Citation: | LIU Tao, ZHANG Ya-li. Non-contact blood oxygenin saturation measurement dynamic head scenarios[J]. Chinese Optics. doi: 10.37188/CO.2024-0034 |
Aiming at the low accuracy of existing non-contact blood oxygen saturation measurement methods in dynamic head scenes, a denoising method based on improved adaptive noise complete set empirical mode decomposition and wavelet threshold is proposed to extract pulse wave signals with high signal-to-noise ratio. Firstly, in order to solve the problem of false components and mode aliasing in the early stage of decomposition and reconstruction, white Gaussian noise is added to the decomposition process to make it become an improved ICEEMDAN (ICEEMDAN), so as to reduce the residual noise in the modal components. Then, ICEEMDAN was used for mode decomposition of pulse wave signals of red and blue channels, and db8 wavelet basis function was used for 3-stage decomposition and reconstruction of components conforming to the spectrum range of blood oxygen, and the reconstructed signals were used for subsequent calculation of blood oxygen value. Finally, the experimental comparison and analysis of the blood oxygen saturation results measured in different dynamic head scenes show that the average error of blood oxygen saturation obtained in different head scenes is 0.73%, which is 1.93% lower than the average error of other algorithms. The denoising method proposed in this paper has good stability in different head scenes and can meet the needs of daily blood oxygen saturation measurement.
[1] |
STRUYF T, DEEKS J J, DINNES J, et al. Signs and symptoms to determine if a patient presenting in primary care or hospital outpatient settings has COVID-19 disease[J]. Cochrane Database of Systematic Reviews, 2020, 7(7): CD013665.
|
[2] |
MORO E, PRIORI A, BEGHI E, et al. The international European Academy of Neurology survey on neurological symptoms in patients with COVID-19 infection[J]. European Journal of Neurology, 2020, 27(9): 1727-1737. doi: 10.1111/ene.14407
|
[3] |
TAMURA T. Current progress of photoplethysmography and SPO2 for health monitoring[J]. Biomedical Engineering Letters, 2019, 9(1): 21-36. doi: 10.1007/s13534-019-00097-w
|
[4] |
ALHARBI S, HU S, MULVANEY D, et al. Oxygen saturation measurements from green and orange illuminations of multi-wavelength optoelectronic patch sensors[J]. Sensors, 2019, 19(1): 118.
|
[5] |
BAL U. Non-contact estimation of heart rate and oxygen saturation using ambient light[J]. Biomedical Optics Express, 2015, 6(1): 86-97. doi: 10.1364/BOE.6.000086
|
[6] |
荣猛, 范强, 李凯扬. 基于IPPG非接触式生理参数测量算法的研究[J]. 生物医学工程研究,2018,37(1):27-31,35.
RONG M, FAN Q, LI K Y. Study on the measurement algorithm of contactless physiological parameter based on imaging photoplenthysmography[J]. Journal of Biomedical Engineering Research, 2018, 37(1): 27-31,35. (in Chinese).
|
[7] |
AL-NAJI A, KHALID G A, MAHDI J F, et al. Non-Contact SpO2 prediction system based on a digital camera[J]. Applied Sciences, 2021, 11(9): 4255. doi: 10.3390/app11094255
|
[8] |
WEI B, WU X P, ZHANG CH, et al. Analysis and improvement of non-contact SpO2 extraction using an RGB webcam[J]. Biomedical Optics Express, 2021, 12(8): 5227-5245. doi: 10.1364/BOE.423508
|
[9] |
嵇晓强, 刘振瑶, 李炳霖, 等. 面部视频非接触式生理参数感知[J]. 中国光学,2022,15(2):276-285. doi: 10.37188/CO.2021-0157
JI X Q, LIU ZH Y, LI B L, et al. Non-contact perception of physiological parameters from videos of faces[J]. Chinese Optics, 2022, 15(2): 276-285. doi: 10.37188/CO.2021-0157
|
[10] |
PIRZADA P, MORRISON D, DOHERTY G, et al. Automated remote pulse oximetry system (ARPOS)[J]. Sensors, 2022, 22(13): 4974. doi: 10.3390/s22134974
|
[11] |
HU M, WU X, WANG X H, et al. Contactless blood oxygen estimation from face videos: A multi-model fusion method based on deep learning[J]. Biomedical Signal Processing and Control, 2023, 81: 104487. doi: 10.1016/j.bspc.2022.104487
|
[12] |
KONG L Q, ZHAO Y J, DONG L Q, et al. Non-contact detection of oxygen saturation based on visible light imaging device using ambient light[J]. Optics Express, 2013, 21(15): 17464-17471. doi: 10.1364/OE.21.017464
|
[13] |
ALHARBI S, HU S, MULVANEY D, et al. Oxygen saturation measurements from green and orange illuminations of multi-wavelength optoelectronic patch sensors[J]. Sensors, 2019, 19(1): 118. (查阅网上资料, 本条文献与第4条文献重复, 请确认) .
|
[14] |
VIOLA P, JONES M J, SNOW D. Detecting pedestrians using patterns of motion and appearance[J]. International Journal of Computer Vision, 2005, 63(2): 153-161. doi: 10.1007/s11263-005-6644-8
|
[15] |
MSTAFA R J, ELLEITHY K M. A video steganography algorithm based on Kanade-Lucas-Tomasi tracking algorithm and error correcting codes[J]. Multimedia Tools and Applications, 2016, 75(17): 10311-10333. doi: 10.1007/s11042-015-3060-0
|
[16] |
KHANAM F T Z, AL-NAJI A, CHAHL J. Remote monitoring of vital signs in diverse non-clinical and clinical scenarios using computer vision systems: A review[J]. Applied Sciences, 2019, 9(20): 4474. doi: 10.3390/app9204474
|
[17] |
KONG L Q, ZHAO Y J, DONG L Q, et al. Non-contact detection of oxygen saturation based on visible light imaging device using ambient light[J]. Optics Express, 2013, 21(15): 17464-17471. (查阅网上资料, 本条文献与第12条文献重复, 请确认) .
|
[18] |
NIU X S, HU H, SHAN SH G, et al. VIPL-HR: A multi-modal database for pulse estimation from less-constrained face video[C]. Proceedings of the 14th Asian Conference on Computer Vision, Springer, 2018: 562-576.
|
[19] |
NIU X S, SHAN SH G, HAN H, et al. RhythmNet: End-to-end heart rate estimation from face via spatial-temporal representation[J]. IEEE Transactions on Image Processing, 2020, 29: 2409-2423. doi: 10.1109/TIP.2019.2947204
|