Reduction in the Motion Artifacts in Noncontact ECG Measurements Using a Novel Designed Electrode Structure

Ding, Jianwen; Tang, Yue; Chang, Ronghui; Liu, Yu; Zhang, Limin; Yan, Feng

doi:10.3390/s23020956

Cited by 12 publications

(4 citation statements)

References 28 publications

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“…The signal acquisition ICs in these sensors are designed to amplify the captured signals, providing a gain of around 32 dB and a bandwidth of 370 Hz [26]. Moreover, advancements in electrode structures have been made to suppress motion artifacts, thereby maintaining the stability of the signal quality during non-contact ECG acquisition [27]. It's worth noting that the energy efficiency and transmission delay are also critical factors in the operation of these sensors [28].…”

Section: Electrocardiogram (Ecg)mentioning

confidence: 99%

A Review of Intelligent Garment System for Bioelectric Monitoring During Long-Lasting Intensive Sports

Shen,

Tao,

Koncar

et al. 2023

IEEE Access

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Bioelectric signals are significant indicators of the state of health of the human body, especially in sports monitoring, where athletes' fatigue state and performance need to be monitored in realtime to develop a proper training plan. Due to the characteristics of sports, it is difficult to obtain the dynamic bioelectrical signals of the human body during exercise. This paper provides a comprehensive overview of the current knowledge on Intelligent Garment Systems (IGS) for long-lasting bioelectric monitoring in sports. This review includes a detailed examination of human bioelectric signals, focusing on ECG, EMG, and GSR signals and their applications in intelligent wearable technologies. The definition and development history of IGS is also discussed, along with a review of the primary research components of IGS, including dry textile electrodes, methods for connecting sensors to IGS, and processing methods for bioelectric signals. The paper concludes by highlighting the current challenges faced by IGS in terms of real-time dynamic monitoring and connection problems and outlining the future directions for this field, including the need for further advancements in bioelectric signal processing and analysis, the development of new materials and connection technologies, and the integration of artificial intelligence and machine learning into IGS.

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Section: Electrocardiogram (Ecg)mentioning

confidence: 99%

A Review of Intelligent Garment System for Bioelectric Monitoring During Long-Lasting Intensive Sports

Shen,

Tao,

Koncar

et al. 2023

IEEE Access

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“…However, this study does not provide a comprehensive literature review of existing motion artifact reduction methods, and does not compare the proposed method with other state-of-the-art methods. Jianwen Ding et al, designed a new ECG electrode structure that can measure ECG signals and reference signals simultaneously [17]. A reference signal is used to reduce motion artifacts originating from accuracy and variations in the skin-electrode interface.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Digital Filter Orders in Single Lead Electrocardiographic Design for Reducing Interference Motion Artifact

Maghfiroh,

Setiawan,

Mujahid

2023

j.teknokes

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Developed a new method to overcome motion artifacts in Electrocardiogram (ECG) signals, which often interfere with accurate clinical analysis. Motion artifacts, such as body movements, can cause significant distortions in the ECG signal, resulting in incorrect interpretation and affecting medical diagnosis. The main objective of this research is to design and implement an infinite impulse response (IIR) filter with a predetermined sequence, namely orders 2, 4, 6, and 8 to reduce motion artifacts in the ECG signal. We aim to improve ECG signal quality by preserving important ECG signal information and reducing noise caused by motion artifacts. This research contributes to developing more precise and reliable ECG signal processing techniques. The proposed method provides an effective approach to handling motion artifacts, enabling more accurate and reliable ECG interpretation by medical professionals. We used an ECG simulator that provides body movement simulation as a basis for experiments. The detected ECG signal is processed with a predetermined order IIR filter. We compare the filtered signal to the original signal to measure the effectiveness of reducing motion artifacts. Experimental results show that the applied IIR filter efficiently reduces motion artifacts in the ECG signal. The SNR assessment showed a significant improvement, proving the success of this method in maintaining ECG signal quality. The result is that in the 2nd order, the SNR value is 22.25 dB, in the 4th order the SNR value is 22.75 dB, in the 6th order the SNR value is 22.99 dB, in the 6th order the SNR value is 22.99 dB. 8 obtained an SNR value of 23dB. This study successfully demonstrated that using IIR filters in a specified order effectively reduces motion artifacts in the ECG signal, increases SNR, and maintains the integrity of clinical information in the ECG signal. The implications of this research extend to medical technology development and clinical applications, providing a strong foundation for continued research in more efficient and reliable ECG signal processing.

