Filter bank-based processing of the stress ECG

Afonso, V.X.; Tompkins, W.J.; Nguyen, Truong Q.; Trautmann, Steven; Luo, Shen

doi:10.1109/iembs.1995.579254

Cited by 27 publications

(12 citation statements)

References 1 publication

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“…Thus, at each step we decrease the correlation between the most correlated two output channels. In effect the algorithm performs (18) incrementally. Let…”

Section: Theory: Time Domain Orthogonalization Of Long Ecg Recordsmentioning

confidence: 99%

“…Jane et al used a cascade of two adaptive filters to remove BW and gave a comparison of this to cubic spline filter [16] whereas Meyer et al used a cubic spline technique to estimate and remove BW from ECG [17]. Recently, Afonso et al proposed a filter-bank-based approach for the processing of stress ECG [18]. They decomposed the signal into different subbands and processed each subband independently.…”

Section: Introductionmentioning

confidence: 99%

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SVD-based on-line exercise ECG signal orthogonalization

Açar

Köymen

1999

IEEE Trans. Biomed. Eng.

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An orthogonalization method to eliminate unwanted signal components in standard 12-lead exercise electrocardiograms (ECG's) is presented in this work. A singular-value-decomposition-based algorithm is proposed to decompose the signal into two time-orthogonal subspaces; one containing the ECG and the other containing artifacts like baseline wander and electromyogram. The method makes use of redundancy in 12-lead ECG. The same method is also tested for reconstruction of a completely lost channel. The online implementation of the method is given. It is observed that the first two decomposed channels with highest energy are sufficient to reconstruct the ST-segment and J-point. The dimension of the signal space, on the other hand, does not exceed three. Data from 23 patients, with duration ranging from 9 to 21 min, are used.

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“…Thus, at each step we decrease the correlation between the most correlated two output channels. In effect the algorithm performs (18) incrementally. Let…”

Section: Theory: Time Domain Orthogonalization Of Long Ecg Recordsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

SVD-based on-line exercise ECG signal orthogonalization

Açar

Köymen

1999

IEEE Trans. Biomed. Eng.

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“…However, the Hilbert transform does not improve the SNR; hence it is common for investigators to filter the signal before applying the Hilbert transform. Other technique applied for QRS enhancement is the filter banks, which significantly improve the SNR for Gaussian noise and for muscle noise in comparison with the median or mean averaging methods (Afonso et al, 1995). In Afonso et al (1999), authors applied filter banks to ECG signal followed by threshold.…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction of Electrocardiogram Signals by Applying Adaptive Threshold and Principal Component Analysis

Rodriguez¹,

Mexicano²,

Bíla³

et al. 2015

Journal of Applied Research and Technology

107

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This paper presents a novel approach for QRS complex detection and extraction of electrocardiogram signals for different types of arrhythmias. Firstly, the ECG signal is filtered by a band pass filter, and then it is differentiated. After that, the Hilbert transform and the adaptive threshold technique are applied for QRS detection. Finally, the Principal Component Analysis is implemented to extract features from the ECG signal. Nineteen different records from the MIT-BIH arrhythmia database have been used to test the proposed method. A 96.28% of sensitivity and a 99.71% of positive predictivity are reported in this testing for QRS complexity detection, being a positive result in comparison with recent researches. All Rights Reserved

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“…However, the cut-off frequency is difficult to determine and it may introduce some additional artifacts to the signal, especially on the QRS wave. Other filtering techniques that have been proposed concentrate on the design of hybrid filters and filter banks, such as [10].…”

Section: Introductionmentioning

confidence: 99%

Electrocardiogram (ECG) signal modeling and noise reduction using wavelet neural networks

Poungponsri

2009

2009 IEEE International Conference on Automation and Logistics

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-Electrocardiogram (ECG) signal has been widely used in cardiac pathology to detect heart disease. In this paper, wavelet neural network (WNN) is studied for ECG signal modeling and noise reduction. WNN combines the multiresolution nature of wavelets and the adaptive learning ability of artificial neural networks, and is trained by a hybrid algorithm that includes the Adaptive Diversity Learning Particle Swarm Optimization (ADLPSO) and the gradient descent optimization. Computer simulation results demonstrate this proposed approach can successfully model the ECG signal and remove high-frequency noise.Index Terms -Wavelet neural networks, ECG signal, particle swarm optimization.

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Filter bank-based processing of the stress ECG

Cited by 27 publications

References 1 publication

SVD-based on-line exercise ECG signal orthogonalization

SVD-based on-line exercise ECG signal orthogonalization

Feature Extraction of Electrocardiogram Signals by Applying Adaptive Threshold and Principal Component Analysis

Electrocardiogram (ECG) signal modeling and noise reduction using wavelet neural networks

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