the elasticity of the vascular walls. In this context, the arterial system is treated as a fluid-structure dynamic system, where:

[0022] • p represents the density of blood,

[0023] • v is the velocity vector of blood flow,

[0024] • p is blood pressure,

[0025] • p is the dynamic viscosity of blood,

[0026] • t is time.

[0027] The modified Navier-Stokes equations for a fluid-structure system are:

[0028] V ■ v - 0

[0029] Where f represents external forces, including the elastic forces of the vascular walls.

[0030] Analytical challenges and use of artificial intelligence:

[0031] The analytical solution of these differential equations is complex due to the non-linear nature of the system and the dynamic interactions between blood flow and the elastic vessel walls. Fluid-structure differential equations require sophisticated numerical approaches for their solution, which often cannot be solved in real time using traditional methods.

[0032] To overcome these challenges, the invention uses artificial intelligence algorithms that are trained on a vast set of clinical data to recognise patterns of abnormal blood pressure propagation. Artificial intelligence analyses delay times, waveforms and other characteristics collected by sensors to determine the presence of vascular or cardiac pathologies. Through machine learning, the system is able to provide rapid and accurate diagnoses without the need for invasive procedures.

[0033] Conclusion

[0034] The PDS system represents a significant advance in non-invasive diagnostics for vascular and cardiac diseases, using complex mathematical models to understand the behaviour of the arterial system and applying artificial intelligence for data analysis. This technology reduces the need for invasive procedures, improves the timeliness of diagnosis and provides an effective means of monitoring patients' cardiovascular health.

Claims

1. Independent claim: A non-invasive disease detection system, as described in Figure 1 , characterised by an ECG sensor for heartbeat detection, an infrared reflectometer and an echo Doppler for blood flow detection, where the ECG, infrared reflectometer and echo Doppler are synchronised on the same time axis; a data acquisition system connected to a cloud server via a Wi-Fi connection, and an artificial intelligence module for processing and analysing the collected data, aimed at non- invasively diagnosing clinical pathologies.

2. Dependent claim: The system according to claim 1 , which allows the transmission of raw data to servers via an internet connection.

3. Dependent claim: The system according to any of the preceding claims, usable in both human and veterinary medicine, with the necessary adaptations.

4. Dependent claim: The system according to claims 1 and 3, which includes five points of application on the body, namely the wrists, ankles and chest.

5. Dependent claim: The system according to any of the preceding claims, which uses the patient's own heartbeat as the starting pulse for the measurement process.

6. Dependent claim: The system according to any of the preceding claims, usable both in a fixed location and in a mobile station.

7. Dependent claim: The system according to any of the preceding claims, which can use a PC, smartphone or tablet as a user interface.

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