A Novel Cyclic Convolution Based Regularization Method for Power-Line Interference Removal in ECG Signal

Sujadevi, V. G.; Soman, K P; Kumar, Sachin; Mohan, Neethu

doi:10.1007/978-3-319-67934-1_11

Cited by 1 publication

(1 citation statement)

References 17 publications

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“…ECG signal Sujadevi et al (2017) has presented the ECG signals from PLI cancellation based on the circular convolution approach. Subsequently the future method involves FFT process; it can produce the prototype fabrications in less computation intensive for the sensor hardware in standalone and wearable types.…”

Section: Pli Cancellation Inmentioning

confidence: 99%

PLI cancellation in ECG signal using intrinsic time scale decomposition with adaptive gain control

Bharadwaj

Chennupati

2021

JEDT

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Purpose The purpose of this manuscript is to detect heart fault using Electrocardiogram. Mutually low and high frequency noises such as electromyography (EMG) and power line interference (PLI) degrades the performance of ECG signals. Design/methodology/approach The ECG record depicts the procedural electrical movement of the heart, which is non-invasive foot age obtained by placing surface electrodes on designated locations of the patient’s skin. The main concept of this manuscript is to present a novel filtering method to cancel the unwanted noises in ECG signal. Here, intrinsic time scale decomposition (ITD) is introduced to suppress the effect of PLI from ECG signals. Findings In the existing ITD, the gain control parameter is a constant value; however, in this paper it is an adaptive feature that varies according to certain constraints. Simulation outcomes show that the proposed method effectively reduces the effect of PLI and quantitatively express the effectiveness with different evaluation metrics. Originality/value The results found by the proposed method are compared with Fourier decomposition technique and eigen value decomposition methods (EDM) to validate the effectiveness of the proposed method.

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Section: Pli Cancellation Inmentioning

confidence: 99%