Cardiac Artifacts Research Articles

Improving Localization Accuracy of Neural Sources by Pre-processing: Demonstration With Infant MEG Data.

We discuss specific challenges and solutions in infant MEG, which is one of the most technically challenging areas of MEG studies. Our results can be generalized to a variety of challenging scenarios for MEG data acquisition, including clinical settings. We cover a wide range of steps in pre-processing, including movement compensation, suppression of magnetic interference from sources inside and outside the magnetically shielded room, suppression of specific physiological artifact components such as cardiac artifacts. In the assessment of the outcome of the pre-processing algorithms, we focus on comparing signal representation before and after pre-processing and discuss the importance of the different components of the main processing steps. We discuss the importance of taking the noise covariance structure into account in inverse modeling and present the proper treatment of the noise covariance matrix to accurately reflect the processing that was applied to the data. Using example cases, we investigate the level of source localization error before and after processing. One of our main findings is that statistical metrics of source reconstruction may erroneously indicate that the results are reliable even in cases where the data are severely distorted by head movements. As a consequence, we stress the importance of proper signal processing in infant MEG.

Comparison of high-pitch prospective electrocardiogram-gated pulmonary CT angiography with standard CT pulmonary angiography on dual-source CT for detection of subsegmental pulmonary embolism in patients suspected of acute pulmonary embolism.

PurposeObjective of this study was to compare high-pitch prospective electrocardiogram (ECG)-gated computed tomography (CT) pulmonary angiography (HP-PECG-gated CTPA) with standard-pitch non-ECG-gated CT pulmonary angiography (SP-NECG-gated CTPA) on 128-slice dual-source CT (DSCT) for the detection of subsegmental pulmonary embolism (SSPE) in patients suspected of acute pulmonary embolism (APE) with radiation and contrastoptimized protocols. Cardiac-related motion artefacts, lung image quality, and quantitative parameter (pulmonary arterial enhancement, radiation exposure, and contrast) volumes were also compared.Material and methodsThis prospective study enrolled 87 patients clinically suspected of APE and randomly distributed to either group by software. Two radiologists blinded to each other interpreted the images for assessment of SSPE, image quality, and quantitative parameters.ResultsSSPE was diagnosed in 15/44 (34.09%) patients in HP-PECG-gated CTPA, in comparison to 8/43 (18.60%) patients in SP-NECG-gated CTPA. Cardiac motion-related artefacts (blurring of bronchovascular structures and double-line artefacts) were statistically significantly less, with p-value < 0.05. Lung image quality was also better, with p-value < 0.001. Effective radiation dose and contrast volume in HP-PECG-gated CTPA were (2.54 ± 0.80 mSv, 45.05 ± 6 ml) versus SP-NECG-gated CTPA (3.17 ± 1.20 mSv, 74.19 ± 7.63 ml) with p-values of 0.007 and 0.001, respectively.ConclusionsRadiation and contrast volume-optimized HP-PECG-gated CTPA provides reduced cardiac motion related artefacts of pulmonary arteries, which allows enhanced detection of SSPE. It also provides better image quality of lung and parenchyma with lower radiation exposure and less contrast volume

Cardiac Artifact Noise Removal From Sleep EEG Signals Using Hybrid Denoising Model

Sleep is one of the prime natural activities for human well-being in physical, emotional, and mental aspects. The assessment of sleep Electroencephalography (EEG) signals is required to diagnose sleep-related neurological disorders. It is found that sleep EEG signals are extremely vulnerable to highly energetic electrocardiogram (ECG) signals. The intermixing of ECG into EEG, commonly known as cardiac artifacts, might severely affect the sleep EEG data. In order to have artifact-free EEG signal, a hybrid signal denoising methodology which includes empirical wavelet transforms (EWT), adaptive threshold-based nonlinear Teager-Kaiser energy operator (TEO), and customized morphological filter in accompanying with modified ensemble average subtraction (MEAS) is proposed for automatic detection and suppression of cardiac artifact from a single-channel EEG. The efficacy of the proposed methodology presented in the paper has been evaluated over standard public datasets such as CinC Challenge 2014 dataset (synthetic), and MIT-BIH polysomnography data (clinical). It has been observed that the proposed method outperforms other state-of-the-art automated EEG artifact elimination methods in terms of few popular denoising performance indexes such as signal to artifact ratio, percentage root mean square difference, percentage distortion in power spectral density, structural similarity index measure, and execution time. The proposed method is robust, time-efficient, and preserves the majority of EEG data with minimal loss, making it suitable for neuro clinical EEG analysis.

