Cardiac Artifacts Research Articles

A planar mounted SQUID full-tensor module for magnetoenterogram denoising detection

Magntoenterogram (MENG) is a functional imaging method with great clinical potential for non-invasive diagnosis of intestinal diseases. In measurements of MENG, the cardiac artifacts are a strong biological source of interference. Based on wire-wound SQUID gradiometers, we constructed a planar mounted full-tensor compensation module for MENG system, which can obtain cardiac artifacts while suppressing ambient noise, so as to denoise MENG signals without increasing external ECG recording channels. The cardiac artifacts are regressed out from the full-tensor compensation module outputs, which can denoise the MENG measurements. Using this module, the SNR is about 70 dB better than the raw data. At last, 36-point array MENG signals were detected successfully and the contour map of jejunal slow-wave frequency (SWF) was consistent with the jejunal area (one part of the small intestine). This method will save costs and avoid the new electromagnetic interferences introduced by ECG equipment. It will lay a good foundation for the rapid clinical application of the MENG system.

Mechanical ventilation in a conscious male during exercise: a case report.

We recently explored the cardiopulmonary interactions during partial unloading of the respiratory muscles during exercise. Expanding upon this work, we present a noteworthy case study whereby we eliminated the influence of respiration on cardiac function in a conscious but mechanically ventilated human during exercise. This human was a young healthy endurance-trained male who was mechanically ventilated during semi-recumbent cycle exercise at 75 Watts (W) (∼30% Wmax). During mechanically ventilated exercise, esophageal pressure was reduced to levels indistinguishable from the cardiac artefact which led to a 94% reduction in the power of breathing. The reduction in respiratory pressures and respiratory muscle work led to a decrease in cardiac output (-6%), which was due to a reduction in stroke volume (-13%), left ventricular end-diastolic volume (-15%), and left-ventricular end-systolic volume (-17%) that was not compensated for by heart rate. Our case highlights the influence of extreme mechanical ventilation on cardiac function while noting the possible presence of a maximal physiological limit to which respiration (and its associated pressures) impacts cardiac function when the power of breathing is maximally reduced.

P.077 Reducing artifact during in bi-directional brain interfacing

Background: Bi-directional brain interfacing (closed loop DBS) is a modern focus of neuroengineering research. Most current clinical systems are open loop, allowing one way communication from the IPG battery to the brain. Bi-directional systems allow both stimulation and recording of neural activity (local field potential, LFP). The system algorithm can measure known pathologic LFPs to guide change in stimulation. However, recording LFPs from the brain encounters electrical artifact from the heart. Reducing artifact is imperative to accurate measurement of neural activity. Artifact will cause the bi-directional system to miscalculate stimulation parameters. This project evaluated reduction of artifact by moving the IPG further away from the heart in a device implanted into the skull. Methods: LFP data from ongoing clinical trials was collected and analysed for artifact using open source code. Anatomic targets include STN, PPN, CMT, and PAG. Results: Cardiac artifact is reduced in skull mounted DBS as shown by power spectral density of LFPs in each region. Conclusions: This project shows the importance of surgical placement of DBS sensing devices to reduce cardiac artifact in bi-directional brain interfacing. This has important engineering and surgical design implications for safety and performance as the field of closed loop DBS transitions from research to clinical settings.

Effect of Breath Training on Image Quality of Chest Magnetic Resonance Free-breathing Sequence.

Magnetic Resonance Imaging (MRI) plays a role in demonstrating substantial utility in lung lesion imaging, detection, diagnosis, and evaluation. Previous studies have found that free-breathing star VIBE sequences not only have high image quality but also have a high ability to detect and display nodules. However, in our routine clinical practice, we have encountered suboptimal image quality in the free-breathing sequences of certain patients. This study aims to assess the impact of breath training on the quality of chest magnetic resonance imaging obtained during free-breathing sequences. A total of 68 patients with lung lesions, such as nodules or masses detected via Computed Tomography (CT) examination, were prospectively gathered. They were then randomly divided into two groups: an observation group and a control group. Standard preparation was performed for all patients in both groups before the examination. The observation group underwent 30 minutes of breath training prior to the MRI examination additionally, followed by the acquisition of MRI free-breathing sequence images. The signal intensity (SI) and standard deviation (SD) of the lesion and adjacent normal lung tissue were measured, and the image signal-to-noise ratio (SNR) and contrast signal-to-noise ratio (CNR) of the lesion were calculated for objective image quality evaluation. The subjective image quality of the two groups of images was also evaluated using a 5-point method. MRI examinations were completed in both groups. Significantly better subjective image quality (edge and internal structure clarity, vascular clarity, breathing and cardiac artifacts, and overall image quality) was achieved in the observation group compared to the control group (P<0.05). In addition, higher SNR and CNR values for disease lesions were observed in the observation group compared to the control group (t=4.35, P<0.05; t=5.35, P<0.05). It is concluded that the image quality of free-breathing sequences MRI can be improved through breath training before examination.

