213 related articles for article (PubMed ID: 30524025)
1. Deep learning in the cross-time frequency domain for sleep staging from a single-lead electrocardiogram.
Li Q; Li Q; Liu C; Shashikumar SP; Nemati S; Clifford GD
Physiol Meas; 2018 Dec; 39(12):124005. PubMed ID: 30524025
[TBL] [Abstract][Full Text] [Related]
2. Transfer learning from ECG to PPG for improved sleep staging from wrist-worn wearables.
Li Q; Li Q; Cakmak AS; Da Poian G; Bliwise DL; Vaccarino V; Shah AJ; Clifford GD
Physiol Meas; 2021 May; 42(4):. PubMed ID: 33761477
[No Abstract] [Full Text] [Related]
3. Sleep stage classification with ECG and respiratory effort.
Fonseca P; Long X; Radha M; Haakma R; Aarts RM; Rolink J
Physiol Meas; 2015 Oct; 36(10):2027-40. PubMed ID: 26289580
[TBL] [Abstract][Full Text] [Related]
4. The research of sleep staging based on single-lead electrocardiogram and deep neural network.
Wei R; Zhang X; Wang J; Dang X
Biomed Eng Lett; 2018 Feb; 8(1):87-93. PubMed ID: 30603193
[TBL] [Abstract][Full Text] [Related]
5. Automatic identification of insomnia using optimal antisymmetric biorthogonal wavelet filter bank with ECG signals.
Sharma M; Dhiman HS; Acharya UR
Comput Biol Med; 2021 Apr; 131():104246. PubMed ID: 33631498
[TBL] [Abstract][Full Text] [Related]
6. Interbeat interval-based sleep staging: work in progress toward real-time implementation.
Garcia-Molina G; Jiang J
Physiol Meas; 2022 Mar; 43(2):. PubMed ID: 35297780
[No Abstract] [Full Text] [Related]
7. Validation of ECG-derived sleep architecture and ventilation in sleep apnea and chronic fatigue syndrome.
Decker MJ; Eyal S; Shinar Z; Fuxman Y; Cahan C; Reeves WC; Baharav A
Sleep Breath; 2010 Sep; 14(3):233-9. PubMed ID: 19816726
[TBL] [Abstract][Full Text] [Related]
8. Measuring dissimilarity between respiratory effort signals based on uniform scaling for sleep staging.
Long X; Yang J; Weysen T; Haakma R; Foussier J; Fonseca P; Aarts RM
Physiol Meas; 2014 Dec; 35(12):2529-42. PubMed ID: 25407770
[TBL] [Abstract][Full Text] [Related]
9. A deep learning model based on the combination of convolutional and recurrent neural networks to enhance pulse oximetry ability to classify sleep stages in children with sleep apnea.
Vaquerizo-Villar F; Alvarez D; Gutierrez-Tobal GC; Del Campo F; Gozal D; Kheirandish-Gozal L; Penzel T; Hornero R
Annu Int Conf IEEE Eng Med Biol Soc; 2023 Jul; 2023():1-4. PubMed ID: 38082822
[TBL] [Abstract][Full Text] [Related]
10. Automated sleep state classification of wide-field calcium imaging data via multiplex visibility graphs and deep learning.
Zhang X; Landsness EC; Chen W; Miao H; Tang M; Brier LM; Culver JP; Lee JM; Anastasio MA
J Neurosci Methods; 2022 Jan; 366():109421. PubMed ID: 34822945
[TBL] [Abstract][Full Text] [Related]
11. The addition of entropy-based regularity parameters improves sleep stage classification based on heart rate variability.
Aktaruzzaman M; Migliorini M; Tenhunen M; Himanen SL; Bianchi AM; Sassi R
Med Biol Eng Comput; 2015 May; 53(5):415-25. PubMed ID: 25690323
[TBL] [Abstract][Full Text] [Related]
12. A Deep Learning Framework for Automatic Sleep Apnea Classification Based on Empirical Mode Decomposition Derived from Single-Lead Electrocardiogram.
Setiawan F; Lin CW
Life (Basel); 2022 Sep; 12(10):. PubMed ID: 36294943
[TBL] [Abstract][Full Text] [Related]
13. A method of REM-NREM sleep distinction using ECG signal for unobtrusive personal monitoring.
Singh J; Sharma RK; Gupta AK
Comput Biol Med; 2016 Nov; 78():138-143. PubMed ID: 27741420
[TBL] [Abstract][Full Text] [Related]
14. Performance of a Convolutional Neural Network Derived From PPG Signal in Classifying Sleep Stages.
Habib A; Motin MA; Penzel T; Palaniswami M; Yearwood J; Karmakar C
IEEE Trans Biomed Eng; 2023 Jun; 70(6):1717-1728. PubMed ID: 36342994
[TBL] [Abstract][Full Text] [Related]
15. Respiration amplitude analysis for REM and NREM sleep classification.
Long X; Foussier J; Fonseca P; Haakma R; Aarts RM
Annu Int Conf IEEE Eng Med Biol Soc; 2013; 2013():5017-20. PubMed ID: 24110862
[TBL] [Abstract][Full Text] [Related]
16. Arrhythmia detection using deep convolutional neural network with long duration ECG signals.
Yıldırım Ö; Pławiak P; Tan RS; Acharya UR
Comput Biol Med; 2018 Nov; 102():411-420. PubMed ID: 30245122
[TBL] [Abstract][Full Text] [Related]
17. Electrocardiogram Classification Based on Faster Regions with Convolutional Neural Network.
Ji Y; Zhang S; Xiao W
Sensors (Basel); 2019 Jun; 19(11):. PubMed ID: 31195603
[TBL] [Abstract][Full Text] [Related]
18. Automatic sleep-stage scoring based on photoplethysmographic signals.
Wu X; Yang J; Pan Y; Zhang X; Luo Y
Physiol Meas; 2020 Jun; 41(6):065008. PubMed ID: 32392540
[TBL] [Abstract][Full Text] [Related]
19. Comparison of HRV indices obtained from ECG and SCG signals from CEBS database.
Siecinski S; Tkacz EJ; Kostka PS
Biomed Eng Online; 2019 Jun; 18(1):69. PubMed ID: 31153383
[TBL] [Abstract][Full Text] [Related]
20. 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; 16():974192. PubMed ID: 36278001
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