The Feasibility of Low-dose Chest CT Acquisition Protocol for the Imaging of COVID-19 Pneumonia.

This study aimed to investigate the feasibility of low-dose chest CT acquisition protocol for the imaging of COVID 19 disease or suspects of this disease in adults. In this retrospective case-control study, the study group consisted of 141 patients who were imaged with low dose chest CT acquisition protocol. The control group consisted of 92 patients who were imaged with standard protocol. Anteroposterior and lateral diameters of chest, effective diameter and scan length, qualitative and quantitative noise levels, volumetric CT dose index (CTDIvol), dose length product (DLP), and size-specific dose estimations were compared between groups. Radiation dose reduction by nearly 90% (CTDIvol and DLP values 1.06 mGy and 40.3 mGy.cm vs. 8.07 mGy and 330 mGy.cm, respectively; p < 0.001) was achieved with the use of low-dose acquisition chest CT protocol. Despite higher image noise with low-dose acquisition protocol, no significant effect on diagnostic confidence was encountered. Cardiac and diaphragm movement-related artifacts were similar in both groups (p=0.275). Interobserver agreement was very good in terms of diagnostic confidence assessment. For the imaging of COVID-19 pneumonia or suspects of this disease in adults, lowdose chest CT acquisition protocol provides remarkable radiation dose reduction without adversely affecting image quality and diagnostic confidence.

Recording activity in proximal muscle networks with surface EMG in assessing infant motor development

ObjectiveTo develop methods for recording and analysing infant’s proximal muscle activations. MethodsSurface electromyography (sEMG) of truncal muscles was recorded in three months old infants (N = 18) during spontaneous movement and controlled postural changes. The infants were also divided into two groups according to motor performance. We developed an efficient method for removing dynamic cardiac artefacts to allow i) accurate estimation of individual muscle activations, as well as ii) quantitative characterization of muscle networks. ResultsThe automated removal of cardiac artefacts allowed quantitation of truncal muscle activity, which showed predictable effects during postural changes, and there were differences between high and low performing infants.The muscle networks showed consistent change in network density during spontaneous movements between supine and prone position. Moreover, activity correlations in individual pairs of back muscles linked to infant́s motor performance. ConclusionsThe hereby developed sEMG analysis methodology is feasible and may disclose differences between high and low performing infants. Analysis of the muscle networks may provide novel insight to central control of motility. SignificanceQuantitative analysis of infant’s muscle activity and muscle networks holds promise for an objective neurodevelopmental assessment of motor system.

Artifact Removal Methods in EEG Recordings: A Review

To obtain the correct analysis of electroencephalogram (EEG) signals, non-physiological and physiological artifacts should be removed from EEG signals. This study aims to give an overview on the existing methodology for removing physiological artifacts, e.g., ocular, cardiac, and muscle artifacts. The datasets, simulation platforms, and performance measures of artifact removal methods in previous related research are summarized. The advantages and disadvantages of each technique are discussed, including regression method, filtering method, blind source separation (BSS), wavelet transform (WT), empirical mode decomposition (EMD), singular spectrum analysis (SSA), and independent vector analysis (IVA). Also, the applications of hybrid approaches are presented, including discrete wavelet transform - adaptive filtering method (DWT-AFM), DWT-BSS, EMD-BSS, singular spectrum analysis - adaptive noise canceler (SSA-ANC), SSA-BSS, and EMD-IVA. Finally, a comparative analysis for these existing methods is provided based on their performance and merits. The result shows that hybrid methods can remove the artifacts more effectively than individual methods.

