Fetal ECG Arrhythmia Detection Based on DensNet Transfer Learning
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Abstract
Purpose: The mortality rate of fetuses due to heart defects is a major concern for clinicians. The fetus's heart is monitored non-invasively using the abdominal Electrocardiogram (ECG) of the mother. Most of the methods in literature diagnose fetal arrhythmia based on fetal heart rate. However, there are various challenges in fetal heart rate monitoring and arrhythmia detection. Therefore, very few methods are explored for fetal arrhythmia classification and have not achieved promising results.
Materials and Methods: In this article, a fetal arrhythmia classification method is investigated. The method has exploited the transfer learning principle where DenseNet architecture is utilized to learn fetal ECG patterns. Fetal ECG (fECG) signal extracted from the mothers abdominal has been processed for denoising and heartbeats are segmented using signal processing techniques. The extracted heartbeats have transformed into 2D fECG images to re-train the pre-trained DenseNet architecture.
Results: The proposed method has been evaluated on the publicly available Non-Invasive Fetal Arrhythmia Database (NIFADB) of Physionet and achieved 98.56% classification accuracy, thus outperforming other existing methods.
Conclusion: The arrhythmia in a fetus can be detected using a non-invasive fetal ECG. Due to the faster convergence of the learning algorithm, the proposed method offers better fetal diagnosis in real-time.
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24- Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger, "Densely connected convolutional networks." in Proceedings of the IEEE conference on computer vision and pattern recognition, (2017), pp. 4700-08.
25- Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei, "Imagenet: A large-scale hierarchical image database." in 2009 IEEE conference on computer vision and pattern recognition, (2009): Ieee, pp. 248-55.
26- Ali Isin and Selen Ozdalili, "Cardiac arrhythmia detection using deep learning." Procedia computer science, Vol. 120pp. 268-75, (2017).
27- Ranjeet Srivastva and Yogendra Narain Singh, "ECG analysis for human recognition using non‐fiducial methods." IET Biometrics, Vol. 8 (No. 5), pp. 295-305, (2019).
28- Peter Van Leeuwen, Silke Lange, Anita Klein, Daniel Geue, and Dietrich HW Grönemeyer, "Dependency of magnetocardiographically determined fetal cardiac time intervals on gestational age, gender and postnatal biometrics in healthy pregnancies." BMC pregnancy and childbirth, Vol. 4pp. 1-10, (2004).
29- Jiapu Pan and Willis J Tompkins, "A real-time QRS detection algorithm." IEEE transactions on biomedical engineering, (No. 3), pp. 230-36, (1985).
30- Yogendra Narain Singh and Phalguni Gupta, "Quantitative evaluation of normalization techniques of matching scores in multimodal biometric systems." in Advances in Biometrics: International Conference, ICB 2007, Seoul, Korea, August 27-29, 2007. Proceedings, (2007): Springer, pp. 574-83.
31- Chuanqi Tan, Fuchun Sun, Tao Kong, Wenchang Zhang, Chao Yang, and Chunfang Liu, "A survey on deep transfer learning." in Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, (2018): Springer, pp. 270-79.
32- Joachim A Behar, Laurent Bonnemains, Vyacheslav Shulgin, Julien Oster, Oleksii Ostras, and Igor Lakhno, "Noninvasive fetal electrocardiography for the detection of fetal arrhythmias." Prenatal diagnosis, Vol. 39 (No. 3), pp. 178-87, (2019).
33- Ranjeet Srivastva, Ashutosh Singh, and Yogendra Narain Singh, "PlexNet: A fast and robust ECG biometric system for human recognition." Information Sciences, Vol. 558pp. 208-28, (2021).
2- A review WITHIN CDC, "Centers for disease control and prevention." ed, (2020).
3- Americal Heart Association, "Heart Disease & Stroke Statistical Update Fact Sheet." ed, (2021).
