Camera-based neonatal respiratory monitoring
Creators: name: Eastwood-Sutherland, Caillin
Publisher: name: University Of Tasmania
Year: 2023
Subjects: classificationCode: 400305
subject: Biomedical instrumentation
classificationCode: 400399
subject: Biomedical engineering not elsewhere classified

description: Real-time monitoring of respiratory activity is a standard part of clinical care for neonates in Neonatal Intensive Care Units, allowing respiratory rate information to be available to bedside staff, and for high-risk situations such as apnoea to be detected and appropriate clinical interventions made. Respiratory monitoring usually takes the form of a visual display of respiratory waveform and numerical respiratory rate, both derived from Impedance Pneumography (IP) via in-situ electrocardiogram (ECG) leads. Although IP has a wide uptake, it is known to be susceptible to cardiac interference, as well as signal errors such as motion artefact due to the physical nature of the ECG leads. Respiratory monitoring using a neonatal respiratory monitor and Pressure Capsule Pneumography (CP) is also effective, however this method is not in widespread or continuous use. Non-contact respiratory monitoring methods have been proposed as an alternative or supplement to IP, with a range of 2D and 3D camera-based systems showing potential.This thesis investigates novel use of non-contact Infrared Respiratory Monitoring (IRM) for detecting neonatal respiratory motion. Aims were to implement an IRM system, gather raw monitoring data using the system in a clinical study and investigate development of automated data analysis methods for detection of respiratory motion and determination of respiratory rate together with determining the performance and limitations of IRM in these applications.The IRM system was based on a consumer-grade device (Microsoft Kinect™ V2) configured to acquire an overhead image stream (video) of neonates lying supine in open cribs in neonatal intensive care. The images produced were depth-maps, utilising infrared time-of-flight capabilities of the Kinect™ in sensing the distance from the camera to each image pixel target.The clinical study gathered raw IRM video images of neonate respiration for subsequent processing. The study aim was to gather 15 minutes of IRM system recordings of each neonate lying supine in neonatal intensive care, either with or without respiratory support, along with respiratory data from IP and CP.Image and signal processing methods were then developed to extract and process respiratory motion waveforms from trial recordings, aiming to identify segments containing reliable signals. This involved dividing depth-map images into upper and lower torso regions, applying techniques to give a depth waveform for each region, and then processing the IRM, IP, and CP signals to determine the existence of authentic respiratory motion and to estimate respiratory rate. Each recording was also manually annotated by two observers, and these annotations were converted to waveforms for later comparison. Key processing steps investigated included filtering the signals based on the expected range of respiratory rates for a neonate, dividing the signals into analysis epochs and using a Signal Quality Index (SQI) threshold and minimum breath count to determine how authentically a section of signal represented respiration. Respiration rate was determined using peak detection and compared to manual observation in epochs determined to contain authentic respiratory motion.Recorded video footage was analysed using these processing methods. The performance of the IRM, IP and CP systems in identifying respiration and respiration rate during each epoch were compared with observer observations.Trial data was recorded from 35 infants in a neonatal and paediatric intensive care with post-birth age of 17 ±15 days and weight of 1750 ±1035 g (mean±SD). Twenty-six infants were classified as being very preterm (&lt;32 weeks gestation at birth), three were between 32- and 36-weeks gestation, with six being born at term (37 weeks gestation or more). Respiratory support types included Continuous Positive Airway Pressure (CPAP) (9 infants), nasal high-flow (3 infants), and intubated with mechanical ventilation (18 infants). Five infants received no respiratory support, and those on HFOV were excluded. Approximately 31915 seconds of video recordings suitable for use were obtained across 39 trials (four infants had multiple trials).Processing resulted in each recording being divided into 15 second analysis epochs. An epoch was determined to contain authentic respiratory motion if a manual observation signal for that epoch contained an 8-second window which contained a minimum of 5 breaths, with the signal for those 5 breaths having an SQI≥ 0.75.In identification of authentic respiratory motion, pooled results showed CP was the best performer, satisfying authenticity requirements in 65% of eligible measurement epochs compared to 50% for IP, and 36% and 48% for the upper and lower torso regions using IRM (IRM<sub>upper</sub> and IRM<sub>lower</sub>). Median SQI values were 0.79, 0.75, 0.70 and 0.74 overall for CP, IP, IRM<sub>upper</sub> and IRM<sub>lower</sub> respectively. Per patient results showed authenticity requirements being met 64%, 50%, 29%, and 49% for CP, IP, IRM<sub>upper</sub> and IRM<sub>lower</sub> respectively, with median SQI values of 0.79, 0.75, 0.69, and 0.74, respectively.Respiration rate investigation showed overall respiratory rate errors of less than 5 bpm were observed in 74%, 59%, 59% and 65% of eligible epochs for CP, IP, IRM<sub>upper</sub> and IRM<sub>lower</sub>, respectively, with respiratory rate errors of less than 10 bpm being observed in 86%, 74%, 76% and 82% of epochs containing authentic respiratory motion.The IRM system was shown to perform similarly to the existing IP system in the neonatal intensive care setting. IRM signals performed more reliably in the abdominal region (IRM<sub>lower</sub>). This matched what was expected as physical motion during abdominally located respiration was noticeably larger, and often what was used during vision observation of respiration.In contrast to many other camera-based studies which have generally used a 2D camera and motion detection techniques to measure respiratory rate, the 3D nature of the IRM system allowed the use of depth information from the acquired images.As with other camera-based studies, the developed IRM system did not require sensors placed on the infant at the expense of suffering from potential occlusion by care staff.Aside from camera hardware improvements (higher frame rate and higher resolution IRM system), implementing specifically developed filtering methods could potentially lead to more representative signals and thus more accurate estimations of respiratory motion and respiratory rate.Future research, particularly in areas such as real-time torso detection and dealing with factors such as patient occlusion and extraneous motion, will expand the scope of usability to potential clinical applications.Although not yet at the level where it can replace existing hospital respiratory monitoring systems, the IRM system has shown that continuous camera-based respiratory monitoring shows promise and warrants further research aiming to achieve the goal of enabling respiratory monitoring over extended periods without the need for contact-based sensors.