ECG Signal Analysis, Classification, and Interpretation: A Framework of Computational Intelligence

Gacek, Adam; Pedrycz, Witold

doi:10.1007/978-0-85729-868-3_3

Cited by 45 publications

(47 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Thus, when the cells in the ventricles depolarize, the depolarization has a much greater effect on the ECG signal. The subsequent T wave represents the repolarization of the ventricles (Gacek and Pedrycz 2011;Roberts 1986;Tortora and Grabowski 2002). Incidentally, the typical "lub-dup" heart sounds represent turbulence created by the rapid closure of the cardiac valves.…”

Section: The Autonomic Nervous System and Its Regulation Of The Bodymentioning

confidence: 99%

“…Low-pass filters are utilized to counteract these noises by reducing high-frequency components. However, muscle noise filtering remains a largely unsolved problem (Wei et al 1999;Gacek and Pedrycz 2011). Collectively, the selection of filters and their adjustments bias the (ECG) output data significantly.…”

Section: Overview Of Current Studies Using Physiology Sensors In Engimentioning

confidence: 99%

See 1 more Smart Citation

Capturing emotion reactivity through physiology measurement as a foundation for affective engineering in engineering design science and engineering practices

Balters

Steinert

2015

J Intell Manuf

View full text Add to dashboard Cite

Section: The Autonomic Nervous System and Its Regulation Of The Bodymentioning

confidence: 99%

Section: Overview Of Current Studies Using Physiology Sensors In Engimentioning

confidence: 99%

Capturing emotion reactivity through physiology measurement as a foundation for affective engineering in engineering design science and engineering practices

Balters

Steinert

2015

J Intell Manuf

View full text Add to dashboard Cite

“…The approximated component of ( ) After wavelet decomposition, the WTF is also involved in deleting the wavelet coefficients of the noisy signal whose modulus is below a threshold and reconstructing the denoised signal from the remaining coefficients. To summarize, the conventional wavelet thresholding filtering method includes the following three steps [3,13]:…”

Section: S N mentioning

confidence: 99%

“…However, electromagnetic disturbances such as thermal noise existing in measurement circuits have a significant influence on ECG signals. Thus, the noise reduction of ECG signals is a key requirement prior to pathological feature analysis [3].…”

Section: Introductionmentioning

confidence: 99%

Optimal Base Wavelet Selection for ECG Noise Reduction Using a Comprehensive Entropy Criterion

Tan

Wang

2015

Entropy

View full text Add to dashboard Cite

Abstract:The selection of an appropriate wavelet is an essential issue that should be addressed in the wavelet-based filtering of electrocardiogram (ECG) signals. Since entropy can measure the features of uncertainty associated with the ECG signal, a novel comprehensive entropy criterion Ecom based on multiple criteria related to entropy and energy is proposed in this paper to search for an optimal base wavelet for a specific ECG signal. Taking account of the decomposition capability of wavelets and the similarity in information between the decomposed coefficients and the analyzed signal, the proposed Ecom criterion integrates eight criteria, i.e., energy, entropy, energy-to-entropy ratio, joint entropy, conditional entropy, mutual information, relative entropy, as well as comparison information entropy for optimal wavelet selection. The experimental validation is conducted on the basis of ECG signals of sixteen subjects selected from the MIT-BIH Arrhythmia Database. The Ecom is compared with each of these eight criteria through four filtering performance indexes, i.e., output signal to noise ratio (SNRo), root mean square error (RMSE), percent root mean-square difference (PRD) and correlation coefficients. The filtering results of ninety-six ECG signals contaminated by noise have verified that Ecom has outperformed the other eight criteria in the selection of best base wavelets for ECG signal filtering. The wavelet identified by the Ecom has achieved the best filtering performance than the other comparative criteria. A hypothesis test also validates that SNRo, RMSE, PRD and correlation coefficients of Ecom are significantly different from those of the shape-matched approach ( 0.05, two-sided t-test).

show abstract

“…A typical ECG tracing of the cardiac cycle consists of a P wave, QRS complex, and T wave [2]. The performance of an ECG analysis system depends upon the accurate and reliable detection of the QRS complex, as well as the T and P waves [3].…”

Section: Introductionmentioning

confidence: 99%

Atrial Fibrillation Analysis for Real Time Patient Monitoring

Aliami

Stranieri

Marzbanrad

et al. 2017

Computing in Cardiology Conference (CinC)

View full text Add to dashboard Cite

show abstract

ECG Signal Analysis, Classification, and Interpretation: A Framework of Computational Intelligence

Cited by 45 publications

References 42 publications

Capturing emotion reactivity through physiology measurement as a foundation for affective engineering in engineering design science and engineering practices

Capturing emotion reactivity through physiology measurement as a foundation for affective engineering in engineering design science and engineering practices

Optimal Base Wavelet Selection for ECG Noise Reduction Using a Comprehensive Entropy Criterion

Atrial Fibrillation Analysis for Real Time Patient Monitoring

Contact Info

Product

Resources

About