Ongoing EEG artifact correction using blind source separation

Objective: Analysis 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. Methods: The 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. Results: The 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. Conclusions: Fast ongoing artifact correction successfully removed a good proportion of artifacts, while preserving most of the EEG signals. Significance: The 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.Comment: 16 pages, 4 figures, 3 table

Outcomes of small for gestational age micropremies depending on how young or how small they are

PurposeThe outcomes of small for gestational age (SGA) infants especially in extremely low birth weight infants (ELBWIs) are controversial. This study evaluated the mortality and morbidity of ELBWIs, focusing on whether or not they were also SGA.MethodsThe medical records of 415 ELBWIs (birth weight <1,000 g), who were inborn and admitted to the Samsung Medical Center neonatal intensive care unit from January 2000 to December 2008, were reviewed retrospectively. Mortality and morbidities were compared by body size groups: very SGA (VSGA), birth weight ≤3rd percentile; SGA, 3rd to 10th percentile; and appropriate for gestational age (AGA) infants, >10th percentile for gestational age. For gestational subgroup analysis, groups were divided into infants with gestational age ≤24+6 weeks (subgroup I), 25+0 to 26+6 weeks (subgroup II), and ≥27+0 weeks (subgroup III).ResultsGestational age was 29+2±2+6 weeks in the VSGA infants (n=49), 27+5±2+2 weeks in the SGA infants (n=45), and 25+4±1+4 weeks in AGA infants (n=321). Birth weight was 692±186.6 g, 768±132.9 g, and 780±142.5 g in the VSGA, SGA, and AGA groups, respectively. Cesarean section rate and maternal pregnancy-induced hypertension were more common in the VSGA and SGA than in AGA pregnancies. However, chorioamnionitis was more common in the AGA group. The mortalities of the lowest gestational group (subgroup I), and also of the lower gestational group (subgroup I+II) were significantly higher in the VSGA group than the SGA or AGA groups (P=0.020 and P=0.012, respectively). VSGA and SGA infants showed lower incidence in respiratory distress syndrome, ductal ligation, bronchopulmonary dysplasia, intraventricular hemorrhage than AGA group did. However, by multiple logistic regression analysis of each gestational subgroup, the differences were not significant.ConclusionOf ELBWIs, extremely SGA in the lower gestational subgroups, had an impact on mortality, which may provide information useful for prenatal counseling

Prognostic value of pre-procedural left ventricular strain for clinical events after transcatheter aortic valve implantation.

BACKGROUND:Transcatheter aortic valve implantation (TAVI) is an alternative therapy for surgically high-risk patients with severe aortic stenosis (AS). Although TAVI improves survival of patients with severe AS, the mechanism of this effect remains to be clarified. We investigated the effects of TAVI on left ventricular (LV) function and identified the predictive parameters for cardiac events after TAVI. METHODS AND RESULTS:We studied 128 patients with severe symptomatic AS who underwent TAVI. Echocardiographic assessments were performed before and after TAVI. In addition to the conventional echocardiographic parameters such as LV ejection fraction (LVEF) and LV mass index (LVMI), the LV global longitudinal strain (GLS) and early diastolic peak strain rate (SR_E) using two-dimensional speckle tracking echocardiography were also evaluated. All patients were assessed for clinical events including major adverse cardiac events and stroke according to Valve Academic Research Consortium-2 criteria. GLS, early diastolic peak velocity (e'), aortic regurgitation (AR) severity, and SR_E were significantly improved after TAVI. Thirteen patients had an event during the observational period of 591 days (median). Patients with events had higher LVMI, more severe AR, and worse GLS compared to those without events. Furthermore, receiver-operating curve analysis revealed that GLS was the strongest predictor for clinical events (p = 0.009; area under the curve, 0.73). CONCLUSION:Preoperative LV geometric deformation and dysfunction, as a consequence of the cumulative burden of pressure overload, improved after TAVI and could predict cardiac events after TAVI

Artificial intelligence-enabled fully automated detection of cardiac amyloidosis using electrocardiograms and echocardiograms.

Patients with rare conditions such as cardiac amyloidosis (CA) are difficult to identify, given the similarity of disease manifestations to more prevalent disorders. The deployment of approved therapies for CA has been limited by delayed diagnosis of this disease. Artificial intelligence (AI) could enable detection of rare diseases. Here we present a pipeline for CA detection using AI models with electrocardiograms (ECG) or echocardiograms as inputs. These models, trained and validated on 3 and 5 academic medical centers (AMC) respectively, detect CA with C-statistics of 0.85-0.91 for ECG and 0.89-1.00 for&nbsp;echocardiography. Simulating deployment on 2 AMCs indicated a positive predictive value (PPV) for the ECG model of 3-4% at 52-71% recall. Pre-screening with ECG enhance the echocardiography model performance at 67% recall from PPV of 33% to PPV of 74-77%. In conclusion, we developed an automated strategy to augment CA detection, which should be generalizable to other rare cardiac diseases