Automated extraction of heart rate variability from magnetoencephalography signals

Magnetoencephalography (MEG) measures magnetic fluctuations in the brain generated by neural processes, some of which, such as cardiac signals, are generally removed as artifacts and discarded. However, heart rate variability (HRV) has long been regarded as a biomarker related to autonomic function, suggesting the cardiac signal in MEG contains valuable information that can provide supplemental health information about a patient. To enable access to these ancillary HRV data, we created an automated extraction tool capable of capturing HRV directly from raw MEG data with artificial intelligence. Five scans were conducted with simultaneous MEG and electrocardiogram (ECG) acquisition, which provides a ground truth metric for assessing our algorithms and data processing pipeline. In addition to directly comparing R-peaks between the MEG and ECG signals, this work explores the variation of the corresponding HRV output in time, frequency, and non-linear domains. After removing outlier intervals and aligning the ECG and derived cardiac MEG signals, the RMSE between the RR-intervals of each was RMSE1 = 2 ms, RMSE2 = 2 ms, RMSE3 = 8 ms, RMSE4 = 4 ms, RMSE5 = 13 ms. The findings indicate that cardiac artifacts from MEG data carry sufficient signal to approximate an individual's HRV metrics.

Ongoing EEG artifact correction using blind source separation

ObjectiveAnalysis of the electroencephalogram (EEG) for epileptic spike and seizure detection or brain-computer interfaces can be severely hampered by the presence of artifacts. The aim of this study is to describe and evaluate a fast automatic algorithm for ongoing correction of artifacts in continuous EEG recordings, which can be applied offline and online. MethodsThe automatic algorithm for ongoing correction of artifacts is based on fast blind source separation. It uses a sliding window technique with overlapping epochs and features in the spatial, temporal and frequency domain to detect and correct ocular, cardiac, muscle and powerline artifacts. ResultsThe approach was validated in an independent evaluation study on publicly available continuous EEG data with 2035 marked artifacts. Validation confirmed that 88% of the artifacts could be removed successfully (ocular: 81%, cardiac: 84%, muscle: 98%, powerline: 100%). It outperformed state-of-the-art algorithms both in terms of artifact reduction rates and computation time. ConclusionsFast ongoing artifact correction successfully removed a good proportion of artifacts, while preserving most of the EEG signals. SignificanceThe presented algorithm may be useful for ongoing correction of artifacts, e.g., in online systems for epileptic spike and seizure detection or brain-computer interfaces.

Downsloping TP Segment in the Precordial Leads on a Standard 12-lead Electrocardiogram: Suspected Cardiac Impulse-tapping Artifact.

We herein report an 80-year-old man showing a downsloping TP segment together with an increase in the height of the T wave in the precordial leads on a standard 12-lead electrocardiogram (ECG). Separately, an 87-year-old woman showed only a downsloping TP segment in the precordial leads on a standard 12-lead ECG. Neither patient reported chest pain or dyspnea when ECGs was obtained. This downsloping TP segment in the precordial leads on the standard 12-lead ECG is thought to be due to a cardiac impulse-tapping artifact. Differential diagnoses are also discussed.

