Electrocardiography Interference Research Articles

Automated detection and removal of artifacts from sEMG signals based on fuzzy inference system and signal decomposition methods

Surface electromyography (sEMG) signal quality decreases when it is contaminated by different types of artifacts. Detection and removal of the contaminants from sEMG signals were a major and interesting task for several research works in recent years. In this paper, a novel automatic approach was designed for the detection and removal of artifacts from sEMG signals. The proposed method consists of a combined model that based on a fuzzy inference system (FIS), a signal decomposition method, and a denoising technique. Based on three spectral statistical features, which are calculated in frequency domain using power spectral density: composite multiscale entropy (CMSE), kurtosis (K), and skewness (S), FIS detects the artifactual epochs of the sEMG signal. Thereafter, those epochs are decomposed into their components by a signal decomposition. In this work, we have tested the performance of two decomposition methods namely stationary wavelet transform (SWT) and variational mode decomposition (VMD). A second FIS is used to select the contaminated components. Depending on the type of artifact, two procedures are considered, the first aims to denoise motion artifact (MOA), electrocardiography interference (ECG) and power line interference (PLI), whereas the second procedure aims to eliminate the additive white gaussian noise (AWGN). Finally, the denoised signal is reconstructed. The evaluation performance of the proposed method is compared with the performance of some existing methods using the following metrics: SNR improvement (ΔSNR), the reduction in artifact (λ(%)), improvement in signal to noise and distortion ratio (ΔSNDR) and improvement in the root mean square error (ΔRMSE). The results emphazed the superiority of our method over others in terms of all the used metrics and computational time.

Noise Reduction in ECG Signal Using Combined Ensemble Empirical Mode Decomposition Method with Stationary Wavelet Transform

The diagnostic study of electrocardiography (ECG) signals plays a vital role in the diagnosis of cardiac problems. But the powerline interference in ECG causes an artifact in the interpretation of the original signal. In this paper, a new method for the removal of such noise/artifact from the ECG signal by combining stationary wavelet transform with empirical mode decomposition (EMD-SWT) and ensemble empirical mode decomposition (EEMD-SWT) is proposed. SWT is applied after the decomposition of ECG signals into various intrinsic mode functions (IMFs) for further removal of noise. The proposed methods are tested for various datasets available in MIT-BIH Arrhythmia database, and the performance has been validated with existing methods. From the simulated results, it is found that combining SWT with EMD and EEMD yields better SNR enhancement when compared to the traditional methods.

Signal-to-Noise Ratio Estimation in Electromyography Signals Contaminated with Electrocardiography Signals

When electromyography (EMG) signals are collected from muscles in the torso, they can be perturbed by the electrocardiography (ECG) signals from heart activity. In this paper, we present a novel signal-to-noise ratio (SNR) estimate for an EMG signal contaminated by an ECG signal. We use six features that are popular in assessing EMG signals, namely skewness, kurtosis, mean average value, waveform length, zero crossing and mean frequency. The features were calculated from the raw EMG signals and the detail coefficients of the discrete stationary wavelet transform. Then, these features are used as inputs to a neural network that outputs the estimate of SNR. While we used simulated EMG signals artificially contaminated with simulated ECG signals as the training data, the testing was done with simulated EMG signals artificially contaminated with real ECG signals. The results showed that the waveform length determined with raw EMG signals was the best feature for estimating SNR. It gave the highest average correlation coefficient of 0.9663. These results suggest that the waveform length could be deployed not only in EMG recognition systems but also in EMG signal quality measurements when the EMG signals are contaminated by ECG interference.

Accounting for SNR in an Algorithm Using Wavelet Transform to Remove ECG Interference from EMG Signals

When the electromyography (EMG) signal is acquired from muscles in the torso, the electrocardiography (ECG) signal coming from heart activity can interfere. As a result, the EMG signal can be contaminated during data collection. In this paper, a technique based on discrete stationary wavelet transform (DSWT) is proposed to remove ECG interference from the EMG signal while taking into account the signal-to-noise ratio (SNR). The contaminated EMG signal is decomposed using 5-level DSWT with the Symlet wavelet function. The coefficients for levels 4 and 5, which are contaminated by ECG, are set to zero when their absolute values are less than or equal to a threshold determined for each SNR level. A clean EMG signal can then be obtained by inverse DSWT mapping of the new thresholded coefficients. We evaluated the performance of the proposed algorithm using simulated EMG contaminated with both simulated and real ECG signals, at 9 SNR levels from [Formula: see text]20 to 20[Formula: see text]dB with 5[Formula: see text]dB increments. The performance based on mean absolute error, correlation coefficient and relative error shows that the DSWT method is better than a high-pass filter.

