Methods of Hidden Periodicity Discovering for Gearbox Fault Detection.

It is shown that the models of gear pair vibration, proposed in literature, are particular cases of the bi-periodically correlated random processes (BPCRPs), which describe its stochastic recurrence with two periods. The possibility of vibration and analysis within the framework of BPCRP approximation, in the form of periodically correlated random processes (PCRPs), is grounded and the implementation of vibration processing procedures using PCRP techniques, which are worked out by the authors, is given. Searching for hidden periodicities of the first and the second orders was considered as the main issue of this approach. The estimation of the non-stationary period (basic frequency) allowed us to carry out a detailed analysis of the deterministic part, the covariance structure of the stochastic part, and to form, using their parameters, the sensitive indicators for fault detection. The results of the processing of the wind turbine gearbox vibration signals are presented. The amplitude spectra of the deterministic oscillations and the time changes of the stochastic part power for different fault stages are analyzed. The most efficient indicators, which are formed using the amplitude spectra for practical applications, are proposed. The presented approach was compared with known in literature cyclostationary analysis and envelope techniques, and its advantages are shown.

Preliminary Study on Assessing Delaminated Cracks in Cement Asphalt Mortar Layer of High-Speed Rail Track Using Traditional and Normalized Impact-Echo Methods.

The severe deterioration of a cement asphalt (CA) mortar layer may lead to the movement of the upper concrete slab and impair the safety of the speedy train. In this study, a test specimen simulating the structure of high-speed rail track slabs was embedded with delaminated cracks in various lateral sizes inside the CA mortar layer. Impact-echo tests (IE) were performed above the flawed and flawless locations. In present study, the IE method is chosen to assess defects in the CA mortar layer. Both traditional IE and normalized IE are used for data interpolation. The normalized IE are the simulated transfer function of the original IE response. The peak amplitudes in the normalized amplitude spectrum and the peak frequency in the traditional amplitude spectrum for the top concrete overlay were used to develop simple indicators for identifying the integrity of the CA mortar layer. The index was based on the difference of the experimental peak amplitude and frequency of the ones calculated from previously developed formulas for plates without substrates. As a result, the technique does not require an experimental baseline for the crack assessment. A field test and analysis procedure for evaluating high-speed rail slab systems are proposed.

Acute sleep loss induces signs of visual discomfort in young men.

Acute sleep loss influences visual processes in humans, such as recognizing facial emotions. However, to the best of our knowledge, no study till date has examined whether acute sleep loss alters visual comfort when looking at images. One image statistic that can be used to investigate the level of visual comfort experienced under visual encoding is the slope of the amplitude spectrum, also referred to as the slope constant. The slope constant describes the spatial distribution of pixel intensities and deviations from the natural slope constant can induce visual discomfort. In the present counterbalanced crossover design study, 11 young men with normal or corrected-to-normal vision participated in two experimental conditions: one night of sleep loss and one night of sleep. In the morning after each intervention, subjects performed a computerized psychophysics task. Specifically, they were required to adjust the slope constant of images depicting natural landscapes and close-ups with a randomly chosen initial slope constant until they perceived each image as most natural looking. Subjects also rated the pleasantness of each selected image. Our analysis showed that following sleep loss, higher slope constants were perceived as most natural looking when viewing images of natural landscapes. Images with a higher slope constant are generally perceived as blurrier. The selected images were also rated as less pleasant after sleep loss. No such differences between the experimental conditions were noted for images of close-ups. The results suggest that sleep loss induces signs of visual discomfort in young men. Possible implications of these findings are discussed.

Influence of Uniaxial Stress on the Shear-Wave Spectrum Propagating in Steel Members.

Structural health monitoring technologies have provided extensive methods to sense the stress of steel structures. However, monitored stress is a relative value rather than an absolute value in the structure's current state. Among all the stress measurement methods, ultrasonic methods have shown great promise. The shear-wave amplitude spectrum and phase spectrum contain stress information along the propagation path. In this study, the influence of uniaxial stress on the amplitude and phase spectra of a shear wave propagating in steel members was investigated. Furthermore, the shear-wave amplitude spectrum and phase spectrum were compared in terms of characteristic frequency (CF) collection, parametric calibration, and absolute stress measurement principles. Specifically, the theoretical expressions of the shear-wave amplitude and phase spectra were derived. Three steel members were used to investigate the effect of the uniaxial stress on the shear-wave amplitude and phase spectra. CFs were extracted and used to calibrate the parameters in the stress measurement formula. A linear relationship was established between the inverse of the CF and its corresponding stress value. The test results show that both the shear-wave amplitude and phase spectra can be used to evaluate uniaxial stress in structural steel members.

Stationary Wavelet-Fourier Entropy and Kernel Extreme Learning for Bearing Multi-Fault Diagnosis.

