137 related articles for article (PubMed ID: 33048831)
1. Dimensionality reduction for EEG-based sleep stage detection: comparison of autoencoders, principal component analysis and factor analysis.
Tăuţan AM; Rossi AC; de Francisco R; Ionescu B
Biomed Tech (Berl); 2021 Apr; 66(2):125-136. PubMed ID: 33048831
[TBL] [Abstract][Full Text] [Related]
2. Automatic Sleep Stage Detection: A Study on the Influence of Various PSG Input Signals.
Tautan AM; Rossi AC; de Francisco R; Ionescu B
Annu Int Conf IEEE Eng Med Biol Soc; 2020 Jul; 2020():5330-5334. PubMed ID: 33019187
[TBL] [Abstract][Full Text] [Related]
3. Automatic sleep scoring with LSTM networks: impact of time granularity and input signals.
Tăuțan AM; Rossi AC; Ionescu B
Biomed Tech (Berl); 2022 Aug; 67(4):267-281. PubMed ID: 35660133
[TBL] [Abstract][Full Text] [Related]
4. Cascaded LSTM recurrent neural network for automated sleep stage classification using single-channel EEG signals.
Michielli N; Acharya UR; Molinari F
Comput Biol Med; 2019 Mar; 106():71-81. PubMed ID: 30685634
[TBL] [Abstract][Full Text] [Related]
5. Multivariate analysis of full-term neonatal polysomnographic data.
Gerla V; Paul K; Lhotska L; Krajca V
IEEE Trans Inf Technol Biomed; 2009 Jan; 13(1):104-10. PubMed ID: 19129029
[TBL] [Abstract][Full Text] [Related]
6. Automatic sleep stage classification based on EEG signals by using neural networks and wavelet packet coefficients.
Ebrahimi F; Mikaeili M; Estrada E; Nazeran H
Annu Int Conf IEEE Eng Med Biol Soc; 2008; 2008():1151-4. PubMed ID: 19162868
[TBL] [Abstract][Full Text] [Related]
7. A new approach for arrhythmia classification using deep coded features and LSTM networks.
Yildirim O; Baloglu UB; Tan RS; Ciaccio EJ; Acharya UR
Comput Methods Programs Biomed; 2019 Jul; 176():121-133. PubMed ID: 31200900
[TBL] [Abstract][Full Text] [Related]
8. 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
[TBL] [Abstract][Full Text] [Related]
9. Recognition of wake-sleep stage 1 multichannel eeg patterns using spectral entropy features for drowsiness detection.
Sriraam N; Padma Shri TK; Maheshwari U
Australas Phys Eng Sci Med; 2016 Sep; 39(3):797-806. PubMed ID: 27550443
[TBL] [Abstract][Full Text] [Related]
10. A PCA aided cross-covariance scheme for discriminative feature extraction from EEG signals.
Zarei R; He J; Siuly S; Zhang Y
Comput Methods Programs Biomed; 2017 Jul; 146():47-57. PubMed ID: 28688489
[TBL] [Abstract][Full Text] [Related]
11. Long Short-Term Memory Networks for Unconstrained Sleep Stage Classification Using Polyvinylidene Fluoride Film Sensor.
Choi SH; Kwon HB; Jin HW; Yoon H; Lee MH; Lee YJ; Park KS
IEEE J Biomed Health Inform; 2020 Dec; 24(12):3606-3615. PubMed ID: 32149661
[TBL] [Abstract][Full Text] [Related]
12. A fast approximation method for principal component analysis applied to ECG derived respiration for OSA detection.
Sadr N; de Chazal P
Annu Int Conf IEEE Eng Med Biol Soc; 2016 Aug; 2016():6198-6201. PubMed ID: 28269667
[TBL] [Abstract][Full Text] [Related]
13. A Fast Principal Component Analysis Method For Calculating The ECG Derived Respiration.
Sadr N; de Chazal P
Annu Int Conf IEEE Eng Med Biol Soc; 2018 Jul; 2018():5294-5297. PubMed ID: 30441532
[TBL] [Abstract][Full Text] [Related]
14. Diagnosis of epilepsy from electroencephalography signals using multilayer perceptron and Elman Artificial Neural Networks and Wavelet Transform.
Işik H; Sezer E
J Med Syst; 2012 Feb; 36(1):1-13. PubMed ID: 20703754
[TBL] [Abstract][Full Text] [Related]
15. Automated Analysis of Sleep Study Parameters Using Signal Processing and Artificial Intelligence.
Sohaib M; Ghaffar A; Shin J; Hasan MJ; Suleman MT
Int J Environ Res Public Health; 2022 Oct; 19(20):. PubMed ID: 36293844
[TBL] [Abstract][Full Text] [Related]
16. A Residual Based Attention Model for EEG Based Sleep Staging.
Qu W; Wang Z; Hong H; Chi Z; Feng DD; Grunstein R; Gordon C
IEEE J Biomed Health Inform; 2020 Oct; 24(10):2833-2843. PubMed ID: 32149700
[TBL] [Abstract][Full Text] [Related]
17. Expert-level automated sleep staging of long-term scalp electroencephalography recordings using deep learning.
Abou Jaoude M; Sun H; Pellerin KR; Pavlova M; Sarkis RA; Cash SS; Westover MB; Lam AD
Sleep; 2020 Nov; 43(11):. PubMed ID: 32478820
[TBL] [Abstract][Full Text] [Related]
18. Accurate Deep Learning-Based Sleep Staging in a Clinical Population With Suspected Obstructive Sleep Apnea.
Korkalainen H; Aakko J; Nikkonen S; Kainulainen S; Leino A; Duce B; Afara IO; Myllymaa S; Toyras J; Leppanen T
IEEE J Biomed Health Inform; 2020 Jul; 24(7):2073-2081. PubMed ID: 31869808
[TBL] [Abstract][Full Text] [Related]
19. A Holistic Strategy for Classification of Sleep Stages with EEG.
Prabhakar SK; Rajaguru H; Ryu S; Jeong IC; Won DO
Sensors (Basel); 2022 May; 22(9):. PubMed ID: 35591246
[TBL] [Abstract][Full Text] [Related]
20. Sleep stage classification using covariance features of multi-channel physiological signals on Riemannian manifolds.
Jiang D; Ma Y; Wang Y
Comput Methods Programs Biomed; 2019 Sep; 178():19-30. PubMed ID: 31416548
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