Complexity Analysis of Surface EMG for Overcoming ECG Interference toward Proportional Myoelectric Control

Electromyographic (EMG) signals from muscles in the body torso are often contaminated by electrocardiography (ECG) interferences, which consequently compromise EMG intensity estimation. The ECG interference has become a barrier to proportional control of myoelectric prosthesis using a neural machine interface called targeted muscle reinnervation (TMR), which involves transferring the residual amputated nerves to nonfunctional muscles (typically pectoralis muscles for high level amputations). This study investigates a novel approach toward implementation of proportional myoelectric control by applying sample entropy (SampEn) analysis of surface EMG signals for robust intensity estimation in the presence of significant ECG interference. Surface EMG data from able-bodied and TMR amputee subjects with different degrees of ECG interference were used for performance evaluation. The results showed that the SampEn analysis had high correlation with surface EMG amplitude measurement but low sensitivity to different degrees of ECG interference. Taking this advantage, SampEn analysis of surface EMG signal can be used to facilitate implementation of proportional myoelectric control against ECG interference. This is particularly important for TMR prosthesis users.

Design and validation of a periodic leg movement detector.

Periodic Limb Movements (PLMs) are episodic, involuntary movements caused by fairly specific muscle contractions that occur during sleep and can be scored during nocturnal polysomnography (NPSG). Because leg movements (LM) may be accompanied by an arousal or sleep fragmentation, a high PLM index (i.e. average number of PLMs per hour) may have an effect on an individual’s overall health and wellbeing. This study presents the design and validation of the Stanford PLM automatic detector (S-PLMAD), a robust, automated leg movement detector to score PLM. NPSG studies from adult participants of the Wisconsin Sleep Cohort (WSC, n = 1,073, 2000–2004) and successive Stanford Sleep Cohort (SSC) patients (n = 760, 1999–2007) undergoing baseline NPSG were used in the design and validation of this study. The scoring algorithm of the S-PLMAD was initially based on the 2007 American Association of Sleep Medicine clinical scoring rules. It was first tested against other published algorithms using manually scored LM in the WSC. Rules were then modified to accommodate baseline noise and electrocardiography interference and to better exclude LM adjacent to respiratory events. The S-PLMAD incorporates adaptive noise cancelling of cardiac interference and noise-floor adjustable detection thresholds, removes LM secondary to sleep disordered breathing within 5 sec of respiratory events, and is robust to transient artifacts. Furthermore, it provides PLM indices for sleep (PLMS) and wake plus periodicity index and other metrics. To validate the final S-PLMAD, experts visually scored 78 studies in normal sleepers and patients with restless legs syndrome, sleep disordered breathing, rapid eye movement sleep behavior disorder, narcolepsy-cataplexy, insomnia, and delayed sleep phase syndrome. PLM indices were highly correlated between expert, visually scored PLMS and automatic scorings (r2 = 0.94 in WSC and r2 = 0.94 in SSC). In conclusion, The S-PLMAD is a robust and high throughput PLM detector that functions well in controls and sleep disorder patients.

Application of adaptive wavelet scalogram threshold in diaphragmatic electromyographic signal denoising

As weak bioelectricity signals, diaphragmatic electromyographic( EMGdi) signals are always corrupted by strong electrocardiography( ECG) signals. A denoising algorithm based on wavelet scalogram adaptive threshold was proposed in this paper to improve the precision of threshold in EMGdi signal denoising. This algorithm found the position of ECG interference by performing wavelet transform on the EMGdi signals and conveying wavelet coefficients to wavelet scalogram,and then automatically adjusted the threshold by ECG neighborhood wavelet energy in order to remove ECG interference. By comparing the results with the wavelet threshold, it shows that the proposed method can eliminate the ECG interference from EMGdi and reserve EMGdi signal characteristics effectively.

