Automated Respiration Detection from Neonatal Video Data

In the interest of the neonatal comfort, the need for noncontact respiration monitoring increases. Moreover, home respiration monitoring would be beneficial. Therefore, the goal is to extract the respiration rate from video data included in a polysomnography. The presented method first uses Eulerian video magnification to amplify the respiration movements. A respiration signal is obtained through the optical flow algorithm. Independent component analysis and principal component analysis are applied to improve the signal quality, with minor enhancement of the signal quality. The respiratory rate is extracted as the dominant frequency in the spectrograms obtained using the short-time Fourier transform. Respiratory rate detection is successful (94.12%) for most patients during quiet sleep stages. Real-time monitoring could possibly be achieved by lowering the spatial and temporal resolutions of the input video data. The outline for successful video-aided detection of the respiration pattern is shown, thereby paving the way for improvement of the overall assessment in the NICU and application in a home-friendly environment.

[1]  H. C. Miller,et al.  Changing patterns of respiration in newborn infants. , 1953, Pediatrics.

[2]  Changzhi Li,et al.  A Review on Recent Advances in Doppler Radar Sensors for Noncontact Healthcare Monitoring , 2013, IEEE Transactions on Microwave Theory and Techniques.

[3]  P. O'donovan.  Optical Flow : Techniques and Applications , 2005 .

[4]  Joachim Hornegger,et al.  Robust real-time 3D respiratory motion detection using time-of-flight cameras , 2008, International Journal of Computer Assisted Radiology and Surgery.

[5]  R. Saatchi,et al.  Respiration rate monitoring methods: A review , 2011, Pediatric pulmonology.

[6]  Reza Saatchi,et al.  Real-time vision based respiration monitoring system , 2010, 2010 7th International Symposium on Communication Systems, Networks & Digital Signal Processing (CSNDSP 2010).

[7]  Abbas K. Abbas,et al.  Non-Contact Respiratory Monitoring Based on Real-Time IR-Thermography , 2009 .

[8]  Michael J. Black,et al.  Learning Optical Flow , 2008, ECCV.

[9]  B. Hök,et al.  Critical review of non-invasive respiratory monitoring in medical care , 2003, Medical and Biological Engineering and Computing.

[10]  David J. Fleet,et al.  Optical Flow Estimation , 2006, Handbook of Mathematical Models in Computer Vision.

[11]  W. Verkruysse,et al.  Non-contact heart rate monitoring utilizing camera photoplethysmography in the neonatal intensive care unit - a pilot study. , 2013, Early human development.

[12]  Frédo Durand,et al.  Eulerian video magnification for revealing subtle changes in the world , 2012, ACM Trans. Graph..

[13]  T Tamura,et al.  Development of real-time image sequence analysis for evaluating posture change and respiratory rate of a subject in bed , 2001, Physiological measurement.

[14]  Won Jong Jeon,et al.  Spatio-temporal pyramid matching for sports videos , 2008, MIR '08.

[15]  Berthold K. P. Horn,et al.  Determining Optical Flow , 1981, Other Conferences.

[16]  Kwang Suk Park,et al.  Validation of heart rate extraction using video imaging on a built-in camera system of a smartphone , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.