A hybrid GPFA-EEMD_Fuzzy threshold method for ECG signal de-noising

Thakran, Snekha

doi:10.3233/jifs-191518

Cited by 6 publications

(3 citation statements)

References 10 publications

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“…As an effective evaluation method of influencing factors, the fuzzy threshold method can judge the critical influencing factors by comparing the calculated fuzzy recognition value with the actual set threshold [25]. This method is suitable for complex and fuzzy evaluation criteria and can combine experience with objective facts to make the evaluation results more accurate.…”

Section: Fuzzy Threshold Methodsmentioning

confidence: 99%

Sustainable Investment Forecasting of Power Grids Based on the Deep Restricted Boltzmann Machine Optimized by the Lion Algorithm

Wang¹,

Yang²,

Pu³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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This paper proposes a new power grid investment prediction model based on the deep restricted Boltzmann machine (DRBM) optimized by the Lion algorithm (LA). Firstly, two factors including transmission and distribution price reform (TDPR) and 5G station construction were comprehensively incorporated into the consideration of influencing factors, and the fuzzy threshold method was used to screen out critical influencing factors. Then, the LA was used to optimize the parameters of the DRBM model to improve the model's prediction accuracy, and the model was trained with the selected influencing factors and investment. Finally, the LA-DRBM model was used to predict the investment of a power grid enterprise, and the final prediction result was obtained by modifying the initial result with the modifying factors. The LA-DRBM model compensates for the deficiency of the single model, and greatly improves the investment prediction accuracy of the power grid. In this study, a power grid enterprise was taken as an example to carry out an empirical analysis to prove the validity of the model, and a comparison with the RBM, support vector machine (SVM), back propagation neural network (BPNN), and regression model was conducted to verify the superiority of the model. The conclusion indicates that the proposed model has a strong generalization ability and good robustness, is able to abstract the combination of low-level features into high-level features, and can improve the efficiency of the model's calculations for investment prediction of power grid enterprises.

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Section: Fuzzy Threshold Methodsmentioning

confidence: 99%

Sustainable Investment Forecasting of Power Grids Based on the Deep Restricted Boltzmann Machine Optimized by the Lion Algorithm

Wang¹,

Yang²,

Pu³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

show abstract

“…Kapoor and Birok (2020) [59] Takran (2020) [60] Overall noise EEMD Herein, the noise removal procedure follows two phases. Te IMF's were generated from the noisy signal using the EEMD method in the frst phase.…”

Section: Rachakonda and Maheshmentioning

confidence: 99%

A Review on the Applications of Time‐Frequency Methods in ECG Analysis

Pradhan

Neelappu

Jayaraman

et al. 2023

Journal of Healthcare Engineering

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The joint time-frequency analysis method represents a signal in both time and frequency. Thus, it provides more information compared to other one-dimensional methods. Several researchers recently used time-frequency methods such as the wavelet transform, short-time Fourier transform, empirical mode decomposition and reported impressive results in various electrophysiological studies. The current review provides comprehensive knowledge about different time-frequency methods and their applications in various ECG-based analyses. Typical applications include ECG signal denoising, arrhythmia detection, sleep apnea detection, biometric identification, emotion detection, and driver drowsiness detection. The paper also discusses the limitations of these methods. The review will form a reference for future researchers willing to conduct research in the same field.

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“…To overcome this limitation, methods based on EMD are used, the signal is disintegrated into a sequence of intrinsic mode functions (IMFs), and noisy IMFs are removed, but this technique can remove some useful information when eliminating the noisy IMFs. EEMD is used to overcome this problem by removing the mode-mixing [32].…”

Section: B Related Work Based Ecg Denoisingmentioning

confidence: 99%

Speeding up an Adaptive Filter based ECG Signal Pre-processing on Embedded Architectures

Mejhoudi¹,

Latif²,

Saddik³

et al. 2021

IJACSA

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Medical applications increasingly require complex calculations with constraints of accelerated processing time. These applications are therefore oriented towards the integration of high-performance embedded architectures. In this context, the detection of cardiac abnormalities is a task that remains a high priority in emergency medicine. ECG analysis is a complex task that requires significant computing time since a large amount of information must be analyzed in parallel with high frequencies. Real-time processing is the biggest challenge for researchers, when talking about applications that require time constraints like that of cardiac activity monitoring. This work evaluates the Adaptive Dual Threshold Filter (ADTF) algorithm dedicated to ECG signal filtering using various embedded architectures: A Raspberry 3B+ and Odroid XU4. The implementation has been based on C/C++ and OpenMP to exploit the parallelism in the used architectures. The evaluation was validated using several ECG signals proposed in MIT-BIH Arrhythmia database with a sampling frequency of 360 Hz. Based on an algorithmic complexity study and a parallelization of the functional blocks which present significant workloads, the evaluation results show a mean execution time of 7.5 ms on the Raspberry 3B+ and 0.34 ms on the Odroid XU4. With an efficient parallelization on the Odroid XU4 architecture, real-time performance can be achieved.

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A hybrid GPFA-EEMD_Fuzzy threshold method for ECG signal de-noising

Cited by 6 publications

References 10 publications

Sustainable Investment Forecasting of Power Grids Based on the Deep Restricted Boltzmann Machine Optimized by the Lion Algorithm

Sustainable Investment Forecasting of Power Grids Based on the Deep Restricted Boltzmann Machine Optimized by the Lion Algorithm

A Review on the Applications of Time‐Frequency Methods in ECG Analysis

Speeding up an Adaptive Filter based ECG Signal Pre-processing on Embedded Architectures

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