Electrocardiogram Artifacts Research Articles

Performance analysis of two stage adaptive FIR Filter Algorithms for PLI and BW artifact cancellation in ECG

Electrocardiogram (ECG) is a measure of the electrical movement of the heart, and is obtained by surface electrodes at standardized locations on the patient’s chest. During acquisition, various artifacts/noises such as power-line interference (PLI), baseline wander (BW), muscle artifacts (MA) and motion artifacts (EM) obscure the ECG. It is important that these artifacts are minimized for the clinicians to make better diagnosis on heart problems. This paper researches the creative idea of adaptive noise cancellation (ANC) using two stage form of adaptive filters. The concept of cascading and its algorithm for real-time application is simulated on MATLAB. The proposed algorithm utilizes two adaptive filters to estimate gradients accurately which results in good adaptation and performance. The objective of the present investigation is to provide solution in order to improve the performance of noise canceller in terms of filter parameters which are obtained with the help of adaptive algorithms. Different kinds of two stage ANC algorithms are used to eliminate artifacts in ECG by considering the noises such as power line interference and baseline wander. The simulation results show that the performance of the two stage ANC is superior to the conventional single stage ANC system in terms of higher signal-to-noise ratio. Two stage adaptive algorithms are applied on real time ECG signals and compared their performance with the conventional single stage adaptive algorithms in terms of parameters Signal-to-Noise Ratio (SNR), Mean Square Error (MSE), Root Mean Square Error (RMSE) and Distortion.

Investigation of the Effect of Video Game Play on Cognitive Behaviour using Adaptive Noise Cancellation and Wavelet Transform

This work shows the effect on cognitive performance while playing action video game based on the beta waves variation. The Present work initially shows the Signal to Noise Ratio (SNR) improvement by using an adaptive noise cancellation technique for suppressing the noise, here is to eliminate the ECG artifact from noisy EEG signal and then the wavelet analysis of EEG signals is done to study the brain activity. EEG signal is acquired experimentally from the subject by using the 10-20 system of electrode placement. In first stage the adaptive noise cancellation technique is implemented with the help of an adaptive filter to remove the artifact and hence to improve the signal to noise ratio (SNR). The adaptive algorithm used for the filter is the LMS which is a FIR based digital filter. A synthetic signal is also simulated to give a concept of the effect of the proposed active noise cancellation method to remove the noise or artifact. Synthetic signal analysis shows the suitability of the adaptive noise cancellation technique which is a de-noising tool, and here the de-noised signal is the error signal. Then in second stage, the de-noised signal is processed and the Wavelet Transform is used to provide the information of the β-activity. EEG signals from the frontal and parietal lobes are recorded and the effect is analyzed as the frontal and parietal lobe is associated with reasoning, planning, problem solving and orientation, recognition and perception of stimuli respectively. However, the presence of artifacts like electrocardiogram(ECG) in the EEG signal is a major problem in the study of brain activity. Though it is needed to record cerebral signals, it also records the signals that are not of cerebral origin called artifacts. Artifact removal from EEG signals is essential for better analysis and diagnosis. The EEG signal acquired from a patient is generally noisy and non-stationary by nature. So an adaptive noise cancellation technique is used to remove the artifact and hence the signal to noise ratio of the acquired EEG signal is improved. The EEG signal alone, is acquired from the subject and is used as the reference signal in the ANC noise cancellation technique to get the de-noise EEG signal. Then the de-noised signal is processed and the Wavelet Transform (WT) is used to provide the information of the β-activity. Experiments reveal that the proposed ANC method of noise or artifact removal along with the Wavelet Transform can be used to provide a clear information of the β-activity during the action video game play. The increase in the Beta activity in the eleventh hour of game play than the game play of the first hour indicates increase in cognition due to training in action video game play.

