Non-contact respiratory rate monitoring using thermal and visible imaging: a pilot study on neonates

Abstract

Respiratory rate (RR) monitoring is essential in neonatal intensive care units. Despite its importance, RR is still monitored intermittently by manual counting instead of continuous monitoring due to the risk of skin damage with prolonged use of contact electrodes in preterm neonates and false signals due to displacement of electrodes. Thermal imaging has recently gained significance as a non-contact method for RR detection because of its many advantages. However, due to the lack of information in thermal images, the selection and tracking of the region of interest (ROI) in thermal images for neonates are challenging. This paper presents the integration of visible (RGB) and thermal (T) image sequences for the selection and tracking of ROI for breathing rate extraction. The deep-learning based tracking-by-detection approach is employed to detect the ROI in the RGB images, and it is mapped to the thermal images using the RGB-T image registration. The mapped ROI in thermal spectrum sequences gives the respiratory rate. The study was conducted first on healthy adults in different modes, including steady, motion, talking, and variable respiratory order. Subsequently, the method is tested on neonates in a clinical settings. The findings have been validated with a contact-based reference method.The average absolute error between the proposed and belt-based contact method in healthy adults reached 0.1 bpm and for more challenging conditions was approximately 1.5 bpm and 1.8 bpm, respectively. In the case of neonates, the average error is 1.5 bpm, which are promising results. The Bland–Altman analysis showed a good agreement of estimated RR with the reference method RR and this pilot study provided the evidence of using the proposed approach as a contactless method for the respiratory rate detection of neonates in clinical settings.

Motion Detectors as Additional Monitoring Devices in the Intensive Care Unit—A Proof-of-Concept Study

“…The detection of motions in this area was performed primarily to reduce or blank out motion artefacts [30]. These techniques were primarily tested and applied in pediatric cohorts, as the significance of contactless monitoring is more meaningful here [31][32][33]. Noncontact monitoring using imaging techniques was also introduced in adult patients with dementia, but the procedure was still susceptible to errors, and safety was not regarded high enough to justify substituting the current standard in clinical practice [34,35].…”

Use of Infrared Thermography in Medical Diagnosis, Screening, and Disease Monitoring: A Scoping Review

“…Medical staff usually measure respiratory rates by counting abdominal and chest movements, as alternative adhesive sensors on the skin can be uncomfortable and even painful. A method that integrates non-contact visual and thermal imaging to estimate respiratory rate could be a major advance for this sensitive area [109].…”

Thermal Image Processing for Respiratory Estimation from Cubical Data with Expandable Depth

“…Thermal imaging has already been successfully used to evaluate human RR from a sequence of facial images; however, most of the methods are still multi-step procedures, requiring tuning of each phase separately, which is time-consuming and may affect final performance both in terms of accuracy and computational overhead [ 13 ]. Such systems are usually based on three data processing phases: (1) manual or automatic identification of an ROI and extraction of the respiratory signal using a chosen aggregation operator for each ROI datum (e.g., mean [ 14 ], skewness [ 15 ]), (2) signal pre- or post-processing to improve its quality (e.g., Hampel and bandpass filters [ 16 ], moving-average filtration [ 14 ], etc. ), (3) identification of respiratory signal parameters such as RR (e.g., peak detection [ 17 ], dominating peak in the frequency domain [ 18 ], wavelet analysis [ 19 ], auto-correlation techniques [ 20 ]), or other respiration-related properties (e.g., respiratory patterns [ 18 ]).…”