Signal quality evaluation of single-channel respiratory sEMG recordings

This work presents a learning-based approach to respiratory surface electromyography (sEMG) quality evaluation. For this purpose, we define the signal-to-disturbance ratio (SDR), which quantifies the relative impact of disturbances on affected signal components. The SDR values for different disturbance types are estimated based on signal and disturbance characteristics and serve as a measure of signal quality. For the multivariate regression task, a fully connected neural network with three layers is trained based on standard and handcrafted signal features. The features are extracted before and after removing cardiac artifacts. This integration of domain-specific knowledge enables us to leverage shallow neural networks, which contributes to the interpretability of the method. For training and testing, artificially disturbed signals are generated based on undisturbed clinical sEMG recordings of mechanically ventilated patients and different disturbance models. This includes single and combined disturbances with different SDRs. The results show that the root-mean-square error (RMSE) of the estimated to the applied SDR regarding powerline (2.3dB) and spike-like disturbances (2.1dB) is smaller than the RMSE referring to high-frequency disturbances (3.6dB) and motion artifacts (5.8dB). Our findings also indicate that the SDR is estimated similarly well regardless of whether one or more contaminants are present simultaneously. Overall, the SDR can be determined with an RMSE of 3.8dB and a correlation coefficient of 0.97. This work contributes to automatically quantifying the impact of disturbances and objectively assessing the quality of sEMG signals of the respiratory muscles.

Reliability and validity of brain-gastric phase synchronization.

Recent studies have reported that various brain regions, mainly sensory, unimodal regions, display phase synchronization with the stomach's slow (0.05 Hz) myoelectrical rhythm. These gastric-brain interactions have broad implications, from feeding behavior to functional gastrointestinal disorders. However, in contrast to other interoceptive signals (e.g., heart rate) and their relation to the brain, little is known about the reliability of these gastric-brain interactions, their robustness to artifacts such as motion, and whether they can be generalized to new samples. Here we examined these aspects in 43 subjects that had undergone multiple runs of concurrent electrogastrography (EGG), brain fMRI, and pulse oximetry. We also repeated all analyses in an open dataset of a highly sampled individual. We found a set of brain regions that were coupled with the EGG signal after controlling for non-grey matter (GM) signals, head motion, and cardiac artifacts. These regions exhibited significant overlap with previous work. However, we also showed that prior to confound regression, the spatial extent of the gastric network was largely overestimated. Finally, we found substantial test-retest reliability in both the brain and the gastric signals when estimated alone, but not for measures of gastric-brain synchrony. Together, these results provide methodological scaffolding for future research into brain-stomach interactions and for a better understanding of the role of the gastric network.

Investigation of the Common Independent Component Analysis Approaches in Biological Signals for Removing Cardiac Field Artefact from EEG.

Electroencephalography (EEG) signals are often impacted by the cardiac field artefact (CFA), which can compromise EEG analysis. Independent component analysis (ICA) has proven effective in removing such artefacts, including CFA. This paper examines three well-known ICA algorithms commonly utilized in EEG signal processing and assesses their ability to decompose EEG into independent components (ICs) to remove CFA. The paper also investigates whether a new two-level ICA approach can improve performance. Results are evaluated using a synthetic dataset of 10 subjects.

Lack of Association between Diaphragm Thickening Fraction and Transdiaphragmatic Pressure Swing in COVID-19 Pneumonia during Helmet Continuous Positive Airway Pressure: Research Letter.

