Comparison Among ECG Filtering Methods for Non-linear Noise

Patwary, Adnan Basir; Chowdhury, Md. Thouhidul Islam; Mamun, Nursadul

doi:10.1109/icaeee.2018.8642991

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…A low pass filter can separate the noise after chopping and unnecessary filter noise in high frequency since the ECG signal's information lies mainly in the low-frequency domain. However, the filter's cutoff frequency should be chosen carefully, which impacts the QRS complex wave and may lead to distortion [17].…”

Section: Low-pass Filtermentioning

confidence: 99%

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Highly Efficient Low Noise Solutions in ECG Signals

Liu

Yuan

2022

J. Phys.: Conf. Ser.

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This paper aims at helping researchers develop low-noise ECG monitors and analyze existing solutions for eliminating noise in ECG signals. A brief introduction of ECG signal, main kinds of noise in ECG signal and a summary of different kinds of low noise solutions is given. The components of the ECG signal and the origin of noise in the ECG signal are analyzed first. In order to find optimized solutions for eliminating the noise, highly effective solutions are listed and analyzed. Solutions are divided into three main directions, which are circuit design, signal processing and machine learning. Specific practical examples from former research and analyses for each direction help the reader understand and select different ways of eliminating noise in ECG signals. After introducing three directions, a summary and further improvement are given to help readers have a horizontal comparison between solutions.

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Section: Low-pass Filtermentioning

confidence: 99%

“…Using certain formulated patterns, with data fed in, can solve almost all kinds of problems related to detection and recognition, which are virtues of machine learning [25]. Of note, machine learning was used in filter selection and noise detection to achieve low-noise ECG signals [17,25].…”

Section: Machine Learningmentioning

confidence: 99%

Highly Efficient Low Noise Solutions in ECG Signals

Liu

Yuan

2022

J. Phys.: Conf. Ser.

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“…The placement of the electrode patches is crucial for measuring ECG signals. The consistent placement of electrode patches is essential to ensuring consistency in ECG signals for individual identification [11][12][13][14]. However, achieving consistent placement in each data-acquisition attempt is challenging.…”

Section: Introductionmentioning

confidence: 99%

Semi-Supervised Domain Adaptation for Individual Identification from Electrocardiogram Signals

Byeon,

Kwak

2023

Applied Sciences

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When acquiring electrocardiogram (ECG) signals, the placement of electrode patches is crucial for acquiring electrocardiographic signals. Constant displacement positions are essential for ensuring the consistency of the ECG signal when used for individual identification. However, achieving constant placement of ECG electrode patches in every trial for data acquisition is challenging. This is because different individuals may attach patches, and even when the same person attaches them, it may be difficult to specify the exact position. Therefore, gathering ECG data from various locations is necessary. However, this process requires a substantial amount of labor and time, owing to the requirement for multiple attempts. Nonetheless, persisting with these efforts enables the endurance of some ECG differences. To reduce labor and time, we propose a semi-supervised domain adaptation for individual identification using ECG signals. The method operates with a full set of original ECG signals and a small set of ECG signals from different placements to account for the differences between the signals in the generative adversarial network (CycleGAN). Specifically, to train the CycleGAN, the ECG signals were transformed into time–frequency representations, and the trained generator was used to generate ECG signals to expand the small set of ECG signals from different placements. Subsequently, both the original and generated signals were used to train the classifier for individual identification. This scenario can also be applied to the classification of ECG signals from different sensors. The PTB-ECG dataset was used for this experiment. We found that the proposed method showed higher accuracy than when only the original ECG signals were used for the training classifier.

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