ECG Changes during Radiofrequency Ablation of Liver Metastasis under General Anaesthesia - A Dilemma with Cardiac Event

Electrocardiogram (ECG) monitoring is one of the mandatory monitoring during surgery under anaesthesia for identifying any cardiac event like arrthymias or ischemic changes. The operating room is increasingly being studded with various electrical equipments. These electrical gadgets may interfere with the ECG monitoring and thus produce various artifacts mimicking arrthymias or ischemic changes.We report a case of ECG changes induced artifactually by the use of radiofrequency ablation (RFA) for ablating hepatic metastasis under general anaesthesia. Our case emphasizes that awareness of such artifacts in ECG during radiofrequency ablation is required and if it occurs it should be cautiously interperated and managed.

Missing RRI interpolation for HRV analysis using locally-weighted partial least squares regression.

The R-R interval (RRI) fluctuation in electrocardiogram (ECG) is called heart rate variability (HRV). Since HRV reflects autonomic nervous function, HRV-based health monitoring services, such as stress estimation, drowsy driving detection, and epileptic seizure prediction, have been proposed. In these HRV-based health monitoring services, precise R wave detection from ECG is required; however, R waves cannot always be detected due to ECG artifacts. Missing RRI data should be interpolated appropriately for HRV analysis. The present work proposes a missing RRI interpolation method by utilizing using just-in-time (JIT) modeling. The proposed method adopts locally weighted partial least squares (LW-PLS) for RRI interpolation, which is a well-known JIT modeling method used in the filed of process control. The usefulness of the proposed method was demonstrated through a case study of real RRI data collected from healthy persons. The proposed JIT-based interpolation method could improve the interpolation accuracy in comparison with a static interpolation method.

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

Abstract Electromyography (EMG) signals provide significant information of muscle activity that may be used, among others, to estimate the activation stages during a certain activity or to predict fatigue. Heart activity or electrocardiogram (ECG) is one of the main contamination sources, especially in trunk muscles. This paper proposes a novel method based on Singular Spectrum Analysis (SSA) and frequency analysis to separate both signals present in the raw data. The performance of the method has been compared in time and frequency domains with traditional high-pass filtering or novel techniques such as Complete Ensemble Empirical Mode Decomposition or Wavelets analysis. The results show that for both time and frequency domains the proposed approach outperforms the other methods. Thus, the proposed SSA approach is a valid method to remove the ECG artifact from the contaminated EMG signals without using an ECG reference signal.

Effect of electrocardiogram interference on cortico-cortical connectivity analysis and a possible solution

BackgroundElectroencephalogram (EEG) signals are often contaminated by the electrocardiogram (ECG) interference, which affects quantitative characterization of EEG. New methodWe propose null-coherence, a frequency-based approach, to attenuate the ECG interference in EEG using simultaneously recorded ECG as a reference signal. After validating the proposed approach using numerically simulated data, we apply this approach to EEG recorded from six newborns receiving therapeutic hypothermia for neonatal encephalopathy. We compare our approach with an independent component analysis (ICA), a previously proposed approach to attenuate ECG artifacts in the EEG signal. The power spectrum and the cortico-cortical connectivity of the ECG attenuated EEG was compared against the power spectrum and the cortico-cortical connectivity of the raw EEG. ResultsThe null-coherence approach attenuated the ECG contamination without leaving any residual of the ECG in the EEG. Comparison with existing methodWe show that the null-coherence approach performs better than ICA in attenuating the ECG contamination without enhancing cortico-cortical connectivity. ConclusionOur analysis suggests that using ICA to remove ECG contamination from the EEG suffers from redistribution problems, whereas the null-coherence approach does not. We show that both the null-coherence and ICA approaches attenuate the ECG contamination. However, the EEG obtained after ICA cleaning displayed higher cortico-cortical connectivity compared with that obtained using the null-coherence approach. This suggests that null-coherence is superior to ICA in attenuating the ECG interference in EEG for cortico-cortical connectivity analysis.

Using a Cardiac Event Recorder in Children with Potentially Arrhythmia-Related Symptoms.

