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WHO suggests a new discrete wavelet transform for compressing ECG signals with minimum loss of diagnostic information? 

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Our results in ECG signal compression can achieve lesser approximation error in comparison with discrete Daubechies wavelet transform.
Wavelet based methods present best performance as irregularity measures and makes them suitable for ECG data analysis.
Wavelet transform is a powerful tool for the analysis of ECG signal.
Proceedings ArticleDOI
Zhao Zhidong, Pan Min 
06 Jul 2007
11 Citations
The results show that on contrast with traditional methods, the novel wavelet shrinkage method can achieve the optimal denoising of the ECG signal.
Interestingly, our results show that CS represents a competitive alternative to state-of-the-art digital wavelet transform (DWT)-based ECG compression solutions in the context of WBSN-based ECG monitoring systems.
Proceedings ArticleDOI
Pradnya B. Patil, Mahesh S. Chavan 
21 Mar 2012
45 Citations
The results show that, on contrast with traditional methods wavelet method can achieve optimal denoising of ECG signal.
It shows that ECG data compression using wavelet transform can achieve better compression performance than FFT and DCT.
Results of the compression and reconstruction of ECG data are given which suggest that the wavelet transform is well suited to this task.
As wavelet transform shows a good performance in de-noising the ECG signal, however the selection of appropriate mother wavelet functions and number of wavelet decomposition levels is still an issue to remove the various kinds of noises from the input signal.
Journal ArticleDOI
Supriya Goel, Pradeep Tomar, Gurjit Kaur 
31 Aug 2016
16 Citations
As wavelet transforms shows a good performance in denoising the ECG signal however, the selection of appropriate mother wavelet functions and number of wavelet decomposition level is still an issue to remove the various kinds of noises from the input signal.

Related Questions

How the wavelet is used to filter cardiac signals?5 answersWavelet transforms are utilized in filtering cardiac signals to enhance diagnostic accuracy and reduce noise interference. In the context of electrocardiogram (ECG) denoising, various wavelet filter banks like the modified Mallat structure are compared for computational complexity, with the modified Mallat structure showing efficiency in ECG signal denoising. Additionally, wavelet filters are crucial in heart rate variability (HRV) analysis, where specific filters like Db-3 (Daubechies) with six-filter length are recommended for adaptive slow and fast variation detection in HRV signals. Furthermore, a study on cardiointervalography discusses the use of wavelet filters to isolate heart sounds from background noise, emphasizing the importance of filtering conditions and signal requirements. These findings collectively highlight the significant role of wavelet filters in enhancing the analysis and interpretation of cardiac signals.
Medical Image Segmentation Using Discrete Wavelet Transform?4 answersMedical image segmentation using discrete wavelet transform is a widely studied topic in the field of medical image processing. Various techniques have been proposed to improve the accuracy and efficiency of image segmentation in medical images. Bagaria et al. proposed an image segmentation system based on wavelet transform that decomposes images at an appropriate level, providing detailed information in different directions. Singh et al. developed a robust and lightweight deep learning network called MISegNet, which incorporates discrete wavelet transform to extract salient features in the frequency domain. Goyal et al. applied discrete wavelet transform for the compression of medical images, specifically magnetic resonance imaging (MRI) images, achieving better compression using Haar wavelet. Jabiyeva implemented discrete wavelet transform for medical image fusion, improving the understanding of medical images. H N et al. proposed a wavelet-based medical image fusion technique for image segmentation, combining texture features and region growing method.
What are the disadvantages of wavelet transform?5 answersThe disadvantages of wavelet transform include shift sensitivity, poor directionality, lack of phase information, and limited ability to capture directional information in certain types of images. Additionally, wavelet transform may not be effective in detecting linear radial structures, which are not dominant in images. Another limitation is that wavelet transform cannot reflect the time evolution of frequency, unlike Fourier transform. However, wavelet transform has been proven to be effective in analyzing and describing the dynamics of complex nonlinear reactive power compensation processes in electric power systems. It is also used for distinguishing the operating states of overhead HV transmission lines with increased capacity. The significance of selecting the right wavelet system is crucial for implementing the spectrum sensing function in cognitive radio networks.
Which wavelet transform is best for feature extraction of eeg data?4 answersThe db4 wavelet is used for feature extraction of EEG data in one of the papers. Another paper mentions the use of wavelet transformation with frequency regions of interest and a genetic algorithm to isolate resonance peaks. The paper also highlights the reduction in regression and classification errors compared to traditional wavelet decomposition techniques. However, there is no specific mention of the best wavelet transform for feature extraction of EEG data in the provided abstracts.
Using Wavelet transform in processing real-time ECG signals?5 answersThe wavelet transform is used in processing real-time ECG signals for various purposes such as denoising, restoration, and arrhythmia classification. An optimized de-noising method based on the wavelet transform is proposed to remove noise from ECG signals, achieving high-precision diagnosis. The wavelet transform is also applied to discriminate mechanical defects in signals and determine their extents, although classification methods are necessary to achieve accurate results. Another study proposes an algorithm for arrhythmia classification using the wavelet transform, which decomposes signals at multiple levels and facilitates simultaneous location in time and frequency domains. A universal ECG signal arrhythmia classification system is proposed, utilizing the discrete wavelet transform and wavelet packet transform, with the wavelet packet transform-based algorithm being the most superior method. Additionally, the wavelet scattering transform is used to automatically classify arrhythmia ECG heartbeats, achieving high accuracy and assisting physicians in ECG interpretation.
What diseases can be detected by ECG?11 answers

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