MEGnet: Automatic ICA-based artifact removal for MEG using spatiotemporal convolutional neural networks

Magnetoencephalography (MEG) is a functional neuroimaging tool that records the magnetic fields induced by neuronal activity; however, signal from non-neuronal sources can corrupt the data. Eye-blinks, saccades, and cardiac activity are three of the most common sources of non-neuronal artifacts. They can be measured by affixing eye proximal electrodes, as in electrooculography (EOG), and chest electrodes, as in electrocardiography (ECG), however this complicates imaging setup, decreases patient comfort, and can induce further artifacts from movement. This work proposes an EOG- and ECG-free approach to identify eye-blinks, saccades, and cardiac activity signals for automated artifact suppression.The contribution of this work is three-fold. First, using a data driven, multivariate decomposition approach based on Independent Component Analysis (ICA), a highly accurate artifact classifier is constructed as an amalgam of deep 1-D and 2-D Convolutional Neural Networks (CNNs) to automate the identification and removal of ubiquitous whole brain artifacts including eye-blink, saccade, and cardiac artifacts. The specific architecture of this network is optimized through an unbiased, computer-based hyperparameter random search. Second, visualization methods are applied to the learned abstraction to reveal what features the model uses and to bolster user confidence in the model's training and potential for generalization. Finally, the model is trained and tested on both resting-state and task MEG data from 217 subjects, and achieves a new state-of-the-art in artifact detection accuracy of 98.95% including 96.74% sensitivity and 99.34% specificity on the held out test-set. This work automates MEG processing for both clinical and research use, adapts to the acquired acquisition time, and can obviate the need for EOG or ECG electrodes for artifact detection.

Predictors of Cardiac Implantable Electronic Device Artifact on Cardiac MRI: The Utility of a Device Related Score

Cardiac magnetic resonance imaging (CMR) image quality can be degraded by artifact in patients with cardiac implantable electronic devices (CIED). We aimed to establish a clinical risk score, so patient selection for diagnostic CMR could be optimised. In this retrospective cohort study, CMRs performed for clinical use in subjects with CIED from January 2016 to May 2019 were reviewed. Subject anthropometry, CIED generator/lead specifications and pre-scan chest X-ray (CXR) measurements were collected. Generator-related artifact size was measured on axial steady state free precession images. Interpretability of late gadolinium enhancement (LGE) imaging was performed based on a three-grade visual score attributed to each of 17 myocardial segments. Fifty-seven (57) patients (59±16 years, 74% male) fitted the inclusion criteria. Artifact precluded left ventricle (LV) evaluation (≥5 segments) in 17 (30%). Artifact was more common with implantable cardioverter-defibrillators, related to generator volume, mass, height, width, thickness, and area, along with right ventricular (RV) lead length and diameter (all p<0.05). Artifact was associated with distance from generator to LV apex, generator to RV lead tip and shortest distance from generator to heart on CXR (all p<0.05). On multivariable regression modelling, RV lead diameter (OR 5.861, 95% CI 1.866-18.407, p=0.002) and distance from generator to LV apex (OR 0.693, 95% CI 0.511-0.940, p=0.019) were independent predictors of artifact. Multivariable predictors were used to develop Device Related CMR Artifact Prediction Score (DR-CAPS), where all patients with DR-CAPS=0 had fully interpretable LGE imaging. Simple, readily available measures, such as lead characteristics and pre-scan CXR measures, can stratify patients via an artifact prediction score to optimise selection for diagnostic CMR.

Three-dimensional static and dynamic parallel transmission of the human heart at 7 T.

Three-dimensional (3D) human heart imaging at ultra-high fields is highly challenging due to respiratory and cardiac motion-induced artifacts as well as spatially heterogeneous profiles. In this study, we investigate the feasibility of applying 3D flip angle (FA) homogenization targeting the whole heart via static phase-only and dynamic kT-point in vivo parallel transmission at 7 T. 3D maps of the thorax were acquired under free breathing in eight subjects to compute parallel transmission pulses that improve excitation homogeneity in the human heart. To analyze the number of kT-points required, excitation homogeneity and radiofrequency (RF) power were compared using different regions of interest in six subjects with different body mass index (BMI) values of 20-34 kg/m2 for a wide range of regularization parameters. One subset of the optimized subject-specific pulses was applied in vivo on a 7 T scanner for six subjects in Cartesian 3D breath-hold scans as well as in two subjects in a radial phase-encoded 3D free-breathing scan. Across all subjects, 3-4 kT-points achieved a good tradeoff between RF power and nominal FA homogeneity. For subjects with a BMI in the normal range, the 4 kT-point pulses reliably improved the coefficient of variation by less than 10% compared with less than 25% achieved by static phase-only parallel transmission. in vivo measurements on a 7 T scanner validated the estimations and the pulse design, despite neglecting ΔB0 in the optimizations and Bloch simulations. This study demonstrates in vivo that kT-point pTx pulses are highly suitable for mitigating nominal FA heterogeneities across the entire 3D heart volume at 7 T. Furthermore, 3-4 kT-points demonstrate a practical tradeoff between nominal FA heterogeneity mitigation and RF power.

