Comparative Performance of Various Wavelet Transformation for the Detection of Normal and Arrhythmia ECG Signal

Mu'thiana Gusnam, Hendra Kusuma, Tri Arief Sardjono


Cardiac Activity forms a signal of electrical potential waves in the heart that can be recorded using an Electrocardiogram (ECG). The results of the ECG signal can determine the conditions and abnormalities experienced by the heart, such as arrhythmias. Medical personnel diagnoses normal and arrhythmia heart conditions by looking at R peaks and R-R interval features. Normal conditions have regular R peaks and R-R intervals, whereas arrhythmias are irregular. The challenges in diagnosing ECG signals are that sometimes the signal has some noises that need reducing noise (denoising) are not required in the signal so it can be easier to detect abnormalities. This paper is a brief study of the comparison of the best performance in detecting ECG signals using various wavelet transforms and optimal threshold values based on empirical methods to obtain R peaks and R-R interval features. Wavelet transform describes the signals that can compress the ECG signal and reduce noise without losing important clinical information that can be achieved by medical personnel. The wavelet transform is suitable for approaching data with a discontinuity signal, so the frequency component will increase if noise or anomalies occur in the ECG signal. The various wavelet transforms used Daubechies (db4), Symlets (sym4), Coiflets (coif4), and Biorthogonal (bior3.7) with four types of Detail and Approximate levels; they are Level 1, 2, 3, and 4. The comparison result for the best performance of the various wavelet transforms is using Daubechies wavelet, and biorthogonal wavelet with an accuracy percentage of 100% at level 2 for diagnosing arrhythmia and 93.1% at level 1 for normal diagnosis from 31 data for arrhythmia and 18 for Normal sourced of the MIT-BIH Database. Hence, the total accuracy results obtained from all the data tested is 96.55%.

Full Text:




K. Ratna, dkk, “Keterampilan Pemasangan Elektrokardiografi

(EKG),” vol. I, no. Elektrokardiografi, pp. 1–10, 2019.

C. S. Kalangi, E. L. Jim, and V. F. F. Joseph, “Gambaran aritmia pada

pasien penyakit jantung koroner di RSUP Prof. Dr. R. D. Kandou

Manado periode 1 Januari 2015 – 31 Desember 2015,” e-CliniC, vol.

, no. 2, 2016.

H. Sulastomo et al., “Buku Manual Keterampilan Klinis Interpretasi

Pemeriksaan Elektrokardiografi ( Ekg ),” Skillslab.Fk.Uns.Ac.Id, pp.

–30, 2019.

O. Heriana, A. Matooq, and A. Misbah, “Denoising Perbandingan

Unjuk Kerja Transformasi Wavelet dalam Denoising Sinyal ECG,”

vol. 17, no. 1, pp. 1–6, 2017

A. Agrawal and D. H. Gawali, “Comparative study of ECG feature

extraction methods,” RTEICT 2017 - 2nd IEEE Int. Conf. Recent

Trends Electron. Inf. Commun. Technol. Proc., vol. 2018–Janua, no.

c, pp. 246–250, 2017

M. Alfaouri and K. Daqrouq, “SCI-PUBLICATIONS Author

Manuscript ECG Signal Denoising By Wavelet Transform

Thresholding SCI-PUBLICATION Author Manuscript,” vol. 5, no. 3,

pp. 276–281, 2008.

Z. Wang, J. Zhu, T. Yan, and L. Yang, “A new modified waveletbased ECG denoising,” Comput. Assist. Surg., vol. 24, no. S1, pp.

–183, 2019

S. Das, S. Mukherjee, S. Chatterjee, and H. K. Chatterjee, “Noise

elimination and ECG R peak detection using wavelet transform,”

IEEE 7th Annu. Ubiquitous Comput. Electron. Mob. Commun.

Conf. UEMCON 2016, no. October, 2016

S. Rahman, C. Karmakar, I. Natgunanathan, J. Yearwood, and M.

Palaniswami, “Robustness of electrocardiogram signal quality

indices,” J. R. Soc. Interface, vol. 19, no. 189, 2022

H. Yang and Z. Wei, “Arrhythmia Recognition and Classification

Using Combined Parametric and Visual Pattern Features of ECG

Morphology,” IEEE Access, vol. 8, pp. 47103–47117, 2020

R. C. Conditions, “Performances of Orthogonal Wavelet Division

Multiplex ( OWDM ) System Under p erformances o f o rthogonal w

avelet d ivision m ultiplex ( owdm ) s ystem u nder awgn , r ayleigh ,

a nd r icean c hannel c onditions,” no. june, 2016

H. Kim, S. Member, R. F. Yazicioglu, and P. Merken, “ECG Signal

Compression and Classification Algorithm With Quad Level Vector

for ECG Holter System,” no. June 2014, 2010

Moody GB, Mark RG. The impact of the MIT-BIH Arrhythmia

Database. IEEE Eng in Med and Biol 20(3):45-50 (May-June 2001).

(PMID: 11446209)

‘PhysioBank MIT-BIH Arrhythmia Database’,

[Online]. Available:

‘PhysioBank MIT-BIH Normal Sinus Database’,

[Online]. Available:

‘PhysioBank ATM’, [Online]. Available:

‘Matlab Wavelet Tools’, [Online]. Available:

R. A. Alharbey, S. Alsubhi, K. Daqrouq, and A. Alkhateeb, “The

continuous wavelet transform using for natural ECG signal

arrhythmias detection by statistical par



  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.