In this study, we reported our experience with the use of cardiac event recorders in pediatric patients. We evaluated 583 patients fitted with an event recorder (15-30 days) between March 2010 and November 2014 at our clinic. Excluded from the study were 117 patients with no recorded events and six with records contaminated by electrocardiogram artifacts. All of the patients received electrocardiograms, Holter monitoring, and echocardiography before the cardiac event recording. The patient sample consisted of 460 patients (64% female). The mean age was 12.8 ± 4.1 years. The median number of recorded events was 7. The indications included palpitations in 336 (73%) patients, syncope in 27 (6%) patients, and chest pain and palpitations in 97 (21%) patients. Whereas 64 patients (14%) had structural heart disease according to echocardiographic examination, the remaining patients had normal echocardiographic examination results. The most frequent cardiac comorbidities were mitral valve prolapse (6%), operated tetralogy of Fallot (1.5%), and complicated congenital heart diseases with single ventricle physiology (1%). The recorded events were sinus tachycardia in 113 (25%) patients, supraventricular tachycardia in 35 (8%) patients, ventricular extrasystole in 20 (4%) patients, supraventricular extrasystole in nine (2%) patients, and ventricular tachycardia in two (0.4%) patients. Based on the event recorder and follow-up electrocardiogram findings, 46 patients received an electrophysiology study/ablation. The symptom-rhythm correlation was 39%. In the presence of possible arrhythmia-related symptoms in children, a cardiac event recorder can be considered a useful primary diagnostic method. More research on this topic is needed.

Estimating actigraphy from motion artifacts in ECG and respiratory effort signals

Recent work in unobtrusive sleep/wake classification has shown that cardiac and respiratory features can help improve classification performance. Nevertheless, actigraphy remains the single most discriminative modality for this task. Unfortunately, it requires the use of dedicated devices in addition to the sensors used to measure electrocardiogram (ECG) or respiratory effort. This paper proposes a method to estimate actigraphy from the body movement artifacts present in the ECG and respiratory inductance plethysmography (RIP) based on the time-frequency analysis of those signals. Using a continuous wavelet transform to analyze RIP, and ECG and RIP combined, it provides a surrogate measure of actigraphy with moderate correlation (for ECG+RIP, , p < 0.001) and agreement (mean bias ratio of 0.94 and 95% agreement ratios of 0.11 and 8.45) with reference actigraphy. More important, it can be used as a replacement of actigraphy in sleep/wake classification: after cross-validation with a data set comprising polysomnographic (PSG) recordings of 15 healthy subjects and 25 insomniacs annotated by an external sleep technician, it achieves a statistically non-inferior classification performance when used together with respiratory features (average κ of 0.64 for 15 healthy subjects, and 0.50 for a dataset with 40 healthy and insomniac subjects), and when used together with respiratory and cardiac features (average κ of 0.66 for 15 healthy subjects, and 0.56 for 40 healthy and insomniac subjects). Since this method eliminates the need for a dedicated actigraphy device, it reduces the number of sensors needed for sleep/wake classification to a single sensor when using respiratory features, and to two sensors when using respiratory and cardiac features without any loss in performance. It offers a major benefit in terms of comfort for long-term home monitoring and is immediately applicable for legacy ECG and RIP monitoring devices already used in clinical practice and which do not have an accelerometer built-in.

Robust and accurate anomaly detection in ECG artifacts using time series motif discovery.

Electrocardiogram (ECG) anomaly detection is an important technique for detecting dissimilar heartbeats which helps identify abnormal ECGs before the diagnosis process. Currently available ECG anomaly detection methods, ranging from academic research to commercial ECG machines, still suffer from a high false alarm rate because these methods are not able to differentiate ECG artifacts from real ECG signal, especially, in ECG artifacts that are similar to ECG signals in terms of shape and/or frequency. The problem leads to high vigilance for physicians and misinterpretation risk for nonspecialists. Therefore, this work proposes a novel anomaly detection technique that is highly robust and accurate in the presence of ECG artifacts which can effectively reduce the false alarm rate. Expert knowledge from cardiologists and motif discovery technique is utilized in our design. In addition, every step of the algorithm conforms to the interpretation of cardiologists. Our method can be utilized to both single-lead ECGs and multilead ECGs. Our experiment results on real ECG datasets are interpreted and evaluated by cardiologists. Our proposed algorithm can mostly achieve 100% of accuracy on detection (AoD), sensitivity, specificity, and positive predictive value with 0% false alarm rate. The results demonstrate that our proposed method is highly accurate and robust to artifacts, compared with competitive anomaly detection methods.

