Abstract
This paper presents the development and evaluation of an algorithm for compressing fetal electrocardiographic signals, taken superficially on the mother’s abdomen. This method for acquiring ECG signals produces a great volumen of information that makes it difficult for the records to be stored and transmitted. The proposed algorithm aims for lossless compression of the signal by applying Wavelet Packet Transform to keep errors below the unit, with compression rates over 20:1 and with conserved energy in reconstruction as comparison parameter. For algorithm validation, the signal files provided by PhysioBank DataBase are used.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Calabria, S.J.C., et al.: Software applications to health sector: a systematic review of literature. J. Eng. Appl. Sci. 13, 3922–3926 (2018). https://doi.org/10.36478/jeasci.2018.3922.3926
ZhiLin, Z., Tzyy-Ping, J.: Compressed sensing for energy-efficient wireless telemonitoring of noninvasive fetal ECG via block sparse bayesian learing. IEEE Trans. Biomed. Eng. 60(2), 300–309 (2013). https://doi.org/10.1109/TBME.2012.2226175
Panifraphy, D., Rakshit, M., An efficient method for fetal ECG extraction from single channel abdominal ECG. In: IEEE International Conference on Industrial Instrumentation and Control (ICIC), Pune, India, pp. 1083–1088 (2015)
Arvinti, B., Costache, M.: The performance of the Daubechies mother wavelets on ECG Compression. In: 11th International Symposium on Electronics and Telecommunications (ISETC), Timisoara, Romania, pp. 1–4 (2014)
Brechet, L.: Compression of biomedical signals with mother wavelet optimization and best-biasis wavelet packet selection. IEEE Trans. Biomed. Eng. 54(12), 2186–2192 (2007)
Castillo, E., Morales, D.P.: Efficient wavelet-based ECG processing for single-lead FHR extraction. Digit. Signal Process. 23(6), 1897–1909 (2013)
Rivas, E., Burgos, J.C.: Condition assessment of power OLTC by vibration analysis using wavelet transformd. IEEE Trans. Power Delivery 24(2), 687–694 (2009). https://doi.org/10.1109/TPWRD.2009.2014268
Bueno, M.C.: Electrocardiografía clínica deductiva, vol. 19. Universidad de Salamanca (2012)
Johnson, B., Bennett, A., Myungjae, K., Choi, A.: Automated evaluation of fetal cardiotocograms using neural network. In: 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Seoul, South Korea, pp. 408–413 (2012)
Ayres-de-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: cardiotocography. Int. J. Gynecol. Obstet. 131, 13–24 (2015)
Kuzilek, J., Lhotska, L., Hanuliak, M.: Processing Holter ECG signal corrupted with noise: using ICA for QRS complex detection. In: 2010 3rd International Symposium on Applied Sciences in Biomedical and Communication Technologies (ISABEL), Rome, Italy, pp. 1–4 (2010)
Chen, Y., Cheng, C.: Reconstruction of sparse multiband wavelet signals from fourier measurements. In: International Conference on Sampling Theory and Applications (SampTA), Washington D.C., USA, pp. 78–81 (2015)
Rao, Y., Zeng, H.: Estimate MECG from abdominal ECG signals using extended Kalman RTS smoother. In: Sixth International Conference on Intelligent Control and Information Processing (ICICIP), Wuhan, China, pp. 73–77 (2015)
Yao, Z., Dong, Y.: Experimental evaluations of sequential adaptive processing for fetal electrocardiograms (ECGs). In: 49th Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, USA, pp. 770-774 (2015)
Lima-Herrera, S.L., Alvarado-Serrano, C.: Fetal ECG extraction based on adaptive filters and wavelet transform: validation and application in fetal heart rate variability analysis. In: 13th International Conference on Electrical Engineering. Computing Science and Automatic Control (CCE), Ciudad de México, México, pp. 1–6 (2016)
Ebrahimzadeh, A., Azarbad, M.: ECG compression using wavelet transform and three-level quantization. In: 6th International Conference on Digital Content, Multimedia Technology and Its Applications (IDC), Seoul, South Korea, pp. 254-256 (2010)
Gerla, V., Paul, K.: Multivariate analysis of full-term neonatal polysomnographic data. IEEE Trans. Inf. Technol. Biomed. 13(1), 104–110 (2009). https://doi.org/10.1109/TITB.2008.2007193
Chae, D.H., Alem, F.: Performance study of compressive sampling for ECG signal compression in noisy and varying sparsity acquisition, pp. 1306–1309. IEEE (2013). https://doi.org/10.1109/ICASSP.2013.6637862
