New aspects to event-synchronous cancellation of ECG interference: an application of the method in diaphragmatic EMG signals

Morphological ECG subtraction method for removing ECG artifacts from diaphragm EMG

BACKGROUND

Diaphragmatic electromyographic (EMGdi) is a helpful method to reflect the respiratory center's activity visually. However, the electrocardiogram (ECG) severely affected its weakness, limiting its use.

OBJECTIVE

To remove the ECG artifact from the EMGdi, we designed a Morphological ECG subtraction method (MES) based on three steps: 1) ECG localization, 2) morphological tracking, and 3) ECG subtractor.

METHODS

We evaluated the MES method against the wavelet-based dual-threshold and stationary wavelet filters using visual and frequency-domain characteristics (median frequency and power ratio).

RESULTS

The results show that the MES method can preserve the features of the original diaphragm signal for both surface diaphragm signal (SEMGdi) and clinical collection of diaphragm signal (EMGdi_clinic), and it is more effective than the wavelet-based dual-threshold and stationary wavelet filtering methods.

CONCLUSION

The MES method is more effective than other methods. This technique may improve respiratory monitoring and assisted ventilation in patients with respiratory diseases.

Reducing Noise, Artifacts and Interference in Single-Channel EMG Signals: A Review

Electromyography (EMG) is gaining importance in many research and clinical applications, including muscle fatigue detection, control of robotic mechanisms and prostheses, clinical diagnosis of neuromuscular diseases and quantification of force. However, EMG signals can be contaminated by various types of noise, interference and artifacts, leading to potential data misinterpretation. Even assuming best practices, the acquired signal may still contain contaminants. The aim of this paper is to review methods employed to reduce the contamination of single channel EMG signals. Specifically, we focus on methods which enable a full reconstruction of the EMG signal without loss of information. This includes subtraction methods used in the time domain, denoising methods performed after the signal decomposition and hybrid approaches that combine multiple methods. Finally, this paper provides a discussion on the suitability of the individual methods based on the type of contaminant(s) present in the signal and the specific requirements of the application.

Flexible Non-contact Electrodes for Wearable Biosensors System on Electrocardiogram Monitoring in Motion

Electrocardiogram (ECG) is a critical physiological indicator that contains abundant information about human heart activities. However, it is a kind of weak low-frequency signal, which is easy to be interfered by various noises. Therefore, wearable biosensors (WBS) technique is introduced to overcome this challenge. A flexible non-contact electrode is proposed for wearable biosensors (WBS) system, which is made up of flexible printed circuits materials, and can monitor the ECG signals during exercise for a long time. It uses the principle of capacitive coupling to obtain high-quality signals, and reduces the impact of external noise through active shielding; The results showed that the proposed non-contact electrode was equivalent to a medical wet electrode. The correlation coefficient was as high as 99.70 ± 0.30% when the subject was resting, while it was as high as 97.53 ± 1.80% during exercise. High-quality ECG could still be collected at subjects walking at 7 km/h. This study suggested that the proposed flexible non-contact electrode would be a potential tool for wearable biosensors for medical application on long-term monitoring of patients' health and provide athletes with physiological signal measurements.

Evaluation of Correlation between Surface Diaphragm Electromyography and Airflow Using Fixed Sample Entropy in Healthy Subjects

In clinic, the acquisition of airflow with nasal prongs, masks, thermistor to monitor respiratory function is more uncomfortable and inconvenience than surface diaphragm electromyography (EMGdi) using electrode pads. The EMGdi with strong electrocardiograph (ECG) interference affect the extraction of its characteristic information. In this work, surface EMGdi and airflow signals of 20 subjects were collected under 5 incremental inspiratory threshold loading protocols from quiet breathing to maximum forced breathing. First, we filtered out the ECG interference in EMGdi based on the combination of stationary wavelet transform and the positioning of ECG to obtain pure EMGdi (EMGdip). Second, the Spearman's rank correlation coefficients between EMGdi and EMGdip quantified by time series fixed sample entropy (fSampEn), root mean square (RMS), and envelope were compared to verify the robustness of the fSampEn to ECG. A comparative analysis of correlation between fSampEn of EMGdi and inspiratory airflow and the correlation between envelope of EMGdip (EMGdie) and inspiratory airflow found that there was no significant difference between the two, indicating the feasibility of using fSampEn to predict airflow. Moreover, fSampEn of EMGdi was used as characteristic parameter to build a quantitative relationship with the airflow by polynomial regression analysis. Mean coefficient of determination of all subjects in any breathing state is greater than 0.88. Finally, nonlinear programming method was used to solve a universal fitting coefficient between fSampEn of EMGdi and airflow for each subject to further evaluate the possibility of using surface EMGdi to monitor and control respiratory activity.