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.

Neural Respiratory Drive Estimation in Respiratory sEMG with Cardiac Arrhythmias.

Neural respiratory drive as measured by the electromyography allows the study of the imbalance between the load on respiratory muscles and its capacity. Surface respiratory electromyography (sEMG) is a non-invasive tool used for indirectly assessment of NRD. It also provides a way to evaluate the level and pattern of respiratory muscle activation. The prevalence of electrocardiographic activity (ECG) in respiratory sEMG signals hinders its proper evaluation. Moreover, the occurrence of abnormal heartbeats or cardiac arrhythmias in respiratory sEMG measures can make even more challenging the NRD estimation. Respiratory sEMG can be evaluated using the fixed sample entropy (fSampEn), a technique which is less affected by cardiac artefacts. The aim of this work was to investigate the performance of the fSampEn, the root mean square (RMS) and the average rectified value (ARV) on respiratory sEMG signals with supraventricular arrhythmias (SVA) for NRD estimation. fSampEn, ARV and RMS parameters increased as the inspiratory load increased during the test. fSampEn was less influenced by ECG with SVAs for the NRD estimation showing a greater response to respiratory sEMG, reflected with a higher percentage increase with increasing load (228 % total increase, compared to 142 % and 135 % for ARV and RMS, respectively).

Performance Evaluation of Fixed Sample Entropy for Lung Sound Intensity Estimation.

Lung sound (LS) signals are often contaminated by impulsive artifacts that complicate the estimation of lung sound intensity (LSI) using conventional amplitude estimators. Fixed sample entropy (fSampEn) has proven to be robust to cardiac artifacts in myographic respiratory signals. Similarly, fSampEn is expected to be robust to artifacts in LS signals, thus providing accurate LSI estimates. However, the choice of fSampEn parameters depends on the application and fSampEn has not previously been applied to LS signals. This study aimed to perform an evaluation of the performance of the most relevant fSampEn parameters on LS signals, and to propose optimal fSampEn parameters for LSI estimation. Different combinations of fSampEn parameters were analyzed in LS signals recorded in a heterogeneous population of healthy subjects and chronic obstructive pulmonary disease patients during loaded breathing. The performance of fSampEn was assessed by means of its cross-covariance with flow signals, and optimal fSampEn parameters for LSI estimation were proposed.

NeuroPycon: An open-source python toolbox for fast multi-modal and reproducible brain connectivity pipelines

Recent years have witnessed a massive push towards reproducible research in neuroscience. Unfortunately, this endeavor is often challenged by the large diversity of tools used, project-specific custom code and the difficulty to track all user-defined parameters. NeuroPycon is an open-source multi-modal brain data analysis toolkit which provides Python-based template pipelines for advanced multi-processing of MEG, EEG, functional and anatomical MRI data, with a focus on connectivity and graph theoretical analyses. Importantly, it provides shareable parameter files to facilitate replication of all analysis steps. NeuroPycon is based on the NiPype framework which facilitates data analyses by wrapping many commonly-used neuroimaging software tools into a common Python environment. In other words, rather than being a brain imaging software with is own implementation of standard algorithms for brain signal processing, NeuroPycon seamlessly integrates existing packages (coded in python, Matlab or other languages) into a unified python framework. Importantly, thanks to the multi-threaded processing and computational efficiency afforded by NiPype, NeuroPycon provides an easy option for fast parallel processing, which critical when handling large sets of multi-dimensional brain data. Moreover, its flexible design allows users to easily configure analysis pipelines by connecting distinct nodes to each other. Each node can be a Python-wrapped module, a user-defined function or a well-established tool (e.g. MNE-Python for MEG analysis, Radatools for graph theoretical metrics, etc.). Last but not least, the ability to use NeuroPycon parameter files to fully describe any pipeline is an important feature for reproducibility, as they can be shared and used for easy replication by others. The current implementation of NeuroPycon contains two complementary packages: The first, called ephypype, includes pipelines for electrophysiology analysis and a command-line interface for on the fly pipeline creation. Current implementations allow for MEG/EEG data import, pre-processing and cleaning by automatic removal of ocular and cardiac artefacts, in addition to sensor or source-level connectivity analyses. The second package, called graphpype, is designed to investigate functional connectivity via a wide range of graph-theoretical metrics, including modular partitions. The present article describes the philosophy, architecture, and functionalities of the toolkit and provides illustrative examples through interactive notebooks. NeuroPycon is available for download via github (https://github.com/neuropycon) and the two principal packages are documented online (https://neuropycon.github.io/ephypype/index.html, and https://neuropycon.github.io/graphpype/index.html). Future developments include fusion of multi-modal data (eg. MEG and fMRI or intracranial EEG and fMRI). We hope that the release of NeuroPycon will attract many users and new contributors, and facilitate the efforts of our community towards open source tool sharing and development, as well as scientific reproducibility.

