Assessing ECG signal quality indices to discriminate ECGs with artefacts from pathologically different arrhythmic ECGs

False and non-actionable alarms in critical care can be reduced by developing algorithms which assess the trueness of an arrhythmia alarm from a bedside monitor. Computational approaches that automatically identify artefacts in ECG signals are an important branch of physiological signal processing which tries to address this issue. Signal quality indices (SQIs) derived considering differences between artefacts which occur in ECG signals and normal QRS morphology have the potential to discriminate pathologically different arrhythmic ECG segments as artefacts. Using ECG signals from the PhysioNet/Computing in Cardiology Challenge 2015 training set, we studied previously reported ECG SQIs in the scientific literature to differentiate ECG segments with artefacts from arrhythmic ECG segments. We found that the ability of SQIs to discriminate between ECG artefacts and arrhythmic ECG varies based on arrhythmia type since the pathology of each arrhythmic ECG waveform is different. Therefore, to reduce the risk of SQIs classifying arrhythmic events as noise it is important to validate and test SQIs with databases that include arrhythmias. Arrhythmia specific SQIs may also minimize the risk of misclassifying arrhythmic events as noise

Size constancy is preserved but afterimages are prolonged in typical individuals with higher degrees of self-reported autistic traits

Deficits in perceptual constancies from early infancy have been proposed to contribute to autism and exacerbate its symptoms (Hellendoorn et al., Frontiers in Psychology 6:1–16, 2015). Here, we examined size constancy in adults from the general population (N = 106) with different levels of self-reported autistic traits using an approach based on negative afterimages. The afterimage strength, as indexed by duration and vividness, was also quantified. In opposition to the Hellendoorn and colleagues’ model, we were unable to demonstrate any kind of relationship between abilities in size constancy and autistic traits. However, our results demonstrated that individuals with higher degrees of autistic traits experienced more persistent afterimages. We discuss possible retinal and post-retinal explanations for prolonged afterimages in people with higher levels of autistic traits

Adults with autism overestimate the volatility of the sensory environment.

Insistence on sameness and intolerance of change are among the diagnostic criteria for autism spectrum disorder (ASD), but little research has addressed how people with ASD represent and respond to environmental change. Here, behavioral and pupillometric measurements indicated that adults with ASD are less surprised than neurotypical adults when their expectations are violated, and decreased surprise is predictive of greater symptom severity. A hierarchical Bayesian model of learning suggested that in ASD, a tendency to overlearn about volatility in the face of environmental change drives a corresponding reduction in learning about probabilistically aberrant events, thus putatively rendering these events less surprising. Participant-specific modeled estimates of surprise about environmental conditions were linked to pupil size in the ASD group, thus suggesting heightened noradrenergic responsivity in line with compromised neural gain. This study offers insights into the behavioral, algorithmic and physiological mechanisms underlying responses to environmental volatility in ASD

Full field electroretinogram in autism spectrum disorder

Purpose To explore early findings that individuals with autism spectrum disorder (ASD) have reduced scotopic ERG b-wave amplitudes. Methods Dark adapted (DA) ERGs were acquired to a range of flash strengths, (-4.0 to 2.3 log phot cd.s.m-2), including and extending the ISCEV standard, from two subject groups: (ASD) N=11 and (Control) N=15 for DA and N=14 for light adapted (LA) ERGs who were matched for mean age and range. Naka-Rushton curves were fitted to DA b-wave amplitude growth over the first limb (-4.0 to -1.0 log phot cd.s.m-2). The derived parameters (Vmax, Km and n) were compared between groups. Scotopic 15 Hz flicker ERGs (14.93Hz) were recorded to 10 flash strengths presented in ascending order from -3.0 to 0.5 log Td.s to assess the slow and fast rod pathways respectively. LA ERGs were acquired to a range of flash strengths, (-0.5 to 1.0 log phot cd.s.m-2). Photopic 30 Hz, flicker ERGs, oscillatory potentials (OPs) and the responses to prolonged 120 ms ON- OFF stimuli were also recorded. Results For some individuals the DA b-wave amplitudes fell below the control 5th centile of the controls with up to four ASD participants (36%) at the 1.5 log phot cd.s.m-2 flash strength and two (18%) ASD participants at the lower -2 log phot cd.s.m-2 flash strength. However, across the thirteen flash strengths there were no significant group differences for b-wave amplitude’s growth (repeated measures ANOVA p=0.83). Nor were there any significant differences between the groups for the Naka-Rushton parameters (p>0.09). No group differences were observed in the 15Hz scotopic flicker phase or amplitude (p>0.1), DA ERG a- wave amplitude or time to peak (p>26). The DA b-wave time to peak at 0.5 log phot cd.s.m-2 were longer in the ASD group (corrected p=0.04). The single ISCEV LA 0.5 log phot cd.s.m-2 (p0.08) to the single flash stimuli although there was a significant interaction between group and flash strength for the b-wave amplitude (corrected p=0.006). The prolonged 120 ms ON-responses were smaller in the ASD group (corrected p=0.003), but the OFF response amplitude (p>0.6) and ON and OFF times to peaks (p>0.4) were similar between groups. The LA OPs showed an earlier bifurcation of OP2 in the younger ASD participants, however no other differences were apparent in the OPs or 30Hz flicker waveforms. Conclusion Some ASD individuals show subnormal DA ERG b-wave amplitudes. Under LA conditions the b-wave is reduced across the ASD group along with the ON response of the ERG. These exploratory findings, suggest there is altered cone-ON bipolar signalling in ASD

