Electrocardiogram Classification Using Wavelet Transformations

Krak, Iurii; Stelia, Oleg; Pashko, Anatolii; Efremov, Mykola; Khorozov, Oleg

doi:10.1109/tcset49122.2020.235573

Cited by 11 publications

(3 citation statements)

References 12 publications

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“…[28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48], [49], [50], and [51].…”

Section: Related Workunclassified

Image Analysis of 12 Lead Electrocardiogram Using Wavelet Transformation

Darwan¹,

Anwar²,

Indra³

et al. 2023

ETJ

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The heart is one of the most important human organs. One of instruments to detect cardiac abnormalities is the electrocardiogram (ECG). This research tries to analyze ECG image in normal heart condition from ECG machine. The previous research related to the pre-processing process is the same, only at the feature extraction process look for peaks P, Q, R, S, T, heart rate, and Deviation-ST. While this research is the characteristic extraction process using wavelet transformation. The image of lead ECG 12 is processed using discrete wavelet transforms with decomposition up to ten levels, by searching for mean square error (MSE). The type of mother wavelet and the wavelet order used are Daubechies (db) with 1 (db1 (Haar)). The smallest MSE value decomposition results are obtained at the level 5, which are lead I, II, III, aVR, aVF, V4 and V5, lead V1 & V2 on level 4, for aVL (level 9), V3 (level 7) and V4 (level 6). It is expected that such research can be followed up to the identification model of cardiac abnormalities using wavelets.

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“…[28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48], [49], [50], and [51].…”

Section: Related Workunclassified

Image Analysis of 12 Lead Electrocardiogram Using Wavelet Transformation

Darwan¹,

Anwar²,

Indra³

et al. 2023

ETJ

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“…A new method is proposed for removing baseline drift from the ECG signal based on authors results on ECG investigation [10][11][12][13]17]. The method is based on the use of a sliding window containing 5 points.…”

Section: Using Spline Functions For Elimination the Baseline Wandermentioning

confidence: 99%

Baseline Wander Correction of the Electrocardiogram Signals for Effective Preprocessing

Pashko

Krak²,

Stelia³

et al. 2021

Lecture Notes in Computational Intelligence and Decision Making

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Thus, to effectively solve the problem of eliminating the ECG baseline drift, it is necessary to filter the ECG signal. In this study for solve the problem of eliminating the ECG baseline drift, various filters were used: a low-pass filters based on the forward and inverse discrete Fourier transform, Butterworth filter, median filter, Savitsky-Golay filter. The results obtained confirmed the efficiency of filtering harmonic noise based on forward and backward DFT on real data. The method makes it possible to implement a narrow-band stop filter in the range from 0 to the Nyquist frequency. In this case, the notch band can be less than 0.1% of the Nyquist frequency, which is important when processing ECG signals.The filtering result was evaluated by the appearance of the filtered ECG. The evaluation criterion was the presence on the signal of characteristic fragments reflecting the work of the atria and ventricles of the heart in the form of a P wave, a QRS complex and a ST-T segment. A new method has been developed for filtering the ECG signal, which is based on the use of a sliding window containing 5 points. A linear function is constructed from a sample of five points using the least squares method. The value of the resulting linear function at the midpoint is used as the new value. Several iterations are performed to achieve a good result. To eliminate the baseline drift for the ECG fragment, it is proposed to use a special cubic interpolation spline. The algorithm for constructing the used spline requires solving a system of equations with a tridiagonal matrix.

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“…In addition, they have also classified the one-dimensional ECG signals using CNN and found an accuracy of 90.93%. Krak et al [4] have transformed ECG signals using CWT and DWT in their study. Furthermore, they have classified using the CNN architecture and obtained an accuracy of 96% in the classification phase.…”

Section: Introductionmentioning

confidence: 99%