Design of Optimal Discrete Wavelet for ECG Signal Using Orthogonal Filter Bank

Dakshayini, M.; Guruprasad, H S; Maheshappa, H. D.; Manjunath, A. S.

doi:10.1109/iccima.2007.273

Cited by 7 publications

(3 citation statements)

References 6 publications

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“…In [6] five types of ECG beats has been classified by proposing a power spectral-based hybrid Genetic Algorithm and Support Vector Machine (SVMGA) technique with accuracy of 96.00%. In [13], a similar method based on designing a new WF has been used and the classification accuracy of 88.4% has been achieved. The research work which is presented in [11] is an appropriate case for comparison with the proposed method, because of the identically of the concepts, same arrhythmias included in the tests and using the entire database.…”

Section: Resultsmentioning

confidence: 99%

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A multi-wavelet optimization approach using similarity measures for electrocardiogram signal classification

Nazarahari

Namin

Markazi

2015

Biomedical Signal Processing and Control

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Section: Resultsmentioning

confidence: 99%

“…Tan et al [12] proposed the Best WF Identification System to identify and select the WF which is better suited to denoising a given ECG signal. Kumari et al [13] designed two new wavelets by using the polyphase representation of wavelet filter banks and considering the perfect reconstruction conditions as the cost function.…”

Section: Introductionmentioning

confidence: 99%

A multi-wavelet optimization approach using similarity measures for electrocardiogram signal classification

Nazarahari

Namin

Markazi

2015

Biomedical Signal Processing and Control

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“…Optimal wavelet design has drawn a lot of attention in the past years [7][8] [9]. The basic idea of optimal wavelet design is to look for some prototype filters that optimize some criteria.…”

Section: Introductionmentioning

confidence: 99%

Wepilet, optimal orthogonal wavelets for epileptic seizure prediction with one single surface channel

Bandarabadi

Teixeira

Sales

et al. 2011

2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Wepilet is a series of novel orthogonal wavelets optimized for Electroencephalogram (EEG) signals, specialized for epileptic seizure prediction. The main idea is to design a mother wavelet that when applied to EEG signal to create the feature space, should enable a better classification of the brain state. Wepilet is developed by an iterative optimization process, employing Genetic Algorithm (GA). Frequency sub-band features are first extracted using wepilet under design for the EEG signal captured by one single surface channel. These features are then fed to Support Vector Machines (SVMs) that classify the cerebral state in preictal and inter-ictal classes. The results of the classification are then used to compute the Probability of Error Rate (PER), which in turn is the GA objective function to be minimized. Results in a group of four patients, indicate the efficiency of optimized mother wavelet compared to the well-known Daubechies wavelet in EEG processing.

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Efficient ECG Approximation Using Chebyshev Polynomials

Yadav

Ray

2018

2018 International Conference on Inventive Research in Computing Applications (ICIRCA)

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Design of Optimal Discrete Wavelet for ECG Signal Using Orthogonal Filter Bank

Cited by 7 publications

References 6 publications

A multi-wavelet optimization approach using similarity measures for electrocardiogram signal classification

A multi-wavelet optimization approach using similarity measures for electrocardiogram signal classification

Wepilet, optimal orthogonal wavelets for epileptic seizure prediction with one single surface channel

Efficient ECG Approximation Using Chebyshev Polynomials

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