TLBO Optimization Algorithm Based-Type2 Fuzzy Adaptive Filter for ECG Signals Denoising

Ouali, Mohammed Assam; Ghanai, Mouna; Chafaa, Kheireddine

doi:10.18280/ts.370401

Cited by 9 publications

(11 citation statements)

References 47 publications

(100 reference statements)

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“…Inspired by visual selectivity, among the four parameters of the Gabor function, frequency and orientation have greater contributions and play the absolute role in the Gabor function. Perform Lipschitz [25][26][27][28][29][30]…”

Section: Improvement 33 Lipschitz-orthogonal-pruning-methods (Lopm Algorithm)mentioning

confidence: 99%

Slow Feature Extraction Algorithm Based on Visual Selection Consistency Continuity and Its Application

Yang¹,

Zhao²,

Su³

et al. 2021

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The conventional slow feature analysis (SFA) algorithm has no support of computational theory of vision for primates, nor does it have the ability to learn the global features with visual selection consistency continuity. And what is more, the algorithm is highly complex. Based on this, Slow Feature Extraction Algorithm Based on Visual selection consistency continuity and Its Application was proposed. Inspired by the visual selection consistency continuity theory for primates, this paper replaced the principal component analysis (PCA) method of the conventional SFA algorithm with the myTICA method, extracted the Gabor basis functions of natural images, initialized the basis function family; it used the feature basis expansion algorithm based on visual selection consistency continuity (the VSCC_FBEA algorithm) to replace the polynomial expansion method in the original SFA algorithm to generates the Gabor basis functions of features with long and short-term visual selectivity in the family of basis functions, which solved the drawbacks of the polynomial prediction algorithm; it also designed the Lipschitz consistency constraint, and proposed the Lipschitz-Orthogonal-Pruning-Method (LOPM algorithm) to optimize the basis function family into an over-complete family of basis functions. In addition, this paper used the feature expression method based on visual invariance theory (visual invariance theory -FEM) to establish the set of features of natural images with visual selection consistency continuity. Subsequently, it adopted three error evaluation methods and mySFA classification method to evaluate the proposed algorithm. According to the experimental results, the proposed algorithm showed good prediction performance with respect to the LSVRC2012 data set; compared with the SFA, GSFA, TICA, myICA and mySFA algorithms, the proposed algorithm is correct and feasible; when the classification threshold of the algorithm was set at 8.0, the recognition rate of the proposed algorithm reached 99.66%, and neither of the false recognition rate and the false rejection rate was higher than 0.33%. The proposed algorithm has good performance in prediction and classification, and also shows good anti-noise capacity under limited noise conditions.

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Section: Improvement 33 Lipschitz-orthogonal-pruning-methods (Lopm Algorithm)mentioning

confidence: 99%

Slow Feature Extraction Algorithm Based on Visual Selection Consistency Continuity and Its Application

Yang¹,

Zhao²,

Su³

et al. 2021

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show abstract

“…The diagnosis of heart diseases highly depends on the electrocardiogram (ECG) [1,2]. However, using computer programs to automatically extract relevant and reliable information from ECG is challenging.…”

Section: Introductionmentioning

confidence: 99%

“…However, using computer programs to automatically extract relevant and reliable information from ECG is challenging. There exist distinct electrical depolarization-repolarization patterns in each heartbeat of the cardiac cycle which lead to different heart's electrical activities [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

“…It was reported by the World Health Organization (WHO) that 31% of the worldwide human deaths in 2016 was caused by cardiovascular disease (CVD) [2] while heart attack caused 85% of these deaths. Traditionally, the medical history and clinical examinations of a patient are used for diagnosing CVD paradigm [1,[5][6][7].…”

Section: Introductionmentioning

confidence: 99%

“…Empirical mode decomposition [20] and wavelet transform [2,[21][22][23] on ECG data are superior tools used in the preprocessing to de-noise the ECG signals with high accuracy.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Hybrid Lightweight 1D CNN-LSTM Architecture for Automated ECG Beat-Wise Classification

Obeidat¹,

Alqudah²

2021

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In this paper we have utilized a hybrid lightweight 1D deep learning model that combines convolutional neural network (CNN) and long short-term memory (LSTM) methods for accurate, fast, and automated beat-wise ECG classification. The CNN and LSTM models were designed separately to compare with the hybrid CNN-LSTM model in terms of accuracy, number of parameters, and the time required for classification. The hybrid CNN-LSTM system provides an automated deep feature extraction and classification for six ECG beats classes including Normal Sinus Rhythm (NSR), atrial fibrillation (AFIB), atrial flutter (AFL), atrial premature beat (APB), left bundle branch block (LBBB), and right bundle branch block (RBBB). The hybrid model uses the CNN blocks for deep feature extraction and selection from the ECG beat. While the LSTM layer will learn how to extract contextual time information. The results show that the proposed hybrid CNN-LSTM model achieves high accuracy and sensitivity of 98.22% and 98.23% respectively. This model is light and fast in classifying ECG beats and superior to other previously used models which makes it very suitable for embedded systems designs that can be used in clinical applications for monitoring heart diseases in faster and more efficient manner.

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Comprehensive survey on ECG signal denoising, feature extraction and classification methods for heart disease diagnosis

Velusamy,

Thangavel,

Rahman

2024

INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING &Amp; COMMUNICATION ENGINEERING SYSTEMS: SPACES-2021

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TLBO Optimization Algorithm Based-Type2 Fuzzy Adaptive Filter for ECG Signals Denoising

Cited by 9 publications

References 47 publications

Slow Feature Extraction Algorithm Based on Visual Selection Consistency Continuity and Its Application

Slow Feature Extraction Algorithm Based on Visual Selection Consistency Continuity and Its Application

A Hybrid Lightweight 1D CNN-LSTM Architecture for Automated ECG Beat-Wise Classification

Comprehensive survey on ECG signal denoising, feature extraction and classification methods for heart disease diagnosis

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