ECG Artifacts Research Articles

PCA-Based Preprocessing for Clustering-Based Fetal Heart Rate Extraction in Non-Invasive Fetal Electrocardiograms

Non-invasive fetal electrocardiography (NI-ECG) is based on the acquisition of signals from electrodes on the mother’s abdominal surface. This abdominal ECG (aECG) signal consists of the maternal ECG (mECG) along with the fetal ECG (fECG) and other noises and artifacts. These records allow the acquisition of valuable and reliable information that helps ensure fetal well-being during pregnancy. This paper proposes a procedure based on principal component analysis (PCA) to obtain a single-channel master abdominal ECG record that can be used as input to fetal heart rate extraction techniques. The new procedure requires three main processing stages: PCA-based analysis for fECG-component extraction, polarity test, and curve fitting. To show the advantages of the proposal, this PCA-based method has been used as the feeding stage to a previously developed clustering-based method for single-channel aECG fetal heart rate monitoring. The results obtained for a set of real abdominal ECG recordings from annotated public aECG databases, the Abdominal and Direct Fetal ECG Database and the Challenge 2013 Training Set A, show improved efficiency in fetal heart rate extraction and illustrate the benefits derived from the use of such a master abdominal ECG channel. This allows us to achieve proper fetal heart rate monitoring without the need for manual inspection and selection of channels to be processed, while also allowing us to analyze records that would have been discarded otherwise.

Assessing Skin Sympathetic Nerve Activity and Cardiac Effects in Patients with Gastric Electrical Stimulation

Background:Gastric Electrical Stimulation (GES) is a therapy for gastroparesis patients, alleviating symptoms like nausea, vomiting, and poor gastric emptying. The therapeutic impact of GES is believed to stem from changes in autonomic nerve activity, particularly parasympathetic activity via the vagal nerves. This study examines skin sympathetic nerve activity (SKNA) to assess autonomic nerve bursts, investigating the effects of GES on nerve activity and cardiac function.
 Methods:SKNA signals were recorded from 48 patients at three locations: left side of the neck (SKNA1), right side of the neck (SKNA2), and via ECG Lead 1 across the chest (SKNA3). Signals were band pass filtered (500 Hz to 1000 Hz) to eliminate ECG and muscle artifacts and highlight nerve activity. Using Labchart software, the absolute value integral (iSKNA) was calculated for each 100-millisecond sample, with resulting values exported to Excel. Data was collected during different GES conditions: GES On, GES at ½ voltage, GES Off, and GES back on. The threshold for nerve activity bursts was determined as the mean of iSKNA values + two times the standard deviation in each section.
 Results:Histograms of average aSKNA values showed reduced nerve activity with GES off. Left and vagal nerve activity on SKNA1 was significantly lower with GES fully on or off compared to GES at ½ voltage. No other significant differences were observed in variabilities or average aSKNAs in SKNA1 or SKNA2.
 Future Directions & Potential Impact:Burst analysis of iSKNA data will address key questions. Variations in burst activity with different GES conditions may elucidate GES's therapeutic effects on gastroparesis. Subsequent segmentation of data into patients with resolved and unresolved symptoms could reveal the relationship between burst activity and therapeutic efficacy. Additionally, correlation of burst data with heart rate variability analysis will provide insights into the impact of GES on cardiac function. This study will advance our understanding of GES effects and safety, potentially improving patient outcomes.

Evaluation of myocardial infarction by a 12-lead routine electrocardiogram: a case report of an ST-segment elevation.

The spiked helmet sign (SHS) is a type of ST-segment elevation associated with critical cardiac disease and a high risk of death. We report a case of SHS caused by an ECG artifact. A 60-year-old male patient presented to the clinic after suffering an electric shock. The initial 12-lead routine elec-trocardiogram showed an SHS. The patient received appropriate intravenous fluid replacement therapy, and after 30 minutes, the ST-T changes of the 12-lead electrocardiogram were all restored to normal. The patient was discharged after a 24-hour observation period in the emergency room.Recent studies have pointed out that there may be two different types of SHS. One is the mechanical factor, and the other is the significant prolongation of the QT interval. The two types have different clinical significance. In our report, the radial artery of the patient’s right wrist pulsed strongly, and after the occurrence of SHS, the SHS disappeared after adjusting the contact position of the electrode in his right arm. This SHS caused by mechanical traction was an ECG artifact.Although the SHS may be an essential indicator of critical illness, there are mechanical factors that lead to the appearance of ECG artifacts. Therefore, in clinical work, obtaining a complete medical history and primary conditions of the patient at the time of ECG sampling is necessary to help the diagnosis and thus avoid er-roneous treatment.

