Fetching the paper…
Reading the bibliography…
Information extracted from electrohysterography recordings could potentially prove to be an interesting additional source of information to estimate the risk on preterm birth.
doi:10.1161/01.CIR.101.23.e215
A. L. Goldberger, L. A. N. Amaral, L. Glass, J. M. Hausdorff, P. C. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C.-K. Peng, H. E. Stanley, PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals , Circulation 101 (23) (2000) e215–e220 · 2000
Earlier work this paper cites.
N. V. Chawla, K. W. Bowyer, L. O. Hall, W. P. Kegelmeyer, SMOTE: synthetic minority over-sampling technique, Journal of Artificial Intelligence Research 16 (2002) 321–357
2002
Earlier work this paper cites.
doi:10.1145/1007730.1007735
G. E. A. P. A. Batista, R. C. Prati, M. C. Monard, A study of the behavior of several methods for balancing machine learning training data , SIGKDD Explor. Newsl. 6 (1) (2004) 20–29 · 2004
Earlier work this paper cites.
doi:10.1109/GRC.2006.1635905
D. A. Cieslak, N. V. Chawla, A. Striegel, Combating imbalance in network intrusion datasets, in: 2006 IEEE International Conference on Granular Computing, 2006, pp. 732–737 · 2006
Earlier work this paper cites.
T. Y. Euliano, M. T. Nguyen, D. Marossero, R. K. Edwards, Monitoring contractions in obese parturients: electrohysterography compared with traditional monitoring, Obstetrics & Gynecology 109 (5) (2007) 1136–1140
2007
Earlier work this paper cites.
A. Asuncion, D. Newman, Uci machine learning repository (2007)
2007
Earlier work this paper cites.
G. Fele-Žorž, G. Kavšek, Ž. Novak-Antolič, F. Jager, A comparison of various linear and non-linear signal processing techniques to separate uterine EMG records of term and pre-term delivery groups, Medical & biological engineering & computing 46 (9) (2008) 911–922
2008
Earlier work this paper cites.
doi:10.1109/DAS.2008.74
S. Gazzah, N. E. B. Amara, New oversampling approaches based on polynomial fitting for imbalanced data sets, in: 2008 The Eighth IAPR International Workshop on Document Analysis Systems, 2008, pp. 677–684 · 2008
Earlier work this paper cites.
H. He, E. A. Garcia, Learning from imbalanced data, IEEE Trans Knowl and Data Eng 21 (9) (2009) 1263–1284
2009
Earlier work this paper cites.
G. A. Davies, C. Maxwell, L. McLeod, R. Gagnon, M. Basso, H. Bos, M.-F. Delisle, D. Farine, L. Hudon, S. Menticoglou, et al., Obesity in pregnancy, Journal of Obstetrics and Gynaecology Canada 32 (2) (2010) 165–173
2010
Earlier work this paper cites.
S. M.-S. Baghamoradi, M. Naji, H. Aryadoost, Evaluation of cepstral analysis of EHG signals to prediction of preterm labor, in: Biomedical Engineering (ICBME), 2011 18th Iranian Conference of, IEEE, 2011, pp. 81–83
2011
Earlier work this paper cites.
S. Barua, M. M. Islam, K. Murase, A novel synthetic minority oversampling technique for imbalanced data set learning, in: B.-L. Lu, L. Zhang, J. Kwok (Eds.), Neural Information Processing, Springer Berlin Heidelberg, Berlin, Heidelberg, 2011, pp. 735–744
2011
Earlier work this paper cites.
T. Y. Euliano, M. T. Nguyen, S. Darmanjian, S. P. McGorray, N. Euliano, A. Onkala, A. R. Gregg, Monitoring uterine activity during labor: a comparison of 3 methods, American journal of obstetrics and gynecology 208 (1) (2013) 66–e1
2013
Earlier work this paper cites.
S. Naeem, A. Ali, M. Eldosoky, Kl. comparison between using linear and non-linear features to classify uterine electromyography signals of term and preterm deliveries, in: Radio Science Conference (NRSC), 2013 30th National, IEEE, 2013, pp. 492–502
2013
Earlier work this paper cites.
