Abstract
Pre-term birth is the birth that carries out before the baby’s expected date. Sometimes, it augments the possibility of health problems or death. In this research, the abdominal EHG signal incorporates both the maternal heart beat signal and Fetal ECG signal. The removal of fetal ECG signal from the heart beat signal is difficult in pre-term detection. In this work, the EHG signal is pre-processed using wiener filter that is applied to enhance the signal quality. Then, the attributes are removed from the pre-processed signal to find the distinctive class. In addition, Opposition based Ant Lion Optimization is used to select the multi-kernel Support Vector Machine and training algorithms for predicting the pre-term birth. The proposed methodology is simulated by using MATLAB software and the results are investigated to verify the classification accuracy. From the experimental study, the proposed work enhanced the classification accuracy upto 3–19% related to the existing works.
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06 June 2022
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s12652-022-04055-w
References
Acharya UR, Sudarshan VK, Rong SQ, Tan Z, Lim CM, Koh JE, Nayak S, Bhandary S (2017) Automated detection of premature delivery using empirical mode and wavelet packet decomposition techniques with uterine electromyogram signals. Comput Biol Med 85:33–42
Anderson C, Smitherman AB, Engel SM et al (2018) Modifiable and non-modifiable risk factors for preterm delivery among adolescent and young adult cancer survivors. Cancer Causes Control 29:289–295
Dinkar SK, Deep K (2017) Opposition based Laplacian ant lion optimizer. J Comput Sci 23:71–90
Dohare AK, Kumar V, Kumar R (2018) Detection of myocardial infarction in 12 lead ECG using support vector machine. Appl Soft Comput 64:138–147
Dubey HM, Pandit M, Panigrahi BK (2016) Ant lion optimization for short-term wind integrated hydrothermal power generation scheduling. Int J Electr Power Energy Syst 83:158–174
Fatemi M, Sameni R (2017) An online subspace denoising algorithm for maternal ECG removal from fetal ECG signals. Iran J Sci Technol Trans Electr Eng 41:65–79
Fergus P, Idowu I, Hussain A, Dobbins C (2016) Advanced artificial neural network classification for detecting preterm births using EHG records. Neurocomputing 188:42–49
Hussain AJ, Fergus P, Al-Askar H, Al-Jumeily D, Jager F (2015) Dynamic neural network architecture inspired by the immune algorithm to predict preterm deliveries in pregnant women. Neurocomputing 151:963–974
Kumar A, Komaragiri R, Kumar M (2018) From pacemaker to wearable: techniques for ECG detection systems. J Med Syst 42:34
Lemancewicz A, Borowska M, Kuć P, Jasińska E, Laudański P, Laudański T, Oczeretko E (2016) Early diagnosis of threatened premature labor by electrohysterographic recordings—the use of digital signal processing. Biocybern Biomed Eng 36:302–307
Li X, Mao W, Jiang W, Yao Y (2016) Multi-kernel transfer extreme learning classification. In Proceedings of ELM-2016, Springer, pp 159–170
Liu G, Luan Y (2015) An adaptive integrated algorithm for noninvasive fetal ECG separation and noise reduction based on ICA-EEMD-WS. Med Biol Eng Compu 53:1113–1127
Mengesha H, Wondwossen G, Lerebo T, Kidanemariam A, Gebrezgiabher G, Berhane Y (2016) Pre-term and post-term births: predictors and implications on neonatal mortality in Northern Ethiopia. BMC Nursing 15:48
Mirjalili S (2015) The ant lion optimizer. Adv Eng Softw 83:80–98
Sadi-Ahmed N, Kacha B, Taleb H, Kedir-Talha M (2017) Relevant features selection for automatic prediction of preterm deliveries from pregnancy electrohysterograhic (EHG) records. J Med Syst 41:204
Sutha P, Jayanthi VE (2018) Fetal electrocardiogram extraction and analysis using adaptive noise cancellation and wavelet transformation techniques. J Med Syst 42:21
Tayel MB, Eltrass AS, Ammar AI (2018) A new multi-stage combined kernel filtering approach for ECG noise removal. J Electrocardiol 51:265–275
Upadhyay N, Jaiswal RK (2016) Single channel speech enhancement: using Wiener filtering with recursive noise estimation. Proc Comput Sci 84:22–30
Venkatesan C, Karthigaikumar P, Varatharajan R (2019) FPGA implementation of modified error normalized LMS adaptive filter for ECG noise removal. Cluster Comput 22:12233–12241. https://doi.org/10.1007/s10586-017-1602-0
Wang WF, Yang C, Wu Y (2017) SVM-based classification method to identify alcohol consumption using ECG and PPG monitoring. Person Ubiquitous Comput. pp 1–13. https://www.physionet.org/pn6/tpehgdb/tpehgdb.smr
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Abdullah Mohammed Kaleem declares that he has no conflict of interest. Rajendra D. Kokate declares that he has no conflict of interest.
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Kaleem, A.M., Kokate, R.D. RETRACTED ARTICLE: Prediction of pre-term groups from EHG signals using optimal multi-kernel SVM. J Ambient Intell Human Comput 12, 3689–3703 (2021). https://doi.org/10.1007/s12652-019-01648-w
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DOI: https://doi.org/10.1007/s12652-019-01648-w