Maliha Atteeq
Muhammad Fahad Khan
ABSTRACT
Fetal monitoring through phonocardiography is non-invasive and very challenging technique. It is very crucial to know about the fetus heart status. Extraction of fetus heart beat from mother heart sound is very challenging and difficult task due to the presence of additional sounds like mother organ sound, mother respiration and external noises. Benchmarked datasets and literature are also not available. In this research we extract fetus heart beat from mother beat using Blind source separation technique like STFT. Shiraz University Fetal Heart Sounds Database of Physionet has been used. 92 maternal heart sounds are used. It can be seen that the algorithm well separates the mixed source into maternal and fetal heart sounds.
- Wei, Z., Xiaolong, L., Jin, Z., Xueyun, W., & Hongxing, L., 2018. Foetal heart rate estimation by empirical mode decomposition and MUSIC spectrum, Biomedical Signal Processing and Control, 42, 287--296.Google Scholar
Cross Ref
- Quiceno, A. F., Delgado, E., Vallverd, M., Matijasevic, A. M., & Castellanos-Domnguez, G. Sep 2008. Effective phonocardiogram segmentation using nonlinear dynamic analysis and high-frequency decomposition. In Computers in Cardiology, 2008 (pp. 161--164). IEEE.Google Scholar
- Mondal, A., Bhattacharya, P., & Saha, G., 2013. An automated tool for localization of heart sound components S1, S2, S3 and S4 in pulmonary sounds using Hilbert transform and Heron's formula. SpringerPlus, 2(1), 512.Google ScholarCross Ref
- Pedrosa, J., Castro, A., & Vinhoza, T. T., Aug 2014, Automatic heart sound segmentation and murmur detection in pediatric phonocardiograms. In Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE (pp. 2294--2297). IEEE.Google ScholarCross Ref
- Jeyarani, A. D., & Singh, T. J., 2011. Feature Extraction from Heart sound signal for Anomaly Detection. IJCSNS International Journal of Computer Science and Network Security.Google Scholar
- Ganguly, A., & Sharma, M., 2017. Detection of pathological heart murmurs by feature extraction of phonocardiogram signals. Journal of Applied and Advanced Research, 2(4), 200--205.Google ScholarCross Ref
- Schmidt, S. E., Toft, E., Holst-Hansen, C., Graff, C., & Struijk, J. J., Sep 2008, Segmentation of heart sound recordings from an electronic stethoscope by a duration dependent Hidden-Markov model. In Computers in Cardiology, 2008 (pp. 345--348). IEEE.Google Scholar
- Munia, T. T., Tavakolian, K., Verma, A. K., Zakeri, V., Khosrow-Khavar, F., Fazel-Rezai, R., & Akhbardeh, A., Heart sound classification from wavelet decomposed signal using morphological and statistical features. In Computing in Cardiology Conference (CinC), IEEE, Sep 2016, pp. 597--600.Google ScholarCross Ref
- Latif, S., Usman, M., & Rana, J. Q. R., 2018. Abnormal Heartbeat Detection Using Recurrent Neural Networks. arXiv preprint arXiv:1801.08322.Google Scholar
- Liang, H., & Hartimo, I., Oct 1998, A heart sound feature extraction algorithm based on wavelet decomposition and reconstruction. In Engineering in Medicine and Biology Society, 1998. Proceedings of the 20th Annual International Conference of the IEEE, IEEE, vol. 3, pp. 1539--1542.Google ScholarCross Ref
- Ibrahim, E. A., Al Awar, S., Balayah, Z. H., Hadjileontiadis, L. J., & Khandoker, A. H., 2017, A Comparative Study on Fetal Heart Rates Estimated from Fetal Phonography and Cardiotocography. Frontiers in physiology, 8, 764.Google Scholar
- Adithya, P. C., Sankar, R., Moreno, W. A., & Hart, S., 2017, Trends in fetal monitoring through phonocardiography: Challenges and future directions. Biomedical Signal Processing and Control, 33, 289--305.Google ScholarCross Ref
- Koutsiana, E., Hadjileontiadis, L. J., Chouvarda, I., & Khandoker, A. H., 2017, Fetal heart sounds detection using wavelet transform and fractal dimension. Frontiers in bioengineering and biotechnology, 5, 49.Google Scholar
- Nagarkoti, S. K., Singh, B., & Kumar, M., Mar 2012, An algorithm for fetal heart rate detection using wavelet transform, In Recent Advances in Information Technology (RAIT), 2012 1st International Conference on IEEE, pp. 838--840.Google ScholarCross Ref
- Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 101(23): e215--e220 {Circulation Electronic Pages; http://circ.ahajournals.org/content/101/23/e215}; 2000 (June 13).Google Scholar
- https://ccrma.stanford.edu/~jos/sasp/Mathematical_Definition_STFT.htmlGoogle Scholar
Index Terms
- Fetus Heart Beat Extraction from Mother's PCG Using Blind Source Separation
Recommendations
Computer Aided Detection of Normal and Abnormal Heart Sound using PCG
ICBBT '19: Proceedings of the 2019 11th International Conference on Bioinformatics and Biomedical TechnologyA PCG (phonocardiogram) is a method of plotting of heart sounds and murmurs during a cardiac cycle, with the help of machine called phonocardiograph. A PCG can be visually represented. PCG recordings comprise of bio-acoustic statistics indicating the ...
Generalized Blind Delayed Source Separation Model for Online Non-invasive Twin-fetal Sound Separation: A Phantom Study
The fetal phonocardiogram, which is the acoustic recording of mechanical activity of the fetal heart, facilitates the measurement of the instantaneous fetal heart rate, beat-to-beat differences and duration of systolic and diastolic phases. These ...
Automated detection of heart valve diseases using chirplet transform and multiclass composite classifier with PCG signals
AbstractHeart valve diseases (HVDs) are a group of cardiovascular abnormalities, and the causes of HVDs are blood clots, congestive heart failure, stroke, and sudden cardiac death, if not treated timely. Hence, the detection of HVDs at the ...
Highlights- A new method based on the time–frequency analysis of PCG signal using Chirplet transform has been proposed.
Comments