Requirement-driven model-based development methodology applied to the design of a real-time MEG data processing unit

The paper describes a multidisciplinary work that uses a model-based systems engineering method for developing real-time magnetoencephalography (MEG) signal processing. We introduce a requirement-driven, model-based development methodology (RDD and MBD) to provide a high-level environment and efficiently handle the complexity of computation and control systems. The proposed development methodology focuses on the use of System Modeling Language to define high-level model-based design descriptions for later implementation in heterogeneous hardware/software systems. The proposed approach was applied to the implementation of a real-time artifact rejection unit in MEG signal processing and demonstrated high efficiency in designing complex high-performance embedded systems. In MEG signal processing, biological artifacts in particular have a signal strength that overtop the signal of interest by orders of magnitude and must be removed from the measurement to achieve high-quality source reconstructions with minimal error contributions. However, many existing brain–computer interface studies overlook real-time artifact removal because of the demanding computational process. In this work, an automated real-time artifact rejection method is introduced, which is based on the recently presented method “ocular and cardiac artifact rejection for real-time analysis in MEG” (OCARTA). The method has been implemented using the RDD and MBD approach and successfully verified on a Virtex-6 field-programmable gate array.

Effect of Physiological Noise on Thoracolumbar Spinal Cord Functional Magnetic Resonance Imaging in 3T Magnetic Field.

Introduction:Functional Magnetic Resonance Imaging (fMRI) methods have been used to study sensorimotor processing in the spinal cord. However, these techniques confront unwanted noises to the measured signal from the physiological fluctuations. In the spinal cord imaging, most of the challenges are consequences of cardiac and respiratory movement artifacts that are considered as significant sources of noise, especially in the thoracolumbar region. In this study, we investigated the effect of each source of physiological noise and their contribution to the outcome of the analysis of the blood-oxygen-level-dependent signal in the human thoracolumbar spinal cord.Methods:Fifteen young healthy male volunteers participated in the study, and pain stimuli were delivered on the L5 dermatome between the two malleoli. Respiratory and cardiac signals were recorded during the imaging session, and the generated respiration and cardiac regressors were included in the general linear model for quantification of the effect of each of them on the task-analysis results. The sum of active voxels of the clusters was calculated in the spinal cord in three correction states (respiration correction only, cardiac correction only, and respiration and cardiac noise corrections) and analyzed with analysis of variance statistical test and receiver operating characteristic curve.Results:The results illustrated that cardiac noise correction had an effective role in increasing the active voxels (Mean±SD = 23.46±9.46) compared to other noise correction methods. Cardiac effects were higher than other physiological noise sourcesConclusion:In summary, our results indicate great respiration effects on the lumbar and thoracolumbar spinal cord fMRI, and its contribution to the heartbeat effect can be a significant variable in the individual fMRI data analysis. Displacement of the spinal cord and the effects of this noise in the thoracolumbar and lumbar spinal cord fMRI results are significant and cannot be ignored.