Digitizing ECG Signal using 2D Signal Convolution Approach

ECG signal printed on a graph paper has been widely used by medical examiners to analyze diseases related to the heart. Medical practitioners rely on historical records to perform diagnosis. Constantly accessing the ECG printed graph paper manually could be time consuming as there are bulk of graph papers for examination. The proposed work aims to convert the printed ECG graph paper into digitized ECG for remote diagnosis. The ECG printed graph paper undergoes conversion into ECG artifact before transforming as digitized ECG. In the initial phase, patient information in the ECG artifact is preserved by encoding into a QR Code. In phase two, preliminary processing is done on ECG artifact for removal of gridline in the background. Image convolution method is proposed as the process for background gridline removal. Then, morphological image processing is implemented to enhance the ECG artifact. In phase three, segmentation process takes place, in which the ECG artifact is divided into segments for separating the waveforms. In the final phase of ECG digitization, the location of the signal is traced for reshaping the ECG artifact as digitized ECG. The accuracy of the ECG digitization is measured through the heart rate that is calculated using our approach and compared with the one on ECG printed graph paper. The average sum of squared error of the heart rate between the ECG printed graph paper and digitized ECG is 0.005618. The digitized ECG can be useful for medical examiners and practitioners in telemedicine where remote diagnosis may be needed.

Pulsation artifact removal from intra-operatively recorded local field potentials using sparse signal processing and data-specific dictionary.

Neural recordings frequently get contaminated by ECG or pulsation artifacts. These large amplitude components can mask the neural patterns of interest and make the visual inspection process difficult. The current study describes a sparse signal representation strategy that targets to denoise pulsation artifacts in local field potentials (LFPs) recorded intraoperatively. To estimate the morphology of the artifact, we first detect the QRS-peaks from the simultaneously recorded ECG trace as an anchor point. After the LFP data has been epoched with respect to each beat, a pool of raw data segments of a specific length is generated. Using the K-singular value decomposition (K-SVD) algorithm, we constructed a data-specific dictionary to represent each contaminated LFP epoch in a sparse fashion. Since LFP is aligned to each QRS complex and the background neural activity is uncorrelated to the anchor points, we assumed that constructed dictionary will be formed to mainly represent the pulsation artifact. In this scheme, we performed an orthogonal matching pursuit to represent each LFP epoch as a linear combination of the dictionary atoms. The denoised LFP data is thus obtained by calculating the residual between the raw LFP and its approximation. We discuss and demonstrate the improvements in denoised data and compare the results with respect to principal component analysis (PCA). We noted that there is a comparable change in the signal for visual inspection to observe various oscillating patterns in the alpha and beta bands. We also see a noticeable compression of signal strength in the lower frequency band (<13 Hz), which was masked by the pulsation artifact, and a strong increase in the signal-to-noise ratio (SNR) in the denoised data.Clinical Relevance- Pulsation artifact can mask relevant neural activity patterns and make their visual inspection difficult. Using sparse signal representation, we established a new approach to reconstruct the quasiperiodic pulsation template and computed the residue signal to achieve noise-free neural activity.

0537 Incident Hypertension Prediction in Obstructive Sleep Apnea using Machine Learning

Abstract Introduction Obstructive sleep apnea (OSA) is associated with hypertension due to intermittent hypoxia and sleep fragmentation. Due to the complex pathogenesis of hypertension, it is difficult to predict incident hypertension associated with OSA. A Machine Learning (ML) model to predict incident hypertension identified up to five years after the diagnosis of OSA by polysomnography developed. Methods Polysomnography provides time-series data on multiple physiological signals. We used the sleep heart health study (SHHS) cohort, where 4,797 participants had OSA. After excluding participants with pre-existing hypertension at baseline, the sample size was 2,652. 1,814 participants with follow-up data at 5 years were included (911/1,814, 50% with incident hypertension). In addition to clinical data (i.e. age and race), features extracted from polysomnography (heart rate variability, HRV calculated based on the electrocardiography R-R interval), electroencephalography delta power, statistical information (i.e., mean and standard deviation of signals), and heart rate periodicity functions fed to support vector machine (SVM) ML model to train and validate. The polysomnography features were calculated over the 30-second epochs identified based on respiratory events and EEG arousal and respiratory events annotation, and their corresponding parts in other signals based on sampling frequency. Technical artifacts in oxygen saturation and ECG were reconstructed with the interpolation method and removed from the signal respectively. The SVM is a robust ML method trained in an iterative fashion to find the global optimum. In comparison to the Deep Neural Network (DNN) approaches, SVMs results are interpretable. Each polysomnography signal and its corresponding features were trained on a separate SVM, followed by a fusion of the SVM results. The final results were fused by voting of individual SVM results. Results The SVM ML model thus far has achieved a test accuracy (area under the curve, AUC) of 66.06%, sensitivity 63.21%, and specificity 68.9%. Conclusion This proof-of-concept study suggests that supervised ML models, such as the SVM, may be useful in predicting incident hypertension associated with OSA. Further research is required regarding optimal input features to boost the accuracy, followed by external validation of the model in additional OSA cohorts. Support (if any) Research support 1R56HL157182, NIH/NHLBI

