Electrocardiogram Artifacts Research Articles

Noise Cancellation Signal Processing Method and Computer System for Improved Real-Time Electrocardiogram Artifact Correction During MRI Data Acquisition

A system was developed for real-time electrocardiogram (ECG) analysis and artifact correction during magnetic resonance (MR) scanning, to improve patient monitoring and triggering of MR data acquisitions. Based on the assumption that artifact production by magnetic field gradient switching represents a linear time invariant process, a noise cancellation (NC) method is applied to ECG artifact linear prediction. This linear prediction is performed using a digital finite impulse response (FIR) matrix, that is computed employing ECG and gradient waveforms recorded during a training scan. The FIR filters are used during further scanning to predict artifacts by convolution of the gradient waveforms. Subtracting the artifacts from the raw ECG signal produces the correction with minimal delay. Validation of the system was performed both off-line, using prerecorded signals, and under actual examination conditions. The method is implemented using a specially designed Signal Analyzer and Event Controller (SAEC) computer and electronics. Real-time operation was demonstrated at 1 kHz with a delay of only 1 ms introduced by the processing. The system opens the possibility of automatic monitoring algorithms for electrophysiological signals in the MR environment.

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.

Electrocardiographic artifacts due to electrode misplacement and their frequency in different clinical settings

Misplacement of electrodes can change the morphology of an electrocardiogram (ECG) in clinical important ways. To assess the frequency of these errors in different clinical settings, we collected ECGs routinely performed at the cardiology outpatient clinic and the intensive care unit. Lead misplacement was suspected when one of the following morphological changes occurred: QRS axis between 180° and −90°, positive P wave in lead aVR, negative P waves in lead I and/or II, very low (<0.1 mV) amplitude in an isolated peripheral lead, or abnormal R progression in the precordial leads. We analyzed 838 ECGs and identified 37 ECGs suspicious for electrode misplacement, from which 7 were confirmed. The frequency of ECG artifacts due to switched electrodes was 0.4% (3/739) at the outpatient clinic and 4.0% (4/99) at the intensive care unit ( P = .005). In conclusion, errors in ECG performance do occur with an increasing frequency in an acute medical care setting.

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.

An unusual electrocardiogram artifact: what is its source?

Abstract A diabetic female presented with nausea and vomiting. Her electrocardiogram showed sinus rhythm with two artifactual spikes, not synchronized with the cardiac rhythm. The patient had an implanted gastric electrical stimulation system for treating her diabetic gastroparesis. Recent DC shock for ventricular fibrillation during coronary angiography caused malfunction of the gastric pacemaker.

Comparison of Pleural Pressure and Transcutaneous Diaphragmatic Electromyogram in Obstructive Sleep Apnea Syndrome

Based on studies of the impact of esophageal pressure on cardiovascular variables during sleep, this signal can be used to refine the severity level in the clinical diagnosis of obstructive sleep apnea syndrome. We hypothesized that relative changes in diaphragmatic electromyogram (EMG) can reflect short-term changes in esophageal pressure durng obstructive apneas and hypopneas. Diaphragmatic EMG was sampled at 0.25 kHz; diaphragmatic EMG waveform was band-pass filtered and digitally converted; the electrocardiogram artifact was eliminated; using a gating procedure, the waveform was fast-Fourier transformed and digitally rectified; and a moving average of 200 milliseconds was calculated. For each inspiratory effort during apnea or hypopnea, we calculated maximum diaphragmatic EMG and esophageal pressure. Data were normalized calculating the percentage difference between the first obstructed and each subsequent inspiratory effort during the respiratory event. Sleep disorders laboratory. 9 patients with moderate obstructive sleep apnea syndrome presenting with apneas and hypopneas during sleep. None. 861 respiratory events were scored, and the evolution between esophageal pressure and diaphragmatic EMG were compared. Normalized data showed a good correlation between the 2 measures during apneas and hypopneas. There was a significant difference between the percentage increase in esophageal pressure and diaphragmatic EMG for apneas and hypopneas (esophageal pressure, apnea: 118.1% +/- 118.5%, hypopnea: 76.1% +/- 74.3%, P = .000; diaphragmatic EMG, 123.5% +/- 131.7%, hypopnea: 73.3% +/- 74.2%, P = .000). No significant differences for apnea or hypopnea were noted between the 2 measures under investigation. Diaphragmatic EMG may be clinically useful to describe relative changes in respiratory effort under conditions of apnea and hypopnea during sleep and to reliably dissociate central from obstructive events where esophageal pressure monitoring is not readily available.

Electrocardiogram artefacts caused by an abdominal electrostimulator

Electrocardiogram artefacts caused by an abdominal electrostimulator

Electrocardiogram artifacts caused by deep brain stimulation.