Removing Cardiac Artifacts From Single-Channel Respiratory Electromyograms

Electromyographic (EMG) measurements of the respiratory muscles provide a convenient and noninvasive way to assess respiratory muscle function and detect patient activity during assisted mechanical ventilation. However, surface EMG measurements of the diaphragm and intercostal muscles are substantially contaminated by cardiac activity due to the vicinity of the cardiac muscles. Many algorithmic solutions to this problem have been proposed, yet a conclusive performance comparison of the most promising candidates currently is missing. The objective of this work is to provide a quantitative performance comparison of six previously proposed cardiac artifact removal algorithms operating on single-channel EMG measurements, and two newly proposed, improved versions of these algorithms. Algorithmic performance is evaluated quantitatively based on four different measures of separation success, using both synthetic validation signals and electromyographic measurements of the respiratory muscles in eight subjects. The compared algorithms are two versions of the empirical template subtraction algorithm, two model-based Bayesian filtering procedures, a wavelet denoising approach, an empirical mode decomposition (EMD) based approach, and classical high-pass filtering. Different algorithms perform well with respect to different performance measures. Template subtraction algorithms yield the best results if the characteristics of the raw signal are of interest, while filtering algorithms like simple high-pass filtering, wavelet denoising, and EMD-based denoising show superior performance for calculating a cleaned envelope signal. No algorithm completely removes the cardiac interference, but the characteristic errors introduced by the considered algorithms differ. Hence, the choice of the algorithm to use should be made depending on the target application. Finally, we also demonstrate that our empirical SNR measure, which can be calculated without knowledge of the true, undisturbed signals, correlates strongly with the exact reconstruction error. Thus, it represents a reliable indicator for algorithm performance on real measurement data.

P603Cardiac Magnetic Resonance evaluation and risk stratification of patients with unexplained or suspected arrhythmias

Abstract Introduction The etiological diagnosis of cardiac arrhythmias is often difficult. Cardiac Magnetic Resonance (CMR) is the gold standard exam for anatomical and functional cardiac evaluation and it may be indicated in patients with ventricular arrhythmias when echocardiography does not provide an accurate assessment of left and right ventricles (LV, RV). Purpose The aim of this study was to determine the impact of CMR in the diagnosis and stratification of arrhythmic risk in patients with confirmed or suspected arrhythmias, as well as to describe the changes observed. Methods We performed a prospective registry over a 5-year period of all the patients with arrhythmias who underwent CMR for diagnostic and risk stratification purposes. We followed a protocol to evaluate both anatomically and functionally the ventricles and to look for the presence of late gadolinium enhancement (LGE). Results A total of 78 patients were included, of which 65% were male and a mean age of 46±17 years-old was observed. The indications for CMR evaluation of patients with confirmed or suspected arrhythmias were as follows: 33% (n=26) of the patients had very frequent premature ventricular complexes (PVC), 23% (n=18) had sustained ventricular tachycardia (VT), 17% (n=13) suspected structural heart disease with high arrhythmic potential, 12% (n=9) unexplained recurrent syncope, 6% (n=5) supraventricular tachycardia, 5% (n=4) non-sustained VT and 4% (n=3) aborted sudden cardiac death. Depressed ventricular ejection fraction (&lt;50%) was present in 9% (n=7) for the LV and in 14% (n=11)for the RV. Dilation of the LV was found in 24% of the patients (n=19, mean LV volume: 115±4ml/m2) and RV dilation was present in only 1 patient who had right ventricle arrhythmogenic dysplasia (RVAD) (RV volume: 152ml/m2). Cardiac synchronization artifacts due to the presence of very frequent PVC compromised the calculation of v volumes in only 4% (n=3) of the patients. In total, 6% (n=5) had interventricular septum hypertrophy (mean 15±6g/m2), 10% (n=8) had a slight prolapse of the anterior leaflet of the mitral valve and 19% (n=15) had a dilated left auricle. LGE was present in 13% (n=10) and slight pericardium effusion was detected in 12% (n=9). CMR was considered normal in 65% (n=51), in 15% (n=12) we found nonspecific changes deserving follow-up and in 20% (n=15) it was possible to establish a diagnosis which was previously unknown: 5% (n=4) had hypertrophic cardiomyopathy, 4% (n=3) LV non-compaction, 4% (n=3) a myocarditis sequelae, 3% (n=2) RVAD, 3% (n=2) a myocardial infarction scar and 1 had non-ischemic dilated cardiomyopathy. Conclusions CMR is a technique with high spatial resolution, feasible and safe, which allowed an increase in diagnosis in 20% of the patients, thus contributing to the risk stratification of our study population with suspected high arrhythmic potential when the first-line complementary exams were inconclusive.

