Análisis del filtrado de señales ECG mediante filtro UFIR con ponderación de pesos

Victor Manuel Jiménez Ramos; Floriberto Canseco de la Rosa; Roberto Tamar Castellanos Baltazar; César Hernández Sanchez; Carlos Mauricio Lastre Domínguez

doi:10.61117/ipsumtec.v7i2.325

Authors

Victor Manuel Jiménez Ramos National Technological Institute of Mexico https://orcid.org/0000-0001-8185-5139
Floriberto Canseco de la Rosa National Technological Institute of Mexico https://orcid.org/0009-0001-3968-4839
Roberto Tamar Castellanos Baltazar National Technological Institute of Mexico https://orcid.org/0000-0002-3733-0711
César Hernández Sanchez National Technological Institute of Mexico https://orcid.org/0009-0002-2890-8099
Carlos Mauricio Lastre Domínguez National Technological Institute of Mexico https://orcid.org/0000-0003-2737-7644

DOI:

https://doi.org/10.61117/ipsumtec.v7i2.325

Keywords:

ECG, Signal Estimation, Fitlro Savitzky-Golay, Softener Filter, RMSE, UFIR

Abstract

Electrocardiogram (ECG) is of paramount importance in the diagnosis of heart disease and because it persists, is the leading cause of death worldwide. Various techniques have emerged in recent decades to process ECG signals, and noise removal has played a prominent role in improving feature extraction. However, achieving greater accuracy remains an enduring challenge. This study presents an innovative approach that applies a weighted and unbiased finite impulse response (UFIR) filter. Under the same noise conditions and in terms of mean square error (RMSE) and signal-to-noise ratio (SNR), our proposed method shows decent performance compared to the weighted Savitzky-Golay (SG) filter. This research contributes to the progressive evolution of ECG signal processing, offering the potential for more accurate and reliable detection of heart disease.

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References

Goldberger, A. L., Goldberger, Z. D., & Shvilkin Alexei. (2018). Goldberger’s Clinical Electrocardiography. In Perfusion (Vol. 32, Issue 8). Elsevier. DOI: https://doi.org/10.1016/B978-0-323-40169-2.00005-6

Mac, P. W., Oosterom, A. van, Pahlm, O., Klifield, P., Janse, M., & Camm, J. (2010). Comprehensive Electrocardiology (P. W. Macfarlane, A. van Oosterom, O. Pahlm, P. Kligfield, M. Janse, & J. Camm, Eds.). Springer London.

Armstrong, Michael L. (1974). Los Electrocardiogramas: Método Sistemático Para Su Lectura (Segunda Edición). Editorial Ateneo, Buenos Aires.

Luz, E. J. da S., Schwartz, W. R., Cámara-Chávez, G., & Menotti, D. (2016). ECG-based heartbeat classification for arrhythmia detection: A survey. Computer Methods and Programs in Biomedicine, 127, 144–164. DOI: https://doi.org/10.1016/j.cmpb.2015.12.008

Lastre-Dominguez, C., Shmaliy, Y. S., Ibarra-Manzano, O., & Vazquez-Olguin, M. (2019). Denoising and Features Extraction of ECG Signals in State Space Using Unbiased FIR Smoothing. IEEE Access, 7, 152166–152178. DOI: https://doi.org/10.1109/ACCESS.2019.2948067

Amri, M. F., Rizqyaan, M. I., and Turnip, A. (2016). ECG signal processing using offline wavelet transform method based on ECG-IoT device. 3rd International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE), 1–6. DOI: https://doi.org/10.1109/ICITACEE.2016.7892404

Tripathy, R. K., Dash, D. K., Ghosh S. K. and Pachori R. B., (2023) Detection of Different Stages of Anxiety from Single-Channel Wearable ECG Sensor Signal Using Fourier–Bessel Domain Adaptive Wavelet Transform, in IEEE Sensors Letters, Vol. 7, No. 5, no. 7002304., pp. 1-4. DOI: https://doi.org/10.1109/LSENS.2023.3274668

Basu, S., and Mamud, S. (2020). Comparative Study on the Effect of Order and Cut-off Frequency of Butterworth Low Pass Filter for Removal of Noise in ECG Signal. 2020 IEEE 1st International Conference for Convergence in Engineering (ICCE), pp. 156–160. DOI: https://doi.org/10.1109/ICCE50343.2020.9290646

M. S. Islam, M. N. Islam, N. Hashim, M. Rashid, B. S. Bari, and F. A. Farid, (2022). New Hybrid Deep Learning Approach Using BiGRU-BiLSTM and Multilayered Dilated CNN to Detect Arrhythmia, in IEEE Access, Vol. 10, pp. 58081-58096. DOI: https://doi.org/10.1109/ACCESS.2022.3178710

Y. Hou, R. Liu, M. Shu, X. Xie and C. Chen, (2023). Deep Neural Network Denoising Model Based on Sparse Representation Algorithm for ECG Signal, in IEEE DOI: https://doi.org/10.1109/TIM.2023.3251408

Transactions on Instrumentation and Measurement, vol. 72, No. 2507711, pp. 1-11.

