An Empirical Evaluation of Savitzky-Golay (SG) Filter for Denoising ST Segment

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 801)

Abstract

Electrocardiogram (ECG) is the graphical illustration of the heart’s electrical activity and it is acquired through linking electrodes to body surface. It is useful in determining cardiac diseases non-invasively. The general concerns in ECG signal processing is the restrain of noise. This paper has been encouraged by the necessary to recognize the ability of Savitzky-Golay (SG) filter for denoising the ST segment of ECG signals. It essentially implicates the extraction of essential cardiac components by refusing the background noise with the aid of filtering technique. The simulation is carried out using MATLAB and the experiments are performed on physionet database. Peak Signal-to-Noise Ratio (PSNR), Signal-to-Noise Ratio (SNR), Mean Square Error (MSE) and Percentage Root mean square Difference (PRD) are used as performance evaluation metrics. This work uses SG filter to denoise the ECG signal and comparison is provided for SG filter with Median, Butterworth and SOS filters. The comparison results states the performance of the SG filter is better than that of the other filters.

Keywords

ST segment Denoising ECG signal Filtering Cardiac diseases 

References

  1. 1.
    Ripoll, V.J., Wojdel, A., Romero, E., Ramos, P., Brugada, J.: ECG assessment based on neural networks with pretraining. Appl. Soft Comput. 49, 399–406 (2016)CrossRefGoogle Scholar
  2. 2.
    Salas-Boni, R., Bai, Y., Harris, P.R., Drew, B.J., Hu, X.: False ventricular tachycardia alarm suppression in the ICU based on the discrete wavelet transform in the ECG signal. J. Electrocardiol. 47, 775–780 (2014)CrossRefGoogle Scholar
  3. 3.
    Varanini, M., Tartarisco, G., Balocchi, R., Macerata, A., Pioggia, G., Billeci, L.: A new method for QRS complex detection in multichannel ECG: application to self-monitoring of fetal health. Comput. Biol. Med. 85, 125–134 (2017)CrossRefGoogle Scholar
  4. 4.
    Zidelmal, Z., Amirou, A., Ould-Abdeslam, D., Moukadem, A., Dieterlen, A.: QRS detection using S-Transform and Shannon energy. Comput. Methods Programs Biomed. 116, 1–9 (2014)CrossRefGoogle Scholar
  5. 5.
    Talbi, M.L., Ravier, P.: Detection of PVC in ECG signals using fractional linear prediction. Biomed. Signal Process. Control 23, 42–51 (2016)CrossRefGoogle Scholar
  6. 6.
    Mjahad, A., Rosado-Muñoz, A., Bataller-Mompeán, M., Francés-Víllora, J.V., Guerrero-Martínez, J.F.: Ventricular fibrillation and tachycardia detection from surface ECG using time-frequency representation images as input dataset for machine learning. Comput. Methods Programs Biomed. 41, 119–127 (2017)CrossRefGoogle Scholar
  7. 7.
    Al Rahhal, M.M., Bazi, Y., AlHichri, H., Alajlan, N., Melgani, F., Yager, R.R.: Deep learning approach for active classification of electrocardiogram signals. Inf. Sci. 345, 340–354 (2016)CrossRefGoogle Scholar
  8. 8.
    Awal, M.A., Mostafa, S.S., Ahmad, M., Rashid, M.A.: An adaptive level dependent wavelet thresholding for ECG denoising. Biocybern. Biomed. Eng. 34, 238–249 (2014)CrossRefGoogle Scholar
  9. 9.
    Zidelmal, Z., Amirou, A., Ould-Abdeslam, D., Merckle, J.: ECG beat classification using a cost sensitive classifier. Comput. Methods Programs Biomed. 111, 570–577 (2013)CrossRefGoogle Scholar
  10. 10.
    Sharma, L.D., Sunkaria, R.K.: A robust QRS detection using novel pre-processing techniques and kurtosis based enhanced efficiency. Measurement 87, 194–204 (2016)CrossRefGoogle Scholar
  11. 11.
    Arif, M., Malagore, I.A., Afsar, F.A.: Detection and localization of myocardial infarction using K-nearest neighbor classifier. J. Med. Syst. 36, 279–289 (2012)CrossRefGoogle Scholar
  12. 12.
    Nasario-Junior, O., Benchimol-Barbosa, P.R., Nadal, J.: Principal component analysis in high resolution electrocardiogram for risk stratification of sustained monomorphic ventricular tachycardia. Biomed. Signal Process. Control 10, 275–280 (2014)CrossRefGoogle Scholar
  13. 13.
    Martínez, J.P., Almeida, R., Olmos, S., Rocha, A.P., Laguna, P.: A wavelet-based ECG delineator evaluation on standard databases. IEEE Trans. Biomed. Eng. 51, 570–581 (2004)CrossRefGoogle Scholar
  14. 14.
    Cuomo, S., De Pietro, G., Farina, R., Galletti, A., Sannino, G.: A revised scheme for real time ECG signal denoising based on recursive filtering. Biomed. Signal Process. Control 27, 134–144 (2016)CrossRefGoogle Scholar
  15. 15.
    Roopa, C.K., Harish, B.S.: A survey on various machine learning approaches for ECG analysis. Int. J. Comput. Appl. 163, 25–33 (2017)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.JSS Research FoundationSri Jayachamarajendra College of EngineeringMysoreIndia

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