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
BibRef
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,
Tracking, Biomedical monitoring,
image denoising, signal, analysis
BibRef
Lashkar, S.[Samaher],
Ammar, H.[Heyfa],
A motion-based waveform for the detection of breathing difficulties
during sleep,
MVA(30), No. 5, July 2019, pp. 867-874.
Springer DOI
1907
BibRef
Phan, H.[Huy],
Chén, O.Y.[Oliver Y.],
Tran, M.C.[Minh C.],
Koch, P.[Philipp],
Mertins, A.[Alfred],
de Vos, M.[Maarten],
XSleepNet: Multi-View Sequential Model for Automatic Sleep Staging,
PAMI(44), No. 9, September 2022, pp. 5903-5915.
IEEE DOI
2208
Time-frequency analysis, Sleep apnea, Training data, Training,
Databases, Task analysis, Robustness, Automatic sleep staging,
end-to-end
BibRef
Teng, F.[Fei],
Wang, D.[Dian],
Yuan, Y.[Yue],
Zhang, H.B.[Hai-Bo],
Singh, A.K.[Amit Kumar],
Lv, Z.H.[Zhi-Han],
Multimedia Monitoring System of Obstructive Sleep Apnea via a Deep
Active Learning Model,
MultMedMag(29), No. 3, July 2022, pp. 48-56.
IEEE DOI
2209
Feature extraction, Electrocardiography, Data models, Uncertainty,
Training, Monitoring, Labeling
BibRef
Chan, H.L.[Hei-Long],
Yuen, H.M.[Hoi-Man],
Au, C.T.[Chun-Ting],
Chan, K.C.C.[Kate Ching-Ching],
Li, A.M.[Albert Martin],
Lui, L.M.[Lok-Ming],
Classification of Childhood Obstructive Sleep Apnea based on X-ray
images analysis by Quasi-conformal Geometry,
PR(152), 2024, pp. 110454.
Elsevier DOI
2405
Obstructive sleep apnea, Quasi-conformal theory,
Image analysis, Disease classification, Machine learning
BibRef
Niknazar, H.[Hamid],
Mednick, S.C.[Sara C.],
A Multi-Level Interpretable Sleep Stage Scoring System by Infusing
Experts' Knowledge Into a Deep Network Architecture,
PAMI(46), No. 7, July 2024, pp. 5044-5061.
IEEE DOI
2406
Sleep, Electroencephalography, Feature extraction,
Time-frequency analysis, Brain modeling, Deep learning, Kernel,
sleep stages
BibRef
Khincha, R.[Rishab],
Krishnan, S.[Soundarya],
Parveen, R.[Rizwan],
Goveas, N.[Neena],
ECG Signal Analysis on an Embedded Device for Sleep Apnea Detection,
ICISP20(377-384).
Springer DOI
2009
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 .