Removal of ECG Artifacts Affects Respiratory Muscle Fatigue Detection-A Simulation Study

This work investigates elimination methods for cardiogenic artifacts in respiratory surface electromyographic (sEMG) signals and compares their performance with respect to subsequent fatigue detection with different fatigue algorithms. The analysis is based on artificially constructed test signals featuring a clearly defined expected fatigue level. Test signals are additively constructed with different proportions from sEMG and electrocardiographic (ECG) signals. Cardiogenic artifacts are eliminated by high-pass filtering (HP), template subtraction (TS), a newly introduced two-step approach (TSWD) consisting of template subtraction and a wavelet-based damping step and a pure wavelet-based damping (DSO). Each method is additionally combined with the exclusion of QRS segments (gating). Fatigue is subsequently quantified with mean frequency (MNF), spectral moments ratio of order five (SMR5) and fuzzy approximate entropy (fApEn). Different combinations of artifact elimination methods and fatigue detection algorithms are tested with respect to their ability to deliver invariant results despite increasing ECG contamination. Both DSO and TSWD artifact elimination methods displayed promising results regarding the intermediate, "cleaned" EMG signal. However, only the TSWD method enabled superior results in the subsequent fatigue detection across different levels of artifact contamination and evaluation criteria. SMR5 could be determined as the best fatigue detection algorithm. This study proposes a signal processing chain to determine neuromuscular fatigue despite the presence of cardiogenic artifacts. The results furthermore underline the importance of selecting a combination of algorithms that play well together to remove cardiogenic artifacts and to detect fatigue. This investigation provides guidance for clinical studies to select optimal signal processing to detect fatigue from respiratory sEMG signals.

Assessment of Respiratory Muscle Activity with Surface Electromyographic Signals Acquired by Concentric Ring Electrodes

The assessment of respiratory muscle activity by surface electromyography (sEMG) is a promising noninvasive technique for the diagnosis and monitoring of chronic obstructive pulmonary disease. The diaphragm is the most important muscle in breathing, although in forced inspiration other muscles, such as sternocleidomastoid, are activated and contribute to the respiratory process. The measurement of the sEMG in these muscles (sEMGdi and sEMGscm, respectively) by means of two electrodes in conventional bipolar configuration (BEs) is a common practice to evaluate the respiratory muscle activity and allows to indirectly quantify the level of muscular activation. However, the resulting signals are usually contaminated by electrocardiographic (ECG) activity, hindering the assessment of the activity of these muscles. sEMG signals can also be recorded using concentric ring electrodes (CREs). CREs have greater spatial resolution and attenuate distant bioelectrical interferences. In this scenario, the objective of this work has been to evaluate the applicability of CREs for the acquisition of sEMGdi and sEMGscm. For this purpose, both sEMG signals were recorded simultaneously with BEs and CREs in healthy subjects while performing an inspiratory load protocol. To evaluate the effect of the cardiac interference, the ratio between the mean power in inspiratory segments without ECG and the mean power in expiratory segments with ECG (Rcardio) was calculated. Additionally, the ratio between the mean power in inspiratory segments without ECG and the mean power in expiratory segments without ECG (Rinex) was also calculated. The results revealed that the Rcardio and bandwidth is greater in sEMG signals acquired with the CREs, while the Rinex is higher in the signals acquired with BEs. These results suggest that the use of CREs is a recommended alternative for the acquisition of sEMG in muscles with high cardiac interference, such as the diaphragm muscle.

The application of ECG cancellation in diaphragmatic electromyographic by using stationary wavelet transform

In this paper, we present and investigate a special kind of stationary wavelet algorithm using "inverse" hard threshold to eliminate the electrocardiogram (ECG) interference included in diaphragmatic electromyographic (EMGdi). Differing from traditional wavelet hard threshold, "inverse" hard threshold is used to shrink strong coefficients of ECG interference and reserve weak coefficients of EMGdi signal. Meanwhile, a novel QRS location algorithm is proposed for the position detection of R wave by using low frequency coefficients in this paper. With the proposed method, raw EMGdi is decomposed by wavelet at fifth scale. Then, each ECG interference threshold is calculated by mean square, which is estimated by wavelet coefficients in the ECG cycle at each level. Finally, ECG interference wavelet coefficients are removed by "inverse" hard threshold, and then the de-noised signal is reconstructed by wavelet coefficients. The simulation and clinical EMGdi de-noising results show that the "inverse" hard threshold investigated in this paper removes the ECG interference in EMGdi availably and reserves its signal characteristics effectively, as compared to wavelet threshold.