A modified algorithm of the combined ensemble empirical mode decomposition and independent component analysis for the removal of cardiac artifacts from neuromuscular electrical signals

Neuronal and muscular electrical signals contain useful information about the neuromuscular system, with which researchers have been investigating the relationship of various neurological disorders and the neuromuscular system. However, neuromuscular signals can be critically contaminated by cardiac electrical activity (CEA) such as the electrocardiogram (ECG) which confounds data analysis. The purpose of our study is to provide a method for removing cardiac electrical artifacts from the neuromuscular signals recorded. We propose a new method for cardiac artifact removal which modifies the algorithm combining ensemble empirical mode decomposition (EEMD) and independent component analysis (ICA). We compare our approach with a cubic smoothing spline method and the previous combined EEMD and ICA for various signal-to-noise ratio measures in simulated noisy physiological signals using a surface electromyogram (sEMG). Finally, we apply the proposed method to two real-life sets of data such as sEMG with ECG artifacts and ambulatory dog cardiac autonomic nervous signals measured from the ganglia near the heart, which are also contaminated with CEA. Our method can not only extract and remove artifacts, but can also preserve the spectral content of the neuromuscular signals.

Electrocardiogram artifact caused by rigors mimicking narrow complex tachycardia: a case report

BackgroundThe electrocardiogram (ECG) is useful in the diagnosis of cardiac and non-cardiac conditions. Rigors due to shivering can cause electrocardiogram artifacts mimicking various cardiac rhythm abnormalities.Case presentationWe describe an 80-year-old Sri Lankan man with an abnormal electrocardiogram mimicking narrow complex tachycardia during the immediate post-operative period. Electrocardiogram changes caused by muscle tremor during rigors could mimic a narrow complex tachycardia.ConclusionsIdentification of muscle tremor as a cause of electrocardiogram artifact can avoid unnecessary pharmacological and non-pharmacological intervention to prevent arrhythmias.

Evaluation of a New Shirt-Based Electrocardiogram Device for Cardiac Screening in Soccer Players: Comparative Study With Treadmill Ergospirometry.

BackgroundPrevention of cardiac events during competitive sports is fundamental. New technologies with remote monitoring systems integrated into clothing could facilitate the screening of heart disease. Our aim was to evaluate the feasibility of Nuubo system during a field stress test performed by soccer players, comparing results with treadmill ergospirometry as test reference.MethodsNineteen male professional soccer players (19.2 ± 1.6 years) were studied. Wireless electrocardiographic monitoring during a Yo-Yo intermittent recovery test level 1 in soccer field and subsequent analysis of arrhythmias were firstly performed. Subsequently, in a period no longer than 4 weeks, each player underwent cardiopulmonary exercise testing in hospital.ResultsDuring Yo-Yo test, electrocardiogram (ECG) signal was interpretable in 16 players (84.2%). In the other three players, ECG artifacts did not allow a proper analysis. Estimation of maximum oxygen consumption was comparable between two exercise tests (VO2 max 53.3 ± 2.4 vs. 53.7 ± 3.0 mL/kg/min for Yo-Yo test and ergometry respectively; intra-class correlation coefficient 0.84 (0.63 - 0.93), P < 0.001). No arrhythmias were detected in any player during both tests.ConclusionsThe use of Nuubo’s technology allows an accurate single-lead electrocardiographic recording and estimation of reliable performance variables during exercise testing in field, and provides a new perspective to cardiac remote monitoring in collective sports.

