Araştırma Makalesi
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Decomposition of Fetal Electrocardiogram Sign in Non-Negative Matrix Separation Methods

Yıl 2021, Sayı: 24, 252 - 257, 15.04.2021
https://doi.org/10.31590/ejosat.903201

Öz

Electrocardiogram (ECG) sign is the signals produced by the heart muscles during heart beats and taken with the help of electrodes placed on the surface of the body to learn the electrical activity of the heart. After the obtained signals are strengthened, they can be analyzed by digital signal processing methods. The signs obtained as a result of the analysis will have a decisive effect especially in the diagnosis and treatment of heart diseases or in terms of the health of the person. As a result of the measurement, the mixed form of at least two marks in the marks obtained from the human body will be obtained. In this study, non-negative matrix separation method from blind source decomposition algorithms was used in signal decomposition to obtain the ECG of the baby in the mother's womb. As a source sign, the signal decomposition process was performed in the data that was a mixture of maternal ECG, Fetal ECG (baby’s) and noise signals. Performance analysis and transaction costs were compared using the Multiplicative Update method and Hierarchical Least Squares method, which are non-negative matrix decomposition algorithms. Signal / noise ratio criterion was used to determine the appropriate method.

Kaynakça

  • Badeau, R., Bertin, N., & Vincent, E. (2011). Stability analysis of multiplicative update algorithms for non-negative matrix factorization. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2148–2151.
  • Burred, J. J. (2014). Detailed derivation of multiplicative update rules for NMF.
  • Çelik, H., Ilgın, F. Y., & Sevim, Y. (2019). Kanonik Korelasyon Analiz Tabanlı Ses Ayrıştırma Algoritmalarının İşlem Süresi Azaltımı. Teknik Bilimler Dergisi, 9(2), 55–59.
  • Çeli̇k, H., Ilgın, F. Y., & Sevim, Y. (2019). Müzik İşaretlerin Tek Kanal Kör Kaynak Ayrıştırma İle Ayrıştırılması. Engineering Sciences (NWSAENS), 14(1), 26–38.
  • Çelik, H., & Karaboğa, N. (2020). Ses İşaretlerinin Ayrıştırılmasında Kör Kaynak Algoritmalarının Performans Analizleri. European Journal of Science and Technology Special Issue, 399–404.
  • Choi, H., Park, J., Lim, W., & Yang, Y. M. (2021). Active-beacon-based driver sound separation system for autonomous vehicle applications. Applied Acoustics, 171, 107549.
  • Cichocki, A., & Phan, A. H. (2009). Fast local algorithms for large scale nonnegative matrix and tensor factorizations. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, E92-A(3), 708–721.
  • Çiflikli, C., & Ilgin, F. Y. (2020). Multiple Antenna Spectrum Sensing Based on GLR Detector in Cognitive Radios. Wireless Personal Communications, 110(4), 1915–1927.
  • De Oliveira, D. R., Lima, M. A. A., Silva, L. R. M., Ferreira, D. D., & Duque, C. A. (2021). Second order blind identification algorithm with exact model order estimation for harmonic and interharmonic decomposition with reduced complexity. International Journal of Electrical Power and Energy Systems, 125, 106415.
  • Gillis, N., & Glineur, F. (2008). Nonnegative Factorization and The Maximum Edge Biclique Problem.
  • Gurve, D., & Krishnan, S. (2020). Separation of Fetal-ECG from Single-Channel Abdominal ECG Using Activation Scaled Non-Negative Matrix Factorization. IEEE Journal of Biomedical and Health Informatics, 24(3), 669–680.
  • Ho, N. D. (2008). Nonnegative matrix factorization algorithms and applications. SIAM Conference on Optimization.
  • Https://physionet.org/content/nifecgdb/1.0.0/. (2021). Non-Invasive Fetal ECG Database v1.0.0.
  • Ilgin, F. Y. (2020). Energy-based spectrum sensing with copulas for cognitive radios. Bulletin of the Polish Academy of Sciences: Technical Sciences, 68(4), 829–834.
  • Kim, J. (2011). Nonnegatıve Matrıx And Tensor Factorızatıons, Least Squares Problems, And Applıcatıons A Dissertation Presented To The Academic Faculty. Georgia Institute of Technology.
  • Kim, J., & Park, H. (2012). Fast Nonnegative Tensor Factorization with an Active-Set-Like Method.
  • Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791.
  • Lee, D. D., & Seung, H. S. (2001). Algorithms for non-negative matrix factorization. Neural information processing systems foundation.
  • Lin, X., & Boutros, P. C. (2020). Optimization and expansion of non-negative matrix factorization. BMC Bioinformatics, 21(1), 7.
  • Mohebbian, M. R., Alam, M. W., Wahid, K. A., & Dinh, A. (2020). Single channel high noise level ECG deconvolution using optimized blind adaptive filtering and fixed-point convolution kernel compensation. Biomedical Signal Processing and Control, 57, 101673.
  • Ramli, D. A., Shiong, Y. H., & Hassan, N. (2020). Blind source separation (BSS) of mixed maternal and fetal electrocardiogram (ECG) signal: A comparative Study. Procedia Computer Science, 176, 582–591.
  • Ziani, S., Jbari, A., Bellarbi, L., & Farhaoui, Y. (2018). Blind Maternal-Fetal ECG Separation Based on the Time-Scale Image TSI and SVD - ICA Methods. Procedia Computer Science, 134, 322–327.

