Denoising of weak ECG signals by using wavelet analysis and fuzzy thresholding

Üstündağ, Mehmet; Gökbulut, Muammer; Şengür, Abdulkadir; Ata, Fikret

doi:10.1007/s13721-012-0015-5

Cited by 38 publications

(14 citation statements)

References 10 publications

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“…This probably accounts for poorer performance of -11.2dB as compared to this work of -31dB despite the superiority of the method used. In the third paper [31], wavelet analysis and fuzzy thresholding is used to filter weak ECG signals corrupted by AWGN. The wavelet analysis is used for decomposition and reconstruction while fuzzy s-function determines the threshold.…”

Section: Resultsmentioning

confidence: 99%

Analysis of Abdominal ECG Signal for Fetal Heart Rate Estimation Using Adaptive Filtering Technique

Ahmad¹,

Kuta²,

Loko³

2017

IJIGSP

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Abstract-This paper presents a method for fetal heart rate estimation from an abdominal electrocardiogram (ECG) signal based on adaptive filter analysis using least mean square (LMS) adaptive filtering algorithm in order to determine the health status of a baby in its mother's womb. The fetal ECG signal is extracted from abdominal ECG containing other sources of interference using the maternal ECG signal obtained from mother's chest cavity as the reference signal. Interference/noise model used for this work include the power-line noise, the white noise and the unwanted propagating maternal ECG signal. Thereafter, the heart rate is estimated using an automated peak voltage measurement algorithm at 75 percent threshold voltage. It is found that irrespective of the estimated heart rate of the baby, 100 percent estimation is achieved at signal-to-noise ratio (SNR) greater than or equal to -31dB.

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Section: Resultsmentioning

confidence: 99%

Analysis of Abdominal ECG Signal for Fetal Heart Rate Estimation Using Adaptive Filtering Technique

Ahmad¹,

Kuta²,

Loko³

2017

IJIGSP

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“…Bu çalışmada önerilen ve [36], [22] ve [34] nolu kaynaklarda önerilen yöntemlere ait başarım sonuçları karşılaştırmalı olarak aşağıdaki tablolarda verilmiştir.…”

Section: Proposed Method)unclassified

“…DD ile birlikte bu eşikleme yöntemlerini kullanan, geleneksel gürültü giderim algoritması aşağıda adımlar halinde verilmiştir [34]. E eşik değerini, n ise örnek sayısını göstermektedir.…”

Section: Y[n] = X[n] + D[n]unclassified

Dalgacik Paket Dönüşümü Ve Geneti̇k Algori̇tma Kullanarak Zayif Radar Si̇nyalleri̇ni̇n Gürültüden Arindirilmasi