Comparison of the “Real-life” Diagnostic Value of Two Recently Published Electrocardiogram Methods for the Differential Diagnosis of Wide QRS Complex Tachycardias

The diagnostic values of the aVR lead or "Vereckei algorithm," and the lead II R-wave peak time (RWPT) criterion, recently devised for the differential diagnosis of wide QRS complex tachycardias (WCTs), were compared. A total of 212 WCTs (142 ventricular tachycardias [VTs], 62 supraventricular tachycardias [SVT], and eight preexcitation SVTs) from 145 patients with proven electrophysiologic diagnoses were retrospectively analyzed by seven examiners blinded to the electrophysiologic diagnoses. The overall test accuracy of the Vereckei algorithm was superior to that of the RWPT criterion (84.3% vs. 79.6%; p = 0.0003). The sensitivity of the Vereckei algorithm for VT diagnosis was greater than that of RWPT criterion (92.4% vs. 79.1%; p < 0.0001). The negative predictive value (NPV) for the Vereckei algorithm was also greater (77.8%; 95% confidence interval [CI] = 73.6% to 82.1%) than that of the RWPT criterion (61.6%; 95% CI = 57.6% to 65.6%). The specificity of the Vereckei algorithm was lower than that of the RWPT criterion (64.7% vs. 80.9%; p < 0.0001). The positive predictive value (PPV) was also lower for the Vereckei algorithm (86.4%; 95% CI = 84.4% to 88.4%) than for the RWPT criterion (90.9%; 95% CI = 89.1% to 92.8%). Incorrect diagnoses made by the Vereckei algorithm were mainly due to misdiagnosis of SVT as VT (65.7% of cases), and those made by the RWPT criterion were due to the more dangerous misdiagnosis of VT as SVT (72.5% of cases). The Vereckei algorithm was superior in overall test accuracy, sensitivity, and NPV for VT diagnosis and inferior in specificity and PPV to the RWPT criterion. All of these parameters were lower in "real life" than those reported by the original authors for each of the particular electrocardiographic methods.

Building a Cepstrum-HMM kernel for Apnea identification

Authors present an approach based on the transformation of the Cepstral domain on Hidden Markov Model, which is employed for the automatic diagnosis of the Obstructive Sleep Apnea syndrome. The approach includes an Electrocardiogram artefacts removal and R wave detection stage. In addition, the system is modeled by a transformation of the Cepstral domain sequence using Hidden Markov Models (HMM). Final decisions are taken with two different approaches: A Hidden Markov Model and Support Vector Machine classifiers, where the parameterization is based on the transformation of HMM by a kernel. Two public databases have been used for experiments. Firstly, Physionet Apnea-ECG Database for building algorithms, and finally, The St. Vincent's University Hospital/University College Dublin Sleep Apnea Database for testing out with a blind independent dataset. Achieved results were up to 99.23% for Physionet Apnea-ECG Database, and 98.64% for The St. Vincent's Database.

Electrocardiogram Artifact

Electrocardiogram artifacts are common and can lead to confusion and misdiagnoses. Here, we present an unusual example of an artifact that has not previously been well described.

Optimization of the adult zebrafish ECG method for assessment of drug-induced QTc prolongation

