New Compression Method Proposed for Fetal Heart Sound Signals

Fetal phonocardiography, or FPCG, is a non-invasive acoustic technique used to monitor the heart activity of a fetus. While this method offers a valuable alternative to more invasive monitoring procedures, it generates significant amounts of data. The large size of these signals presents challenges for efficient storage and transmission, particularly in remote healthcare settings and Internet of Things (IoT) based monitoring systems. Bandwidth constraints and the need for real-time processing further complicate the handling of these biomedical signals. Researchers have therefore sought methods to reduce data size without losing diagnostically relevant information.

In a study published in the journal Cluster Computing, Samia A. El-Moneim Kabel and Walid El-Shafai proposed a new compression method that combines the Discrete Wavelet Transform (DWT) with fractal encoding. The approach is designed to exploit the self-similar characteristics inherent in FPCG signals. By identifying recurring patterns within the data, the method aims to represent these patterns using compact transformation information rather than storing the original coefficient blocks. This hybrid strategy seeks to balance high compression ratios with acceptable reconstruction quality for clinical use.

The proposed method begins by applying the Discrete Wavelet Transform to decompose the FPCG signal. This process separates the signal into approximation coefficients and detail coefficients. The approximation coefficients, which capture the broader trends of the signal, are then subjected to fractal encoding. This step involves identifying recurrent signal patterns and representing them through mathematical transformations. The detail coefficients, which contain finer variations, are preserved separately to ensure that important signal features are not lost during the compression process.

During the decompression phase, the method employs iterative fractal decoding to reconstruct the approximation coefficients from the compact transformation data. The preserved detail coefficients are then combined with the reconstructed approximation coefficients through an inverse DWT process. This reconstruction step aims to restore the original signal structure as closely as possible. The entire workflow is designed to be computationally efficient, addressing the real-time processing requirements often necessary in fetal monitoring applications.

The authors implemented the proposed method in MATLAB and evaluated its performance using FPCG signals. The evaluation metrics included compression ratio, mean square error, peak signal-to-noise ratio, compression time, and decompression time. These metrics provide a comprehensive view of both the efficiency of the data reduction and the quality of the reconstructed signal. The study utilized publicly available data, and the authors noted that no new datasets were generated or analyzed during the research, as the work focused on algorithmic development and simulation.

Experimental results indicated that the DWT-assisted fractal compression method achieves substantial data reduction. The study reported that the method maintains acceptable reconstruction quality, suggesting that diagnostically relevant information is preserved despite the significant reduction in file size. The authors highlighted the potential of this approach for efficient storage and transmission of FPCG signals in fetal health monitoring applications. The findings suggest that the method could be particularly useful in scenarios where bandwidth is limited, such as remote patient monitoring.

The research builds upon previous work in biomedical signal compression, which has explored various techniques including wavelet transforms and compressed sensing. Earlier studies have demonstrated the utility of wavelet-based methods for reducing the size of phonocardiogram signals. However, the integration of fractal encoding specifically targeting the approximation coefficients of the DWT decomposition is a distinct contribution of this work. The authors acknowledge the support of the Tanta High Institute of Engineering and Technology and the Automated Systems and Computing Lab at Prince Sultan University in the development of this research.

It is important to note that this study presents a proposed algorithm and its simulation results. The method has not yet been validated in clinical trials or real-world deployment scenarios. The authors did not receive external funding for this research and declared no competing interests. The study did not involve human participants, as it relied on publicly available data, and therefore ethical approval for human subject research was not required. The focus remains on the technical feasibility and performance metrics of the compression algorithm.

The potential application of this compression method extends to improving the infrastructure for remote fetal monitoring. By reducing the data size, the method could facilitate the transmission of FPCG signals over low-bandwidth connections, enabling more widespread access to fetal heart monitoring. This could be particularly beneficial in regions with limited healthcare infrastructure. However, further research is needed to assess the method’s robustness in diverse clinical environments and its impact on diagnostic accuracy in real-time settings. The study provides a foundation for future work in optimizing biomedical signal compression for IoT applications.

In conclusion, the DWT-assisted fractal compression method offers a promising approach to the challenge of storing and transmitting fetal phonocardiography signals. By leveraging the self-similarity of FPCG data through fractal encoding and the frequency decomposition capabilities of the DWT, the method achieves significant data reduction while maintaining signal quality. The results suggest that this hybrid technique could play a role in the development of more efficient remote fetal monitoring systems. As the field of biomedical signal processing continues to evolve, such methods may contribute to making fetal heart monitoring more accessible and efficient.

Subject of Research: Biomedical Signal Processing

Article Title: DWT-assisted fractal compression for efficient storage and transmission of fetal phonocardiography signals

Article References: Kabel, S. A. E.-M., & El-Shafai, W. (2026). DWT-assisted fractal compression for efficient storage and transmission of fetal phonocardiography signals. Cluster Computing, 29(13), Article 764. https://doi.org/10.1007/s10586-026-06501-2

Image Credits: AI Generated

DOI: 10.1007/s10586-026-06501-2

Keywords: Fetal Phonocardiography, Signal Compression, Discrete Wavelet Transform, Fractal Encoding, Remote Monitoring, Biomedical Engineering, DWT-assisted, fractal, compression, efficient, storage, transmission