Recently, evaluation of the diaphragm by means of ultrasound has become popular.1 The increase in diaphragm thickness during inspiration (thickening fraction) has been proposed as a noninvasive bedside measure of diaphragm function,2,3 although previous studies have reported wide variability between thickening, inspiratory effort, and transdiaphragmatic pressure.4–6 We hypothesized that the force-length and force-generating relationship in the diaphragm is altered by different positive end-expiratory pressure (PEEP) levels in patients with SARS-CoV-2.7We investigated the relationship between diaphragm thickening fraction and transdiaphragmatic pressure swing during noninvasive helmet continuous positive airway pressure. From March to December 2021, a total of 26 consecutive patients with laboratory-confirmed SARS-CoV-2 infection admitted to the intensive care unit of Santa Chiara Hospital (Trento, Italy) were prospectively enrolled. The study was approved by the ethics committee (Rep. Int. 282/2022), and written informed consent was obtained. At intensive care unit admission, according to the local protocol, noninvasive respiratory assistance with helmet continuous positive airway pressure was started, and a nasogastric tube provided with two balloons for registering the esophageal and gastric pressures was inserted.8 Intragastric positioning of the tube was checked with standard methods; both balloons were inflated with manufacturer- recommended volume. Intragastric position of the lower balloon was confirmed by positive pressure deflections during gentle abdominal compressions; the mid-lower third esophageal position of the proximal balloon was confirmed by inspiratory negative deflections and the presence of cardiac artifacts. In vivo calibration of the filling volume was performed. Patients underwent a trial with three increasing levels of PEEP, lasting 30 min each. The first level was set at 0 cm H2O. PEEP was then increased to 5 and 10 cm H2O; fractional expired oxygen tension was left unchanged. An average of three measurements was made, with careful attention to select the same breaths for the diaphragm ultrasound and for esophageal/gastric pressure measurements. During the last 5 min of each step, esophageal and transdiaphragmatic pressure swing and diaphragmatic ultrasound were recorded. The right hemidiaphragm was identified in the zone of apposition as a three-layered structure by B-mode ultrasonography, with the probe at the mid-axillary line at the 10th intercostal space.1 Thickness was measured from frozen M-mode images as the distance from the pleural to the peritoneal line. All the examinations were performed by the same expert physician and recorded for a subsequent offline analysis.The study population had a median age of 67.5 [62.3–72.5 interquartile range] yr, 16 (61.5%) were male, body mass index was 27 [24–33 interquartile range] kg/m2, and Pao2/fractional expired oxygen tension ratio and Paco2 at admission were 122 [103–139 interquartile range] and 39 [37–44 interquartile range] mmHg, respectively. No association was found between diaphragm thickening fraction and transdiaphragmatic pressure swings or esophageal pressure swings, at 0 cm H2O, 5 cm H2O, and 10 cm H2O (fig. 1).In a previous investigation, Steinberg et al.9 found that neither thickening fraction nor diaphragmatic excursion was able to estimate esophageal pressure swings in patients with SARS-CoV-2. We sought to extend these findings by investigating the relationship between diaphragm ultrasound and the transdiaphragmatic pressure swing, which should be more representative of the contribution of the diaphragm to the total inspiratory effort. Similar to other authors,4 we confirm the lack of association between diaphragm thickening and the transdiaphragmatic pressure. Despite that it may lie on the nonlinear pressure–volume relationship of the diaphragm, during a single breath, the diaphragm inspiratory thickening and the transdiaphragmatic pressure generated depend on the specific pattern of thoracoabdominal motion (i.e., a descent of the diaphragm rather than expansion of the rib cage). The application of PEEP also causes changes in the diaphragm geometry also modifying the force generated by the diaphragm that may not be uniform across the muscle.The different position of the diaphragm muscle over its force or length relationship is likely explained by changes in end-expiratory lung volume during the PEEP trial. Although not directly measured, diaphragm dysfunction can also be a contributing factor; COVID-19 is associated with a viral myositis,10 which might have affected diaphragm force-generating capacity.However, the impact of the small sample size on any statistical inference must be considered, and the lack of Gilbert index and pressure-time product of diaphragmatic pressure calculation reduces the interpretation of our results.In summary, despite being promising for its feasibility and noninvasive characteristics, the assessment of diaphragm thickening should not be used as a surrogate for inspiratory effort. In SARS-CoV-2 patients during helmet continuous positive airway pressure in the acute setting, an increase in inspiratory effort, as measured by transdiaphragmatic pressure swings, is not related to diaphragm thickening fraction. We suggest caution against using this tool to inform clinical choices about respiratory support, at least until more robust data will be available.Support was provided solely from institutional and/or departmental sources.The authors declare no competing interests.

Removing the cardiac field artifact from the EEG using neural network regression.

When EEG recordings are used to reveal interactions between central-nervous and cardiovascular processes, the cardiac field artifact (CFA) poses a major challenge. Because the electric field generated by cardiac activity is also captured by scalp electrodes, the CFA arises as a heavy contaminant whenever EEG data are analyzed time-locked to cardio-electric events. A typical example is measuring stimulus-evoked potentials elicited at different phases of the cardiac cycle. Here, we present a nonlinear regression method deploying neural networks that allows to remove the CFA from the EEG signal in such scenarios. We train neural network models to predict R-peak centered EEG episodes based on the ECG and additional CFA-related information. In a second step, these trained models are used to predict and consequently remove the CFA in EEG episodes containing visual stimulation occurring time-locked to the ECG. We show that removing these predictions from the signal effectively removes the CFA without affecting the intertrial phase coherence of stimulus-evoked activity. In addition, we provide the results of an extensive grid search suggesting a set of appropriate model hyperparameters. The proposed method offers a replicable way of removing the CFA on the single-trial level, without affecting stimulus-related variance occurring time-locked to cardiac events. Disentangling the cardiac field artifact (CFA) from the EEG signal is a major challenge when investigating the neurocognitive impact of cardioafferent traffic by means of the EEG. When stimuli are presented time-locked to the cardiac cycle, both sources of variance are systematically confounded. Here, we propose a regression-based approach deploying neural network models to remove the CFA from the EEG. This approach effectively removes the CFA on a single-trial level and is purely data-driven, providing replicable results.