IntroductionRecent studies have shown the utility of adult zebrafish ECG (electrocardiogram) in assessing drug-induced QTc prolongation. While the method has significant advantages over current ECG animal models including ethical issues, low compound requirement and expense, adoption of the method into drug discovery programs has been hampered by specific limitations. The limitations include the inability to determine the exact dose of test compound administered, and potential effects due to variables such as flow rate of oral perfusion and immobilization method. We describe a refined method for the reproducible recording of the adult zebrafish ECG and illustrate its application in investigating drug-induced QTc prolongation using the histamine receptor antagonist Terfenadine as a test drug. MethodWe chose to perform parenteral administration of test drug instead of perfusion on the basis of mg per kg body weight of adult zebrafish. Acclimatization and immobilization methods were optimized to avoid ECG artifacts due to sudden environmental changes. We further modified the formula for QT correction and ensured reproducible recording of stable ECGs. Various concentrations of Terfenadine were used and the resultant proarrhythmic effects were analyzed as compared to the baseline and untreated controls. ResultsNormal, stable and reproducible ECGs were recorded in all zebrafish. Terfenadine at the rate of 0.1mg/kg body weight was found to be the NOAEL. We found an excellent correlation between known QTc effects in humans and those observed in adult zebrafish at all concentrations. All Terfenadine-induced proarrhythmic effects observed in zebrafish were dose and time dependent. DiscussionWe report a refined method for reproducible recording of stable zebrafish ECGs to facilitate its routine application in preclinical investigation of QTc-prolonging drugs with reliable estimation of NOAEL. Our study is of relevance to the development and use of alternate animal models in drug discovery.

Artifact Removal from Biosignal using Fixed Point ICA Algorithm for Pre-processing in Biometric Recognition

In the modern world of automation, biological signals, especially Electroencephalogram (EEG) and Electrocardiogram (ECG), are gaining wide attention as a source of biometric information. Earlier studies have shown that EEG and ECG show versatility with individuals and every individual has distinct EEG and ECG spectrum. EEG (which can be recorded from the scalp due to the effect of millions of neurons) may contain noise signals such as eye blink, eye movement, muscular movement, line noise, etc. Similarly, ECG may contain artifact like line noise, tremor artifacts, baseline wandering, etc. These noise signals are required to be separated from the EEG and ECG signals to obtain the accurate results. This paper proposes a technique for the removal of eye blink artifact from EEG and ECG signal using fixed point or FastICA algorithm of Independent Component Analysis (ICA). For validation, FastICA algorithm has been applied to synthetic signal prepared by adding random noise to the Electrocardiogram (ECG) signal. FastICA algorithm separates the signal into two independent components, i.e. ECG pure and artifact signal. Similarly, the same algorithm has been applied to remove the artifacts (Electrooculogram or eye blink) from the EEG signal.

Automatic Apnea Identification by Transformation of the Cepstral Domain

A new approach based on the transformation of the Cepstral domain is developed on this work. This approach reaches an automatic diagnosis for the syndrome of obstructive sleep apnea that includes a specific block for the removal of electrocardiogram (ECG) artifacts and the R wave detection. The system is modeled by a transformation of the Cepstral domain sequence using hidden Markov model (HMM). The final decision is done with two different approaches: one based on HMM as a classifier and a second one based on support vector machines classification and a parameterization based on the transformation of HMM by a kernel. The later approach reached results up to 99.23 %, using all test samples from Physionet Apnea-ECG Database.

Electrocardiographic “Bleed Through” to the Electroencephalogram on an Electroconvulsive Therapy (ECT) Tracing

Improper recording electrode placement can cause artifacts on electroconvulsive therapy tracings. This shows an example of electrocardiogram artifact in the electroencephalogram.

ECG and PLI Artifacts Cancellation from Electromyogram

Electromyography (EMG) is a test used to discern primary muscle conditions from muscle weakness caused by neurological disorders. EMG recordings are frequently contaminated by noise. The objective of this research was to remove or reduce the two main noises which disturb the EMG signal with help of filtering. The most common noises which are present in the EMG signal are the electrocardiogram (ECG) and the power line interference (PLI). The butter worth filter is proposed to cancel the ECG noise ,and notch filter for removal of PLI artifacts from the main EMG signal and artificial neural network (ANN) which give the error signal (i.e. difference between the actual and target values ) and enhance the signal-to-noise ratio of the output signal. The performance evaluation of the proposed technique is done in terms of signal- to- noise ratio, mean square error, and convergence time. Keywords - Artificial neural network, Digital Filters, Electromyogram (EMG), ECG artifact, PLI.