Application of a Reduced Band-pass Filter in the Extraction of Power-line Interference from ECG Signals

Mihov, Georgy; Badarov, Dimiter

doi:10.1109/et50336.2020.9238202

Cited by 5 publications

(2 citation statements)

References 11 publications

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“…Removing noises is particularly important in applications that require using biosignals and must be performed during the preprocessing step. Various digital filtering methods are used, such as a band-pass filter [25], median filter [26], and moving average filter [27], which are useful and easy tools for removing noises recorded in ECGs including interference of power cables or electrodes. However, removing noises by applying only digital filters to original signals cannot effectively remove outlier signals of which the morphological nature of signals has been damaged by motion artifacts, as shown in Figure 2.…”

Section: A Outlier Signal Removal Based On the Rr Interval Of Ecgsmentioning

confidence: 99%

Stress Classification Using ECGs Based on a Multi-Dimensional Feature Fusion of LSTM and Xception

Song,

Kim,

Kim

et al. 2024

IEEE Access

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In the information age, people are increasingly being exposed to stress as societies are experiencing sudden changes fueled by advancements in cutting-edge scientific technology and the Information Technology (IT) industry. Consequently, research has been actively conducted on stress classification to mitigate psychological and physical diseases caused by constantly feeling stressed. Specifically, the number of studies examining electrocardiograms (ECGs), which record biosignals that provide insight into the response level of the body's autonomic nervous system, has increased. However, previous studies on stress classification based on ECG used only one-dimensional feature data, thus entailing difficulties in analyzing the data more closely and comprehensively owing to bias toward a specific aspect. Therefore, to overcome the limitations of conventional stress classification based on ECGs, this paper developed a stress classification method based on multi-dimensional feature fusion of LSTM and Xception using ECGs from which outliers have been removed. Experimental results showed that applying multidimensional feature fusion of the weighted average method using ECG data with outlier signals removed resulted in a stress classification of 99.51%, a 1.25% improvement from previous studies which used only one-dimensional feature data of ECGs, thus highlighting the excellent performance of the proposed stress classification method using ECG based on multi-dimensional feature fusion of LSTM and Xception.

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Section: A Outlier Signal Removal Based On the Rr Interval Of Ecgsmentioning

confidence: 99%

Stress Classification Using ECGs Based on a Multi-Dimensional Feature Fusion of LSTM and Xception

Song,

Kim,

Kim

et al. 2024

IEEE Access

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“…Accurate medical equipment is also called electrocardiogram (ECG) machine using CVC analysis. The presence of noise can be controlled by electronically receiving the source of the ECG signal (Mihov and Badarov, 2020; Prashar et al , 2021). The ECG signal in noise is one of the important and unwanted sources for wrong elucidation near definite answers.…”

Section: Introductionmentioning

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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Filters for Electrocardiogram Signal Processing: A Review

Kandić,

Pokvić

2024

IFMBE Proceedings

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Application of a Reduced Band-pass Filter in the Extraction of Power-line Interference from ECG Signals

Cited by 5 publications

References 11 publications

Stress Classification Using ECGs Based on a Multi-Dimensional Feature Fusion of LSTM and Xception

Stress Classification Using ECGs Based on a Multi-Dimensional Feature Fusion of LSTM and Xception

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

Filters for Electrocardiogram Signal Processing: A Review

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