A Novel Technique for Fetal ECG Extraction Using Single-Channel Abdominal Recording

Non-invasive fetal electrocardiograms (FECGs) are an alternative method to standard means of fetal monitoring which permit long-term continual monitoring. However, in abdominal recording, the FECG amplitude is weak in the temporal domain and overlaps with the maternal electrocardiogram (MECG) in the spectral domain. Research in the area of non-invasive separations of FECG from abdominal electrocardiograms (AECGs) is in its infancy and several studies are currently focusing on this area. An adaptive noise canceller (ANC) is commonly used for cancelling interference in cases where the reference signal only correlates with an interference signal, and not with a signal of interest. However, results from some existing studies suggest that propagation of electrocardiogram (ECG) signals from the maternal heart to the abdomen is nonlinear, hence the adaptive filter approach may fail if the thoracic and abdominal MECG lack strict waveform similarity. In this study, singular value decomposition (SVD) and smooth window (SW) techniques are combined to build a reference signal in an ANC. This is to avoid the limitation that thoracic MECGs recorded separately must be similar to abdominal MECGs in waveform. Validation of the proposed method with r01 and r07 signals from a public dataset, and a self-recorded private dataset showed that the proposed method achieved F1 scores of 99.61%, 99.28% and 98.58%, respectively for the detection of fetal QRS. Compared with four other single-channel methods, the proposed method also achieved higher accuracy values of 99.22%, 98.57% and 97.21%, respectively. The findings from this study suggest that the proposed method could potentially aid accurate extraction of FECG from MECG recordings in both clinical and commercial applications.

Onset and Offset Estimation of the Neural Inspiratory Time in Surface Diaphragm Electromyography: A Pilot Study in Healthy Subjects

This study evaluates the onset and offset of neural inspiratory time estimated from surface diaphragm electromyographic (EMGdi) recordings. EMGdi and airflow signals were recorded in ten healthy subjects according to two respiratory protocols based on respiratory rate (RR) increments, from 15 to 40 breaths per minute (bpm), and fractional inspiratory time (T_i/T_tot) decrements, from 0.54 to 0.18. The analysis of EMGdi signal amplitude is an alternative approach for the quantification of neural respiratory drive. The EMGdi amplitude was estimated using the fixed sample entropy computed over a 250 ms moving window of the EMGdi signal (EMGdi_fse). The neural onset was detected through a dynamic threshold over the EMGdi_fse using the kernel density estimation method, while neural offset was detected by finding when the EMGdi_fse had decreased to 70% of the peak value reached during inspiration. The Bland-Altman analysis between airflow and neural onsets showed a global bias of 46 ms in the RR protocol and 22 ms in the T_i /T_tot protocol. The Bland-Altman analysis between airflow and neural offsets reveals a global bias of 11 ms in the RR protocol and -2 ms in the T_i/T _tot protocol. The relationship between pairs of RR values (Pearson's correlation coefficient of 0.99, Bland-=Altman limits of -2.39 to 2.41 bpm, and mean bias of 0.01 bpm) and between pairs of T_i/T_tot values (Pearson's correlation coefficient of 0.86, Bland-Altman limits of -0.11 to 0.10, and mean bias of -0.01) showed a good agreement. In conclusion, we propose a method for determining neural onset and neural offset based on noninvasive recordings of the electrical activity of the diaphragm that requires no filtering of cardiac muscle interference.

FastICA peel-off for ECG interference removal from surface EMG

BACKGROUND

Multi-channel recording of surface electromyographyic (EMG) signals is very likely to be contaminated by electrocardiographic (ECG) interference, specifically when the surface electrode is placed on muscles close to the heart.

METHODS

A novel fast independent component analysis (FastICA) based peel-off method is presented to remove ECG interference contaminating multi-channel surface EMG signals. Although demonstrating spatial variability in waveform shape, the ECG interference in different channels shares the same firing instants. Utilizing the firing information estimated from FastICA, ECG interference can be separated from surface EMG by a "peel off" processing. The performance of the method was quantified with synthetic signals by combining a series of experimentally recorded "clean" surface EMG and "pure" ECG interference.