P. Fergus, P. Cheung, A. Hussain, D. Al-Jumeily, C. Dobbins, S. Iram, Prediction of preterm deliveries from EHG signals using machine learning, PloS one 8 (10) (2013) e77154
2013
Earlier work this paper cites.
M. Nakamura, Y. Kajiwara, A. Otsuka, H. Kimura, LVQ-SMOTE – learning vector quantization based synthetic minority over–sampling technique for biomedical data, in: BioData Mining, 2013
2013
Earlier work this paper cites.
S. M. Naeem, A. F. Seddik, M. A. Eldosoky, New technique based on uterine electromyography nonlinearity for preterm delivery detection, Journal of Engineering and Technology Research 6 (7) (2014) 107–114
2014
Earlier work this paper cites.
S. Sim, H. Ryou, H. Kim, J. Han, K. Park, Evaluation of electrohysterogram feature extraction to classify the preterm and term delivery groups, in: The 15th International Conference on Biomedical Engineering, Springer, 2014, pp. 675–678
2014
Cited alongside, same era.
doi:10.1109/ICPR.2014.245
B. A. Almogahed, I. A. Kakadiaris, NEATER: Filtering of over-sampled data using non-cooperative game theory, in: 2014 22nd International Conference on Pattern Recognition, 2014, pp. 1371–1376 · 2014
Cited alongside, same era.
F. Koto, SMOTE-Out, SMOTE-Cosine, and Selected-SMOTE: An enhancement strategy to handle imbalance in data level, 2014 International Conference on Advanced Computer Science and Information System (2014) 280–284
2014
Cited alongside, same era.
L. Lusa, et al., Joint use of over-and under-sampling techniques and cross-validation for the development and assessment of prediction models, BMC bioinformatics 16 (1) (2015) 363
2015
Cited alongside, same era.
N. Sadi-Ahmed, B. Kacha, H. Taleb, M. Kedir-Talha, Relevant features selection for automatic prediction of preterm deliveries from pregnancy electrohysterograhic (EHG) records, Journal of medical systems 41 (12) (2017) 204
2017
Later among the works it cites.
U. R. Acharya, V. K. Sudarshan, S. Q. Rong, Z. Tan, C. M. Lim, J. E. Koh, S. Nayak, S. V. Bhandary, Automated detection of premature delivery using empirical mode and wavelet packet decomposition techniques with uterine electromyogram signals, Computers in biology and medicine 85 (2017) 33–42
2017
Later among the works it cites.
S. Janjarasjitt, Evaluation of performance on preterm birth classification using single wavelet-based features of EHG signals, in: Biomedical Engineering International Conference (BMEiCON), 2017 10th, IEEE, 2017, pp. 1–4
2017
Later among the works it cites.
M. S. Santos, J. P. Soares, P. H. Abreu, H. Araujo, J. Santos, Cross-validation for imbalanced datasets: Avoiding overoptimistic and overfitting approaches [research frontier], ieee ComputatioNal iNtelligeNCe magaziNe 13 (4) (2018) 59–76
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
D. T. Far, M. Beiranvand, M. Shahbakhti, Prediction of preterm labor from EHG signals using statistical and non-linear features, in: Biomedical Engineering International Conference (BMEiCON), 2015 8th, IEEE, 2015, pp. 1–5
2015
Cited alongside, same era.
P. Ren, S. Yao, J. Li, P. A. Valdes-Sosa, K. M. Kendrick, Improved prediction of preterm delivery using empirical mode decomposition analysis of uterine electromyography signals, PloS one 10 (7) (2015) e0132116
2015
Cited alongside, same era.
A. J. Hussain, P. Fergus, H. Al-Askar, D. Al-Jumeily, F. Jager, Dynamic neural network architecture inspired by the immune algorithm to predict preterm deliveries in pregnant women, Neurocomputing 151 (2015) 963–974
2015
Cited alongside, same era.
I. O. Idowu, P. Fergus, A. Hussain, C. Dobbins, M. Khalaf, R. V. C. Eslava, R. Keight, Artificial intelligence for detecting preterm uterine activity in gynecology and obstetric care, in: Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing (CIT/IUCC/DASC/PICOM), 2015 IEEE International Conference on, IEEE, 2015, pp. 215–220
2015
Cited alongside, same era.