Negatif Olmayan Matris Ayrıştırma Yöntemlerinde Fetal Elektrokardiyogram İşaretin Ayrıştırılması

Yıl 2021, Sayı: 24, 252 - 257, 15.04.2021
https://doi.org/10.31590/ejosat.903201

Öz

Elektrokardiyogram (EKG) işareti, kalp atımları esnasında kalp kasları tarafından üretilen ve kalbin elektriksel aktivitesini öğrenmek için vücudun yüzeyine yerleştirilen elektrotlar yardımıyla alınan işaretlerdir. Elde edilen işaretler kuvvetlendirildikten sonra sayısal işaret işleme yöntemleri ile analiz edilebilirler. Analiz sonucunda elde edilen işaretler özellikle kalp hastalıklarının teşhis ve tedavisinde veya kişinin sağlığı açısından belirleyici etkiye sahip olacaktır. Ölçüm sonucunda insan vücudundan elde edilen işaretlerde en az iki işaretin karışmış şekli elde edilecektir. Bu çalışmada anne karnındaki bebeğin EKG’ sinin elde edilmesi için işaret ayrıştırmada kör kaynak ayrıştırma algoritmalarından negatif olmayan matris ayrıştırma yöntemi kullanılmıştır. Kaynak işareti olarak anne EKG, Fetal EKG (bebeğin) ve gürültü işaretlerinin karışımı olan veride işaret ayrıştırma işlemi yapılmıştır. Negatif olmayan matris ayrıştırma algoritmalarından Çarpımsal Güncelleme yöntemi ve Hiyerarşik Değişen En Küçük Kareler yöntemi kullanılarak performans analizleri ile birlikte işlem maliyetleri karşılaştırılmıştır. Uygun olan yöntemi belirlemek için işaret/gürültü oranı ölçütü kullanılmıştır.