Zhang, Y., Liu, B., Ji, X., Huang, D.: Classification of EEG signals based on autoregressive model and wavelet packet decomposition. Neural Process. Lett. 45, 365–378 (2016). https://doi.org/10.1007/s11063-016-9530-1
Jarisch, W., Detwiler, J.S.: Statistical modeling of fetal heart rate variability. IEEE Trans. Biomed. Eng. (BME) 27(10), 582–589 (1980). https://doi.org/10.1109/TBME.1980.326580
Mukhopadhyay, S.K., Mitra, M.: ECG signal processing: lossless compression, transmission via GSM network and feature extraction using Hilbert transform. In: Point-of-Care Healthcare Technologies (PHT), Bangalore, India, pp. 85–88 (2013)
Jin, W., Xiaomei, L.: ECG data compression research based on wavelet neural network. In: Computer, Mechatronics, Control and Electronic Engineering (CMCE), Changchun, China, pp. 361–363 (2010)
Arvinti, B., Isar, A.: An adaptive compression algorithm for ECG signals. In: IEEE 12th International Symposium on Computational Intelligence and Informatics (CINTI), Budapest, Hungary, pp. 91–95 (2011)
Hongteng, X., Guangtao, Z.: ECG data compression based on wave atom transform. In: IEEE 13th International Workshop on Multimedia Signal Processing (MMSP), Boston, MA, USA, pp. 1–5 (2011)
Hernando-Ramiro, C., Blanco-Velasco, M.: Efficient thresholding-based ECG compressors for high quality applications using cosine modulated filter banks. In: Engineering in Medicine and Biology Society (EMBC), pp. 7079–7082 (2011)
Seong-Beom, C., Young-Dong, L.: Implementation of novel ECG compression algorithm using template matching. In: 7th International Conference on Computing and Convergence Technology (ICCCT), Seoul, South Korea, pp. 305–308 (2012)
Li, Z., Deng, Y.: ECG signal compressed sensing using the wavelet tree model. In: 8th International Conference on Biomedical Engineering and Informatics (BMEI), Shenyang, China, pp. 194–199 (2015)
Jha, C.K., Kolekar, M.H.: Efficient ECG data compression and transmission algorithm for telemedicine. In: 8th International Conference on Communication Systems and Networks (COMSNETS), Bangalore, India, pp. 1–6 (2016). https://doi.org/10.1109/COMSNETS.2016.7439988
Motinath, V.A., Jha, C.K.: A novel ECG data compression algorithm using best mother wavelet selection. In: International Conference on Advances in Computing, Communications and Informatics (ICACCI), Jaipur, India, pp. 682–686 (2016)
Wang, X., Chen, Z.: ECG compression based on combining of EMD and wavelet transform. Electron. Lett. 52(1), 1588–1590 (2016). https://doi.org/10.1049/el.2016.2174
Santamaría, F., Cortés, C.A., et al.: Uso de la Transformada de Ondeletas (Wavelet Transform) en la Reducción de Ruidos en las Señales de Campo Eléctrico producidas por Rayos. Información Tecnológica 23, 65–78 (2012)
Craven, D., McGinley, B.: Energy-Efficient Compressed Sensing for Ambulatory ECG Monitoring, vol. 71, pp. 1–13. Elsevier, Amsterdam (2016). https://doi.org/10.1016/j.compbiomed.2016.01.013
Goldberger, A.L., et al.: PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23), 215–220 (2000)
Singh, O., Sunkaria, R.K.: The utility of wavelet packet transform in QRS complex detection - a comparative study of different mother wavelets. In: 2nd International Conference on Computing for Sustainable Global Development (INDIACom), pp. 1242–1247 (2015)
Lee, S., Kim, J.: A real-time ECG data compression and transmission algorithm for an e-health device. IEEE Trans. Biomed. Eng. 58(9), 2448–2455 (2011). https://doi.org/10.1109/TBME.2011.2156794
Ma, J., Zhang, T.: A novel ECG data compression method using adaptive fourier decomposition with security guarantee in e-health applications. IEEE J. Biomed. Health Inf. 19(3), 986–994 (2015). https://doi.org/10.1109/JBHI.2014.2357841
Zhao, C., Chen, Z.: Electrocardiograph compression based on sifting process of empirical mode decomposition. Electron. Lett 52(3)(9), 688–690 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Jiménez, G., Collazos Morales, C.A., De-la-Hoz-Franco, E., Ariza-Colpas, P., González, R.E.R., Maldonado-Franco, A. (2020). Wavelet Transform Selection Method for Biological Signal Treatment. In: Tiwary, U., Chaudhury, S. (eds) Intelligent Human Computer Interaction. IHCI 2019. Lecture Notes in Computer Science(), vol 11886. Springer, Cham. https://doi.org/10.1007/978-3-030-44689-5_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-44689-5_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-44688-8
Online ISBN: 978-3-030-44689-5
eBook Packages: Computer ScienceComputer Science (R0)