20.3.1 Sleep Apnea Analysis

Chapter Contents (Back)
Sleep Apnea.

Kermit, M.[Martin], Eide, A.J.[Age J.], Lindblad, T.[Thomas], Waldemark, K.[Karina],
Treatment of obstructive sleep apnea syndrome by monitoring patients airflow signals,
PRL(21), No. 3, March 2000, pp. 277-281. 0003
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Pavlidis, I., Dowdall, J., Sun, N., Puri, C., Fei, J., Garbey, M.,
Interacting with human physiology,
CVIU(108), No. 1-2, October-November 2007, pp. 150-170.
Elsevier DOI 0710
Human-computer interaction; Thermal imaging; Facial tracking; Blood flow; Cardiac pulse; Breath rate; Stress; Sleep apnea BibRef

Yang, C.[Cheng], Cheung, G.[Gene], Stankovic, V.[Vladimir], Chan, K.[Kevin], Ono, N.[Nobutaka],
Sleep Apnea Detection via Depth Video and Audio Feature Learning,
MultMed(19), No. 4, April 2017, pp. 822-835.
IEEE DOI 1704
Cameras BibRef

Yang, C.[Cheng], Cheung, G.[Gene], Stankovic, V.[Vladimir],
Estimating Heart Rate and Rhythm via 3D Motion Tracking in Depth Video,
MultMed(19), No. 7, July 2017, pp. 1625-1636.
IEEE DOI 1706
Head, Heart rate, Image restoration, Noise reduction, Sensors, Three-dimensional displays, Tracking, Biomedical monitoring, image denoising, signal, analysis BibRef


Grimm, T., Martinez, M., Benz, A., Stiefelhagen, R.,
Sleep position classification from a depth camera using Bed Aligned Maps,
ICPR16(319-324)
IEEE DOI 1705
Cameras, Computer architecture, Gravity, Microprocessors, Monitoring, Sleep apnea, Three-dimensional, displays BibRef

Ammar, H., Lashkar, S.,
Obstructive sleep apnea diagnosis based on a statistical analysis of the optical flow in video recordings,
ISIVC16(18-23)
IEEE DOI 1704
Estimation BibRef

Sharma, S., Bhattacharyya, S., Mukherjee, J., Purkait, P.K., Biswas, A., Deb, A.K.,
Automated detection of newborn sleep apnea using video monitoring system,
ICAPR15(1-6)
IEEE DOI 1511
image motion analysis BibRef

Zhang, Z.[Zhong], Sawamura, I., Toda, H., Akiduki, T., Miyake, T.,
A new approach to diagnose Sleep Apnea Syndrome using a continuous wavelet transform,
ICWAPR15(128-132)
IEEE DOI 1511
See also Achieving complex discrete wavelet transform by lifting scheme using Meyer wavelet. diseases BibRef

Belo, D.[David], Coito, A.L.[Ana Luísa], Paiva, T.[Teresa], Sanches, J.M.[João Miguel],
Topographic EEG Brain Mapping before, during and after Obstructive Sleep Apnea Episodes,
IbPRIA11(564-571).
Springer DOI 1106
BibRef

Xu, W.L.[Wen-Long], Liu, X.F.[Xiao-Fang],
Sleep Apnea Assessment by ECG Pattern,
CISP09(1-4).
IEEE DOI 0910
BibRef

de Chazal, P., Reilly, R.B., Heneghan, C.,
Automatic sleep apnoea detection using measures of amplitude and heart rate variability from the electrocardiogram,
ICPR02(I: 775-778).
IEEE DOI 0211
BibRef

de Chazal, P., Reilly, R.B.,
A Comparison of the Use of Different Wavelet Coefficients for the Classification of the Electrocardiogram,
ICPR00(Vol II: 255-258).
IEEE DOI 0009
BibRef

Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Cell, DNA, Analysis and Extraction, Microarray .


Last update:Dec 15, 2017 at 20:32:53