Performance analysis of wavelet-based denoising techniques for ECG signal

Mandala, Satria; Fuadah, Yunendah Nur; Arzaki, Muhammad; Pambudi, Faida Esti

doi:10.1109/icoict.2017.8074701

Cited by 20 publications

(6 citation statements)

References 5 publications

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“…These low-frequency components need to be eliminated before wrist pulse analysis for proper diagnosis. Baseline wanders present in wrist pulse, can be removed by various methods like multi-rate empirical mode decomposition, Hilbert decomposition, adaptive mean filter and high pass filter (7,8) . Low-frequency wrist pulse signals can easily interfere with 50 Hz signal during acquisition.…”

Section: Methodsmentioning

confidence: 99%

Denoising wrist pulse signals using variance thresholding technique

Suguna¹

2020

IJST

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Background/Objectives: Denoising of the wrist pulse is a significant preprocessing stage for accurate investigation of the disease. The objective is to improve and analyze performance metrics of denoising techniques. Methods/Statistical analysis: Denoising of wrist pulse with the evaluation parameters such as PSNR, SNR, AE and RMSE has been implemented using wavelets such as Daubechies, Symlet and Biorthogonal. The performance of wavelets depends on the choice of decomposition level N and thresholding techniques. Findings: Variance thresholding technique showed significant improvement in Peak Signal to Noise Ratio (PSNR), Signal to Noise Ratio (SNR) and reduction in Absolute Error (AE) and Root Mean Square Error (RMSE) compared to other thresholding methods. Novelty/Applications: Experimental results showed drastic improvement in PSNR and SNR retaining the pathophysiological information of the wrist pulse signal for future analysis.

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Section: Methodsmentioning

confidence: 99%

Denoising wrist pulse signals using variance thresholding technique

Suguna¹

2020

IJST

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“…Dalam beberapa literature banyak pendekatan yang telah diusulkan untuk menghilangkan noise pada sinyal EKG. Dalam studi yang dilakukan oleh D. Zhang, dkk [11] menggunakan metode Discrete Wavelet Transform (DWT) dengan 5 data pasien yang open akses dari MIT-BIH database, dan menyatakan metode ini dapat meningkatkan kualitas dari sinyal EKG dibandingkan dengan metode yang lain berdasarkan evaluasi dari Signal-to-noise (SNR), sedangkan S. Mandala, dkk [12] mengeksplor DWT dengan 5 data pasien untuk menghilangkan noise yang ada pada EKG sinyal dan memberikan hasil bahwa DWT metode terbaik dalam denoising berdasarkan SNR, sementara W. Jenkal , dkk [13] mengusulkan DWT adalah salah satu teknik denoising yang banyak digunakan dan memberikan solusi penting untuk sinyal EKG, dan studi juga yang dilakukan oleh G. Kaushik, dkk [14] menyajikan pendekatan denoising EKG dengan menggunakan DWT untuk data sinyal EKG dengan 1 pasien.…”

Section: Pendahuluanunclassified

“…Jenis wavelet metode ambang batas dan pemilihan jenis yang akan memvariasikan nilai SNR. Semakin besar SNR yang diukur, semakin baik parameter dari DWT yang akan dipilih atau parameter wavelet memiliki kinerja yang semakin baik [12], [26]. Perhitungan SNR dinyatakan dalam logarithmic decibel scale [27] dengan persamaan dibawah ini :…”

Section: Evaluasi Snrunclassified

Perfoma Discrete Wavelet Transform dalam Denoising Sinyal EKG Berdasarkan Evaluasi Signal-to-Noise Rasio

Panjaitan

Amalia

2022

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Denoising adalah cara dalam menghilangkan noise yang ada pada sinyal elektrodiagram, sehingga gelombang yang ada pada elektrodiagram dapat dianalisis dengan menggunakan mesin untuk mendapatkan prediksi penyakit jantung yang diderita. Beberapa penelitian yang telah menganalisis sinyal elektrodiagram mengusulkan metode denoising dengan menggunakan Discrete Wavelet Transform, karena metode ini memberikan solusi kualitas yang lebih baik dibandingkan dengan metode denoising lainnya. Penelitian ini akan bertujuan melakukan denoising dengan menggunakan metode Discrete Wavelet Transform serta membandingkan performa dari wavelet family yang terdapat pada metode tersebut dengan persamaan evaluasi Signal-to-Noise Rasio. Data yang digunakan adalah data pasien yang memiliki diagnosis Venticular Tachycardia yang diambil dari data MITB. Penelitian memberikan hasil dan gambaran hasil denoising untuk setiap wavelet family yang ada pada Discrete Wavelet Transform. Berdasarkan hasil evaluasi Signal-to-Noise Rasio bahwa sym6 dan db6 memberikan perfoma yang lebih baik dibandingkan dengan wavelet family yang lainnya, karena kedua wavelet ini memiliki nilai yang lebih tinggi.

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“…As most of the signal shows spatially non-uniform energy distribution, this inspires the decision of a non-uniform threshold. The thresholding technique is a signal estimation approach that has been applied to detail coefficients obtained at each level of wavelet decomposition to achieve signal denoising (Mandala et al, 2017;Priyaa et al, 2016;Sharma and Kashyap, 2017).…”

Section: Introductionmentioning

confidence: 99%

Dual-tree complex wavelet transform technique-based optimal threshold tuning system to deliver denoised ECG signal

Prashar

Sood

Jain

2020

Transactions of the Institute of Measurement and Control

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The primary output for any health monitoring system that offers telecardiology services is the recovery of the electrocardiographic (ECG) signal from the noised signal. The mechanized investigation of the ECG signal is the most inspiring challenge for accurate detection of cardiac disease. This could be accomplished by eliminating the various noises from the acquired signal. In this paper, a noise reduction approach employing DTCWT is executed on an ECG signal by proposing a noise estimator along with a detailed assessment of the effect of the choice of the threshold value, threshold algorithm and distribution function. The thresholding technique is executed by varying the threshold value ( γ) and its function ( fn) applied to the proposed estimator (α*). The proposed estimator is scaled by 2n factor to study its impact on performance metrics and the nature of the reconstructed signal utilizing different distribution functions. The best combination of threshold function with threshold value selection has been chosen in this work from eight different sets of threshold value selection rules along with six distinct threshold functions. The experimental results show that the proposed noise reduction approach using a universal modified threshold level-dependent threshold with non-negative garrote threshold function for normal distribution with n = 3 delivers 80.72dB SNR with a subsequent reduction in MSE and PRD as compared with other standard techniques. An elaborate empirical analysis for selecting the distribution function for obtaining the best possible threshold function and technique is the prime objective and novelty of this research work.

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Performance analysis of wavelet-based denoising techniques for ECG signal

Cited by 20 publications

References 5 publications

Denoising wrist pulse signals using variance thresholding technique

Denoising wrist pulse signals using variance thresholding technique

Perfoma Discrete Wavelet Transform dalam Denoising Sinyal EKG Berdasarkan Evaluasi Signal-to-Noise Rasio

Dual-tree complex wavelet transform technique-based optimal threshold tuning system to deliver denoised ECG signal

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