Non-invasive measurement of JVP
Jugular Venous Pressure is the pressure of the right atrium that is obtained from the jugular vein. The normal JVP is 6-8 cm H2O or 4-5 mm Hg. Values above or below this indicates some serious problems associated with the right side of the heart such as Bradycardia, atrial fibrillation, cardiac tamp
2025-06-28 16:28:40 - Adil Khan
Non-invasive measurement of JVP
Project Area of Specialization Biomedical EngineeringProject SummaryJugular Venous Pressure is the pressure of the right atrium that is obtained from the jugular vein. The normal JVP is 6-8 cm H2O or 4-5 mm Hg. Values above or below this indicates some serious problems associated with the right side of the heart such as Bradycardia, atrial fibrillation, cardiac tamponade, tricuspid regurgitation etc. Currently there are two widely used techniques to measure the JVP. One technique is invasive that uses a catheter that is inserted in-vivo to the Internal Jugular Vein (IJV). The other technique is non-invasive and uses a ruler to measure the vertical distance between the sternal angle and the top pulsation of the IJV when the patient/subject’s head is slightly raised and tilted to a 45º angle. Both these methods come with disadvantages such as the invasive technique is highly time consuming and puts patients with anesthetic allergies to risk. It also requires an operative room to be booked for the procedure. The other method is often misleading and inaccurate in many cases.
Hence, to resolve these issues, we are proposing designing of a non-invasive device that measures jugular venous pressure accurately. We will be using two different techniques, Plethysmography and ECG along with accelerometers to obtain the JVP signal waveform.
Our aim is to design these two methods of signal acquisition and run a comparative study on both to determine the best approach based on accuracy. Another target is to create and utilize an Artificial Intelligence system based on Machine Learning algorithms that is trained on real patients’ JVP waveform data. The system would then be able to differentiate between a normal and abnormal waveform. The signal that we obtain using our device would also be interfaced with this AI system. This would allow us to have correct and faster determination of JVP’s normality.
Project ObjectivesWe aim to:
1. Design and compare different approaches/techniques which can accurately measure the JVP non-invasively and resolve the issues discussed earlier. These includes signal extraction using Plethysmography, Electrodes, and accelerometer. We will then determine the most accurate method of JVP waveform extraction.
For the goal above, we require:
- Creating a plethysmography sensor for our signal acquisition.
- Extracting signals using electrode & accelerometer method.
- Processing the acquired signals digitally using MATLAB or LabVIEW.
2. To create and utilize an Artificial Intelligence program using machine learning to differentiate abnormal waveform from a normal waveform.
The main goal of the project is the extraction of the JVP signal utilizing different types of extraction methodologies and giving a comparative analysis on which method yields the best outcome. Research is still being carried out on finding the best method for the extraction of the JVP signal, in this project we hope to clarify which technique would be best suited to obtain the optimal results and should be focus on.
Project Implementation MethodWe have aimed to follow the methodology below to successfully reach our target:
1. Gathering research papers regarding Non-Invasive JVP extraction
2. Gathering patient dataset for AI program’s testing and training.
3. Getting information about the components frequently used for the extraction techniques.
4. Since we are using two different approaches to extract the JVP signal non-invasively, we will be creating the sensor (for the plethysmography approach) and device using accelerometers (for the ECG and accelerometer approach) to extract the JVP signal.
5. Digitize the acquired signal and transfer them to a system using Arduino UNO.
6. Process the signals acquired using both techniques through MATLAB and LabVIEW.
7. Comparative study of both techniques to determine their accuracy on a test circuit that mimics JVP.
8. Creating an AI program using machine learning (utilizing patient dataset for JVP signals) for waveform normality determination.
9. Completion of thesis report that includes all details of the project, the comparative study of the methods tested and the best approach depending on their accuracy. The report would also include results of the JVP extracted using our devices that would be categorized into normal or abnormal waves based on the AI system developed.
Benefits of the ProjectSince our proposed project is regarding the Jugular Venous Pressure, it directly corresponds to cardiology. In this case we aim this project to be a vital device that would be used in emergency rooms (ER) and cardiology departments such as Echocardiography and Cath Lab.
The device that we aim to design would be time-efficient and a lower risk to patients. Hence if a patient comes in an emergency and has a cardiac history too, instead of being prepared for the invasive JVP measurement using catheters or having an inaccurate reading using the ruler method, this device will give a quick and accurate result to determine if the JVP waveform is normal or if the patient has an issue with the right side of the heart.
Technical Details of Final DeliverableThe project will finally have a software and hardware both. The hardware would include a JVP measurement device or sensor made using the most suitable and accurate method observed while testing the multiple various techniques. The pressure waveform extracted through the created sensor or device would be transmitted to the computer where digital filters will be utilized to extract the important information from the signal and filter out the noise and then passed on to an AI program. The trained AI program through KNN and Decision Tree Machine Learning algorithms will be used to determine the JVP's normality (change of classification technique may occur if a better method can be utilized).
We aim this project (upon completion) to be a vital device that would be used in emergency rooms (ER), cardiology departments, and would be of assistance to cardiologists in making better diagnosis in lesser time and reduce misdiagnosis of certain diseases.
Final Deliverable of the Project HW/SW integrated systemCore Industry MedicalOther Industries Health Core Technology Artificial Intelligence(AI)Other Technologies Internet of Things (IoT)Sustainable Development Goals Good Health and Well-Being for People, Industry, Innovation and InfrastructureRequired Resources| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| Total in (Rs) | 65910 | |||
| PD438C/S46 Photodiode | Equipment | 10 | 135 | 1350 |
| 660nm 5mW Laser diode module | Equipment | 5 | 751 | 3755 |
| MPU6050 Accelerometer | Equipment | 2 | 500 | 1000 |
| Disposable ECG electrode pack | Equipment | 2 | 1000 | 2000 |
| Arduino UNO | Equipment | 2 | 600 | 1200 |
| Shipping charges | Miscellaneous | 1 | 3755 | 3755 |
| Diaphragm Pump PWM 5V | Equipment | 1 | 6850 | 6850 |
| MASTERFLEX® pulse dampener (07596-20) | Equipment | 1 | 25000 | 25000 |
| Digital Manometer | Equipment | 1 | 15000 | 15000 |
| Miscellaneous | Miscellaneous | 1 | 6000 | 6000 |