Difference between revisions of "Contactless monitoring of blood pressure using photoplethysmography"
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|Summary=A camera-based system for non-invasive monitoring of blood pressure | |Summary=A camera-based system for non-invasive monitoring of blood pressure | ||
| + | |Keywords=Photoplethysmography, blood pressure, computer vision, neural networks | ||
| + | |TimeFrame=Fall 2018 | ||
| + | |Prerequisites=Computer vision or digital image processing, digital signal processing, Machine learning, Artificial Intelligence | ||
|Supervisor=Taha Khan | |Supervisor=Taha Khan | ||
| + | |Level=Master | ||
| + | |Status=Open | ||
}} | }} | ||
In emergency medicine and urgent care, the first assessment and triage are crucial to examine the severity of disease, condition or injury, for prioritizing tests and procedures. Vital signs are fundamental in this process and are investigated in all validated clinical assessment methods. This proposal aims to develop a camera-based system based on photoplethysmography to enable non-contact scanning of vital signs, specifically pulse rate and blood pressure (BP), replacing the conventional setup of devices that are time-consuming and require physical contact. The method will work by recording a video for a few seconds using a high-speed RGB camera. This video will then be processed by image and signal processing algorithms, and machine learning to classify blood pressure, and trigger a flag in case if a patient is in need of emergency care, enabling the staff in the care unit to decide on the level of caution to be taken. | In emergency medicine and urgent care, the first assessment and triage are crucial to examine the severity of disease, condition or injury, for prioritizing tests and procedures. Vital signs are fundamental in this process and are investigated in all validated clinical assessment methods. This proposal aims to develop a camera-based system based on photoplethysmography to enable non-contact scanning of vital signs, specifically pulse rate and blood pressure (BP), replacing the conventional setup of devices that are time-consuming and require physical contact. The method will work by recording a video for a few seconds using a high-speed RGB camera. This video will then be processed by image and signal processing algorithms, and machine learning to classify blood pressure, and trigger a flag in case if a patient is in need of emergency care, enabling the staff in the care unit to decide on the level of caution to be taken. | ||
Research Question: How to classify blood pressure levels using photoplethysmography and machine learning? | Research Question: How to classify blood pressure levels using photoplethysmography and machine learning? | ||
Latest revision as of 15:54, 18 October 2018
| Title | Contactless monitoring of blood pressure using photoplethysmography |
|---|---|
| Summary | A camera-based system for non-invasive monitoring of blood pressure |
| Keywords | Photoplethysmography, blood pressure, computer vision, neural networks |
| TimeFrame | Fall 2018 |
| References | |
| Prerequisites | Computer vision or digital image processing, digital signal processing, Machine learning, Artificial Intelligence |
| Author | |
| Supervisor | Taha Khan |
| Level | Master |
| Status | Open |
In emergency medicine and urgent care, the first assessment and triage are crucial to examine the severity of disease, condition or injury, for prioritizing tests and procedures. Vital signs are fundamental in this process and are investigated in all validated clinical assessment methods. This proposal aims to develop a camera-based system based on photoplethysmography to enable non-contact scanning of vital signs, specifically pulse rate and blood pressure (BP), replacing the conventional setup of devices that are time-consuming and require physical contact. The method will work by recording a video for a few seconds using a high-speed RGB camera. This video will then be processed by image and signal processing algorithms, and machine learning to classify blood pressure, and trigger a flag in case if a patient is in need of emergency care, enabling the staff in the care unit to decide on the level of caution to be taken.
Research Question: How to classify blood pressure levels using photoplethysmography and machine learning?
