Grasshopper optimization algorithm based improved variational mode decomposition technique for muscle artifact removal in ECG using dynamic time warping

Malghan, Pavan G; Hota, Malaya Kumar

doi:10.1016/j.bspc.2021.103437

Cited by 15 publications

(7 citation statements)

References 31 publications

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“…The ECG signal should be denoised to remove the muscular artifacts and power line interference [ 24 ]. Therefore, a low-pass finite impulse response (FIR) filter is designed by using the Parks-McClellan algorithm.…”

Section: Methodsmentioning

confidence: 99%

ECG based apnea detection by multirate processing hybrid of wavelet-empirical decomposition Hjorth features extraction and neural networks

Khandelwal,

Salankar,

Mian Qaisar

et al. 2023

PLoS ONE

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Sleep Apnea (SA) can cause health complications including heart stroke and neurological disorders. The Polysomnography (PSG) test can detect the severity of sleep disturbance. However, it is expensive and requires a dedicated sleep laboratory and expertise to examine the patients. Therefore, it is not available to a large population in developing countries. This leads to the development of cost-effective and automated patient examination methods for the detection of sleep apnea. This study suggests an approach of using the ECG signals to categorize sleep apnea. In this work, we have devised an original technique of feature space designing by intelligently hybridizing the multirate processing, a mix of wavelet-empirical mode decomposition (W-EMD), modes-based Hjorth features extraction, and Adam-based optimized Multilayer perceptron neural network (MLPNN) for automated categorization of apnea. A publicly available ECG dataset is used for evaluating the performance of the suggested approach. Experiments are performed for four different sub-bands of the considered ECG signals. For each selected sub-band, five "Intrinsic Mode Functions" (IMFs) are extracted. Onward, three Hjorth features: complexity, activity, and mobility are mined from each IMF. In this way, four feature sets are formed based on wavelet-driven selected sub-bands. The performance of optimized MLPNN, for the apnea categorization, is compared for each feature set. Five different evaluation parameters are used to assess the performance. For the same dataset, a systematic comparison with current state-of-the-artwork has been done. Results have shown a classification accuracy of 98.12%.

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Section: Methodsmentioning

confidence: 99%

ECG based apnea detection by multirate processing hybrid of wavelet-empirical decomposition Hjorth features extraction and neural networks

Khandelwal,

Salankar,

Mian Qaisar

et al. 2023

PLoS ONE

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“…It decomposes the signal into multiple intrinsic mode functions with well-defined instantaneous frequencies, and enables estimating the HR data from the distorted signals with the decomposed components [93]. Other techniques such as the short time Fourier transform (STFT) [133], and the variational mode decomposition (VMD) [134] has also been used for MA removal.…”

Section: Uncertainties In Ppg Measurementmentioning

confidence: 99%

Uncertainties in the Analysis of Heart Rate Variability: A Systematic Review

Lü

Zhu

Morelli

et al. 2024

IEEE Rev. Biomed. Eng.

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Heart rate variability (HRV) is an important metric with a variety of applications in clinical situations such as cardiovascular diseases, diabetes mellitus, and mental health. HRV data can be potentially obtained from electrocardiography and photoplethysmography signals, then computational techniques such as signal filtering and data segmentation are used to process the sampled data for calculating HRV measures. However, uncertainties arising from data acquisition, computational models, and physiological factors can lead to degraded signal quality and affect HRV analysis. Therefore, it is crucial to address these uncertainties and develop advanced models for HRV analysis. Although several reviews of HRV analysis exist, they primarily focus on clinical applications, trends in HRV methods, or specific aspects of uncertainties such as measurement noise. This paper provides a comprehensive review of uncertainties in HRV analysis, quantifies their impacts, and outlines potential solutions. To the best of our knowledge, this is the first study that presents a holistic review of uncertainties in HRV methods and quantifies their impacts on HRV measures from an engineer's perspective. This review is essential for developing robust and reliable models, and could serve as a valuable future reference in the field, particularly for dealing with uncertainties in HRV analysis.

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“…Te results suggested that the inclusion of the SWT method along with EMD and EEMD has reduced the noise up to 9 dB. Malghan and Kumar Hota (2021) [73] Muscle artefact VMD Te study proposes a unique fltering technique based on the grasshopper optimization algorithm (Goa) based VMD method that uses the dynamic time warping (DTW) distance concept. Te method was also compared with the existing methods and showed better performance in eliminating the muscle artifacts.…”

Section: Dwivedi Et Al (2021) [67]mentioning

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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Grasshopper optimization algorithm based improved variational mode decomposition technique for muscle artifact removal in ECG using dynamic time warping

Cited by 15 publications

References 31 publications

ECG based apnea detection by multirate processing hybrid of wavelet-empirical decomposition Hjorth features extraction and neural networks

ECG based apnea detection by multirate processing hybrid of wavelet-empirical decomposition Hjorth features extraction and neural networks

Uncertainties in the Analysis of Heart Rate Variability: A Systematic Review

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

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