The emperical mode decomposition for ECG signal preprocessing

Tychkov, Alexander Yu.; Alimuradov, Alan K.; Churakov, Pyotr P.

doi:10.1109/dcnair.2019.8875613

Cited by 8 publications

(5 citation statements)

References 9 publications

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“…The following are the main observations that have been made from the literature on ECG signal preprocessing ( (Uwaechia & Ramli, 2021); (Tychkov et al, 2019); (Rahman et al, 2019) b) Filtering from FIR ranges at a very high frequency, which makes it more time consuming and requires large storage.…”

Section: Figure 1: Ecg Signalmentioning

confidence: 99%

Denoising and R-Peak Detection in ECG Signals: A Performance Evaluation

Poonam Chhabra,

Rosy Madaan

2023

J. Adv. Zool.

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An electrocardiogram (ECG) is a continuous electrical signal from the heart that is recorded to understand the activity and condition of the heart. A recorded ECG signal always follows a defined pattern for a normal heart condition. Variation in the normal ECG pattern can be seen in cases of numerous cardiac abnormalities. A recorded ECG is also affected by a number of noises and distortions, resulting in a low SNR. A variation in ECG pattern can lead to incorrect study and improper diagnosis of heart condition. Thus, to perform an efficient analysis, it is necessary to preprocess the ECG waveform. ECG preprocessing requires noise removal and analysis of necessary features needed to study cardiac activity. In this paper, ECG preprocessing is evaluated by using two noise removal techniques, i.e., finite and infinite impulse response. After this, the R-peaks are detected using discrete wavelet transform (DWT), maximal optimal DWT, principal component analysis and independent component analysis. A wavelet transform technique is further proposed using Savitzky-Golay filtering and DWT. The results obtained from the proposed methodology represent the best results compared to those of other methods explicated in this paper.

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Section: Figure 1: Ecg Signalmentioning

confidence: 99%

Denoising and R-Peak Detection in ECG Signals: A Performance Evaluation

Poonam Chhabra,

Rosy Madaan

2023

J. Adv. Zool.

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“…Despite being challenging for preserving important signal information, and adapting to the patient's features, preprocessing has attracted the attention of researchers. Cleaning and transformation are also performed during the preprocessing stage [91]. Other techniques are also used during the preprocessing stage, such as downsampling, resampling and signal normalization [58].…”

Section: Preprocessingmentioning

confidence: 99%

ECG Monitoring Systems: Review, Architecture, Processes, and Key Challenges

Serhani

Kassabi

Ismail

et al. 2020

Sensors

211

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Health monitoring and its related technologies is an attractive research area. The electrocardiogram (ECG) has always been a popular measurement scheme to assess and diagnose cardiovascular diseases (CVDs). The number of ECG monitoring systems in the literature is expanding exponentially. Hence, it is very hard for researchers and healthcare experts to choose, compare, and evaluate systems that serve their needs and fulfill the monitoring requirements. This accentuates the need for a verified reference guiding the design, classification, and analysis of ECG monitoring systems, serving both researchers and professionals in the field. In this paper, we propose a comprehensive, expert-verified taxonomy of ECG monitoring systems and conduct an extensive, systematic review of the literature. This provides evidence-based support for critically understanding ECG monitoring systems’ components, contexts, features, and challenges. Hence, a generic architectural model for ECG monitoring systems is proposed, an extensive analysis of ECG monitoring systems’ value chain is conducted, and a thorough review of the relevant literature, classified against the experts’ taxonomy, is presented, highlighting challenges and current trends. Finally, we identify key challenges and emphasize the importance of smart monitoring systems that leverage new technologies, including deep learning, artificial intelligence (AI), Big Data and Internet of Things (IoT), to provide efficient, cost-aware, and fully connected monitoring systems.

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“…This makes it an uphill task to identify and isolate such low energy level fault signals; however, time-frequency-domain signal processing techniques have strong capabilities in decomposing vibrational signals into several energy levels [24][25][26] and this makes it possible for them to identify and isolate fault signals at various levels. The short-time Fourier transform (STFT), wavelet transform (WT) and the empirical mode decomposition (EMD) are some of the popular techniques for time-frequencydomain feature extraction; however, due to their limitations (STFT is limited by the choice of window function [24], the EMD has a high sensitivity to noise [25] and WT is unreliable for long-range dependencies [26]), the use of MFCCs and their derivatives as reliable features for diagnostics (and prognostics) problems have recently attracted global attention because of their remarkably significant immunity to noise and robustness in extracting underlying nonlinear characteristics from stationary and non-stationary signals [14][15][16]. Figure 1 shows the schematic procedure for extracting MFCCs from a signal; however, the stages summarized below provide the stages for their extraction.…”

Section: Time-frequency-domain Feature Extractionmentioning

confidence: 99%

“…where σ is the standard deviation and X is the mean of X i (i = 1, 2, ..., N) Some studies have tried solving this problem [24] by expanding the nonlinear RBF kernel into its Maclaurin series, and then computing the weight vector w from the series according to the contribution made to classification hyperplane by each feature; however, the high level of assumptions on which its effectiveness depends. High computational costs associated with this approach makes it non-feasible for near real-time industrial applications.…”

Section: Svm-rbf-rfe Algorithm For Fault Isolationmentioning

confidence: 99%

A Cost-Efficient MFCC-Based Fault Detection and Isolation Technology for Electromagnetic Pumps

Akpudo

Hur

2021

Electronics

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Fluid pumps serve critical purposes in hydraulic systems so their failure affects productivity, profitability, safety, etc. The need for proper condition monitoring and health assessment of these pumps cannot be overemphasized and this has resulted in extensive research studies on standard techniques for ensuring optimum fault detection and isolation (FDI) results for these pumps. Interestingly, mechanical vibrational signals reflect operating conditions and by exploring the robust time–frequency-domain feature extraction techniques, the underlying nonlinear characteristics can be captured for reliable fault diagnosis/condition assessment. This study is based on the use of vibrational signals for fault isolation of electromagnetic pumps. From the vibrational signals, Mel frequency cepstral coefficients (MFCCs), the first-order and the second-order differentials were extracted and the salient features selected by a rank-based recursive feature elimination (RFE) of uncorrelated features. The proposed framework was tested and validated on five VSC63A5 electromagnetic pumps at various fault conditions and isolated/classified using the Gaussian kernel SVM (SVM-RBF-RFE). Results show that the proposed feature selection approach is computationally cheaper and significantly improves diagnostics performance. In addition, the proposed framework yields a comparatively better diagnostics results on electromagnetic pumps in comparison with other diagnostics methods, hence a more reliable diagnostics tool for electromagnetic pumps.

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The emperical mode decomposition for ECG signal preprocessing

Cited by 8 publications

References 9 publications

Denoising and R-Peak Detection in ECG Signals: A Performance Evaluation

Denoising and R-Peak Detection in ECG Signals: A Performance Evaluation

ECG Monitoring Systems: Review, Architecture, Processes, and Key Challenges

A Cost-Efficient MFCC-Based Fault Detection and Isolation Technology for Electromagnetic Pumps

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