4- World Health Organization, "Cardiovascular diseases (CVDs) [WWW Document]." URL https://www. who. int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds)(accessed 8.17. 20), (2017).
5- Ashleigh A Richards and Vidu Garg, "Genetics of congenital heart disease." Current cardiology reviews, Vol. 6 (No. 2), pp. 91-97, (2010).
6- Yu Sun, Sijung Hu, Vicente Azorin-Peris, Stephen Greenwald, Jonathon Chambers, and Yisheng Zhu, "Motion-compensated noncontact imaging photoplethysmography to monitor cardiorespiratory status during exercise." Journal of Biomedical Optics, Vol. 16 (No. 7), pp. 077010-10-9, (2011).
7- Roger K Freeman, Thomas J Garite, Michael P Nageotte, and Lisa A Miller, Fetal heart rate monitoring. Lippincott Williams & Wilkins, (2012).
8- Zarko Alfirevic, Gillian ML Gyte, Anna Cuthbert, and Declan Devane, "Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour." Cochrane database of systematic reviews, (No. 2), (2017).
9- P Van Leeuwen, H Bettermann, M Schüßler, and S Lange, "Magnetocardiography in the determination of fetal heart rate complexity." in Biomag 96: Volume 1/Volume 2 Proceedings of the Tenth International Conference on Biomagnetism, (1996): Springer, pp. 573-76.
10- Michael P Nageotte, "Fetal heart rate monitoring." in Seminars in Fetal and Neonatal Medicine, (2015), Vol. 20 (No. 3): Elsevier, pp. 144-48.
11- Emad A Ibrahim, Shamsa Al Awar, Zuhur H Balayah, Leontios J Hadjileontiadis, and Ahsan H Khandoker, "A comparative study on fetal heart rates estimated from fetal phonography and cardiotocography." Frontiers in physiology, Vol. 8p. 764, (2017).
12- Muhammad Asfarul Hasan, MBI Reaz, MI Ibrahimy, MS Hussain, and J Uddin, "Detection and processing techniques of FECG signal for fetal monitoring." Biological procedures online, Vol. 11pp. 263-95, (2009).
13- Kamakshi Sharma and Sarfaraz Masood, "Deep learning-based non-invasive fetal cardiac arrhythmia detection." in Applications of Artificial Intelligence and Machine Learning: Select Proceedings of ICAAAIML 2020, (2021): Springer, pp. 511-23.
14- Fetal Arrhythmia - American Pregnancy Associtioan”. [Online]. Available: https://americanpregnancy.org/pregnancycomplications/ fetal-arrhythmia/. [accessed 18 April 2021].
15- Nathalie Jeanne Bravo-Valenzuela, Luciane Alves Rocha, Luciano Marcondes Machado Nardozza, and Edward Araujo Júnior, "Fetal cardiac arrhythmias: Current evidence." Annals of pediatric cardiology, Vol. 11 (No. 2), p. 148, (2018).
16- MG Devika, C Gopakumar, RP Aneesh, and Gayathri R Nayar, "Myocardial infarction detection using hybrid BSS method." in 2016 International Conference on Communication Systems and Networks (ComNet), (2016): IEEE, pp. 167-72.
17- S Apsana, Manju G Suresh, and RP Aneesh, "A novel algorithm for early detection of fetal arrhythmia using ICA." in 2017 International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT), (2017): IEEE, pp. 1277-83.
18- Prachi Patel and Priyamwada Mahajani, "Fetal ECG separation from abdominal ECG recordings using compressive sensing approach." in 2018 International Conference on Inventive Research in Computing Applications (ICIRCA), (2018): IEEE, pp. 831-34.
19- Evaggelos C Karvounis, Markos G Tsipouras, Dimitrios I Fotiadis, and Katerina K Naka, "An automated methodology for fetal heart rate extraction from the abdominal electrocardiogram." IEEE Transactions on Information technology in Biomedicine, Vol. 11 (No. 6), pp. 628-38, (2007).
