An optimized hybrid methodology for non-invasive fetal electrocardiogram signal extraction and monitoring
Background and objective: Electronic fetal heart monitoring is currently used during pregnancy throughout most of the developed world to detect risk conditions for both the mother and the fetus. Non-invasive fetal electrocardiogram (NI-fECG), recorded in the maternal abdomen, represents an alternati...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-09-01
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590005623000279 |
| _version_ | 1797680750064041984 |
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| author | Theodoros Lampros Konstantinos Kalafatakis Nikolaos Giannakeas Markos G. Tsipouras Euripidis Glavas Alexandros T. Tzallas |
| author_facet | Theodoros Lampros Konstantinos Kalafatakis Nikolaos Giannakeas Markos G. Tsipouras Euripidis Glavas Alexandros T. Tzallas |
| author_sort | Theodoros Lampros |
| collection | DOAJ |
| description | Background and objective: Electronic fetal heart monitoring is currently used during pregnancy throughout most of the developed world to detect risk conditions for both the mother and the fetus. Non-invasive fetal electrocardiogram (NI-fECG), recorded in the maternal abdomen, represents an alternative to cardiotocography, which could provide a more accurate estimate of fetal heart rate. Different methodologies, with varying advantages and disadvantages, have been developed for NI-fECG signal detection and processing. Methods: In this context, we propose a hybrid methodology, combining independent component analysis, signal quality indices, empirical mode decomposition, wavelet thresholding and correlation analysis for NI-fECG optimized signal extraction, denoising, enhancement and addressing the intrinsic mode function selection problem. Results: The methodology has been applied in four different datasets, and the obtained results indicate that our method can produce accurate fetal heart rate (FHR) estimations when tested against different datasets of variable quality and acquisition protocols, on the FECGDARHA dataset our method achieved average values of Sensitivity = 98.55%, Positive Predictive Value = 91.73%, F1 = 94.92%, Accuracy = 90.91%, while on the ARDNIFECG dataset it achieved average values of Sensitivity = 92.96%, Positive Predictive Value = 91.66%, F1 = 93.60%, Accuracy = 90.45%. Conclusions: The proposed methodology is completely unsupervised, has been proven robust in different signal-to-noise ratio scenarios and abdominal signals, and could potentially be applied to the development of real-time fetal monitoring systems. |
| first_indexed | 2024-03-11T23:34:38Z |
| format | Article |
| id | doaj.art-3b0b65b3925d416298712abeb01163d6 |
| institution | Directory Open Access Journal |
| issn | 2590-0056 |
| language | English |
| last_indexed | 2024-03-11T23:34:38Z |
| publishDate | 2023-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Array |
| spelling | doaj.art-3b0b65b3925d416298712abeb01163d62023-09-20T04:21:46ZengElsevierArray2590-00562023-09-0119100302An optimized hybrid methodology for non-invasive fetal electrocardiogram signal extraction and monitoringTheodoros Lampros0Konstantinos Kalafatakis1Nikolaos Giannakeas2Markos G. Tsipouras3Euripidis Glavas4Alexandros T. Tzallas5Human-Computer Interaction Laboratory, Department of Informatics and Telecommunications, School of Informatics and Telecommunications, University of Ioannina, 47100, Arta, GreeceHuman-Computer Interaction Laboratory, Department of Informatics and Telecommunications, School of Informatics and Telecommunications, University of Ioannina, 47100, Arta, Greece; Institute of Health Sciences Education, Barts and the London School of Medicine & Dentistry, Queen Mary University of London (Malta Campus), Triq L-Arċisqof Pietru Pace, VCT 2520, Victoria (Gozo), MaltaHuman-Computer Interaction Laboratory, Department of Informatics and Telecommunications, School of Informatics and Telecommunications, University of Ioannina, 47100, Arta, GreeceDepartment of Electrical and Computer Engineering, School of Engineering, University of Western Macedonia, 50100, Kozani, GreeceHuman-Computer Interaction Laboratory, Department of Informatics and Telecommunications, School of Informatics and Telecommunications, University of Ioannina, 47100, Arta, GreeceHuman-Computer Interaction Laboratory, Department of Informatics and Telecommunications, School of Informatics and Telecommunications, University of Ioannina, 47100, Arta, Greece; Corresponding author. Department of Informatics & Telecommunications School of Informatics & Telecommunications University of Ioannina Kostakioi, GR-47100, Arta GreeceBackground and objective: Electronic fetal heart monitoring is currently used during pregnancy throughout most of the developed world to detect risk conditions for both the mother and the fetus. Non-invasive fetal electrocardiogram (NI-fECG), recorded in the maternal abdomen, represents an alternative to cardiotocography, which could provide a more accurate estimate of fetal heart rate. Different methodologies, with varying advantages and disadvantages, have been developed for NI-fECG signal detection and processing. Methods: In this context, we propose a hybrid methodology, combining independent component analysis, signal quality indices, empirical mode decomposition, wavelet thresholding and correlation analysis for NI-fECG optimized signal extraction, denoising, enhancement and addressing the intrinsic mode function selection problem. Results: The methodology has been applied in four different datasets, and the obtained results indicate that our method can produce accurate fetal heart rate (FHR) estimations when tested against different datasets of variable quality and acquisition protocols, on the FECGDARHA dataset our method achieved average values of Sensitivity = 98.55%, Positive Predictive Value = 91.73%, F1 = 94.92%, Accuracy = 90.91%, while on the ARDNIFECG dataset it achieved average values of Sensitivity = 92.96%, Positive Predictive Value = 91.66%, F1 = 93.60%, Accuracy = 90.45%. Conclusions: The proposed methodology is completely unsupervised, has been proven robust in different signal-to-noise ratio scenarios and abdominal signals, and could potentially be applied to the development of real-time fetal monitoring systems.http://www.sciencedirect.com/science/article/pii/S2590005623000279Fetal electrocardiogramICAEMDWavelet thresholdingSignal quality indicesCorrelation analysis |
| spellingShingle | Theodoros Lampros Konstantinos Kalafatakis Nikolaos Giannakeas Markos G. Tsipouras Euripidis Glavas Alexandros T. Tzallas An optimized hybrid methodology for non-invasive fetal electrocardiogram signal extraction and monitoring Array Fetal electrocardiogram ICA EMD Wavelet thresholding Signal quality indices Correlation analysis |
| title | An optimized hybrid methodology for non-invasive fetal electrocardiogram signal extraction and monitoring |
| title_full | An optimized hybrid methodology for non-invasive fetal electrocardiogram signal extraction and monitoring |
| title_fullStr | An optimized hybrid methodology for non-invasive fetal electrocardiogram signal extraction and monitoring |
| title_full_unstemmed | An optimized hybrid methodology for non-invasive fetal electrocardiogram signal extraction and monitoring |
| title_short | An optimized hybrid methodology for non-invasive fetal electrocardiogram signal extraction and monitoring |
| title_sort | optimized hybrid methodology for non invasive fetal electrocardiogram signal extraction and monitoring |
| topic | Fetal electrocardiogram ICA EMD Wavelet thresholding Signal quality indices Correlation analysis |
| url | http://www.sciencedirect.com/science/article/pii/S2590005623000279 |
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