Kaynakça

  • Badeau, R., Bertin, N., & Vincent, E. (2011). Stability analysis of multiplicative update algorithms for non-negative matrix factorization. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2148–2151.
  • Burred, J. J. (2014). Detailed derivation of multiplicative update rules for NMF.
  • Çelik, H., Ilgın, F. Y., & Sevim, Y. (2019). Kanonik Korelasyon Analiz Tabanlı Ses Ayrıştırma Algoritmalarının İşlem Süresi Azaltımı. Teknik Bilimler Dergisi, 9(2), 55–59.
  • Çeli̇k, H., Ilgın, F. Y., & Sevim, Y. (2019). Müzik İşaretlerin Tek Kanal Kör Kaynak Ayrıştırma İle Ayrıştırılması. Engineering Sciences (NWSAENS), 14(1), 26–38.
  • Çelik, H., & Karaboğa, N. (2020). Ses İşaretlerinin Ayrıştırılmasında Kör Kaynak Algoritmalarının Performans Analizleri. European Journal of Science and Technology Special Issue, 399–404.
  • Choi, H., Park, J., Lim, W., & Yang, Y. M. (2021). Active-beacon-based driver sound separation system for autonomous vehicle applications. Applied Acoustics, 171, 107549.
  • Cichocki, A., & Phan, A. H. (2009). Fast local algorithms for large scale nonnegative matrix and tensor factorizations. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, E92-A(3), 708–721.
  • Çiflikli, C., & Ilgin, F. Y. (2020). Multiple Antenna Spectrum Sensing Based on GLR Detector in Cognitive Radios. Wireless Personal Communications, 110(4), 1915–1927.
  • De Oliveira, D. R., Lima, M. A. A., Silva, L. R. M., Ferreira, D. D., & Duque, C. A. (2021). Second order blind identification algorithm with exact model order estimation for harmonic and interharmonic decomposition with reduced complexity. International Journal of Electrical Power and Energy Systems, 125, 106415.
  • Gillis, N., & Glineur, F. (2008). Nonnegative Factorization and The Maximum Edge Biclique Problem.
  • Gurve, D., & Krishnan, S. (2020). Separation of Fetal-ECG from Single-Channel Abdominal ECG Using Activation Scaled Non-Negative Matrix Factorization. IEEE Journal of Biomedical and Health Informatics, 24(3), 669–680.
  • Ho, N. D. (2008). Nonnegative matrix factorization algorithms and applications. SIAM Conference on Optimization.
  • Https://physionet.org/content/nifecgdb/1.0.0/. (2021). Non-Invasive Fetal ECG Database v1.0.0.
  • Ilgin, F. Y. (2020). Energy-based spectrum sensing with copulas for cognitive radios. Bulletin of the Polish Academy of Sciences: Technical Sciences, 68(4), 829–834.
  • Kim, J. (2011). Nonnegatıve Matrıx And Tensor Factorızatıons, Least Squares Problems, And Applıcatıons A Dissertation Presented To The Academic Faculty. Georgia Institute of Technology.
  • Kim, J., & Park, H. (2012). Fast Nonnegative Tensor Factorization with an Active-Set-Like Method.
  • Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791.
  • Lee, D. D., & Seung, H. S. (2001). Algorithms for non-negative matrix factorization. Neural information processing systems foundation.
  • Lin, X., & Boutros, P. C. (2020). Optimization and expansion of non-negative matrix factorization. BMC Bioinformatics, 21(1), 7.
  • Mohebbian, M. R., Alam, M. W., Wahid, K. A., & Dinh, A. (2020). Single channel high noise level ECG deconvolution using optimized blind adaptive filtering and fixed-point convolution kernel compensation. Biomedical Signal Processing and Control, 57, 101673.
  • Ramli, D. A., Shiong, Y. H., & Hassan, N. (2020). Blind source separation (BSS) of mixed maternal and fetal electrocardiogram (ECG) signal: A comparative Study. Procedia Computer Science, 176, 582–591.
  • Ziani, S., Jbari, A., Bellarbi, L., & Farhaoui, Y. (2018). Blind Maternal-Fetal ECG Separation Based on the Time-Scale Image TSI and SVD - ICA Methods. Procedia Computer Science, 134, 322–327.
Toplam 22 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Hüsamettin Çelik 0000-0002-7662-0674

Nurhan Karaboğa 0000-0002-4550-5251

Yayımlanma Tarihi 15 Nisan 2021
Yayımlandığı Sayı Yıl 2021 Sayı: 24

Kaynak Göster

APA Çelik, H., & Karaboğa, N. (2021). Negatif Olmayan Matris Ayrıştırma Yöntemlerinde Fetal Elektrokardiyogram İşaretin Ayrıştırılması. Avrupa Bilim Ve Teknoloji Dergisi(24), 252-257. https://doi.org/10.31590/ejosat.903201