RESULTS

It was demonstrated that the new method can remove ECG interference efficiently with little distortion to surface EMG amplitude and frequency. The proposed method was also validated using experimental surface EMG signals contaminated by ECG interference.

CONCLUSIONS

The proposed FastICA peel-off method can be used as a new and practical solution to eliminating ECG interference from multichannel EMG recordings.

ECG-EMG separation by using enhanced non-negative matrix factorization

We present a novel approach to single-channel ECG-EMG signal separation by means of enhanced non-negative matrix factorization (NMF). The approach is based on a linear decomposition of the input signal spectrogram in two non-negative components, which represent the ECG and EMG spectrogram estimates. As ECG and EMG have different time-frequency (TF) patterns, the decomposition is enhanced by reshaping the input mixture spectrogram in order to emphasize a sparse ECG over a noisy-like EMG. Moreover, initialization of the classical NMF algorithm with accurately designed ECG and EMG structures further increases its separation performance. The comparative study suggests that the proposed method outperforms two reference methods for both synthetic and real signal mixture scenarios.

Cardiac interference reduction in diaphragmatic MMG signals during a Maintained Inspiratory Pressure Test

A recursive least square (RLS) adaptive filtering algorithm for reduction of cardiac interference in diaphragmatic mecanomyographic (MMGdi) signals is addressed in this paper. MMGdi signals were acquired with a capacitive accelerometer placed between 7th and 8th intercostal spaces, on the right anterior axillary line, during a maintained inspiratory pressure test. Subjects were asked to maintain a constant inspiratory pressure with a mouthpiece connected to a closed tube (without breathing). This maneuver was repeated at five different contraction efforts: apnea (no effort), 20 cmH2O, 40 cmH2O, 60 cmH2O and maximum voluntary contraction. An adaptive noise canceller (ANC) using the RLS algorithm was applied on the MMGdi signals. To evaluate the behavior of the ANC, the MMGdi signals were analyzed in two segments: with and without cardiac interference (WCI and NCI, respectively). In both segments it was analyzed the power spectral density (PSD), and the ARV and RMS amplitude parameters for each contraction effort. With the proposed ANC algorithm the amplitude parameters of the WCI segments were reduced to a level similar to the one of the NCI segments. The obtained results showed that ANC using the RLS algorithm allows to significantly reduce the cardiac interference in MMGdi signals.

Improvement in Neural Respiratory Drive Estimation From Diaphragm Electromyographic Signals Using Fixed Sample Entropy

Diaphragm electromyography is a valuable technique for the recording of electrical activity of the diaphragm. The analysis of diaphragm electromyographic (EMGdi) signal amplitude is an alternative approach for the quantification of the neural respiratory drive (NRD). The EMGdi signal is, however, corrupted by electrocardiographic (ECG) activity, and this presence of cardiac activity can make the EMGdi interpretation more difficult. Traditionally, the EMGdi amplitude has been estimated using the average rectified value (ARV) and the root mean square (RMS). In this study, surface EMGdi signals were analyzed using the fixed sample entropy (fSampEn) algorithm, and compared to the traditional ARV and RMS methods. The fSampEn is calculated using a tolerance value fixed and independent of the standard deviation of the analysis window. Thus, this method quantifies the amplitude of the complex components of stochastic signals (such as EMGdi), and being less affected by changes in amplitude due to less complex components (such as ECG). The proposed method was tested in synthetic and recorded EMGdi signals. fSampEn was less sensitive to the effect of cardiac activity on EMGdi signals with different levels of NRD than ARV and RMS amplitude parameters. The mean and standard deviation of the Pearson's correlation values between inspiratory mouth pressure (an indirect measure of the respiratory muscle activity) and fSampEn, ARV, and RMS parameters, estimated in the recorded EMGdi signal at tidal volume (without inspiratory load), were 0.38±0.12, 0.27±0.11 , and 0.11±0.13, respectively. Whereas at 33 cmH2O (maximum inspiratory load) were 0.83±0.02, 0.76±0.07, and 0.61±0.19 , respectively. Our findings suggest that the proposed method may improve the evaluation of NRD.