ECG Changes Occurring During Chest Liposuction for Gynecomastia - Artifacts or a Cause for Concern?

Currently, liposuction alone or combined with various methods of gland excision has become the standard of care in the surgical management of gynecomastia. Although liposuction is considered a safe and straightforward method of body contouring, serious complications related to the procedure, including deaths, have been reported in the literature. We report the occurrence of ECG artifacts intra-operatively while performing chest liposuction under general anesthesia. Patients may receive unnecessary and potentially dangerous therapeutic interventions if these ECG artifacts are not identified correctly. A careful and rational evaluation of the patient and the ECG by the clinician can accurately identify these pseudo-abnormalities and avoid unnecessary therapies.

Sequential events of asystole and cranial nerve monitor induced narrow QRS complex ECG artefacts: An uncommon combination during a common neurosurgery.

Sequential events of asystole and cranial nerve monitor induced narrow QRS complex ECG artefacts: An uncommon combination during a common neurosurgery.

Electromechanical Association as a Stemi Mimicry - Case Report

Abstract Electromechanical association is a very unique physiological type of ECG artifact caused by the radial artery pulse tapping. ECG presentation of this artifact may imitate primary repolarization changes characteristic for acute coronary syndrome or electrolyte abnormalities. The peculiarity of electromechanical association is the synchronization with regular cardiac cycles, making a diagnostic challenge even for experience physicians. The present case is a rare example of an ECG artifact localized in a specific pattern mimics ECG changes of acute myocardial infarction with ST elevation (STEMI), including reciprocal ST depression. Moreover, serial prehospital ECGs verify evolution from hyperacute T waves to true ST elevation, as we seen in acute myocardial infarction. The key for artefact recognition was the knowledge about ECG leads derivation and identification the affected electrode. Electromechanical association frequency is unknown: it may be common, but often unrecognized finding. So far, electromechanical association in serial ECGs wasn’t reported, as per our knowledge.

Exploring decision making ‘noise’ when interpreting the electrocardiogram in the context of cardiac cath lab activation

In this commentary paper, we discuss the use of the electrocardiogram to help clinicians make diagnostic and patient referral decisions in acute care settings. The paper discusses the factors that are likely to contribute to the variability and noise in the clinical decision making process for catheterization lab activation. These factors include the variable competence in reading ECGs, the intra/inter rater reliability, the lack of standard ECG training, the various ECG machine and filter settings, cognitive biases (such as automation bias which is the tendency to agree with the computer-aided diagnosis or AI diagnosis), the order of the information being received, tiredness or decision fatigue as well as ECG artefacts such as the signal noise or lead misplacement. We also discuss potential research questions and tools that could be used to mitigate this ‘noise’ and improve the quality of ECG based decision making.

Deep-Learning-Based Approach for Automatic Detection and Correction of Ecg Artifacts

Deep-Learning-Based Approach for Automatic Detection and Correction of Ecg Artifacts

Spectral Characteristics of Motion Artifacts in Wireless ECG and their Correlation with Reference Motion Sensors.

The increasing population size of the elderly is fostering the development of telehealth and assisted living systems. In this respect, monitoring vital biophysical conditions using wireless devices, such as the wireless electrocardiogram (WECG), plays a pivotal role in telemonitoring. However, the freedom of movement brings with it motion artifacts, the magnitude of which can be significant enough to interfere with the cardiac signals. To reason about and remove the artifacts, reference models (signals) are needed. In the context of WECGs, one way to construct these models is to employ motion sensors that can pick up the motion affecting the electrodes of the WECGs. In this paper, we experimentally examine the spectra of motion artifacts and the existence of correlations between inertial sensors and motion artifacts. We make use of three different types of sensors (3D accelerometer, 3D gyroscope, and skin-electrode impedance sensor) to assess the characteristics of different movement types. We found that the spectra of motion artifacts are determined by the type of movement. While lower-intensity motion artifacts (e.g., bending forward) are most pronounced below 2 Hz, others (e.g., running) manifest themselves in a series of distinct peaks between 1-10 Hz.Index Terms- accelerometer, electrocardiogram, gyroscope, inertial sensor, motion artifacts, skin-electrode impedance, tele-monitoring.

