참고문헌
- World Health Organization, Born Too Soon: The Global action Report on Preterm birth 2012, 1-111
- Robert E. Garfield, Miha Lucovnik, Ruben J. Kuon, "Diagnosis and Effective Management of Preterm Labor," MGM J. Med. Sci., vol. 2014, no. 1, pp. 22-37 https://doi.org/10.5005/jp-journals-10036-1005
- Miha Luconvik, Z. Novak-Antolic and Robert E. Garfield, "Use of Non-invasive Uterine Electromyography in the Diagnosis of Preterm Labour," Facts, Views & Vis. in Obgyn., vol. 2012, no. 4
- Cheolsoo Park, David Looney, Naveed ur Rehman, Alireza Ahrabian, and Danilo P. Mandic, "Classification of Motor Imagery BCI Using Multivariate Empirical Mode Decomposition," IEEE Trans. on Neural Systems and Rehab. Eng., vol. 2013, no. 21
- Robert E. Garfield, William L. Maner, Lyn B. MacKay, Dietmar Schlembach, and George R. Saade, "Comparing uterine electromyography activity of antepartum patients versus term labor patients," American Journal of Obstetrics and Gynecology, vol. 2005, no. 193, pp. 23-9 https://doi.org/10.1016/j.ajog.2005.01.050
- Shao Q. Shi, William L. Maner, Lynette B. Mackay and Robert E. Garfield, "Identification of term and pre-term labor in rats using artificial neural networks on uterine emg signals," American Journal of Obstetrics and Gynecology, vol. 2008, no. 198, pp. 235.e1-234.e4 https://doi.org/10.1016/j.ajog.2007.08.039
- J. G. Planes, J. P. Morucci, H. Grandjean, and R. Favretto, "External recording and processing of fast electrical activity of the uterus in human parturition," Med. Biol. Eng. Comput., vol. 1984, no. 22, pp. 585-591 https://doi.org/10.1007/BF02443874
- Robert E. Garfield, William L. Maner, Lyn B. MacKay, Dietmar Schlembach, and George R. Saade, "Comparing uterine electromyography activity of antepartum patients versus term labor patients," American Journal of Obstetrics and Gynecology, vol. 2005, no. 193, pp. 23-9 https://doi.org/10.1016/j.ajog.2005.01.050
- Mohamad O. Diab, Catherine Marque, and Mohamad A. Khalil, "Classification of uterine EMG signals: comparison between AR model and statistical classification method," Int. Jounal of Comput. Cog., vol. 2007, no. 5, pp. 8-14
- Bassam Moslem, Mohamad Diab, Mohamad Khalil, and Catherine Marque, "Combining data fusion with multiresolution analysis for improving the classification accuracy of uterine EMG signals," EURASIP Journal on Advances in Signal Processing, vol. 2012, no. 167
- S. Mohammad-Sina Baghamoradi, Mohsen Naji Aryadoost H., "Evaluation of Cepstral Analysis of EHG Signals to Prediction of Preterm Labor," 18th Iranian Conf. on BioMed. Eng., vol. 2011, pp. 81-83
- SooYoung Sim, HoSuk Ryu, HanByul Kim, JungMin Han, and KwangSuk Park, "Evaluation of Electrohysterogram Feature Extraction to Classify the Preterm and Term Delivery Groups," The 15th Int. Conf. on Biomed. Eng., vol. 2013, pp. 675-678
- Paul Fergus, Paulin Cheung, Abir Hussain, Dhiya Al-Jumeily, Chelsea Dobbins, and Shamaila Iram, "Prediction of Preterm Deliveries form EHG Signals Using Machine Learning", vol. 2013, no. 8
- Saffa M, Naeem, Ahmed F. Seddik and Mohamed A. Eldosoky, "New Technique based on uterine electromyography nonlinearity for preterm delivery detection," Journal of Eng. And Tech. Research, vol. 2014, no. 6
- G. Fele-Zorz, G. Kavsek, Z. Novak-Antolic, and 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," Med. Biol. Eng. Comput., vol. 2008, no. 46, pp. 911-922 10 https://doi.org/10.1007/s11517-008-0350-y
- Ivan Verdenik, Marjan Pajntar, Brane Leskosek, "Uterine electrical activity as predictor of preterm birth in women with preterm contractions," European Jounal of Obsterics Gynecology and Reproductive Biology, vol. 2001, no. 95
- Catalin Buhimschi, B. Boyle, and Robert E. Garfield, "Electrical Activity of the Human Uterus During Pregnancy as Recorded from the Abdominal Surface," Obstertrics and Gynecology, vol. 1997, no. 90, pp. 102-111 https://doi.org/10.1016/S0029-7844(97)83837-9
- Branimir Leskosek and Marjan Pajntar, "Electromyography of the pregnant uterus in humans and sheep," 2nd European Med. Biol. Eng. Conf., vol. 2002, no. 3, pp. 544-545
- Mohamad O. Diab, Amira El-Merhie, Nour El-Halabi, Layal Khoder, "Classification of uterine EMG signals using supervised classification method," J. Biol. Sci. and Eng., vol. 2010, no. 3, pp. 837-842 https://doi.org/10.4236/jbise.2010.39113
- Wiliam L. Maner and Robert E. Garfield, "Identification of Human Term and Preterm Labor using Artificial Neural Networks on Uterine Electromyography Data," Ann. Biomed. Eng., vol. 2007, no. 35, pp. 365-473
- Miha Lucovnik, William L. Manver, Linda R. Chambliss, Richard Blumrick, James Balducci, Ziva Novak-Antolic, Robert E. Garfield, "Noninvasive Uterine Electromyography For Prediction of Preterm Delivery," American Journal of Obstetrics and Gynecology, vol. 2011, no. 204, pp. 228.e1-228.10 https://doi.org/10.1016/j.ajog.2010.09.024
- Dima Alamedine, Mohamad Khalil, Catherine Marque, "Parameters Extraction and Monitoring in Uterine EMG Signals. Detection of Preterm Deliveries," IRBM, vol. 2013, no. 34, pp. 322-325 https://doi.org/10.1016/j.irbm.2013.08.003
- Bassam Moslem, Mohamad Khalil, Catherine Marque, Mohamad Diab, "Energy Distribution Analysis of Uterine Electromyography Signals," Journal of Medical and Biological Engineering, vol. 2010, no. 30, pp. 361-366 https://doi.org/10.5405/jmbe.768
- Sondhiya Arora and Girisha Garg, "A Novel Scheme to Classify EHG Signal for Term and Pre-Term Pregnancy," Analysis International Journal of Computer Applications, vol. 2012, no. 51, pp. 37-41
피인용 문헌
- Multivariate Time–Frequency Analysis of Electrohysterogram for Classification of Term and Preterm Labor vol.14, pp.2, 2019, https://doi.org/10.1007/s42835-019-00118-9