A combination method for electrocardiogram rejection from surface electromyogram

The electrocardiogram signal which represents the electrical activity of the heart provides interference in the recording of the electromyogram signal, when the electromyogram signal is recorded from muscles close to the heart. Therefore, due to impurities, electromyogram signals recorded from this area cannot be used. In this paper, a new method was developed using a combination of artificial neural network and wavelet transform approaches, to eliminate the electrocardiogram artifact from electromyogram signals and improve results. For this purpose, contaminated signal is initially cleaned using the neural network. With this process, a large amount of noise can be removed. However, low-frequency noise components remain in the signal that can be removed using wavelet. Finally, the result of the proposed method is compared with other methods that were used in different papers to remove electrocardiogram from electromyogram. In this paper in order to compare methods, qualitative and quantitative criteria such as signal to noise ratio, relative error, power spectrum density and coherence have been investigated for evaluation and comparison. The results of signal to noise ratio and relative error are equal to 15.6015 and 0.0139, respectively.

Evaluation and adaptive attenuation of the cardiac vibration interference in mechanomyographic signals

The study of the mechanomyographic signal of the diaphragm muscle (MMGdi) is a promising technique in order to evaluate the respiratory muscles effort. The relationship between amplitude and frequency parameters of this signal with the respiratory effort performed during respiration is of great interest for researchers and physicians due to its diagnostic potentials. However, MMGdi signals are frequently contaminated by a cardiac vibration or mechanocardiographic (MCG) signal. An adaptive noise cancellation (ANC) can be used to reduce the MCG interference in the recorded MMGdi activity. In this paper, it is evaluated the proposed ANC scheme by means of a synthetic MMGdi signal with a controlled MCG interference. The Pearson's correlation coefficient (PCC) between both root mean square (RMS) and mean frequency (fm) of the synthetic MMGdi signal are considerably reduced with the presence of cardiac vibration noise (from 0.95 to 0.87, and from 0.97 to 0.76, respectively). With the ANC algorithm proposed the effect of the MCG noise on the amplitude and frequency of MMG parameters is reduced considerably (PCC of 0.93 and 0.97 for the RMS and fm, respectively). The ANC method proposed in this work is an interesting technique to attenuate the cardiac interference in respiratory MMG signals. Further investigation should be carried out to evaluate the performance of the ANC algorithm in real MMGdi signals.

Local Wavelet-Based Filtering of Electromyographic Signals to Eliminate the Electrocardiographic-Induced Artifacts in Patients with Spinal Cord Injury

Surface Electromyography (EMG) is a standard method used in clinical practice and research to assess motor function in order to help with the diagnosis of neuromuscular pathology in human and animal models. EMG recorded from trunk muscles involved in the activity of breathing can be used as a direct measure of respiratory motor function in patients with spinal cord injury (SCI) or other disorders associated with motor control deficits. However, EMG potentials recorded from these muscles are often contaminated with heart-induced electrocardiographic (ECG) signals. Elimination of these artifacts plays a critical role in the precise measure of the respiratory muscle electrical activity. This study was undertaken to find an optimal approach to eliminate the ECG artifacts from EMG recordings. Conventional global filtering can be used to decrease the ECG-induced artifact. However, this method can alter the EMG signal and changes physiologically relevant information. We hypothesize that, unlike global filtering, localized removal of ECG artifacts will not change the original EMG signals. We develop an approach to remove the ECG artifacts without altering the amplitude and frequency components of the EMG signal by using an externally recorded ECG signal as a mask to locate areas of the ECG spikes within EMG data. These segments containing ECG spikes were decomposed into 128 sub-wavelets by a custom-scaled Morlet Wavelet Transform. The ECG-related sub-wavelets at the ECG spike location were removed and a de-noised EMG signal was reconstructed. Validity of the proposed method was proven using mathematical simulated synthetic signals and EMG obtained from SCI patients. We compare the Root-mean Square Error and the Relative Change in Variance between this method, global, notch and adaptive filters. The results show that the localized wavelet-based filtering has the benefit of not introducing error in the native EMG signal and accurately removing ECG artifacts from EMG signals.

Elimination of ECG Artifacts from Myoelectric Prosthesis Control Signals Developed by Targeted Muscle Reinnervation

We investigated elimination of electrocardiogram (ECG) artifacts from the myoelectric prosthesis control signals, taken from the reinnervated pectoralis muscles of a patient with bilateral amputations at shoulder disarticulation level. The performance of various ECG artifact removal methods including high pass filtering, spike clipping, template subtracting, wavelet thresholding and adaptive filtering was presented. In particular, considering the clinical requirements and memory limitation of commercial prosthesis controllers, we further explored suitable means of ECG artifact removal for clinical application.

