A simple SSA-based de-noising technique to remove ECG interference in EMG signals

Barrios-Muriel, Jorge; Romero, Francisco; Sánchez, Francisco Javier Alonso; Gianikellis, Kostas

doi:10.1016/j.bspc.2016.06.001

Cited by 43 publications

(12 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The amplitude is generally between 0‐4mv, while the spectral energy is distributed in 0.5‐100 Hz, and most of the energy is in 0.5‐20 Hz. The noise of electrocardiograph (ECG) signal includes power frequency interference, 7 baseline drift, 8 motion artifacts, 9 and EMG interference 10 …”

Section: Electrocardiograph (Ecg) Signal Collection and Feature Extractionmentioning

confidence: 99%

ECGsignal analysis for fatigue and abnormal event detection during sport and exercise

Luo

2020

Internet Technology Letters

View full text Add to dashboard Cite

With the continuous improved quality of life, more and more persons have paid attention on their health. Sport and exercise have become a popular way to keep health. However, improper and excessive sport and exercise may cause physical injury. In order to solve this issue, this paper proposes a system to monitor the state during sport and exercise for detecting fatigue and abnormal event. If a person is under fatigue state, he or she stops exercising to rest. In the fatigue detection system, first the electrocardiograph (ECG) signal is collected by a smart wearable device, then the noises in the signal are removed by several filters and the denoised signal are represented as nine features from time domain and frequency domain, lastly the extracted features are input into a trained weighted one‐class support vector machine (WOC‐SVM) model to determine the state during sport. The WOC‐SVM is learnt offline on a training set which consists of many collected data. The experimental results show the effectiveness of the proposed system.

show abstract

Section: Electrocardiograph (Ecg) Signal Collection and Feature Extractionmentioning

confidence: 99%

ECGsignal analysis for fatigue and abnormal event detection during sport and exercise

Luo

2020

Internet Technology Letters

View full text Add to dashboard Cite

show abstract

“…The eigenvalues of ECG artifacts are also usually smaller than EEG, but larger than EMG. Some studies have already used the information given by the eigenvalues to remove ECG from EMG [52]. Since EOG signals usually have larger amplitudes leading to large eigenvalues, the first several decomposition components extracted by SSA usually represent the EOG artifacts.…”

Section: A Case I: Single Kind Of Artifactsmentioning

confidence: 99%

Remove Diverse Artifacts Simultaneously From a Single-Channel EEG Based on SSA and ICA: A Semi-Simulated Study

Cheng

et al. 2019

IEEE Access

View full text Add to dashboard Cite

Electroencephalogram (EEG) signals are often contaminated with diverse artifacts, such as electromyogram (EMG), electrooculogram (EOG), and electrocardiogram (ECG) artifacts. These artifacts make subsequent EEG analysis inaccurate and prevent practical usage. Recently, the use of wearable EEG devices in ambulatory systems has been developed. For practical reasons, these systems usually contain a single EEG channel. Several studies have proposed to combine single-channel decomposition methods with blind source separation (BSS) methods to denoise the single-channel EEG. However, the existing methods have their own limitations since most of them only focus on removing one single kind of artifacts. Unfortunately, the EEG is prone to be contaminated by various kinds of artifacts simultaneously. Yet to our knowledge, there are no existing methods to remove diverse artifacts simultaneously from the singlechannel EEG. To address this issue, we propose an effective method to remove diverse artifacts simultaneously for the single-channel EEG case. This method is a combination of singular spectrum analysis (SSA) and second-order blind identification (SOBI) method. We conduct a semi-simulated study to investigate all possible cases of the single-channel EEG been contaminated by EMG, EOG, and ECG artifacts. The results show that the proposed method can successfully remove diverse artifacts from the single-channel EEG. It is a promising tool for biomedical signal processing applications.

show abstract

“…Various external factors during the acquisition process, such as 50 or 60 Hz power frequency interference, motion artifact, and ECG interference can easily interfere with the sEMG signals (Phinyomark et al, 2012; Barrios-Muriel et al, 2016). To eliminate ECG interference and motion artifacts, a 20–200 Hz bandpass filtering for sEMG signals was implemented by using a Butterworth filter with 0.1 dB passband ripple and 50 dB stopband attenuation.…”

Section: Trunk Compensation Detection Proceduresmentioning

confidence: 99%

sEMG-Based Trunk Compensation Detection in Rehabilitation Training

Chen

Zhang

et al. 2019

Front. Neurosci.

View full text Add to dashboard Cite

Stroke patients often use trunk to compensate for impaired upper limb motor function during upper limb rehabilitation training, which results in a reduced rehabilitation training effect. Detecting trunk compensations can improve the effect of rehabilitation training. This study investigates the feasibility of a surface electromyography-based trunk compensation detection (sEMG-bTCD) method. Five healthy subjects and nine stroke subjects with cognitive and comprehension skills were recruited to participate in the experiments. The sEMG signals from nine superficial trunk muscles were collected during three rehabilitation training tasks (reach-forward-back, reach-side-to-side, and reach-up-to-down motions) without compensation and with three common trunk compensations [lean-forward (LF), trunk rotation (TR), and shoulder elevation (SE)]. Preprocessing like filtering, active segment detection was performed and five time domain features (root mean square, variance, mean absolute value (MAV), waveform length, and the fourth order autoregressive model coefficient) were extracted from the collected sEMG signals. Excellent TCD performance was achieved in healthy participants by using support vector machine (SVM) classifier (LF: accuracy = 94.0%, AUC = 0.97, F1 = 0.94; TR: accuracy = 95.8%, AUC = 0.99, F1 = 0.96; SE: accuracy = 100.0%, AUC = 1.00, F1 = 1.00). By using SVM classifier, TCD performance in stroke participants was also obtained (LF: accuracy = 74.8%, AUC = 0.90, F1 = 0.73; TR: accuracy = 67.1%, AUC = 0.85, F1 = 0.71; SE: accuracy = 91.3%, AUC = 0.98, F1 = 0.90). Compared with the methods based on cameras or inertial sensors, better detection performance was obtained in both healthy and stroke participants. The results demonstrated the feasibility of the sEMG-bTCD method, and it helps to prompt the stroke patients to correct their incorrect posture, thereby improving the effectiveness of rehabilitation training.

show abstract

A simple SSA-based de-noising technique to remove ECG interference in EMG signals

Cited by 43 publications

References 31 publications

ECGsignal analysis for fatigue and abnormal event detection during sport and exercise

ECGsignal analysis for fatigue and abnormal event detection during sport and exercise

Remove Diverse Artifacts Simultaneously From a Single-Channel EEG Based on SSA and ICA: A Semi-Simulated Study

sEMG-Based Trunk Compensation Detection in Rehabilitation Training

Contact Info

Product

Resources

About