Optimized variational mode decomposition algorithm based on adaptive thresholding method and improved whale optimization algorithm for denoising magnetocardiography signal

Chen, Mingyuan; Cheng, Qiaorui; Feng, Xie; Zhao, Kaiming; Zhou, Yafeng; Xing, Biao; Tang, Sujin; Wang, Ruiqi; Duan, Junping; Wang, Jiayun; Zhang, Binzhen

doi:10.1016/j.bspc.2023.105681

Cited by 10 publications

(3 citation statements)

References 26 publications

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“…Before visualisation, it is important to correct baseline drifts across the signal. The application of a thresholding algorithm using the moving average method has demonstrated applicability for various signals, such as magnetocardiography (MCG) [13]. Open-source signal correction toolkits like Neurokit2 have been widely acclaimed and proven effective for EMG, electrocardiography (ECG) and EEG data [14].…”

Section: The Current State Of Research On Image-to-signal Analysismentioning

confidence: 99%

Deep learning and predictive modelling for generating normalised muscle function parameters from signal images of mandibular electromyography

Farook,

Haq,

Ramees

et al. 2024

Med Biol Eng Comput

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Challenges arise in accessing archived signal outputs due to proprietary software limitations. There is a notable lack of exploration in open-source mandibular EMG signal conversion for continuous access and analysis, hindering tasks such as pattern recognition and predictive modelling for temporomandibular joint complex function. To Develop a workflow to extract normalised signal parameters from images of mandibular muscle EMG and identify optimal clustering methods for quantifying signal intensity and activity durations. A workflow utilising OpenCV, variational encoders and Neurokit2 generated and augmented 866 unique EMG signals from jaw movement exercises. k-means, GMM and DBSCAN were employed for normalisation and cluster-centric signal processing. The workflow was validated with data collected from 66 participants, measuring temporalis, masseter and digastric muscles. DBSCAN (0.35 to 0.54) and GMM (0.09 to 0.24) exhibited lower silhouette scores for mouth opening, anterior protrusion and lateral excursions, while K-means performed best (0.10 to 0.11) for temporalis and masseter muscles during chewing activities. The current study successfully developed a deep learning workflow capable of extracting normalised signal data from EMG images and generating quantifiable parameters for muscle activity duration and general functional intensity. Graphical Abstract

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Section: The Current State Of Research On Image-to-signal Analysismentioning

confidence: 99%

Deep learning and predictive modelling for generating normalised muscle function parameters from signal images of mandibular electromyography

Farook,

Haq,

Ramees

et al. 2024

Med Biol Eng Comput

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“…The energy usage of all nodes is aggregated to calculate the total energy consumption using Eq. (16) are energy-intensive as compared to the fog nodes due to their sheer size. However, fog nodes have limited processing capability, less memory and restricted bandwidth, while cloud nodes have theoretically unlimited resources.…”

Section: ) Energy Consumptionmentioning

confidence: 99%

“…However, despite its numerous strengths, the exploration potential of WOA is limited. Being an exploitation driven meta-heuristic algorithm, it often converges prematurely, providing a solution that is not globally optimal [16]. This limitation also prolongs the convergence rate of WOA.…”

Section: Introductionmentioning

confidence: 99%

Ripple-Induced Whale Optimization Algorithm for Independent Tasks Scheduling on Fog Computing

Ali Khan,

Abdul Aziz

2024

IEEE Access

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Due to the revolution of Internet of Things (IoT), the amount of data generation has been redoubling, leading to higher latency and network traffic. This has resulted in delays in services and increased energy consumption of cloud servers. Fog computing tackles the issues associated with long geographical distance between end-users and cloud servers by extending service provision closer to the network edge, reducing latency and makespan, and optimizing energy consumption during workload execution. Instead of offloading all tasks to the cloud, delay-sensitive tasks are executed at fog nodes, while others are offloaded to the cloud. However, the resources at the fog layer are limited, posing a challenge for task scheduling in fog computing, particularly as a multi-objective optimization problem. Meta-heuristic algorithms have been potent to find an optimal solution for such problems within a reasonable amount of time. The Whale Optimization Algorithm (WOA) is a relatively new meta-heuristic algorithm that has received significant attention from researchers due to its impressive optimization characteristics. However, being an exploitationoriented technique, it falls into local optima due to a lack of generating new solutions over time. Limited exploration capabilities also compromise the diversity of the solution space and prolong convergence time. Therefore, in this study, an enhanced Ripple-induced Whale Optimization Algorithm (RWOA) is proposed, utilizing ripple effects to schedule independent tasks in fog computing. It aims to minimize makespan and energy consumption while maximizing throughput in a fog-cloud infrastructure by improving poor solutions through substantial changes. Extensive simulations are performed to assess the effectiveness of the proposed algorithm. The proposed RWOA outperformed TCaS, HFSGA, MGWO, and WOAmM on two workload datasets: Random and NASA Ames iPSC. The statistical significance of the results is validated by the Friedman test and Wilcoxon Signed-rank test.

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Multi-channel MCG signals filtering method based on multivariate variational mode decomposition

Yang,

Xu,

Pan

et al. 2025

Biomedical Signal Processing and Control

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Optimized variational mode decomposition algorithm based on adaptive thresholding method and improved whale optimization algorithm for denoising magnetocardiography signal

Cited by 10 publications

References 26 publications

Deep learning and predictive modelling for generating normalised muscle function parameters from signal images of mandibular electromyography

Deep learning and predictive modelling for generating normalised muscle function parameters from signal images of mandibular electromyography

Ripple-Induced Whale Optimization Algorithm for Independent Tasks Scheduling on Fog Computing

Multi-channel MCG signals filtering method based on multivariate variational mode decomposition

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