Automatic video detection of body movement during sleep based on optical flow in pediatric patients with epilepsy
Tóm tắt
The aim of our work is to investigate whether the optical flow algorithm applied to video recordings can be used to detect movement during sleep in pediatric patients with epilepsy. The optical flow algorithm allocates intensities to pixels proportional to their involvement in movement of an object. The average of a percentage of the highest movement vectors was plotted as a function of time (R(t)). The used dataset contains video data acquired at the University Hospital of Leuven consisting of normal sleep movement and seizure movement. We investigated R(t), to make a distinction between movement and non-movement. We used the acquisition parameters (320 × 240 at 12.5 fps), derived from a previous study (Cuppens et al., Proceedings of the 4th European congress of the international federation for medical and biological engineering (MBEC 2008), ECIFBME 2008, Antwerp, Belgium, IFMBE Proceedings, vol 22, pp 784–789, 2008). Two experiments were concluded, one with global thresholds of R(t) in all datasets and one with a variable threshold in each dataset. The latter is obtained by inspecting a non-movement epoch and calculating the mean and standard deviations of R(t) over time. The variable threshold on R(t) was then obtained for each dataset by adding to the mean a fixed multiple of the standard deviation. Optimal thresholds were derived based on a three-fold cross-validation. The best result was achieved when using a variable threshold, which resulted in a sensitivity of one in all the test sets and a PPV of 1, 0.821, and 1, respectively, for the three test sets.
Tài liệu tham khảo
Bonroy B, Schiepers P, Leysens G, Miljkovic D, Wils M, De Maesschalck L, Quanten S, Triau E, Exadaktylos V, Berckmans D, Vanrumste B (2009) Acquiring a dataset of labeled video images showing discomfort in demented elderly. Telemed J E-Health 15(4):370–378
Bradski G, Kaehler A (2008) Learning OpenCV. O’Reilly Media
Chase J, Nelson B, Bodily J, Wei Z, Lee DJ (2008) Real-time optical flow calculations on FPGA and GPU architectures: a comparison study. In: FCCM ’08: proceedings of the 2008 16th international symposium on field-programmable custom computing machines, IEEE Computer Society, Washington, DC, USA, pp 173–182
Conradsen I, Beniczky S, Wolf P, Terney D, Sams T, Sorensen HBD (2009) Multi-modal intelligent seizure acquisition (misa)? A new approach towards seizure detection based on full body motion measures. In: Proceedings of the 31st annual international conference of the IEEE EMBS, Minneapolis, Minnesota, USA, pp 2591–2595
Cuppens K, Lagae L, Vanrumste B (2008) Towards automatic detection of movement during sleep in pediatric patients with epilepsy by means of video recordings and the optical flow algorithm. In: Vander Sloten J, Verdonk P, Nyssen M, Haueisen J (eds) Proceedings of the 4th European congress of the international federation for medical and biological engineering (MBEC 2008), ECIFBME 2008, Antwerp, Belgium, IFMBE Proceedings, vol 22, pp 784–789
Cuppens K, Lagae L, Ceulemans B, Van Huffel S, Vanrumste B (2009) Detection of nocturnal frontal lobe seizures in pediatric patients by means of accelerometers: a first study. In: Proceedings of the 31st annual international conference of the IEEE EMBS, Minneapolis, Minnesota, USA, pp 6608–6611
Horn BKP, Schunck BG (1981) Determining optical-flow. Artif Intell 17(1–3):185–203
Hu WM, Tan TN, Wang L, Maybank S (2004) A survey on visual surveillance of object motion and behaviors. IEEE Trans Syst Man Cybern C 34(3):334–352
Jallon P, Bonnet S, Antonakios M, Guillemaud R (2009) Detection system of motor epileptic seizures through motion analysis with 3D accelerometers. In: Proceedings of the 31st annual international conference of the IEEE EMBS, Minneapolis, Minnesota, USA, pp 2466–2469
Karayiannis NB, Tao GZ, Frost JD, Wise MS, Hrachovy RA, Mizrahi EM (2006) Automated detection of videotaped neonatal seizures based on motion segmentation methods. Clin Neurophysiol 117(7):1585–1594
Kearney J, Thompson W (1988) Bounding constraint propagation for optical flow estimation. In: Aggarwal J, Martin W (eds) Motion understanding: robot and human vision. Kluwer Academic Publishers, Boston
Kim YH, Kak AC (2006) Error analysis of robust optical flow estimation by least median of squares methods for the varying illumination model. IEEE Trans Pattern Anal Mach Intell 28(9):1418–1435
Ko T (2008) A survey on behavior analysis in video surveillance for homeland security applications. 2008 37th IEEE Applied Imagery Pattern Recognition Workshop pp 84–91, 37th Applied Imagery and Pattern Recognition Workshop OCT 15–17, 2008 Washington, DC
Liu Q, Sclabassi RJ, Sun M (2004) Change detection in epilepsy monitoring video based on markov random field theory. In: Intelligent signal processing and communication systems, 2004. ISPACS 2004
Lucas BD, Kanade T (1981) An iterative image registration technique with an application to stereo vision. In: DARPA imaging understanding workshop, pp 121–130
Nijsen TME, Cluitmans PJM, Arends J, Griep PAM (2007) Detection of subtle nocturnal motor activity from 3-D accelerometry recordings in epilepsy patients. IEEE Transactions on Biomedical Engineering 54(11):2073–2081
Rowekamp T, Peters L (1997) A real-time smart sensor system for visual motion estimation. In: EDTC ’97: proceedings of the 1997 European conference on design and test, IEEE Computer Society, Washington, DC, USA, p 613
Schepers HM, Roetenberg D, Veltink PH (2010) Ambulatory human motion tracking by fusion of inertial and magnetic sensing with adaptive actuation. Med Biol Eng Comput 48(1):27–37
Sonka M, Hlavac V, Boyle R (2008) Motion analysis. In: Image processing, analysis, and machine vision. Kluwer Academic Publishers, Dordrecht
Suzuki S, Matsui T, Kawahara H, Ichiki H, Shimizu J, Kondo Y, Gotoh S, Yura H, Takase B, Ishihara M (2009) A non-contact vital sign monitoring system for ambulances using dual-frequency microwave radars. Med Biol Eng Comput 47(1):101–105
Turaga P, Chellappa R, Subrahmanian VS, Udrea O (2008) Machine recognition of human activities: a survey. IEEE Trans Circuits Syst Video Technol 18(11):1473–1488
Wang LA, Hu WM, Tan TN (2003) Recent developments in human motion analysis. Pattern Recognit 36(3):585–601