Automatic Estimation of Respiratory Effort using Esophageal Pressure.

Esophageal pressure is currently seen as the gold standard to quantify the respiratory effort during assisted spontaneous ventilation. Yet, the assessment of waveforms at the bedside is often complicated due to heavy interference by cardiac artifacts and due to the unknown dependency on the lung volume. We propose an algorithm that automatically removes artifacts and gives an estimate for the respiratory effort of a patient. The estimator is based on fitting a respiratory system model to the Campbell diagram and, thus, also gives insight into important patient parameters like the chest wall elastance. The feasibility of our approach is demonstrated using clinical datasets of patients on pressure support ventilation. The algorithm facilitates the interpretation of ventilatory waveforms and may support the overall assessment of patients.

Detailed measurements of oesophageal pressure during mechanical ventilation with an advanced high-resolution manometry catheter

BackgroundOesophageal pressure (PES) is used for calculation of lung and chest wall mechanics and transpulmonary pressure during mechanical ventilation. Measurements performed with a balloon catheter are suggested as a basis for setting the ventilator; however, measurements are affected by several factors. High-resolution manometry (HRM) simultaneously measures pressures at every centimetre in the whole oesophagus and thereby provides extended information about oesophageal pressure. The aim of the present study was to evaluate the factors affecting oesophageal pressure using HRM.MethodsOesophageal pressure was measured using a high-resolution manometry catheter in 20 mechanically ventilated patients (15 in the ICU and 5 in the OR). Different PEEP levels and different sizes of tidal volume were applied while pressures were measured continuously. In 10 patients, oesophageal pressure was also measured using a conventional balloon catheter for comparison. A retrospective analysis of oesophageal pressure measured with HRM in supine and sitting positions in 17 awake spontaneously breathing patients is also included.ResultsHRM showed large variations in end-expiratory PES (PESEE) and tidal changes in PES (ΔPES) along the oesophagus. Mean intra-individual difference between the minimum and maximum end-expiratory oesophageal pressure (PESEE at baseline PEEP) and tidal variations in oesophageal pressure (ΔPES at tidal volume 6 ml/kg) recorded by HRM in the different sections of the oesophagus was 23.7 (7.9) cmH2O and 7.6 (3.9) cmH2O respectively. Oesophageal pressures were affected by tidal volume, level of PEEP, part of the oesophagus included and patient positioning. HRM identified simultaneous increases and decreases in PES within a majority of individual patients. Compared to sitting position, supine position increased PESEE (mean difference 12.3 cmH2O), pressure variation within individual patients and cardiac artefacts. The pressure measured with a balloon catheter did not correspond to the average pressure measured with HRM within the same part of the oesophagus.ConclusionsThe intra-individual variability in PESEE and ΔPES is substantial, and as a result, the balloon on the conventional catheter is affected by many different pressures along its length. Oesophageal pressures are not only affected by lung and chest wall mechanics but are a complex product of many factors, which is not obvious during conventional measurements. For correct calculations of transpulmonary pressure, factors influencing oesophageal pressures need to be known. HRM, which is available at many hospitals, can be used to increase the knowledge concerning these factors.Trial registrationClinicalTrials.gov, NCT02901158

Automatic ECG Artefact Removal from EEG Signals

Abstract Electroencephalography (EEG) signals are frequently contaminated by ocular, muscle, and cardiac artefacts whose removal normally requires manual inspection or the use of reference channels (EOG, EMG, ECG). We present a novel, fully automatic method for the detection and removal of ECG artefacts that works without a reference ECG channel. Independent Component Analysis (ICA) is applied to the measured data and the independent components are examined for the presence of QRS waveforms using an adaptive threshold-based QRS detection algorithm. Detected peaks are subsequently classified by a rule-based classifier as ECG or non-ECG components. Components manifesting ECG activity are marked for removal, and then the artefact-free signal is reconstructed by removing these components before performing the inverse ICA. The performance of the proposed method is evaluated on a number of EEG datasets and compared to results reported in the literature. The average sensitivity of our ECG artefact removal method is above 99 %, which is better than known literature results.