Daubechies algorithm for highly accurate ECG feature extraction

Balachandran, Athira; Ganesan, M.; Sumesh, E.P.

doi:10.1109/icgccee.2014.6922266

Cited by 21 publications

(3 citation statements)

References 4 publications

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“…Kaur et al [46] have demonstrated the advantage of the Db6 based DWT for detecting the QRS complexes in the ECG signals. Balachandran et al [47] have leveraged the Db6 based DWT to process the ECG signals for R peak finding. Wang and Eklund [48] have utilized the Db6 based DWT to generate the masks for the potential QRS complexes in the ECG signals, and then narrowed down the searching to the masked regions for accurately finding the QRS complexes.…”

Section: ) Methodology A: It Is Best To Use the Wavelet Similar To Tmentioning

confidence: 99%

Wavelets for Electrocardiogram: Overview and Taxonomy

2019

IEEE Access

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Physiological and pathological information within electrocardiogram (ECG) is crucial for the diagnosis of heart diseases. Computer-aided diagnosis for the ECG signals has drawn growing research attention up to date. Automatic ECG analysis mainly includes signal denoising, wave detection, and heartbeat classification. These three issues are relevant that the signal denoising can help attenuate the noises and accentuate the typical waves in ECG signals for wave detection, and wave detection can help locate the typical ECG waves and acquire the diagnostically valuable heartbeats based on these waves for the heartbeat classification. The wavelet-based methods play important roles in the three issues, but these methods are scattered and unorganized in the literature. In order to manifest the value of these methods, this paper contributes an overview and taxonomy on them. This paper does the comprehensive summary and systematic categorization on the methods for signal denoising, wave detection, and heartbeat classification according to the deep analysis of their methodological characteristics. By doing so, this paper not only uncovers the inner mechanism that why wavelet-based methods are suitable for ECG analysis but also reveals the designing principles that these methods potentially follow. Finally, this paper has provided an outlook for the developing prospect of ''wavelets for ECG'' in the future. INDEX TERMS Wavelets for electrocardiogram, signal denoising, wave detection, heartbeat classification, overview and taxonomy. I. OUTLINE AND INTRODUCTION A. OUTLINE AND CONTRIBUTION

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Section: ) Methodology A: It Is Best To Use the Wavelet Similar To Tmentioning

confidence: 99%

Wavelets for Electrocardiogram: Overview and Taxonomy

2019

IEEE Access

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“…Additional details about wavelets can be explored in [26,47]. In this work, we adopted the db6 wavelet function because it can effectively capture the morphology of the ECG signal [50][51][52][53][54].…”

Section: Wavelet Decompositionmentioning

confidence: 99%

Obstructive sleep apnea detection using discrete wavelet transform-based statistical features

Rajesh¹,

Dhuli

Kumar

2021

Computers in Biology and Medicine

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“…By increasing the order of the Daubechies wavelet, better characteristics are obtained, but the complexity of the implementation, the price of the system and errors in the calculations rise. In practical applications, orders 2 to 10 are most commonly used [10,12,13].…”

Section: Introductionmentioning

confidence: 99%

New Approach to Edge Detection on Different Level of Wavelet Decomposition

Maksimovic

Jakšić

Petrović

et al. 2019

cai

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This paper proposes a new approach to edge detection on the images over which the wavelet decomposition was done to the third level and consisting of different levels of detail (small, medium and high level of detail). Images from the BSD (Berkeley Segmentation Dataset) database with the corresponding ground truth were used. Daubechies wavelet was used from second to tenth order. Gradient and Laplacian operators were used for edge detection. The proposed approach is applied in systems where information is processed in real time, where fast image processing is required and in systems where high compression ratio is used. That is, it can find practical application in many systems, especially in television systems where the level of details in the image changes. The new approach consists in the fact that when wavelet transform is applied, an edge detection is performed over the level 1 image to create a filter. The filter will record only those pixels that can be potential edges. The image is passed through a median filter that filters only the recorded pixels and 8 neighbors of pixel. After that, the edge detection with one of the operators is applied onto the filtered image. F measure, FoM (Figure of Merit) and PR (Performance Ratio) were used as an objective measure. Based on the obtained results, the application of the proposed approach achieves significant improvements and these improvements are very good depending on the number of details in the image and the compression ratio. These results and improvements can be used to improve the quality of edge detection in many systems where compressed images are processed, that is, where work with images with a high compression ratio is required.

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Daubechies algorithm for highly accurate ECG feature extraction

Cited by 21 publications

References 4 publications

Wavelets for Electrocardiogram: Overview and Taxonomy

Wavelets for Electrocardiogram: Overview and Taxonomy

Obstructive sleep apnea detection using discrete wavelet transform-based statistical features

New Approach to Edge Detection on Different Level of Wavelet Decomposition

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