A new multi-stage combined kernel filtering approach for ECG noise removal

Tayel, Mazhar B.; Eltrass, Ahmed S.; Ammar, Abeer I.

doi:10.1016/j.jelectrocard.2017.10.009

Cited by 25 publications

(12 citation statements)

References 20 publications

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“…Note that ECG signals are frequently contaminated with several artifacts and noise sources which affect the diagnosis efficiency [55,56]. In this work, an automated approach based on adaptive filtering [57] is employed to suppress all unwanted artifact components from the input raw ECG data, while keeping all essential characteristics of the ECG signal. After eliminating all unwanted noises and artifacts, each filtered ECG record is partitioned into 6 segments each with a particular length of 10,000 samples (78 s), yielding a total number of 972 ECG segments.…”

Section: Ecg Data Acquisition and Preprocessingmentioning

confidence: 99%

“…For automated ECG classification framework, either the artifacts are suppressed with an automatic technique or the system should be evaluated on unprocessed data. In the current study, an automated adaptive filter method [57] was utilized to suppress all unwanted artifacts from the raw ECG signal. Also, some studies reported very high diagnosis results without any cross-validation techniques (classification results without standard deviation values).…”

Section: Overall Classification Performancementioning

confidence: 99%

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Automated ECG multi-class classification system based on combining deep learning features with HRV and ECG measures

Eltrass

Tayel

Ammar

2022

Neural Comput & Applic

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Electrocardiogram (ECG) serves as the gold standard for noninvasive diagnosis of several types of heart disorders. In this study, a novel hybrid approach of deep neural network combined with linear and nonlinear features extracted from ECG and heart rate variability (HRV) is proposed for ECG multi-class classification. The proposed system enhances the ECG diagnosis performance by combining optimized deep learning features with an effective aggregation of ECG features and HRV measures using chaos theory and fragmentation analysis. The constant-Q non-stationary Gabor transform technique is employed to convert the 1-D ECG signal into 2-D image which is sent to a pre-trained convolutional neural network structure, called AlexNet. The pair-wise feature proximity algorithm is employed to select the optimal features from the AlexNet output feature vector to be concatenated with the ECG and HRV measures. The concatenated features are sent to different types of classifiers to distinguish three distinct subjects, namely congestive heart failure, arrhythmia, and normal sinus rhythm (NSR). The results reveal that the linear discriminant analysis classifier has the highest accuracy compared to the other classifiers. The proposed system is investigated with real ECG data taken from well-known databases, and the experimental results show that the proposed diagnosis system outperforms other recent state-of-the-art systems in terms of accuracy 98.75%, specificity 99.00%, sensitivity of 98.18%, and computational time 0.15 s. This demonstrates that the proposed system can be used to assist cardiologists in enhancing the accuracy of ECG diagnosis in real-time clinical setting.

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Section: Ecg Data Acquisition and Preprocessingmentioning

confidence: 99%

Section: Overall Classification Performancementioning

confidence: 99%

Automated ECG multi-class classification system based on combining deep learning features with HRV and ECG measures

Eltrass

Tayel

Ammar

2022

Neural Comput & Applic

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“…However, it is difficult to capture the local regeneration phenomena due to high nonlinearity. Recently, a nonlinear kernel-based recursive least square tracker (KRLST) was utilized to track the highly nonlinear signal of electromyogram ( Bakshi et al., 2018 ) and electrocardiogram ( Tayel et al., 2018 ). Therefore, it is meaningful to check the accuracy of KRLST for battery state estimation and prediction.…”

Section: Introductionmentioning

confidence: 99%

Kernel recursive least square tracker and long-short term memory ensemble based battery health prognostic model

et al. 2021

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Summary A data-driven approach is developed to predict the future capacity of lithium-ion batteries (LIBs) in this work. The empirical mode decomposition (EMD), kernel recursive least square tracker (KRLST), and long short-term memory (LSTM) are used to derive the proposed approach. First, the LIB capacity data is split into local regeneration and monotonic global degradation using the EMD approach. Next, the KRLST is used to track the decomposed intrinsic mode functions, and the residual signal is predicted using the LSTM sub-model. Finally, all the predicted intrinsic mode functions and the residual are ensembled to get the future capacity. The experimental and comparative analysis validates the high accuracy (RMSE of 0.00103) of the proposed ensemble approach compared to Gaussian process regression and LSTM fused model. Furthermore, two times lesser error than other fused models makes this approach an efficient tool for battery health prognostics.

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“…[ 3 ] However, the most widely recognized noises are PLI, baseline wandering, and motion artifacts. [ 4 ] Thus, for accurate and reliable analysis, these noises should be removed from the corrupted signal. The techniques to denoise the ECG signal can be separated into three classifications: (1) frequency domain, (2) spatiotemporal technique, and (3) statistical method.…”

Section: Introductionmentioning

confidence: 99%

Electrocardiogram Signals Denoising Using Improved Variational Mode Decomposition

Malhotra

Sandhu

2021

Journal of Medical Signals &Amp; Sensors

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Background: Electrocardiogram (ECG) plays a vital role in the analysis of heart activity. It can be used to analyze the different heart diseases and mental stress assessment also. Various noises, such as baseline wandering, muscle artifacts and power line interface disturbs the information within the ECG signal. To acquire correct information from ECG signal, these noises should be removed. Methods: In the proposed work, the improved variational mode decomposition (IVMD) method for the removal of noise in ECG signals is used. In the proposed method, the weighted signal amplitude integrated over the timeframe of the ECG signal varies the window size during decomposition. Raw ECG data are extracted from 10 subjects and ECG data are also taken from the MIT BIH database for the proposed method. Results: The performance comparison of traditional variational mode decomposition (VMD) and the proposed technique is also calculated using mean square error, percentage root mean square difference, signal to noise ratio and correlation coefficient. The extracted highest signal to noise ratio (SNR) value of acquired ECG signals using traditional VMD is 42db whereas highest value of signal to noise ratio (SNR) using improved VMD (IVMD) is 83db. Conclusion: The proposed IVMD technique represented better performance than traditional VMD for denoising of ECG signals.

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A new multi-stage combined kernel filtering approach for ECG noise removal

Cited by 25 publications

References 20 publications

Automated ECG multi-class classification system based on combining deep learning features with HRV and ECG measures

Automated ECG multi-class classification system based on combining deep learning features with HRV and ECG measures

Kernel recursive least square tracker and long-short term memory ensemble based battery health prognostic model

Electrocardiogram Signals Denoising Using Improved Variational Mode Decomposition

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