Rectus abdominis electromyography and MechanoMyoGraphy comparison for the detection of cough

We recently developed a novel active implant for the treatment of severe stress urinary incontinence. This innovative medical device has been developed with the main purpose of reducing the mean urethral occlusive pressure of the current prosthesis. This goal is achieved by detecting circumstances implying either high or low intra-abdominal pressures by a single 3-axis accelerometer. In fact, posture and activity of the patient are monitored in real time. We investigated in this study the possibility of detecting cough events (one of the main causes of urine loss in incontinent patient) by MechanoMyoGraphy (MMG) of the Rectus Abdominis (RA) using the same accelerometer. We compared MMG signal detection characteristics (burst onset times and RMS values) to the method of reference, the ElectroMyoGraphy (EMG). It is shown that detection of cough effort by MMG presents lower performances, mostly in terms of cough anticipation, than EMG detection. However, MMG still remains a good option for an implantable system comparing to implantable EMG disadvantages.

A wavelet-based adaptive filter for removing ECG interference in EMGdi signals

Diaphragmatic electromyogram (EMGdi) signals convey important information on respiratory diseases. In this paper, an adaptive filter for removing the electrocardiographic (ECG) interference in EMGdi signals based on wavelet theory is proposed. Power spectrum analysis was performed to evaluate the proposed method. Simulation results show that the power spectral density (PSD) of the extracted EMGdi signal from an ECG corrupted signal is within 1.92% average error relative to the original EMGdi signal. Testing on clinical EMGdi data confirm that this method is also efficient in removing ECG artifacts from the corrupted clinical EMGdi signal.

The event synchronous canceller algorithm removes maternal ECG from abdominal signals without affecting the fetal ECG

Fetal monitoring using abdominally recorded signals (ADS) allows physicians to detect occurring changes in the well-being state of the fetus from the beginning of pregnancy. Mainly based on the fetal electrocardiogram (fECG), it provides the long-term fetal heart rate (fHR) and assessment of the fetal QRS morphology. But the fECG component in ADS is obscured by the maternal ECG (mECG), thus removal of the mECG from ADS improves fECG analysis. This study demonstrates the performance of the event-synchronous interference canceller (ESC) in mECG removal from ADS data, recorded during pregnancy and labor. Its advantage as a compensation method for extended ADS processing is discussed.

Effect of electrocardiographic contamination on surface electromyography assessment of back muscles

The purpose of this study was to demonstrate the relative effect of electrocardiography (ECG) on back muscle surface electromyography (SEMG) parameters and their corresponding sensitivity in low back pain (LBP) assessment. Back muscle SEMG activities were recorded from 17 healthy subjects and 18 chronic LBP patients under static postures (straight sitting and upright standing), and dynamic action (flexion-extension). ECG cancellation based on independent component analysis (ICA) method was performed. Root mean square (RMS) and median frequency (MF) of raw and denoised SEMG data were computed respectively. Multiple comparisons were then performed. A consistent trend of change (increased MF and decreased RMS) followed ECG removal was noticed. In particular, in SEMG measurements under static postures, a significant decrease in RMS (p<0.05) and increase in MF (p<0.05) were found in all recording muscle groups. Level of corruption by ECG artifacts on SEMG measurements was found to be more serious and prominent in static postures than that in dynamic action. After ECG removal, significant improvements in the ability of SEMG to discriminate LBP patients from healthy subjects were seen in RMS amplitude recorded while standing (p<0.05) and MF in all measuring conditions (p<0.05). This study provides a more complete understanding on the relative effect of ECG contamination on back muscles SEMG parameters and LBP assessment.

Improved method for fetal heart rate monitoring

The fetal ECG can be detected in the recorded abdominal signals. A new procedure to compute the fetal heart rate (FHR) is proposed. The abdominal signal is first preprocessed in order to remove the baseline and the uterine contractions. Then the ECG of the mother (MECG) is removed using coherent averaging and optimizing the averaged MECG template. The channels containing the clearest fetal ECG signal (FECG) are identified by the autocorrelation function. The FECG is enhanced by the cross correlation between the two channels that show the strongest FECG. This enhancement is possible since the residual noise in the abdominal signal after removal of baseline, uterine contractions and maternal ECG is not correlated among the channels. The fetal R-Peaks are then detected and the FHR is computed. The obtained FHR is further corrected, using the information about the MECG and about the FECG.