Heartbeat-Evoked Potential in Major Depressive Disorder: A Biomarker for Differential Treatment Prediction between Venlafaxine and rTMS?

Introduction: Currently, major depressive disorder (MDD) treatment plans are based on trial-and-error, and remission rates remain low. A strategy to replace trial-and-error and increase remission rates could be treatment stratification. We explored the heartbeat-evoked potential (HEP) as a biomarker for treatment stratification to either antidepressant medication or rTMS treatment. Methods: Two datasets were analyzed: (1) the International Study to Predict Optimized Treatment in Depression (iSPOT-D; n = 1,008 MDD patients, randomized to escitalopram, sertraline, or venlafaxine, and n = 336 healthy controls) and (2) a multi-site, open-label rTMS study (n = 196). The primary outcome measure was remission. Cardiac field artifacts were removed from the baseline EEG using independent component analysis (ICA). The HEP-peak was detected in a bandwidth of 20 ms around 8 ms and 270 ms (N8, N270) after the R-peak of the electrocardiogram signal. Differences between remitters and non-remitters were statistically assessed by repeated-measures ANOVAs for electrodes Fp1, Cz, and Oz. Results: In the venlafaxine subgroup, remitters showed a lower HEP around the N8 peak than non-remitters on electrode site Cz (p = 0.004; d = 0.497). The rTMS group showed a non-significant difference in the opposite direction (d = −0.051). Retrospective stratification to one of the treatments based on the HEP resulted in enhanced treatment outcome prediction for venlafaxine (+22.98%) and rTMS (+10.66%). Conclusion: These data suggest that the HEP could be used as a stratification biomarker between venlafaxine and rTMS; however, future out-of-sample replication is warranted.

Clinical evaluation of cylindrical regional suppression in dynamic contrast-enhanced breast MRI: An intra-individual comparison study on image quality and lesion conspicuity

PurposeTo evaluate the effect of a cylindrical regional-suppression technique (CREST) on image quality and lesion conspicuity in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast. MethodThis was a comparative study of 67 women with 44 lesions who underwent breast DCE-MRI with CREST (CREST-DCE) and had a previous DCE-MRI without CREST (conv-DCE) available. Two radiologists assessed image quality parameters and lesion conspicuity using five-point Likert scales. In an intra-individual comparison, the effects of CREST on image quality (strong degradation to strong improvement) were assessed. Moreover, both radiologists identified the post-contrast phase, which benefited the most from using CREST in direct comparison. The statistical analysis included the Wilcoxon signed-rank test. ResultsCardiac motion-rated artefacts were significantly reduced in CREST-DCE compared to conv-DCE (3.6 ± 1.2 [CREST-DCE] vs 2.1 ± 0.8 [conv-DCE], p < 0.001). At the axilla, the visualisation of anatomical structures (3.9 ± 1.0 vs 2.3 ± 1.2, p < 0.001) and the skin contour (4.3 ± 0.8 vs 3.0 ± 1.1, p < 0.001) were significantly improved in CREST-DCE, whereas ghosting artefacts were significantly less pronounced (3.8 ± 1.1 vs 2.4 ± 1.0, p < 0.001). The parasternal region was similarly assessable using both techniques (4.3 ± 1.1 vs 4.2 ± 1.2, p = 0.47). In direct comparison, CREST-DCE images were classified as “improved” in 54/67 and “equivalent” in 13/67 exams. The effects of CREST were found to be most pronounced in the very early post-contrast phase (32/67). The lesion conspicuity was rated similar for CREST and conv-DCE (4.7 ± 0.7 vs 4.8 ± 0.2, p = 0.18). ConclusionsCREST appears to be an effective tool to reduce cardiac motion-related artefacts and, therefore, may improve image quality in breast DCE-MRI without impairing lesion conspicuity.