Xiao, Qiao, Khuan Lee, Siti Aisah Mokhtar, Iskasymar Ismail, Ahmad Luqman bin Md Pauzi, Qiuxia Zhang, and Poh Ying Lim. (2023) "Deep Learning-Based ECG Arrhythmia Classification: A Systematic Review" Applied Sciences Vol. 13, No. 8, pp. 1-25. DOI: https://doi.org/10.3390/app13084964

Kiranyaz S, Devecioglu OC, Ince T, Malik J, Chowdhury M, Hamid T, Mazhar R, Khandakar A, Tahir A, Rahman T, Gabbouj M. (2022). Blind ECG Restoration by Operational Cycle-GANs. IEEE Trans Biomed Eng. Vol. 69, No. 12, pp.3572-3581. DOI: https://doi.org/10.1109/TBME.2022.3172125

A. M. Shaker, M. Tantawi, H. A. Shedeed and M. F. Tolba, (2020). Generalization of Convolutional Neural Networks for ECG Classification Using Generative Adversarial Networks,” in IEEE Access, vol. 8, pp. 35592-35605. DOI: https://doi.org/10.1109/ACCESS.2020.2974712

D. Nankani and R. D. Baruah, Investigating Deep Convolution Conditional GANs for Electrocardiogram Generation, 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, UK, 2020, pp. 1-8. DOI: https://doi.org/10.1109/IJCNN48605.2020.9207613

S. Janbhasha, S. N. Bhavanam and K. Harshita, GAN-Based Data Imbalance Techniques for ECG Synthesis to Enhance Classification Using Deep Learning Techniques and Evaluation, 2023 Third International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), Bhilai, India, 2023, pp. 1-8. DOI: https://doi.org/10.1109/ICAECT57570.2023.10118167

Berger L, Haberbusch M, Moscato F., (2023). Generative adversarial networks in electrocardiogram synthesis: Recent developments and challenges, Artificial Intelligence in Medicine, Vol. 143, No. 102632, pp. 1-13. DOI: https://doi.org/10.1016/j.artmed.2023.102632

Lastre-dominguez, C., Shmaliy, Y. S., & Ibarra-manzano, O. (2018). APC Heartbeats UFIR Smoothing and P-wave Features Analysis using Rice Distribution 2 q -Lag UFIR Smoothing Filtering. WSEAS Transaction on Signal Processing, 14, 36–42.

Lastre-Dominguez, C., Shmaliy, Y. S., Ibarra-Manzano, O., Vazquez-Olguin, M., & Morales-Mendoza, L. J. (2017). Unbiased FIR denoising of ECG data for features extraction. 2017 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2017, 1–6. DOI: https://doi.org/10.1109/ROPEC.2017.8261616

Lastre-Domínguez, C., Shmaliy, Y. S., Ibarra-Manzano, O., Munoz-Minjares, J., & Morales-Mendoza, L. J. (2019). ECG Signal Denoising and Features Extraction Using Unbiased FIR Smoothing. BioMed Research International, 2019, 1–16. DOI: https://doi.org/10.1155/2019/2608547

Lastre-Dominguez, C., Shmaliy, Y. S., & Ibarra-Manzano, O. (n.d.). UFIR Smoothing in State Space for T-wave Features Analysis.

Lastre-Dominguez, C., Shmaliy, Y. S., Ibarra-Manzano, O., & Morales-Mendoza, L. J. (2017). Unbiased FIR denoising of ECG signals. 2017 14th International Conference on Electrical Engineering, DOI: https://doi.org/10.1109/ICEEE.2017.8108834

Computing Science and Automatic Control (CCE), 1,1–6.

Olivera Reyna, R., Rivera-Romero, C., Munoz-Minjares, J., Lastre, C., & Lopez Ramirez, M.. ECG waveform detection based on Modified Iterative UFIR algorithm. Revista de Investigación y Desarrollo de Ingeniería Eléctrica, 16, 7-13, 2022.

Y. Shmaliy and S. Zhao, Optimal and Robust State Estimation: Finite Impulse Response and Kalman Approaches. John Wiley and Sons, Inc., 2022 DOI: https://doi.org/10.1002/9781119863106

Sun J, Fu JB, Wang J. (2014) Improved Manoeuvring Target Tracking Method Based on Unbiased Finite Impulse Response (UFIR) filter, US patent 103 500 455 A Jan. 8.

J. B. Fu, J. Sun, G. Fei, and S. Lu, Manoeuvring Target Tracking with Improved Unbiased FIR Filter, (2014). International Radar Conference, pp. 1–5. DOI: https://doi.org/10.1109/RADAR.2014.7060289

Moody GB, Mark RG. The impact of the MIT-BIH Arrhythmia Database. (2001). IEEE Eng in Med and Biol 20(3):45-50. (PMID: 11446209). DOI: https://doi.org/10.1109/51.932724

Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220. DOI: https://doi.org/10.1161/01.CIR.101.23.e215