An intelligent biological inspired evolutionary algorithm for the suppression of incubator interference in premature infants ECG

Electromagnetic interference produced by the incubator medical equipments may interrupt or degrade the premature infant's electrocardiography (ECG) signal. The premature infant's ECG is usually contaminated by an interference caused by the incubator devices. The interference cancellation system is designed using an adaptive learning ability of artificial neural network Levenberg---Marquardt (LM) algorithm. In this paper the swarm intelligent-LM algorithm is used for the electromagnetic interference cancellation in infant ECG signal. The swarm intelligent algorithm is used for the optimization by selecting the optimized number of neurons in the hidden layer, learning rate and momentum factor of the neural network. Also, this paper presents a comparison of residual mean square error (RMSE) values for neural network trained by LM algorithm, hybrid genetic-LM algorithm and hybrid swarm intelligent-LM algorithm. The LM algorithm is used for the weight updating and reducing the content of electromagnetic interference noise present in the signal. The performance analysis of the proposed noise cancellation approach is compared with gradient based and evolutionary based algorithms. The result analysis shows that the interferences in infant ECG signal is removed successfully using the proposed approach.

Artefactual spikes in electrocardiography: a worthwhile introspection

Electrical devices, which have become an integral part of our daily life, may influence the electrical recording of the heart. These disturbances from external sources outside of the heart's own activity produce changes in the electrocardiography (ECG) of the patient, simulating rhythmic disturbances of the heart. Understanding these disturbances is essential in order to better interprete the ECG. Common sources of electrical interferences include external devices, such as alternating current and improper earthing, and surgical procedures like diathermy. We report a case of electrical interference in a patient's ECG due to an inserted bladder stimulator. This case report highlights the importance of precise identification of artefacts in the interpretation of ECG, as well as prompt localisation and elimination of the source of interference.

FPGA-Based Implementation of an Adaptive Canceller for 50/60-Hz Interference in Electrocardiography

The implementation of an adaptive canceller of interference using a field-programmable gate array (Xilinx) programmable logic device/mechanism is shown in this paper. The adaptive canceller automatically adjusts its parameters to eliminate noise. This ability allows the canceller to adapt to changes in the characteristics of the signal. The use of this adaptive canceller to filter the interference noise that is caused by the power line is shown.

Adaptive noise cancelling: Principles and applications

This paper describes the concept of adaptive noise cancelling, an alternative method of estimating signals corrupted by additive noise or interference. The method uses a "primary" input containing the corrupted signal and a "reference" input containing noise correlated in some unknown way with the primary noise. The reference input is adaptively filtered and subtracted from the primary input to obtain the signal estimate. Adaptive filtering before subtraction allows the treatment of inputs that are deterministic or stochastic, stationary or time variable. Wiener solutions are developed to describe asymptotic adaptive performance and output signal-to-noise ratio for stationary stochastic inputs, including single and multiple reference inputs. These solutions show that when the reference input is free of signal and certain other conditions are met noise in the primary input can be essentiany eliminated without signal distortion. It is further shown that in treating periodic interference the adaptive noise canceller acts as a notch filter with narrow bandwidth, infinite null, and the capability of tracking the exact frequency of the interference; in this case the canceller behaves as a linear, time-invariant system, with the adaptive filter converging on a dynamic rather than a static solution. Experimental results are presented that illustrate the usefulness of the adaptive noise cancelling technique in a variety of practical applications. These applications include the cancelling of various forms of periodic interference in electrocardiography, the cancelling of periodic interference in speech signals, and the cancelling of broad-band interference in the side-lobes of an antenna array. In further experiments it is shown that a sine wave and Gaussian noise can be separated by using a reference input that is a delayed version of the primary input. Suggested applications include the elimination of tape hum or turntable rumble during the playback of recorded broad-band signals and the automatic detection of very-low-level periodic signals masked by broad-band noise.

60-HZ interference in electrocardiography.

One of the major problems encountered in recording ECG's is the appearance of unwanted 60-Hz interference in the output. This paper examines the many possible sources of interference, and for each provides a description, an identifying test, and a remedy. Then we look at possible equipment imperfections and guidelines for amplifier design to aid in the evaluation of new developments in the field of electrocardiography. As an illustrative example, we apply the procedure for isolating the source of interference to an exposition display in which each visitor could rapidly view his own ECG.