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“…Furthermore, each electrode is also affected by other biomedical signal components during signal acquisition, such as electromyogram, electroencephalogram, and Electrooculogram..., especially the motion artifacts that occurs with voluntary or involuntary patient movement during ECG recording. Removal algorithm (QRSMR) [9]; Stationary Wavelet Movement Artifact Reduction (SWMAR); Normalized Least Mean Square Adaptive Filter technique (NLMSAF) [10]; moving average filtering, and wavelet transform have been used to reduce the motion ECG artefact [11,12]; removing such ECG artifacts from local field potentials (LFPs) recorded by a sensing-enabled neurostimulato [13]; ECG Artifact Removal from Single-Channel Surface EMG Using Fully Convolutional Networks [14], Channel-Wise Average Pooling and 1D Pixel-Shuffle Denoising Autoencoder for Electrode Motion Artifact Removal in ECG [15], Removing Cardiac Artifacts From Single-Channel Respiratory Electromyograms [16].…”

mentioning

confidence: 99%

“…Figure 1. Amplitude-frequency distribution graph in ECG recording signal Many studies have focused on eliminating artifact on ECG recording signals with different approaches such as Motion artifact removal (MR); QRS detection based Motion ArtifactRemoval algorithm (QRSMR)[9]; Stationary Wavelet Movement Artifact Reduction (SWMAR); Normalized Least Mean Square Adaptive Filter technique (NLMSAF)[10]; moving average filtering, and wavelet transform have been used to reduce the motion ECG artefact[11,12]; removing such ECG artifacts from local field potentials (LFPs) recorded by a sensing-enabled neurostimulato[13]; ECG Artifact Removal from Single-Channel Surface EMG Using Fully Convolutional Networks[14], Channel-Wise Average Pooling and 1D Pixel-Shuffle Denoising Autoencoder for Electrode Motion Artifact Removal in ECG[15], Removing Cardiac Artifacts From Single-Channel Respiratory Electromyograms[16].The ECG signal recorded at the electrodes is a complex mixture of many components that come from different sources and are difficult to isolate. Using Independent Component Analysis (ICA) can help separate these sources into independent components (ICs), making it possible to remove unwanted components.However, the accuracy of ICA is highly dependent on the size of the analytical database[16], usually the number of signal sources in the body always exceeds the number of data recording channels; and in this case, ICA will not be able to separate the interference from the remaining components, or the components that are considered as artifacts, when removed still contain useful information, so the artifact cancellation will…”

mentioning

confidence: 99%

Enhanced ECG Record Quality: Integrated Artifact Suppression Using Soft Threshold on Wavelet Coefficients and Adaptive Filter Model

Hai¹

2023

MMEP

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The Independent Component Analysis (ICA) method has been demonstrated as an effective tool for separating desired signals and artifacts in the processing of biomedical signals, particularly in electrocardiogram (ECG) recordings through blind source separation (BSS). Unwanted components, which propagate through the body to the electrodes and mix with the recorded signal, are analyzed into independent components (ICs). However, the unwanted ICs identified as artifacts may also contain valuable information, resulting in a loss of information if these ICs are removed entirely. To address this issue, a combined solution of wavelet decomposition and ICA is proposed. Wavelet decompositions are performed on the unwanted ICs, and the application of a threshold level to the wavelet coefficients minimizes the loss of information in the received signal. A proposed solution utilizing the wavelet-based ICA (wICA) algorithm effectively removes artifacts, reducing distortion in the amplitude and phase of the ECG signal. Consequently, the resulting electrocardiogram closely corresponds to the patient's actual heart electrical signal variations, aiding in accurate clinical diagnoses. ECG signals are affected by various artifact components, including highly influential EMG or motion artifacts, which can manifest simultaneously, randomly, or intermittently. An inflexible threshold level is not entirely appropriate for these cases. In this study, a solution integrating the wICA system with an adaptive filter model is proposed to overcome the limitation of a fixed threshold level. This combined system can provide the best prediction of artifact impacts to establish adaptive threshold values. Experimental results have shown that this new approach significantly improves the ability to remove artifacts from ECG records, with a correlation value of 0.9832 compared to the reference clean signal.

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Reduction in the Motion Artifacts in Noncontact ECG Measurements Using a Novel Designed Electrode Structure

Cited by 12 publications

References 28 publications

A Review of Intelligent Garment System for Bioelectric Monitoring During Long-Lasting Intensive Sports

A Review of Intelligent Garment System for Bioelectric Monitoring During Long-Lasting Intensive Sports

Comparison of Digital Filter Orders in Single Lead Electrocardiographic Design for Reducing Interference Motion Artifact

Enhanced ECG Record Quality: Integrated Artifact Suppression Using Soft Threshold on Wavelet Coefficients and Adaptive Filter Model

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