Evaluating a Preprocessing Pipeline for Fetal Phonocardiography Using FIR Filtering
Abstract
This study evaluates the effectiveness of a preprocessing pipeline consisting of resampling, normalization, and Finite Impulse Response (FIR) filtering to improve signal consistency for further signal analyses such as feature extraction and classification. Resampling standardizes the sampling rate to 16 kHz, ensuring uniform temporal resolution. Normalization adjusts amplitude across recordings, yielding a mean of 0,0015 and a standard deviation 0,0462. FIR filtering reduces noise, eliminating 77,69% of signal energy above 200 Hz while retaining 29,65% of the main signal. Pipeline evaluation shows a Signal-to-Noise Ratio (SNR) of -1,88 dB, indicating a significant power reduction, but normalization ensures amplitude stability. These results demonstrate that this preprocessing combination effectively reduces noise, although balancing noise reduction and signal preservation remains challenging.
Keywords
Full Text:
PDFReferences
Ballas, Aristotelis, Papapanagiotou, V., Delopoulos, A., & Diou, C. (2022). Listen2YourHeart: A Self-Supervised Approach for Detecting Murmur in Heart-Beat Sounds. 2022 Computing in Cardiology Conference. https://doi.org/10.22489/CinC.2022.298
Ben Hamza, M. F. A., & Sjarif, N. N. A. (2025). Comparative Analysis of Feature Selection Based on Metaheuristic Methods for Human Heart Sounds Classification Using PCG Signal. International Journal of Advanced Computer Science and Applications (IJACSA), 16(1). https://dx.doi.org/10.14569/IJACSA.2025.0160175
Bruna, J., & Mallat, S. (2013). Invariant Scattering Convolution Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8), 1872–1886. https://doi.org/10.1109/TPAMI.2012.230
Chen, W.-K. (Ed.). (2009). Passive, active, and digital filters (3. ed). CRC Press.
Chetlur Adithya, P., 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. https://doi.org/10.1016/j.bspc.2016.11.007
Faradisa, I. S., Anggriawan, D. O., Sardjono, T. A., & Purnomo, M. H. (2016). Identification of phonocardiogram signal based on STFT and Marquart Lavenberg Backpropagation. 2016 International Seminar on Intelligent Technology and Its Applications (ISITIA), 25–30. https://doi.org/10.1109/ISITIA.2016.7828628
Faradisa, I. S., Putra, O. V., Sardjono, T. A., & Purnomo, M. H. (2023). Arrhythmia Feotus Heartbeat Detection Using Optimized Neural Network Based on Phonocardiograph Ensemble Feature and Principal Component Analysis. International Journal of Intelligent Engineering and Systems, 16(1). https://doi.org/10.22266/ijies2023.0228.48
Farahi, M., Casals, A., Sarrafzadeh, O., Zamani, Y., Ahmadi, H., Behbood, N., & Habibian, H. (2021). Beat-to-Beat Fetal Heart Rate Analysis Using Portable Medical Device and Wavelet Transformation Technique. https://doi.org/10.48550/ARXIV.2103.01014
He, Y., Li, W., Zhang, W., Zhang, S., Pi, X., & Liu, H. (2021). Research on Segmentation and Classification of Heart Sound Signals Based on Deep Learning. Applied Sciences, 11(2), 651. https://doi.org/10.3390/app11020651
Johansson, H., & Gustafsso, O. (2013). Two-Rate Based Structures for Computationally Efficient Wide- Band FIR Systems. In F. P. Garca Mrquez (Ed.), Digital Filters and Signal Processing. InTech. https://doi.org/10.5772/52198
Kahankova, R., Mikolasova, M., Jaros, R., Barnova, K., Ladrova, M., & Martinek, R. (2023). A Review of Recent Advances and Future Developments in Fetal Phonocardiography. IEEE Reviews in Biomedical Engineering, 16, 653–671. https://doi.org/10.1109/RBME.2022.3179633