J. Ryu, C. Park, Time-frequency analysis of electrohysterogram for classification of term and preterm birth, IEIE Transactions on Smart Processing & Computing 4 (2) (2015) 103–109
2015
Cited alongside, same era.
N. Sadi-Ahmed, M. Kedir-Talha, Contraction extraction from term and preterm electrohyterographic signals, in: Electrical Engineering (ICEE), 2015 4th International Conference on, IEEE, 2015, pp. 1–4
2015
Cited alongside, same era.
L. Liu, S. Oza, D. Hogan, Y. Chu, J. Perin, J. Zhu, J. E. Lawn, S. Cousens, C. Mathers, R. E. Black, Global, regional, and national causes of under-5 mortality in 2000–15: an updated systematic analysis with implications for the sustainable development goals, The Lancet 388 (10063) (2016) 3027–3035
2016
Cited alongside, same era.
M. U. Ahmed, T. Chanwimalueang, S. Thayyil, D. P. Mandic, A multivariate multiscale fuzzy entropy algorithm with application to uterine EMG complexity analysis, Entropy 19 (1) (2016) 2
2016
Cited alongside, same era.
2018
Later among the works it cites.
K. Subramaniam, N. V. Iqbal, et al., Classification of fractal features of uterine EMG signal for the prediction of preterm birth, Biomedical and Pharmacology Journal 11 (1) (2018) 369–374
2018
Later among the works it cites.
D. Despotović, A. Zec, K. Mladenović, N. Radin, T. L. Turukalo, A machine learning approach for an early prediction of preterm delivery, in: 2018 IEEE 16th International Symposium on Intelligent Systems and Informatics (SISY), IEEE, 2018, pp. 000265–000270
2018
Later among the works it cites.
M. Shahrdad, M. C. Amirani, Detection of preterm labor by partitioning and clustering the EHG signal, Biomedical Signal Processing and Control 45 (2018) 109–116
2018
Later among the works it cites.
F. Jager, S. Libensek, K. Gersak, Characterization and automatic classification of preterm and term uterine records, bioRxiv (2018) 349266
2018
Later among the works it cites.
S. Hoseinzadeh, M. C. Amirani, Use of electro hysterogram (EHG) signal to diagnose preterm birth, in: Electrical Engineering (ICEE), Iranian Conference on, IEEE, 2018, pp. 1477–1481
2018
Later among the works it cites.
A. Fernandez, S. Garcia, F. Herrera, N. V. Chawla, SMOTE for learning from imbalanced data: Progress and challenges, marking the 15-year anniversary, Journal of Artificial Intelligence Research 61 (2018) 863–905
2018
Later among the works it cites.
G. Vandewiele, I. Dehaene, O. Janssens, F. Ongenae, F. De Backere, F. De Turck, K. Roelens, S. Van Hoecke, T. Demeester, Time-to-birth prediction models and the influence of expert opinions, in: Conference on Artificial Intelligence in Medicine in Europe, Springer, 2019, pp. 286–291
2019
Later among the works it cites.
G. Vandewiele, I. Dehaene, O. Janssens, F. Ongenae, F. De Backere, F. De Turck, K. Roelens, S. Van Hoecke, T. Demeester, A critical look at studies applying over-sampling on the TPEHGDB dataset, in: Conference on Artificial Intelligence in Medicine in Europe, Springer, 2019, pp. 355–364
2019
Later among the works it cites.
M. U. Khan, S. Aziz, S. Ibraheem, A. Butt, H. Shahid, Characterization of term and preterm deliveries using electrohysterograms signatures, in: 2019 IEEE 10th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), IEEE, 2019, pp. 0899–0905
2019
Later among the works it cites.
G. Kovács, Smote-variants: A python implementation of 85 minority oversampling techniques, Neurocomputing 366 (2019) 352–354
2019
Later among the works it cites.
doi:10.1016/j.asoc.2019.105662
G. Kovács, An empirical comparison and evaluation of minority oversampling techniques on a large number of imbalanced datasets, Applied Soft Computing 83 (2019) 105662 · 2019
Later among the works it cites.
J. Peng, D. Hao, L. Yang, M. Du, X. Song, H. Jiang, Y. Zhang, D. Zheng, Evaluation of electrohysterogram measured from different gestational weeks for recognizing preterm delivery: a preliminary study using random forest, 2020
2020
Closest in time.