20- SV Veenadevi, C Padmavathi, B Shanthamma, Balaji Govindraj Abbigeri, and KM Pavithra, "Extraction of fetal electrocardiogram from maternal electrocardiogram and classification of normal and abnormal signals." in 2017 IEEE 2nd International Conference on Signal and Image Processing (ICSIP), (2017): IEEE, pp. 396-401.
21- Fang-Wen Lo and Pei-Yun Tsai, "Deep learning for detection of fetal ECG from multi-channel abdominal leads." in 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), (2018): IEEE, pp. 1397-401.
22- Md Saidur Rahman Pavel, Md Rafi Islam, and Asif Mohammed Siddiqee, "Fetal arrhythmia detection using fetal ECG signal." in 2019 IEEE International Conference on Telecommunications and Photonics (ICTP), (2019): IEEE, pp. 1-4.
23- Biswarup Ganguly et al., "A Non-Invasive Approach for Fetal Arrhythmia Detection and Classification from ECG Signals." in 2020 IEEE VLSI DEVICE CIRCUIT AND SYSTEM (VLSI DCS), (2020): IEEE, pp. 84-88.
24- Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger, "Densely connected convolutional networks." in Proceedings of the IEEE conference on computer vision and pattern recognition, (2017), pp. 4700-08.
25- Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei, "Imagenet: A large-scale hierarchical image database." in 2009 IEEE conference on computer vision and pattern recognition, (2009): Ieee, pp. 248-55.
26- Ali Isin and Selen Ozdalili, "Cardiac arrhythmia detection using deep learning." Procedia computer science, Vol. 120pp. 268-75, (2017).
27- Ranjeet Srivastva and Yogendra Narain Singh, "ECG analysis for human recognition using non‐fiducial methods." IET Biometrics, Vol. 8 (No. 5), pp. 295-305, (2019).
28- Peter Van Leeuwen, Silke Lange, Anita Klein, Daniel Geue, and Dietrich HW Grönemeyer, "Dependency of magnetocardiographically determined fetal cardiac time intervals on gestational age, gender and postnatal biometrics in healthy pregnancies." BMC pregnancy and childbirth, Vol. 4pp. 1-10, (2004).
29- Jiapu Pan and Willis J Tompkins, "A real-time QRS detection algorithm." IEEE transactions on biomedical engineering, (No. 3), pp. 230-36, (1985).
30- Yogendra Narain Singh and Phalguni Gupta, "Quantitative evaluation of normalization techniques of matching scores in multimodal biometric systems." in Advances in Biometrics: International Conference, ICB 2007, Seoul, Korea, August 27-29, 2007. Proceedings, (2007): Springer, pp. 574-83.
31- Chuanqi Tan, Fuchun Sun, Tao Kong, Wenchang Zhang, Chao Yang, and Chunfang Liu, "A survey on deep transfer learning." in Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, (2018): Springer, pp. 270-79.
32- Joachim A Behar, Laurent Bonnemains, Vyacheslav Shulgin, Julien Oster, Oleksii Ostras, and Igor Lakhno, "Noninvasive fetal electrocardiography for the detection of fetal arrhythmias." Prenatal diagnosis, Vol. 39 (No. 3), pp. 178-87, (2019).
33- Ranjeet Srivastva, Ashutosh Singh, and Yogendra Narain Singh, "PlexNet: A fast and robust ECG biometric system for human recognition." Information Sciences, Vol. 558pp. 208-28, (2021).
| Files | ||
| Issue | Vol 10 No 4 (2023) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/fbt.v10i4.13723 | |
| Keywords | ||
| Fetal ECG Arrhythmia Transfer Learning DenseNet | ||
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How to Cite
1.
Kumar Rai R, Singh A, Srivastva R, Kumar G. Fetal ECG Arrhythmia Detection Based on DensNet Transfer Learning. Frontiers Biomed Technol. 2023;10(4):417-426.