Design of optimized adaptive filter for artifact removal from EEG signal

Removal of artifacts from the acquired biomedical signal is a major issue. Various signal processing techniques are developed by the researchers for some decades to extract the clinical information from EEG signals after the removal of unwanted artifacts during the time of its recording. In this work, an optimized weighted RLS filter is designed to suppress cardiac interference from the EEG signal. The cardiac artifact are estimated with the help of an optimized weighted RLS algorithm and eliminated from the EEG signal. Based on validation measures, Mean Square Error(MSE), Gain in Signal to Artifact Ratio(GSAR), Signal to Noise Ratio(SNR), Information Quantity(IQ), and INPS are used. The performance of the proposed method compared with the earlier methods like the Least square notch filter, ICA method are given in the result section. For the proposed method the MSE,GSAR,SNR,IQ, and INPS are found to be 0.1898 μV2 , 10.4807dB, 68.1117dB, 0.330, 10.91dB respectively. In every case, the performance of the proposed method is better than the existing method which proves its robustness.

MLMRS-Net: Electroencephalography (EEG) motion artifacts removal using a multi-layer multi-resolution spatially pooled 1D signal reconstruction network

Electroencephalogram (EEG) signals suffer substantially from motion artifacts when recorded in ambulatory settings utilizing wearable sensors. Because the diagnosis of many neurological diseases is heavily reliant on clean EEG data, it is critical to eliminate motion artifacts from motion-corrupted EEG signals using reliable and robust algorithms. Although a few deep learning-based models have been proposed for the removal of ocular, muscle, and cardiac artifacts from EEG data to the best of our knowledge, there is no attempt has been made in removing motion artifacts from motion-corrupted EEG signals: In this paper, a novel 1D convolutional neural network (CNN) called multi-layer multi-resolution spatially pooled (MLMRS) network for signal reconstruction is proposed for EEG motion artifact removal. The performance of the proposed model was compared with ten other 1D CNN models: FPN, LinkNet, UNet, UNet+, UNetPP, UNet3+, AttentionUNet, MultiResUNet, DenseInceptionUNet, and AttentionUNet++ in removing motion artifacts from motion-contaminated single-channel EEG signal. All the eleven deep CNN models are trained and tested using a single-channel benchmark EEG dataset containing 23 sets of motion-corrupted and reference ground truth EEG signals from PhysioNet. Leave-one-out cross-validation method was used in this work. The performance of the deep learning models is measured using three well-known performance matrices viz. mean absolute error (MAE)-based construction error, the difference in the signal-to-noise ratio (ΔSNR), and percentage reduction in motion artifacts (η). The proposed MLMRS-Net model has shown the best denoising performance, producing an average ΔSNR, η, and MAE values of 26.64 dB, 90.52%, and 0.056, respectively, for all 23 sets of EEG recordings. The results reported using the proposed model outperformed all the existing state-of-the-art techniques in terms of average η improvement.

Combining CEEMDAN with PCA for effective cardiac artifact suppression from single-channel EEG

The large signal due to cardiac activity can easily distort the signals originating from the relatively weak electrical activity of the brain, commonly measured as an Electroencephalogram (EEG). The artifact due to cardiac activity in EEG is called cardiac artifact, which contaminates the EEG data and makes interpretation of the EEG difficult for clinicians. Hence it is crucial to remove the cardiac artifact from EEG data. To suppress the cardiac artifact, we propose a novel approach to effectively extract cardiac artifacts from single-channel contaminated EEG data without using reference Electrocardiogram (EKG) data. The proposed methodology uses Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to decompose EEG data contaminated by cardiac activity into the Intrinsic Mode Functions (IMFs). Principal Component Analysis (PCA) is performed on these IMFs to obtain the principal components arranged in the order of decreasing variance. Effective cardiac artifact extraction is achieved by optimizing the signal reconstruction process so that only those principal components that capture the cardiac activity are retained with the constraint that distortion introduced in EEG data should be minimum. The comparison clearly shows that the proposed method outperforms conventionally employed methods like wavelet-based approach.

Reduction of the cardiac pulsation artifact and improvement of lesion conspicuity in flow-compensated diffusion images in the liver-A quantitative evaluation of postprocessing algorithms.