Real time ECG artifact removal for myoelectric prosthesis control

The electrocardiogram (ECG) artifact is a major noise source contaminating the electromyogram (EMG) of torso muscles. This study investigates removal of ECG artifacts in real time for myoelectric prosthesis control, a clinical application that demands speed and efficiency. Three methods with simple and fast implementation were investigated. Removal of ECG artifacts by digital high-pass filtering was implemented. The effects of the cutoff frequency and filter order of high-pass filtering on the resulting EMG signal were quantified. An alternative adaptive spike-clipping approach was also developed to dynamically detect and suppress the ECG artifacts in the signal. Finally, the two methods were combined. Experimental surface EMG recordings with different ECG/EMG ratios were used as testing signals to evaluate the proposed methods. As a key parameter for clinical myoelectric prosthesis control, the average rectified amplitude of the signal was used as the performance indicator to quantitatively analyze the EMG content distortion and the ECG artifact suppression imposed by the two methods. Aiming at clinical application, the optimal parameter assignment for each method was determined on the basis of the performance using the suite of testing signals with various ECG/EMG ratios.

Fetal ECG extraction during labor using an adaptive maternal beat subtraction technique

Fetal ECG (FECG) monitoring using abdominal maternal signals is a non-invasive technique that allows early detection of changes in fetal wellbeing. Several other signal components have stronger energy than the FECG, the most important being maternal ECG (MECG) and, especially during labor, uterine EMG. This study proposes a new method to subtract MECG after detecting and removing abdominal signal segments with high-amplitude variations due to uterine contractions. The method removes MECG from abdominal signals using an approximation of the current MECG segment based on a linear combination of previous MECG segments aligned on the R-peak. The coefficients of the linear model are computed so that the squared error of the approximation over the whole current segment is minimized. Abdominal signal segments strongly affected by uterine contractions are detected by applying median filtering. The methods proposed are tested on real abdominal data recorded during labor, with FECG recorded using scalp electrodes synchronously recorded for comparison.

Eliminating cardiac contamination from myoelectric control signals developed by targeted muscle reinnervation

The electrocardiogram (ECG) artifact is a major noise contaminating the myoelectric control signals when using shoulder disarticulation prosthesis. This is an even more significant problem with targeted muscle reinnervation to develop additional myoelectric sites for improved prosthesis control in a bilateral amputee at shoulder disarticulation level. This study aims at removal of ECG artifacts from the myoelectric prosthesis control signals produced from targeted muscle reinnervation. Three ECG artifact removal methods based on template subtracting, wavelet thresholding and adaptive filtering were investigated, respectively. Surface EMG signals were recorded from the reinnervated pectoralis muscles of the amputee. As a key parameter for clinical myoelectric prosthesis control, the amplitude measurement of the signal was used as a performance indicator to evaluate the proposed methods. The feasibility of the different methods for clinical application was also investigated with consideration of the clinical speed requirements and memory limitations of commercial prosthesis controllers.

Elimination of electrocardiogram contamination from electromyogram signals: An evaluation of currently used removal techniques

Trunk electromyographic signals (EMG) are often contaminated with heart muscle electrical activity (ECG) due to the proximity of the collection sites to the heart and the volume conduction characteristics of the ECG through the torso. Few studies have quantified ECG removal techniques relative to an uncontaminated EMG signal (gold standard or criterion measure), or made direct comparisons between different methods for a given set of data. Understanding the impacts of both untreated contaminated EMG and ECG elimination techniques on the amplitude and frequency parameters is vital given the widespread use of EMG. The purpose of this study was to evaluate four groups of current and commonly used techniques for the removal of ECG contamination from EMG signals. ECG recordings at two intensity levels (rest and 50% maximum predicted heart rate) were superimposed on 11 uncontaminated biceps brachii EMG signals (rest, 7 isometric and 3 isoinertial levels). The 23 removal methods used were high pass digital filtering (finite impulse response (FIR) using a Hamming window, and fourth-order Butterworth (BW) filter) at five cutoff frequencies (20, 30, 40, 50, and 60 Hz), template techniques (template subtraction and an amplitude gating template), combinations of the subtraction template and high pass digital filtering, and a frequency subtraction/signal reconstruction method. For muscle activation levels between 10% and 25% of maximum voluntary contraction, the template subtraction and BW filter with a 30 Hz cutoff were the two best methods for maximal ECG removal with minimal EMG distortion. The BW filter with a 30 Hz cutoff provided the optimal balance between ease of implementation, time investment, and performance across all contractions and heart rate levels for the EMG levels evaluated in this study.