Martinek, R., Barnova, K., Jaros, R., Kahankova, R., Kupka, T., Jezewski, M., Czabanski, R., Matonia, A., Jezewski, J., & Horoba, K. (2020). Passive Fetal Monitoring by Advanced Signal Processing Methods in Fetal Phonocardiography. IEEE Access, 8, 221942–221962. https://doi.org/10.1109/ACCESS.2020.3043496
Mokeev, A. (2013). Direct Methods for Frequency Filter Performance Analysis. In F. P. Garca Mrquez (Ed.), Digital Filters and Signal Processing. InTech. https://doi.org/10.5772/52192
Sameni, R., & Samieinasab, M. (n.d.). Shiraz University Fetal Heart Sounds Database (Version 1.0.1) [Dataset]. PhysioNet. https://doi.org/10.13026/42EG-8E59
Samieinasab, M., & Sameni, R. (2015). Fetal phonocardiogram extraction using single channel blind source separation. 2015 23rd Iranian Conference on Electrical Engineering, 78–83. https://doi.org/10.1109/IranianCEE.2015.7146186
Sbrollini, A., Strazza, A., Caragiuli, M., Mozzoni, C., Tomassini, S., Agostinelli, A., Morettini, M., Fioretti, S., Di Nardo, F., & Burattini, L. (2017, September 14). Fetal Phonocardiogram Denoising by Wavelet Transformation: Robustness to Noise. 2017 Computing in Cardiology Conference. https://doi.org/10.22489/CinC.2017.331-075
Stergiopoulos, S. (Ed.). (2000). Advanced signal processing handbook: Theory and implementation for radar, sonar, and medical imaging real time systems. CRC Press.
Suryani Faradisa, I., Ananda, A., Arief Sardjono, T., & Hery Purnomo, M. (2020). Denoising of Fetal Phonocardiogram Signal by Wavelet Transformation. E3S Web of Conferences, 188, 00013. https://doi.org/10.1051/e3sconf/202018800013
Tang, H., Li, T., Qiu, T., & Park, Y. (2016). Fetal Heart Rate Monitoring from Phonocardiograph Signal Using Repetition Frequency of Heart Sounds. Journal of Electrical and Computer Engineering, 2016, 1–6. https://doi.org/10.1155/2016/2404267
Tomassini, S., Strazza, A., Sbrollini, A., Marcantoni, I., Morettini, M., Fioretti, S., Burattini, L., 1 Cardiovascular Bioengineering Lab, Department of Information Engineering, Universita Politecnica delle Marche, Ancona, Italy, & 2 Laboratorio di Bioingegneria, Department of Information Engineering, Universita Politecnica delle Marche, Ancona, Italy. (2019). Wavelet filtering of fetal phonocardiography: A comparative analysis. Mathematical Biosciences and Engineering, 16(5), 6034–6046. https://doi.org/10.3934/mbe.2019302
Vetterli, M., Kovacevic, J., & Goyal, V. K. (2014). Foundations of signal processing. Cambridge University Press.
Zhang, L., Lim, C. P., Yu, Y., & Jiang, M. (2022). Sound classification using evolving ensemble models and Particle Swarm Optimization. Applied Soft Computing, 116, 108322. https://doi.org/10.1016/j.asoc.2021.108322
DOI: https://doi.org/10.26760/elkomika.v13i2.155
Refbacks
- There are currently no refbacks.
_______________________________________________________________________________________________________________________
ISSN (print) : 2338-8323 | ISSN (electronic) : 2459-9638
Publisher:
Department of Electrical Engineering Institut Teknologi Nasional Bandung, Indonesia
Address: 20th Building Institut Teknologi Nasional Bandung PHH. Mustofa Street No. 23 Bandung 40124, Indonesia
Contact: +627272215 (ext. 206)
Email: jte.itenas@itenas.ac.id________________________________________________________________________________________________________________________
Jurnal ini terlisensi oleh Creative Commons Attribution-ShareAlike 4.0 International License.