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Journal Abstract Search

206 related items for PubMed ID: 33848253

  • 21. Neonatal sleep stage identification using long short-term memory learning system.
    Fraiwan L, Alkhodari M.
    Med Biol Eng Comput; 2020 Jun; 58(6):1383-1391. PubMed ID: 32281071
    [Abstract] [Full Text] [Related]

  • 22. Performance Evaluation of the Circadia Contactless Breathing Monitor and Sleep Analysis Algorithm for Sleep Stage Classification.
    Lauteslager T, Kampakis S, Williams AJ, Maslik M, Siddiqui F.
    Annu Int Conf IEEE Eng Med Biol Soc; 2020 Jul; 2020():5150-5153. PubMed ID: 33019145
    [Abstract] [Full Text] [Related]

  • 23. Automatic sleep stages classification using respiratory, heart rate and movement signals.
    Gaiduk M, Penzel T, Ortega JA, Seepold R.
    Physiol Meas; 2018 Dec 24; 39(12):124008. PubMed ID: 30524059
    [Abstract] [Full Text] [Related]

  • 24. Deep learning-based sleep stage classification with cardiorespiratory and body movement activities in individuals with suspected sleep disorders.
    Morokuma S, Hayashi T, Kanegae M, Mizukami Y, Asano S, Kimura I, Tateizumi Y, Ueno H, Ikeda S, Niizeki K.
    Sci Rep; 2023 Oct 18; 13(1):17730. PubMed ID: 37853134
    [Abstract] [Full Text] [Related]

  • 25. An Automated Wavelet-Based Sleep Scoring Model Using EEG, EMG, and EOG Signals with More Than 8000 Subjects.
    Sharma M, Yadav A, Tiwari J, Karabatak M, Yildirim O, Acharya UR.
    Int J Environ Res Public Health; 2022 Jun 11; 19(12):. PubMed ID: 35742426
    [Abstract] [Full Text] [Related]

  • 26. A Novel Non-contact Heart Rate Monitor Using Impulse-Radio Ultra-Wideband (IR-UWB) Radar Technology.
    Lee Y, Park JY, Choi YW, Park HK, Cho SH, Cho SH, Lim YH.
    Sci Rep; 2018 Aug 29; 8(1):13053. PubMed ID: 30158545
    [Abstract] [Full Text] [Related]

  • 27. Experimental Comparison of IR-UWB Radar and FMCW Radar for Vital Signs.
    Wang D, Yoo S, Cho SH.
    Sensors (Basel); 2020 Nov 23; 20(22):. PubMed ID: 33238557
    [Abstract] [Full Text] [Related]

  • 28. Automation of classification of sleep stages and estimation of sleep efficiency using actigraphy.
    Kim H, Kim D, Oh J.
    Front Public Health; 2022 Nov 23; 10():1092222. PubMed ID: 36699913
    [Abstract] [Full Text] [Related]

  • 29. A Pilot Study of Impulse Radio Ultra Wideband Radar Technology as a New Tool for Sleep Assessment.
    Pallesen S, Grønli J, Myhre K, Moen F, Bjorvatn B, Hanssen I, Heglum HSA.
    J Clin Sleep Med; 2018 Jul 15; 14(7):1249-1254. PubMed ID: 29991417
    [Abstract] [Full Text] [Related]

  • 30. Validation of novel automatic ultra-wideband radar for sleep apnea detection.
    Zhou Y, Shu D, Xu H, Qiu Y, Zhou P, Ruan W, Qin G, Jin J, Zhu H, Ying K, Zhang W, Chen E.
    J Thorac Dis; 2020 Apr 15; 12(4):1286-1295. PubMed ID: 32395265
    [Abstract] [Full Text] [Related]

  • 31. [Study on the method of polysomnography sleep stage staging based on attention mechanism and bidirectional gate recurrent unit].
    Liu Y, He C, Yuan C, Zhang H, Ji C.
    Sheng Wu Yi Xue Gong Cheng Xue Za Zhi; 2023 Feb 25; 40(1):35-43. PubMed ID: 36854546
    [Abstract] [Full Text] [Related]

  • 32. Estimating sleep stages using cardiorespiratory signals: validation of a novel algorithm across a wide range of sleep-disordered breathing severity.
    Bakker JP, Ross M, Vasko R, Cerny A, Fonseca P, Jasko J, Shaw E, White DP, Anderer P.
    J Clin Sleep Med; 2021 Jul 01; 17(7):1343-1354. PubMed ID: 33660612
    [Abstract] [Full Text] [Related]

  • 33. Sleep stage classification by body movement index and respiratory interval indices using multiple radar sensors.
    Kagawa M, Sasaki N, Suzumura K, Matsui T.
    Annu Int Conf IEEE Eng Med Biol Soc; 2015 Jul 01; 2015():7606-9. PubMed ID: 26738053
    [Abstract] [Full Text] [Related]

  • 34. Convolutional Neural Networks for the Real-Time Monitoring of Vital Signs Based on Impulse Radio Ultrawide-Band Radar during Sleep.
    Choi SH, Yoon H.
    Sensors (Basel); 2023 Mar 14; 23(6):. PubMed ID: 36991833
    [Abstract] [Full Text] [Related]

  • 35. Sleep stage classification by non-contact vital signs indices using Doppler radar sensors.
    Kagawa M, Suzumura K, Matsui T.
    Annu Int Conf IEEE Eng Med Biol Soc; 2016 Aug 14; 2016():4913-4916. PubMed ID: 28325016
    [Abstract] [Full Text] [Related]

  • 36. Deep learning enables sleep staging from photoplethysmogram for patients with suspected sleep apnea.
    Korkalainen H, Aakko J, Duce B, Kainulainen S, Leino A, Nikkonen S, Afara IO, Myllymaa S, Töyräs J, Leppänen T.
    Sleep; 2020 Nov 12; 43(11):. PubMed ID: 32436942
    [Abstract] [Full Text] [Related]

  • 37. Performance evaluation of the open-source Yet Another Spindle Algorithm sleep staging algorithm against gold standard manual evaluation of polysomnographic records in adolescence.
    Benedetti D, Frati E, Kiss O, Yuksel D, Faraguna U, Hasler BP, Franzen PL, Clark DB, Baker FC, de Zambotti M.
    Sleep Health; 2023 Dec 12; 9(6):910-924. PubMed ID: 37709595
    [Abstract] [Full Text] [Related]

  • 38. Automated sleep stage scoring of the Sleep Heart Health Study using deep neural networks.
    Zhang L, Fabbri D, Upender R, Kent D.
    Sleep; 2019 Oct 21; 42(11):. PubMed ID: 31289828
    [Abstract] [Full Text] [Related]

  • 39. Automatic sleep staging by a hybrid model based on deep 1D-ResNet-SE and LSTM with single-channel raw EEG signals.
    Li W, Gao J.
    PeerJ Comput Sci; 2023 Oct 21; 9():e1561. PubMed ID: 37810362
    [Abstract] [Full Text] [Related]

  • 40. An automated heart rate-based algorithm for sleep stage classification: Validation using conventional polysomnography and an innovative wearable electrocardiogram device.
    Pini N, Ong JL, Yilmaz G, Chee NIYN, Siting Z, Awasthi A, Biju S, Kishan K, Patanaik A, Fifer WP, Lucchini M.
    Front Neurosci; 2022 Oct 21; 16():974192. PubMed ID: 36278001
    [Abstract] [Full Text] [Related]

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