Contribution to structural intensity tool: application to the cancellation of ECG interference in diaphragmatic EMG

This paper is concerned with the problem of localizing the typical features of a signal when it is observed with noise in order to align a set of curves. Structural intensity (SI) is a recent tool that computes the "density" of the location of the modulus maxima of a wavelet representation along various scales in order to identify singularities of an unknown signal. As a contribution to this novel approach we establish a modified SI using the Berkner transform which allows maxima linkage to insure accurate localization of singularities. An application to cancellation of ECG interference in diaphragmatic EMG is also proposed.

Real-time ocular artifact suppression using recurrent neural network for electro-encephalogram based brain-computer interface

The paper presents an adaptive noise canceller (ANC) filter using an artificial neural network for real-time removal of electro-oculogram (EOG) interference from electro-encephalogram (EEG) signals. Conventional ANC filters are based on linear models of interference. Such linear models provide poorer prediction for biomedical signals. In this work, a recurrent neural network was employed for modelling the interference signals. The eye movement and eye blink artifacts were recorded by the placing of an electrode on the forehead above the left eye and an electrode on the left temple. The reference signal was then generated by the data collected from the forehead electrode being added to data recorded from the temple electrode. The reference signal was also contaminated by the EEG. To reduce the EEG interference, the reference signal was first low-pass filtered by a moving averaged filter and then applied to the ANC. Matlab Simulink was used for real-time data acquisition, filtering and ocular artifact suppression. Simulation results show the validity and effectiveness of the technique with different signal-to-noise ratios (SNRs) of the primary signal. On average, a significant improvement in SNR up to 27 dB was achieved with the recurrent neural network. The results from real data demonstrate that the proposed scheme removes ocular artifacts from contaminated EEG signals and is suitable for real-time and short-time EEG recordings.

A Novel Technique for the Extraction of Fetal ECG Using Polynomial Networks

In this paper, we propose a novel technique for extracting fetal electrocardiogram (FECG) from a thoracic ECG recording and an abdominal ECG recording of a pregnant woman. The polynomial networks technique is used to nonlinearly map the thoracic ECG signal to the abdominal ECG signal. The FECG is then extracted by subtracting the mapped thoracic ECG from the abdominal ECG signal. Visual test results obtained from real ECG signals show that the proposed algorithm is capable of reliably extracting the FECG from two leads only. The visual quality of the FECG extracted by the proposed technique is found to meet or exceed that of published results using other techniques such as the independent component analysis.

Adaptive filtering for ECG rejection from surface EMG recordings

Surface electromyograms (EMG) of back muscles are often corrupted by electrocardiogram (ECG) signals. This noise in the EMG signals does not allow to appreciate correctly the spectral content of the EMG signals and to follow its evolution during, for example, a fatigue process. Several methods have been proposed to reject the ECG noise from EMG recordings, but seldom taking into account the eventual changes in ECG characteristics during the experiment. In this paper we propose an adaptive filtering algorithm specifically developed for the rejection of the electrocardiogram corrupting surface electromyograms (SEMG). The first step of the study was to choose the ECG electrode position in order to record the ECG with a shape similar to that found in the noised SEMGs. Then, the efficiency of different algorithms were tested on 28 erector spinae SEMG recordings. The best algorithm belongs to the fast recursive least square family (FRLS). More precisely, the best results were obtained with the simplified formulation of a FRLS algorithm. As an application of the adaptive filtering, the paper compares the evolutions of spectral parameters of noised or denoised (after adaptive filtering) surface EMGs recorded on erector spinae muscles during a trunk extension. The fatigue test was analyzed on 16 EMG recordings. After adaptive filtering, mean initial values of energy and of mean power frequency (MPF) were significantly lower and higher respectively. The differences corresponded to the removal of the ECG components. Furthermore, classical fatigue criteria (increase in energy and decrease in MPF values over time during the fatigue test) were better observed on the denoised EMGs. The mean values of the slopes of the energy-time and MPF-time linear relationships differed significantly when established before and after adaptive filtering. These results account for the efficacy of the adaptive filtering method proposed here to denoise electrophysiological signals.