Bearing fault diagnosis methods play an important role in rotating machine health monitoring. In recent years, various intelligent fault diagnosis methods have been proposed, which are mainly based on the features extraction method combined with either shallow or deep learning methods. During the last few years, Shannon entropy features have been widely used in machine health monitoring, improving the accuracy of the bearing fault diagnosis process. Therefore, in this paper, we consider the combination of multi-scale stationary wavelet packet analysis with the Fourier amplitude spectrum to obtain a new discriminative Shannon entropy feature that we call stationary wavelet packet Fourier entropy (SWPFE). Features extracted by our SWPFE method are then passed onto a shallow kernel extreme learning machine (KELM) classifier to diagnose bearing failure types with different severities. The proposed method was applied on two experimental vibration signal databases of a rolling element bearing and compared to two recently proposed methods called stationary wavelet packet permutation entropy (SWPPE) and stationary wavelet packet dispersion entropy (SWPPE). Based on our results, we can say that the proposed method is able to achieve better accuracy levels than both the SWPPE and SWPDE methods using fewer failure features. Further, as our method does not require any hyperparameter calibration step, it is less dependent on user experience/expertise.

Ventricular fibrillation waveform measures combined with prior shock outcome predict defibrillation success during cardiopulmonary resuscitation.

AIM: Amplitude Spectrum Area (AMSA) and Median Slope (MS) are ventricular fibrillation (VF) waveform measures that predict defibrillation shock success. Cardiopulmonary resuscitation (CPR) obscures electrocardiograms and must be paused for analysis. Studies suggest waveform measures better predict subsequent shock success when combined with prior shock success. We determined whether this relationship applies during CPR. METHODS: AMSA and MS were calculated from 5-second pre-shock segments with and without CPR, and compared to logistic models combining each measure with prior return of organized rhythm (ROR). RESULTS: VF segments from 692 patients were analyzed during CPR before 1372 shocks and without CPR before 1283 shocks. Combining waveform measures with prior ROR increased areas under receiver operating characteristic curves for AMSA/MS with CPR (0.66/0.68 to 0.73/0.74, p<0.001) and without CPR (0.71/0.72 to 0.76/0.76, p<0.001). CONCLUSIONS: Prior ROR improves prediction of shock success during CPR, and may enable waveform measure calculation without chest compression pauses.

The tuning of human visual cortex to variations in the 1/fα amplitude spectra and fractal properties of synthetic noise images.

Natural scenes share a consistent distribution of energy across spatial frequencies (SF) known as the 1/fα amplitude spectrum (α≈0.8-1.5, mean 1.2). This distribution is scale-invariant, which is a fractal characteristic of natural scenes with statistically similar structure at different spatial scales. While the sensitivity of the visual system to the 1/f properties of natural scenes has been studied extensively using psychophysics, relatively little is known about the tuning of cortical responses to these properties. Here, we use fMRI and retinotopic mapping techniques to measure and analyze BOLD responses in early visual cortex (V1, V2, and V3) to synthetic noise images that vary in their 1/fα amplitude spectra (α=0.25 to 2.25, step size: 0.50) and contrast levels (10% and 30%) (Experiment 1). To compare the dependence of the BOLD response between the photometric (intensity based) and geometric (fractal) properties of our stimuli, in Experiment 2 we compared grayscale noise images to their binary (thresholded) counterparts, which contain only black and white regions. In both experiments, early visual cortex responded maximally to stimuli generated to have an input 1/f slope corresponding to natural 1/fα amplitude spectra, and lower BOLD responses were found for steeper or shallower 1/f slopes (peak modulation: 0.59% for 1.25 vs. 0.31% for 2.25). To control for changing receptive field sizes, responses were also analyzed across multiple eccentricity bands in cortical surface space. For most eccentricity bands, BOLD responses were maximal for natural 1/fα amplitude spectra, but importantly there was no difference in the BOLD response to grayscale stimuli and their corresponding thresholded counterparts. Since the thresholding of an image changes its measured 1/f slope (α) but not its fractal characteristics, this suggests that neuronal responses in early visual cortex are not strictly driven by spectral slope values (photometric properties) but rather their embedded geometric, fractal-like scaling properties.

An acoustic method (Spectral Flux) to analyze ECG signals for optimizing timing for defibrillation in a porcine model of ventricular fibrillation.