A Robust Multi-Channel EMG System for Lower Back and Abdominal Muscles Training

Abstract EMG is an established method to acquire the action potentials of contracted muscles. Although commercial EMG systems are available and it is one of the most researched biosignals, it has never become widely used in rehabilitation or fitness training monitoring. The reasons are technical challenges of wearable EMG systems regarding electrode placement, motion artefacts and the complex connectivity of multi-channel EMG measurements. We address this problem for the lower back and abdominal musculature, through a novel dry electrodes belt, multi-channel high density EMG circuitry and problem-specific signal processing. The subject can easily strap the dry electrodes belt around himself which provides 16 EMG channels. Interferences from the ECG and motion artefacts are reduced by a stationary wavelet decomposition. Afterwards, an inter-channel filter is applied to increase the robustness of the signals. Subject measurements during different kinds of typical abdominal and lower back training exercises were performed wearing the novel dry electrodes belt. The results show the possibility of robust EMG measurements from the lower back and abdominal muscles by utilizing the gathered redundancy, appropriately. The additional information obtained via the multi-channel EMG circuitry and spatial oversampling can be used to address current problems of EMG applications. It combines the advantages of robustness and the capability of using comfortable dry electrodes. Therefore, the proposed measurement method for acquiring spatial information about the muscle contractions from the lower trunk can be used for rehabilitation or fitness training monitoring.

The “Depression and Elevation” Of ST Segment Settle Down As Momentary “Up and Down “ Of Architect of Artefact : A Case Report Of COVID-19 Patient

A young male was admitted with diagnosis of Covid-19 SARS infection and was having fever, cough &amp; non-cardiac chest pain. There was no past history of cardiac symptoms and physical examination was unremarkable. His vitals were stable. His ECG showed ST elevation and Tall but notched T wave in V3 and ST depression in inferior and lateral leads. Carefully looking these manifestations, they seem the artifacts based on their ECG features and history of the patient. ECG was repeated removing all possible sources of technical errors for these ECG manifestations which showed normal ECG confirming the diagnosis of artifactual ECG. Patient was discharged uneventfully. Recognition of ECG artifact and their technical causes is necessary to avoid inappropriate management.

The “Depression and Elevation” Of ST Segment Settle Down As Momentary “Up and Down “ Of Architect of Artefact : A Case Report Of COVID-19 Patient

A young male was admitted with diagnosis of Covid-19 SARS infection and was having fever, cough &amp; non-cardiac chest pain. There was no past history of cardiac symptoms and physical examination was unremarkable. His vitals were stable. His ECG showed ST elevation and Tall but notched T wave in V3 and ST depression in inferior and lateral leads. Carefully looking these manifestations, they seem the artifacts based on their ECG features and history of the patient. ECG was repeated removing all possible sources of technical errors for these ECG manifestations which showed normal ECG confirming the diagnosis of artifactual ECG. Patient was discharged uneventfully. Recognition of ECG artifact and their technical causes is necessary to avoid inappropriate management.

Artifacts in electrocardiogram interpreted as cardiac arrhythmias: Reports of clinical cases

Background: Artifact waves in the ECG and Holter recording are not rare in clinical practice and can be mistaken for tachyarrhythmia.&#x0D; Objective: To orient the practicing physicians to differentiate these artifacts from cardiac arrhythmias.&#x0D; Patients and Methods: Thirteen patients with incorrectly diagnosed cardiac arrhythmias by ECG or Holter recording then distinguished to be ECG artifacts were included in this study. The patients were collected from the author’s private practice in the northern Iraqi governorate of Sulaimanya during the period from June 2015 to August 2020. The differentiation of the artifact waves from the arrhythmias were made by careful inspection of the ECG, identification of the R waves within the artifact waves and correlating the artifact waves with the patient’s symptoms.&#x0D; Results: The artifacts were mistaken for ventricular fibrillation in two patients, ventricular tachycardia in four, atrial fibrillation in two, atrial flutter in four, and in one patient bradycardia of high grade atrio-ventricular block.&#x0D; Conclusion: Distinguishing artifact in ECG and differentiating them from cardiac arrhythmia is important to avoid mismanagement.

Techniques to Manage ECG Artifacts When Working With Surface EMG

Surface electromyographic (SEMG) amplitude signals can often contain rhythmic spikes due to cardiogenic electrical activity. The author discusses the impact of this activity on SEMG biofeedback training and techniques to reduce the problems caused by cardiogenic electrical activity. In particular, changing the low frequency cutoff of the digital filter settings to reduce cardiogenic electrical activity is recommended as a procedure to improve the fidelity of SEMG amplitude signals.

