Modified Variational Mode Decomposition for Power Line Interference Removal in ECG Signals

Mohan, Neethu; S, Sachin Kumar; Poornachandran, Prabaharan; P, Soman K.

doi:10.11591/ijece.v6i1.pp151-159

Cited by 10 publications

(7 citation statements)

References 20 publications

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“…The performance of VMD algorithm is mainly proportional to the selection of two decomposition parameters namely data fidelity constraint ( ) and number of modes ( N ) [23]. The bandwidth parameter, determine the bandwidth of each mode and the number of modes, N defines the distribution of energy among modes [24]. The very small value of these parameters results in sharing of frequency components with near-by modes.…”

Section: Vmd Decompositionmentioning

confidence: 99%

A hybrid method for fundamental heart sound segmentation using group-sparsity denoising and variational mode decomposition

et al. 2019

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Segmentation of fundamental heart sounds-S1 and S2 is important for automated monitoring of cardiac activity including diagnosis of the heart diseases. This paper proposes a novel hybrid method for S1 and S2 heart sound segmentation using group sparsity denoising and variation mode decomposition (VMD) technique. In the proposed method, the measured phonocardiogram (PCG) signals are denoised using group sparsity algorithm by exploiting the group sparse (GS) property of PCG signals. The denoised GS-PCG signals are then decomposed into subsequent modes with specific spectral characteristics using VMD algorithm. The appropriate mode for further processing is selected based on mode central frequencies and mode energy. It is then followed by the extraction of Hilbert envelope (HEnv) and a thresholding on the selected mode to segment S1 and S2 heart sounds. The performance advantage of the proposed method is verified using PCG signals from benchmark databases namely eGeneralMedical, Littmann, Washington, and Michigan. The proposed hybrid algorithm has achieved a sensitivity of 100%, positive predictivity of 98%, accuracy of 98% and detection error rate of 1.5%. The promising results obtained suggest that proposed approach can be considered for automated heart sound segmentation.

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Section: Vmd Decompositionmentioning

confidence: 99%

A hybrid method for fundamental heart sound segmentation using group-sparsity denoising and variational mode decomposition

et al. 2019

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“…Using the plot command of MATLAB, this synchronization index can be shown in form of graphs .If the graph shows increasing pattern, it shows high amount of synchronization between the two signals else the low synchronization. Following is the diagrammatic representation of the proposed algorithm for better visualization and understanding [15][16][17][18][19][20][21][22][23][24][25][26][27] Next step is to find out the recurrence rate and its normalized value. This is done using the following formulas:…”

Section: The Proposed Methodsmentioning

confidence: 99%

A Novel Approach For Detection of Neurological Disorders through Electrical Potential Developed in Brain

Kidwai¹,

Saeed²

2019

IJECE

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<p>This paper talks about the phenomenon of recurrence and using this concept it proposes a novel and a very simple and user friendly method to diagnose the neurological disorders by using the EEG signals.The mathematical concept of recurrence forms the basis for the detection of neurological disorders,and the tool used is MATLAB. Using MATLAB, an algorithm is designed which uses EEG signals as the input and uses the synchronizing patterns of EEG signals to determine various neurological disorders through graphs and recurrence plots</p>

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“…b. Comparison with model based on signal processing and statistical analysis: The various approaches which use signal processing methods generally use the algorithms like KNN algorithm etc [15][16].…”

Section: Methodsmentioning

confidence: 99%

A Novel Approach to Study the Effects of Anesthesia on Respiratory Signals by using the EEG Signals

Kidwai

Saeed

2017

IJ-ICT

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General anesthesia plays a crucial role in many surgical procedures. It is a drug-induced, reversible state characterized by unconsciousness, anti-nociception or analgesia, immobility and amnesia. On rare occasions, however, the patient can remain unconscious longer than intended, or may regain awareness during surgery. There are no precise measures for maintaining the correct dose of anesthetic, and there is currently no fully reliable instrument to monitor depth of anesthesia. Although a number of devices for monitoring brain function or sympathetic output are commercially available, the anesthetist also relies on clinical assessment and experience to judge anesthetic depth. The undesirable consequences of overdose or unintended awareness might in principle be ameliorated by improved control if we could understand better the changes in function that occur during general anesthesia. Coupling functions prescribe the physical rule specifying how the inter-oscillator interactions occur. They determine the possibility of qualitative transitions between the oscillations, e.g. routes into and out of phase synchronization. Their decomposition can describe the functional contribution from each separate subsystem within a single coupling relationship. In this way, coupling functions offer a unique means of describing mechanisms in a unified and mathematically precise way. It is a fast growing field of research, with much recent progress on the theory and especially towards being able to extract and reconstruct the coupling functions between interacting oscillations from data, leading to useful applications in cardio respiratory interactions.<br />In this paper, a novel approach has been proposed for detecting the changes in synchronism of brain signals, taken from EEG machine. During the effect of anesthesia, there are certain changes in the EEG signals. Those signals show changes in their synchronism. This phenomenon of synchronism can be utilized to study the effect of anesthesia on respiratory parameters like respiration rate etc, and hence the quantity of anesthesia can be regulated, and if any problem occurs in breathing during the effect of anesthesia on patient, that can also be monitored

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Modified Variational Mode Decomposition for Power Line Interference Removal in ECG Signals

Cited by 10 publications

References 20 publications

A hybrid method for fundamental heart sound segmentation using group-sparsity denoising and variational mode decomposition

A hybrid method for fundamental heart sound segmentation using group-sparsity denoising and variational mode decomposition

A Novel Approach For Detection of Neurological Disorders through Electrical Potential Developed in Brain

A Novel Approach to Study the Effects of Anesthesia on Respiratory Signals by using the EEG Signals

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