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Cardio-respiratory signal extraction from video camera data for continuous non-contact vital sign monitoring using deep learning
- Source :
- Physiological Measurement
- Publication Year :
- 2019
-
Abstract
- Non-contact vital sign monitoring enables the estimation of vital signs, such as heart rate, respiratory rate and oxygen saturation (SpO2), by measuring subtle color changes on the skin surface using a video camera. For patients in a hospital ward, the main challenges in the development of continuous and robust non-contact monitoring techniques are the identification of time periods and the segmentation of skin regions of interest (ROIs) from which vital signs can be estimated. We propose a deep learning framework to tackle these challenges. Approach: This paper presents two convolutional neural network (CNN) models. The first network was designed for detecting the presence of a patient and segmenting the patient’s skin area. The second network combined the output from the first network with optical flow for identifying time periods of clinical intervention so that these periods can be excluded from the estimation of vital signs. Both networks were trained using video recordings from a clinical study involving 15 pre-term infants conducted in the high dependency area of the neonatal intensive care unit (NICU) of the John Radcliffe Hospital in Oxford, UK. Main results: Our proposed methods achieved an accuracy of 98.8% for patient detection, a mean intersection-over-union (IOU) score of 88.6% for skin segmentation and an accuracy of 94.5% for clinical intervention detection using two-fold cross validation. Our deep learning models produced accurate results and were robust to different skin tones, changes in light conditions, pose variations and different clinical interventions by medical staff and family visitors. Significance: Our approach allows cardio-respiratory signals to be continuously derived from the patient’s skin during which the patient is present and no clinical intervention is undertaken.
- Subjects :
- Paper
Male
Physiology
Computer science
0206 medical engineering
Biomedical Engineering
Biophysics
Vital signs
Optical flow
Video Recording
Image processing
Video camera
02 engineering and technology
Convolutional neural network
law.invention
03 medical and health sciences
Automation
0302 clinical medicine
law
Physiology (medical)
remote photoplethysmography
Image Processing, Computer-Assisted
Humans
Segmentation
Computer vision
Monitoring, Physiologic
Skin
Artificial neural network
business.industry
Vital Signs
Deep learning
non-contact vital sign monitoring
Respiration
Infant, Newborn
deep learning
Heart
Signal Processing, Computer-Assisted
020601 biomedical engineering
neonatal intensive care unit
3. Good health
Female
Artificial intelligence
Neural Networks, Computer
business
030217 neurology & neurosurgery
physiological monitoring
Infant, Premature
Subjects
Details
- Language :
- English
- ISSN :
- 13616579 and 09673334
- Database :
- OpenAIRE
- Journal :
- Physiological Measurement
- Accession number :
- edsair.doi.dedup.....c84554f8747ca5e50ee3b27aa5f6bb0b