Mitigation of ocular artifacts for EEG signal using improved earth worm optimization-based neural network and lifting wavelet transform

An Electroencephalogram (EEG) is often tarnished by various categories of artifacts. Numerous efforts have been taken to improve its quality by eliminating the artifacts. The EEG involves the biological artifacts (ocular artifacts, ECG and EMG artifacts), and technical artifacts (noise from the electric power source, amplitude artifacts, etc.). From these physiological artifacts, ocular activities are one of the most well-known over other noise sources. Reducing the risks of this event and avoid it is practically very difficult, even impossible, as the ocular activities are involuntary tasks. To trim down the effect of ocular artifacts overlapping with EEG signal and overwhelm the subjected flaws, few intelligent approaches have to be developed. This proposal tempts to implement a novel method for detecting and preventing ocular artifacts from the EEG signal. The developed model involves two main phases: (a) Detection of Ocular artifacts and (b) Removal of ocular artifacts. For detecting the ocular artifacts, initially, the EEG is subjected to decomposition process using 5-level Discrete Wavelet Transform (DWT), and Empirical Mean Curve Decomposition (EMCD). Next to the decomposition process, the features like kurtosis, variance, Shannon’s entropy, and few first-order statistical features are extracted. These features will be helpful for the detection process in the classification side. For detecting the ocular artifacts from the decomposed signal, the extracted features are subjected to a machine learning algorithm called Neural Network (NN). As an improvement to the conventional NN, the training algorithm of ANN is improved by the improved Earth Worm optimization Algorithm (EWA) termed as Dual Positioned Elitism-based EWA (DPE-EWA), which updates the weight of NN to improve the performance. In the Removal phase, the optimized Lifting Wavelet Transform (LWT) is deployed, in which the improvement is made on optimizing the filter coefficients using the proposed DPE-EWA. Thus, the integration of optimized NN and optimized LWT suggests a potential possibility to accommodate the detection and removal of ocular artifacts that exist in the EEG signals.

Physiological Artifacts and the Implications for Brain-Machine-Interface Design.

The accurate measurement of brain activity by Brain-Machine-Interfaces (BMI) and closed-loop Deep Brain Stimulators (DBS) is one of the most important steps in communicating between the brain and subsequent processing blocks. In conventional chest-mounted systems, frequently used in DBS, a significant amount of artifact can be induced in the sensing interface, often as a common-mode signal applied between the case and the sensing electrodes. Attenuating this common-mode signal can be a serious challenge in these systems due to finite common-mode-rejection-ratio (CMRR) capability in the interface. Emerging BMI and DBS devices are being developed which can mount on the skull. Mounting the system on the cranial region can potentially suppress these induced physiological signals by limiting the artifact amplitude. In this study, we model the effect of artifacts by focusing on cardiac activity, using a current- source dipole model in a torso-shaped volume conductor. Performing finite element simulation with the different DBS architectures, we estimate the ECG common mode artifacts for several device architectures. Using this model helps define the overall requirements for the total system CMRR to maintain resolution of brain activity. The results of the simulations estimate that the cardiac artifacts for skull-mounted systems will have a significantly lower effect than non-cranial systems that include the pectoral region. It is expected that with a pectoral mounted device, a minimum of 60-80 dB CMRR is required to suppress the ECG artifact, depending on device placement relative to the cardiac dipole, while in cranially mounted devices, a 0 dB CMRR is sufficient, in the worst-case scenario. In addition, the model suggests existing commercial devices could optimize performance with a right-hand side placement. The methods used for estimating cardiac artifacts can be extended to other sources such as motion/muscle sources. The susceptibility of the device to artifacts has significant implications for the practical translation of closed-loop DBS and BMI, including the choice of biomarkers, the system design requirements, and the surgical placement of the device relative to artifact sources.

ECG Artifact by a Spinal Cord Neurostimulator: A Case Report

Background: Neurostimulator devices produce electrical oscillations that may prevent accurate diagnosis of an ECG.&#x0D; The Case: We present the case of a 68-year-old man who came to the emergency department with chest pain and a spinal cord neuromodulator device in situ to treat his polymyalgia rheumatic. A 12-lead ECG was obtained to determine the cause of the chest pain, and atrial fibrillation was wrongly diagnosed.&#x0D; Conclusion: This case reiterates the value of recognizing this uncommonly encountered ECG artifact to avoid unnecessary mistakes in interpretation of heart rhythms.