To enhance image quality of flow-compensated diffusion-weighted liver MRI data by increasing the lesion conspicuity and reducing the cardiac pulsation artifact using postprocessing algorithms. Diffusion-weighted image data of 40 patients with liver lesions had been acquired at 1.5T. These data were postprocessed with 5 different algorithms (weighted averaging, p-mean, percentile, outlier exclusion, and exception set). Four image properties of the postprocessed data were evaluated for optimizing the algorithm parameters. These properties were the lesion to tissue contrast-to-noise ratio (CNR), the reduction of the cardiac pulsation artifact, the data consistency, and the vessel darkness. They were combined into a total quality score ( set to 1 for the trace-weighted reference image), which was used to rate the image quality objectively. The weighted averaging algorithm performed best according to the total quality score ( ). The further ranking was outlier exclusion algorithm ( ), p-mean algorithm ( ), percentile algorithm ( ), and exception set algorithm ( ). All optimized algorithms except for the exception set algorithm corrected the pulsation artifact and increased the lesion CNR. Changes in were significant for all optimized algorithms except for the percentile algorithm. Liver ADC was significantly reduced (except for the exception set algorithm), particularly in the left lobe. Postprocessing algorithms should be used for flow-compensated liver DWI. The proposed weighted averaging algorithm seems to be suited best to increase the image quality of artifact-corrupted flow-compensated diffusion-weighted liver data.

Novel Outcome Analysis Tool for Hypoglossal Nerve Stimulator Sensor Lead Function and Comparison by Incision Type (2 Versus 3).

No reported outcome measures have been established to evaluate sensor lead function in the hypoglossal nerve stimulator (HNS). This study describes the development of novel functional outcome measures for intraoperative sensor electrode function and compares 2-incision and 3-incision outcomes for HNS. A retrospective cohort study of 100 consecutive patients who underwent HNS between June 2019 and September 2021. Demographic information, intraoperative findings, and immediate postoperative outcomes were recorded. Structured parameters were developed to compare intraoperative waveforms with six outcome measures utilized: waveform syncing, waveform amplitude, sensory current leakage, shark-fin morphology, cardiac artifact, and overall impression. Two sleep surgeons and two sleep medicine specialists compared all waveforms in a blinded fashion and assigned scores on the Likert Scale. The cohort included 50 three-incision and 50 two-incision patients. Age, gender, average body mass index, comorbidity profiles, and sleep endoscopy findings did not significantly differ between the two groups. No major complications occurred. The interclass-correlation-coefficient was greater than 0.7 for all comparisons (good to very good interrater reliability). There was no difference in waveform amplitude, cardiac artifact, sensory current leakage, or shark-fin morphology between the two groups. Waveform syncing and overall impression were statistically better in the 2-incision cohort. This study is the first to define a structured method of HNS sensor electrode outcome measurement and showed consistent measures by surgeons and sleep medicine specialists. This article supports the transition to the 2-incision technique among surgeons for placement of the sensor lead. Consideration should be given to utilizing this novel tool in the clinical/research setting and validating these measures moving forward. 3 Laryngoscope, 133:423-430, 2023.

Blind source separation of inspiration and expiration in respiratory sEMG signals

Objective. Surface electromyography (sEMG) is a noninvasive option for monitoring respiratory effort in ventilated patients. However, respiratory sEMG signals are affected by crosstalk and cardiac activity. This work addresses the blind source separation (BSS) of inspiratory and expiratory electrical activity in single- or two-channel recordings. The main contribution of the presented methodology is its applicability to the addressed muscles and the number of available channels. Approach. We propose a two-step procedure consisting of a single-channel cardiac artifact removal algorithm, followed by a single- or multi-channel BSS stage. First, cardiac components are removed in the wavelet domain. Subsequently, a nonnegative matrix factorization (NMF) algorithm is applied to the envelopes of the resulting wavelet bands. The NMF is initialized based on simultaneous standard pneumatic measurements of the ventilated patient. Main results. The proposed estimation scheme is applied to twelve clinical datasets and simulated sEMG signals of the respiratory system. The results on the clinical datasets are validated based on expert annotations using invasive pneumatic measurements. In the simulation, three measures evaluate the separation success: The distortion and the correlation to the known ground truth and the inspiratory-to-expiratory signal power ratio. We find an improvement across all SNRs, recruitment patterns, and channel configurations. Moreover, our results indicate that the initialization strategy replaces the manual matching of sources after the BSS. Significance. The proposed separation algorithm facilitates the interpretation of respiratory sEMG signals. In crosstalk affected measurements, the developed method may help clinicians distinguish between inspiratory effort and other muscle activities using only noninvasive measurements.