Reduced engagement with social stimuli in 6-month-old infants with later Autism Spectrum Disorder: a longitudinal prospective study of infants at high familial risk

Background: Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that affects more than 1% of the population, and close to 20% of prospectively studied infants with an older sibling with ASD. Although significant progress has been made in characterizing the emergence of behavioral symptoms of ASD, far less is known about the underlying disruptions to early learning. Recent models suggest that core aspects of the causal path to ASD may only be apparent in early infancy. Here, we investigated social attention in 6- and 12-month-old infants who did and did not meet criteria for ASD at 24 months using both cognitive and electrophysiological methods. We hypothesized that a reduction in attention engagement to faces would be associated with later ASD. Methods: In a prospective longitudinal design, we used measures of both visual attention (habituation) and brain function (event-related potentials to faces and objects) at 6 and 12 months, and investigated the relationship to ASD outcome at 24 months. Results: High-risk infants who met criteria for ASD at 24 months showed shorter epochs of visual attention, faster but less prolonged neural activation to faces, and delayed sensitization responses (increases in looking) to faces at 6 months; these differences were less apparent at 12 months. These findings are consistent with disrupted engagement of sustained attention to social stimuli. Conclusions: These findings suggest that there may be fundamental early disruptions to attention engagement that may have cascading consequences for later social functioning

Multivariate physiological recordings in an experimental hemorrhage model

In this paper we describe a data set of multivariate physiological measurements recorded from conscious sheep (N = 8; 37.4 ± 1.1 kg) during hemorrhage. Hemorrhage was experimentally induced in each animal by withdrawing blood from a femoral artery at two different rates (fast: 1.25 mL/kg/min; and slow: 0.25 mL/kg/min). Data, including physiological waveforms and continuous/intermittent measurements, were transformed to digital file formats (European Data Format [EDF] for waveforms and Comma-Separated Values [CSV] for continuous and intermittent measurements) as a comprehensive data set and stored and publicly shared here (Appendix A). The data set comprises experimental information (e.g., hemorrhage rate, animal weight, event times), physiological waveforms (arterial and central venous blood pressure, electrocardiogram), time-series records of non-invasive physiological measurements (SpO2, tissue oximetry), intermittent arterial and venous blood gas analyses (e.g., hemoglobin, lactate, SaO2, SvO2) and intermittent thermodilution cardiac output measurements. A detailed explanation of the hemodynamic and pulmonary changes during hemorrhage is available in a previous publication (Scully et al., 2016) [1]

Assessing ECG signal quality indices to discriminate ECGs with artefacts from pathologically different arrhythmic ECGs

False and non-actionable alarms in critical care can be reduced by developing algorithms which assess the trueness of an arrhythmia alarm from a bedside monitor. Computational approaches that automatically identify artefacts in ECG signals are an important branch of physiological signal processing which tries to address this issue. Signal quality indices (SQIs) derived considering differences between artefacts which occur in ECG signals and normal QRS morphology have the potential to discriminate pathologically different arrhythmic ECG segments as artefacts. Using ECG signals from the PhysioNet/Computing in Cardiology Challenge 2015 training set, we studied previously reported ECG SQIs in the scientific literature to differentiate ECG segments with artefacts from arrhythmic ECG segments. We found that the ability of SQIs to discriminate between ECG artefacts and arrhythmic ECG varies based on arrhythmia type since the pathology of each arrhythmic ECG waveform is different. Therefore, to reduce the risk of SQIs classifying arrhythmic events as noise it is important to validate and test SQIs with databases that include arrhythmias. Arrhythmia specific SQIs may also minimize the risk of misclassifying arrhythmic events as noise