Deep brain stimulation (DBS) is increasingly used to treat a variety of neurological conditions (e.g. movement disorders and chronic pain). This prospective study was designed to detect electrocardiogram (ECG) artifacts induced by deep brain stimulation and to investigate which factors (patient disease, electrode position within the brain or type of stimulation) produced these artifacts. Twelve patients (four women, eight men) with deep brain stimulators were enrolled in the study. Patients were selected to represent the common indications for DBS (Parkinson's disease, tremor, dystonia), the common electrode locations (pallidum, thalamus, subthalamic nucleus) and the two types of stimulation (monopolar, bipolar). Patients had one ECG with the DBS turned 'on' and another with the DBS turned 'off'. The ECGs were then randomized and read by a cardiologist blinded to the status of the patient and DBS and artifacts were noted to be either present or absent. The six patients using monopolar stimulation all had artifacts on their electrocardiograms. These artifacts were severe enough to interfere with ECG interpretation. There were no artifacts detected in the six patients using bipolar stimulation. Electrode location and patient disease appeared to have no effect on ECG artifact. Deep brain stimulation can cause ECG artifacts when monopolar settings are used. These artifacts are not present with bipolar settings or when the DBS is turned 'off'. Knowledge of these potential ECG artifacts and how to avoid them is essential to facilitate accurate ECG interpretation.

Adaptive cancellation of ECG artifacts in the diaphragm electromyographic signals obtained through intraoesophageal electrodes during swallowing and inspiration

Abstract This paper presents a new and effective adaptive filtering method for the cancellation of electrocardiogram (ECG) artifacts in the electromyographic (EMG) signals obtained through intraoesophageal electrodes. This technique requires adjustable filter weights and two input signals. The primary input signal was the EMG signal containing the ECG artifact and the reference or the second input was the ECG signal measured simultaneously by two chest electrodes. The adaptive filter weights were initially set at zero and iteratively adjusted according to a least mean square algorithm proposed in this paper. The filter weights were adjusted in such a way that the output of the adaptive filter was a replica of the contaminating ECG signal, which was then subtracted from the EMG signal, yielding an ECG‐free EMG signal. The performance of the proposed method was investigated using computer simulations. It was found that ECG artifacts could be effectively cancelled while the EMG signal was not affected by adaptive filtering. Its applications showed that the proposed new algorithm had better performance than the conventional least mean square algorithm. The EMG signals were obtained in six healthy subjects, during swallows (oesophageal muscle EMG) and deep inspiration (crural diaphragm EMG). Our results demonstrated the effective cancellation of the ECG artifact in both swallow‐induced and inspiration‐related EMG signals. Spectral analysis of the EMG was performed on the ECG‐free EMG signal. The inspiration‐related EMG signal was found to have higher frequency components than the swallow‐related EMG.

Factors affecting data acquisition during gated radionuclide ventriculography

A 63-YEAR-OLD white male was referred for gated radionuclide ventriculography to evaluate left ventricular reserve and possible wall motion abnormalities during exercise. He had a questionable history of a myocardial infarction 4 years before examination. He had no history of hypertension, diabetes, hypercholesterolemia, or smoking. His family history was also unremarkable. His medications included only salicylates. The patient was evaluated by semiupright bicycle ergometry using one lead for R-wave triggering. A 12-1ead electrocardiogram (ECG) was used to evaluate ST segment changes. Two-minute data acquisitions were attempted during progressive 3-minute exercise stages. The patient exercised for 12.7 minutes to a peak double product of 16,524 and stopped secondary to shortness of breath. The peak heart rate was 102 beats/min, and the actual VO2 was 14.8 mL/kg/min, representing 61% and 55% of predicted maximum, respectively. Blood pressure at rest was normal and increased appropriately during exercise. The ECG at rest showed sinus rhythm with a right bundle branch block. Reduced voltage was also seen in the limb leads. No ST segment or T wave changes were noted after hyperventilation. The patient developed 0.5 mm to 0.75 mm upsloping ST depression in the inferior and anterolateral leads. These changes resolved quickly after exercise and were not diagnostic for ischemia. The patient described no anginal symptoms. The patient's resting ejection fraction was 68% and increased to 79% and 78% during the first and second exercise stages (Fig 1). Using a 10% window for the R-R interval, all beats were accepted during rest; 13 of 143 beats were rejected during the first exercise stage; and 19 of 144 beats were rejected during the second exercise stage. Cornplete data acquisition failure was noted during the third exercise stage as significant ECG artifacts developed (Fig 2). During the postexercise period, the left ventricular ejection fraction was 84%, with 8 of 148 beats rejected. No wall motion abnormalities were noted during the study. A large number of conditions are associated with impaired data acquisition during either resting or exercise gated radionuclide ventriculography. These conditions can be divided into two groups: (1) those associated with R-R interval variability and (2) those associated with increased P or T wave amplitude or with reduced R wave amplitude. The approach to data acquisition will differ with each of these conditions. When R-R variability arises, variable windowing or list mode acquisition (when available) may be helpful. When altered P, R, or T wave amplitude is present, lead repositioning (not possible during exercise) or altered R wave trigger thresholds may be helpful.

Electromyogram processing for sleep research

A computer method for quantifying the submental electromyographic surface interference pattern (EMG) during sleep and wakefulness by amplitude envelope measurement for consecutive 2-sec intervals is described. The method is largely insensitive to electrocardiogram (EKG) artifact. Though this algorithm was developed as part of a program to detect electroencephalographic (EEG), electrooculographic (EOG), tonic and phasic EMG changes during sleep, the method is applicable by itself wherever the envelope width of the EMG interference pattern is of interest. The results obtained correlate well with visual estimates of the amplitude envelope of the raw EMG. It offers increased speed, accuracy and reproducibility compared to visual EMG evaluation and enables a high degree of information extraction. The simplicity of the algorithm permits implementation and on-line processing on a small laboratory computer.