Üstündağ¹,

Avcı²,

Gökbulut³

et al. 2014

Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi

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ÖZETRadar, haberleşme sistemlerinin önemli uygulama alanlarından birini oluşturur. Radar sinyali kaynaktan hedefe varıncaya kadar çevresel ya da insan kaynaklı nedenlerden dolayı asıl sinyale gürültü karışmış olabilir. Böylece, sinyalin doğru okunması imkânsız olabilir. Bu makalede, zayıf radar sinyallerini yüksek başarım ile gürültüden arındırmak amaçlanmıştır. Bu amaç doğrultusunda, geleneksel yöntemlerden farklı olarak Dalgacık Paket Dönüşümü kullanılmıştır. Bu dönüşümler gerçekleştirilirken, uygun dalgacık ailesi türü, entropi türü ve seviye seçimi oldukça önemli olmaktadır. Ayrıca, uygun bir eşik fonksiyonunun seçilmesi de yüksek başarım için önemlidir. Bu makalede, akıllı sistem olan Genetik Algoritma yapısı optimizasyon amaçlı önerilmiştir. Bu yapı ile birlikte, en iyi dalgacık ailesi türü, entropi türü ve seviye sayısı belirlenebilir. Eşikleme fonksiyonu olarak Bulanık s-fonksiyonu tercih edilmiş ve bu fonksiyona ait değişken parametreler en iyi başarım kriterine göre seçilmiştir. Daha sonra, zayıf radar sinyallerini gürültüden arındırabilmek için önerilen bu yöntem, literatürde mevcut olan diğer algoritmalarla karşılaştırılmıştır. Ortalama Karesel Hatanın Karekökü ve Korelasyon Katsayısı kriterleri kullanılarak başarımlar test edilmiştir. Elde edilen sonuçlara göre, önerilen yöntemin başarımının diğer yöntemlere göre oldukça iyi olduğu görülmektedir.Anahtar kelimeler: Zayıf Radar Sinyali, Gürültüden Arındırma, Genetik Algoritma, Dalgacık Dönüşümü, Eşikleme, Ortalama Karesel Hatanın Karekökü, Korelasyon Katsayısı DENOISING OF WEAK RADAR SIGNALS USING WAVELET PACKET TRANSFORM AND GENETIC ALGORITHM ABSTRACTRadar constitutes one of the major application areas of communication systems. Until radar signal reaches from source to target, the noise may be mixed with the original signal because of environmental or human-induced reasons. Thus, it may be impossible to read the signal correctly. In this article, the purpose is to denoise the weak radar signals from noise with high-performance. For this purpose, Wavelet Packet Transform is used as different from the traditional methods. While these transforms are being performed, the selections of the appropriate wavelet family type, entropy type and level are quite important. Moreover, the suitable threshold function selection is important for high performance. In this article, the Genetic Algorithm structure as intelligent system for the optimization is proposed. With this structure, the best wavelet family type, entropy type and level number can be determined. Fuzzy s-function as thresholding function is preferred and variable parameters of this function are selected according to the best performance criteria. Then, this method, which is proposed for denoising the weak radar signals from noise, is compared with other algorithms available in the literature. The achievements are tested by using root mean square error and the correlation coefficient criteria. According to the obtained results, the achievement of proposed method is better than achievements of other me...

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“…The wavelet analysis has been applied to various problems in biomedical engineering including noise removal in ECG signals (Agante and Sa, 1999;AlMahamdy and Riley, 2014;Awal et al, 2014;Bahoura and Ezzaidi, 2010;Chouakri et al, 2006;Garg et al, 2011;Germán-Salló, 2010;Karthikeyan et al, 2012;Li et al, 2009;Patil and Chavan, 2012;Poornachandra and Kumaravel, 2008;Üstündağ et al, 2012). Due to its better time-frequency resolution, it overcomes other classical methods, such as short time Fourier Transform, for instance (Üstündağ et al, 2012).…”

Section: Discrete Wavelet Transformmentioning

confidence: 99%

“…Chouakri et al (2006) compared the performance of Butterworth filters and the multilevel wavelet transform, concluding that improved results were achieved by the wavelet technique. Usually, wavelet-based methods for ECG denoising use thresholding techniques with some additional processing (Agante and Sa, 1999;AlMahamdy and Riley, 2014;Awal et al, 2014;Bahoura and Ezzaidi, 2010;Chouakri et al, 2006;Garg et al, 2011;Germán-Salló, 2010;Karthikeyan et al, 2012;Li et al, 2009;Patil and Chavan, 2012;Poornachandra and Kumaravel, 2008;Üstündağ et al, 2012). Patil and Chavan (2012) compared the PLI removal for different wavelet basis using hard and soft shrinkage functions.…”

Section: Introductionmentioning

confidence: 99%

A wavelet-based method for power-line interference removal in ECG signals

et al. 2018

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Introduction: The analysis of electrocardiogram (ECG) signals allows the experts to diagnosis several cardiac disorders. However, the accuracy of such diagnostic depends on the signals quality. In this paper it is proposed a simple method for power-line interference (PLI) removal based on the wavelet decomposition, without the use of thresholding techniques. Methods: This method consists in identifying the ECG and noise frequency range for further zeroing wavelet detail coefficients in the subbands with no ECG coefficients in the frequency content. Afterward, the enhanced ECG signal is obtained by the inverse discrete wavelet transform (IDWT

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Denoising of weak ECG signals by using wavelet analysis and fuzzy thresholding

Cited by 38 publications

References 10 publications

Analysis of Abdominal ECG Signal for Fetal Heart Rate Estimation Using Adaptive Filtering Technique

Analysis of Abdominal ECG Signal for Fetal Heart Rate Estimation Using Adaptive Filtering Technique

Dalgacik Paket Dönüşümü Ve Geneti̇k Algori̇tma Kullanarak Zayif Radar Si̇nyalleri̇ni̇n Gürültüden Arindirilmasi

A wavelet-based method for power-line interference removal in ECG signals

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