147 related articles for article (PubMed ID: 36236265)
21. Photoplethysmography temporal marker-based machine learning classifier for anesthesia drug detection.
Khalid SG; Ali SM; Liu H; Qurashi AG; Ali U
Med Biol Eng Comput; 2022 Nov; 60(11):3057-3068. PubMed ID: 36063352
[TBL] [Abstract][Full Text] [Related]
22. Preeminently Robust Neural PPG Denoiser.
Kwon JH; Kim SE; Kim NH; Lee EC; Lee JH
Sensors (Basel); 2022 Mar; 22(6):. PubMed ID: 35336253
[TBL] [Abstract][Full Text] [Related]
23. On Robustness of Neural Architecture Search Under Label Noise.
Chen YW; Song Q; Liu X; Sastry PS; Hu X
Front Big Data; 2020; 3():2. PubMed ID: 33693377
[TBL] [Abstract][Full Text] [Related]
24. Bayesian statistics-guided label refurbishment mechanism: Mitigating label noise in medical image classification.
Gao M; Feng X; Geng M; Jiang Z; Zhu L; Meng X; Zhou C; Ren Q; Lu Y
Med Phys; 2022 Sep; 49(9):5899-5913. PubMed ID: 35678232
[TBL] [Abstract][Full Text] [Related]
25. Signal Quality Assessment of PPG Signals using STFT Time-Frequency Spectra and Deep Learning Approaches.
Chen J; Sun K; Sun Y; Li X
Annu Int Conf IEEE Eng Med Biol Soc; 2021 Nov; 2021():1153-1156. PubMed ID: 34891492
[TBL] [Abstract][Full Text] [Related]
26. A supervised machine learning semantic segmentation approach for detecting artifacts in plethysmography signals from wearables.
Guo Z; Ding C; Hu X; Rudin C
Physiol Meas; 2021 Dec; 42(12):. PubMed ID: 34794126
[No Abstract] [Full Text] [Related]
27. Combining deep residual neural network features with supervised machine learning algorithms to classify diverse food image datasets.
McAllister P; Zheng H; Bond R; Moorhead A
Comput Biol Med; 2018 Apr; 95():217-233. PubMed ID: 29549733
[TBL] [Abstract][Full Text] [Related]
28. Hemorrhagic risk prediction in coronary artery disease patients based on photoplethysmography and machine learning.
He Z; Zhang H; Chen X; Shi J; Bai L; Fang Z; Wang R
Sci Rep; 2022 Nov; 12(1):19190. PubMed ID: 36357443
[TBL] [Abstract][Full Text] [Related]
29. Machine-learning models for activity class prediction: A comparative study of feature selection and classification algorithms.
Chong J; Tjurin P; Niemelä M; Jämsä T; Farrahi V
Gait Posture; 2021 Sep; 89():45-53. PubMed ID: 34225240
[TBL] [Abstract][Full Text] [Related]
30. A machine learning approach for hypertension detection based on photoplethysmography and clinical data.
Martinez-Ríos E; Montesinos L; Alfaro-Ponce M
Comput Biol Med; 2022 Jun; 145():105479. PubMed ID: 35398810
[TBL] [Abstract][Full Text] [Related]
31. BadLabel: A Robust Perspective on Evaluating and Enhancing Label-Noise Learning.
Zhang J; Song B; Wang H; Han B; Liu T; Liu L; Sugiyama M
IEEE Trans Pattern Anal Mach Intell; 2024 Jun; 46(6):4398-4409. PubMed ID: 38236681
[TBL] [Abstract][Full Text] [Related]
32. Comparison and Noise Suppression of the Transmitted and Reflected Photoplethysmography Signals.
Li S; Liu L; Wu J; Tang B; Li D
Biomed Res Int; 2018; 2018():4523593. PubMed ID: 30356404
[TBL] [Abstract][Full Text] [Related]
33. Probing machine-learning classifiers using noise, bubbles, and reverse correlation.
Thoret E; Andrillon T; Léger D; Pressnitzer D
J Neurosci Methods; 2021 Oct; 362():109297. PubMed ID: 34320410
[TBL] [Abstract][Full Text] [Related]
34. Compare the performance of multiple binary classification models in microbial high-throughput sequencing datasets.
Xu N; Zhang Z; Shen Y; Zhang Q; Liu Z; Yu Y; Wang Y; Lei C; Ke M; Qiu D; Lu T; Chen Y; Xiong J; Qian H
Sci Total Environ; 2022 Sep; 837():155807. PubMed ID: 35537509
[TBL] [Abstract][Full Text] [Related]
35. Inter-labeler and intra-labeler variability of condition severity classification models using active and passive learning methods.
Nissim N; Shahar Y; Elovici Y; Hripcsak G; Moskovitch R
Artif Intell Med; 2017 Sep; 81():12-32. PubMed ID: 28456512
[TBL] [Abstract][Full Text] [Related]
36. Transfer learning for classification of cardiovascular tissues in histological images.
Mazo C; Bernal J; Trujillo M; Alegre E
Comput Methods Programs Biomed; 2018 Oct; 165():69-76. PubMed ID: 30337082
[TBL] [Abstract][Full Text] [Related]
37. Assessing the signal quality of electrocardiograms from varied acquisition sources: A generic machine learning pipeline for model generation.
Albaba A; Simões-Capela N; Wang Y; Hendriks RC; De Raedt W; Van Hoof C
Comput Biol Med; 2021 Mar; 130():104164. PubMed ID: 33360108
[TBL] [Abstract][Full Text] [Related]
38. fMRI volume classification using a 3D convolutional neural network robust to shifted and scaled neuronal activations.
Vu H; Kim HC; Jung M; Lee JH
Neuroimage; 2020 Dec; 223():117328. PubMed ID: 32896633
[TBL] [Abstract][Full Text] [Related]
39. Do we need different machine learning algorithms for QSAR modeling? A comprehensive assessment of 16 machine learning algorithms on 14 QSAR data sets.
Wu Z; Zhu M; Kang Y; Leung EL; Lei T; Shen C; Jiang D; Wang Z; Cao D; Hou T
Brief Bioinform; 2021 Jul; 22(4):. PubMed ID: 33313673
[TBL] [Abstract][Full Text] [Related]
40. Identification of apnea during respiratory monitoring using support vector machine classifier: a pilot study.
Pradhapan P; Swaminathan M; Salila Vijayalal Mohan HK; Sriraam N
J Clin Monit Comput; 2013 Apr; 27(2):179-85. PubMed ID: 23179018
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