Aim: Spectral Flux (SF), which is based on common algorithms in the audio processing field, was applied to quantitatively analyze ECG signals to optimize the timing of defibrillation. With the aim of proving the performance in optimizing the timing of defibrillation, SF was compared with Amplitude Spectrum Area (AMSA) in a porcine model of ventricular fibrillation (VF) in a retrospective analysis experiment. Methods: A total of 56 male domestic pigs, weighing 40 ± 5 kg, were induced to undergo VF. Animals were then left untreated for 10 min, and after 6 min of cardiopulmonary resuscitation (CPR) defibrillation was performed. The respective SF and AMSA values were calculated every minute during VF and CPR. Comparisons were made through receiver operating characteristic (ROC) curves, one-way analyses of variance (one-way ANOVA), and scatterplots for the successful initial defibrillation sample (positive samples, Group R) and the failed initial defibrillation sample (negative samples, Group N) to illustrate the performance in optimizing the timing of defibrillation for the AMSA and SF methods. Result: Values of SF and AMSA gradually decreased during the 10 min VF period and increased in during the 6 min CPR period. The scatterplots showed that both metrics had the ability to distinguish positive and negative samples (p < .001). Meanwhile, ROC curves showed that SF (area under the curve, AUC = 0.798, p < .001) had the same ability as AMSA (AUC = 0.737, p < .001) to predict the successful defibrillation (Z = 1.35, p = 0.177). Moreover, when comparing the values for AMSA and SF between the successful initial defibrillation samples (Group R) and the failed initial defibrillation samples (Group N), the results showed that the values of both AMSA and SF in Group R were significantly higher than those in Group N (p < .001). Conclusion: In the present study, SF method had the same ability as AMSA to predict successful defibrillation with significantly higher values in cases of successful defibrillation than the instances in which defibrillation failed. Additionally, SF method might be more stable than AMSA for filtering out the higher frequency interference signals due to the narrower frequency range and had higher specificity and predictive accuracy than AMSA. So SF method had high clinical potential to optimize the timing of defibrillation. Nevertheless, further animal and clinical studies are still needed to confirm the effectiveness and practicality of SF as a predictive module for defibrillators in clinical practice.

The contribution of amplitude and phase spectra-defined scene statistics to the masking of rapid scene categorization.

Viewers can recognize the gist of a scene (i.e., its holistic semantic representation, such as its category) in less time than a single fixation, and backward masking has traditionally been employed as a means to determine that time course. The masks used in those paradigms are often characterized by either specific amplitude spectra only, or amplitude and phase spectra-defined structural properties. However, it remains unclear whether there would be a differential contribution of amplitude only or amplitude + phase defined image statistics to the effective backward masking of rapid scene categorization. The current study addresses this issue. Experiments 1-3 explored amplitude spectra defined contributions to category masking and revealed that the slope of the amplitude spectrum was more important for modulating scene category masking strength than amplitude orientation. Further, the masking effects followed an "amplitude spectrum slope similarity principle" whereby the more similar the amplitude spectrum slope of the mask was to the target's amplitude spectrum slope, the stronger the masking. Experiment 5 showed that, when holding mask amplitude spectrum slope approximately constant, both categorically specific unrecognizable amplitude only and amplitude + phase statistical regularities disrupted rapid scene categorization. Specifically, the masking effects observed in Experiment 5 followed a target-mask categorical dissimilarity principle whereby the more dissimilar the mask category is to the target image category, the stronger the masking. Overall, the results support the notion that amplitude only or amplitude + phase-defined image statistics differentially contribute to the effective backward masking of rapid scene gist recognition.

Amplitude spectrum area measured in real-time during cardiopulmonary resuscitation - How does this technology work?

Amplitude spectrum area (AMSA) is one of the most accurate predictors of defibrillation outcome. Details on functioning and use of the available technology to measure AMSA during cardiopulmonary resuscitation (CPR) in the real clinical scenario are described. During chest compression (CC) pauses for ventilations, AMSA is promptly calculated and values displayed through a modified defibrillator. In addition, real-time AMSA analysis has the additional promise to monitor CPR quality, being AMSA threshold values contingent on CC depth. Future larger studies employing this new technology are now needed to demonstrate the impact of AMSA on survival of cardiac arrest.

Impact of induced pseudomyopia and refractive fluctuations of accommodative spasm on visual acuity.

CLINICAL RELEVANCE: High-contrast visual acuity is disproportionately poor in patients with accommodative spasm subtype of near reflex (SNR-A), relative to uncorrected refractive errors of equivalent magnitude. This exaggerated loss of performance in SNR-A may be explained by the combination of pseudomyopia and its fluctuations, vis-à-vis, each factor considered separately. BACKGROUND: To determine how combinations of pseudomyopic refraction and its temporal variations in SNR-A impact high-contrast visual acuity by inducing these patterns in healthy cyclopleged adults, relative to their baseline acuity. METHODS: Refractive profiles of 15 patients with SNR-A were obtained from a previous study, averaged, and induced before the right eye of 14 cyclopleged adults (mean ±1 SD age: 22.7 ± 2.6 yrs) by feeding the profile into a coaxially placed, motorised, Badal optometer. LogMAR acuity was measured using the method of constant stimuli: (1) before cycloplegia, (2) after cycloplegia and post-cycloplegia with (3) combination of pseudomyopia and its temporal fluctuations, (4) only pseudomyopia, (5) only temporal fluctuations in refraction about emmetropia, (6) condition 5 with double the amplitude of induced fluctuations and (7) condition 5 with half the amplitude of induced fluctuations. RESULTS: The induced refractive fluctuations ranged from -0.80 to -1.75D, around a mean pseudomyopia of -1.20D. Visual acuity deterioration was maximum for the combination of pseudomyopia and temporal fluctuations condition (0.51 ± 0.07logMAR), followed by only pseudomyopia (0.27 ± 0.05logMAR) and only refractive fluctuations conditions (0.17 ± 0.04logMAR), all relative to baseline post-cycloplegia (0.13 ± 0.04logMAR) (p < 0.001). Visual acuity loss increased with doubling of refractive fluctuations (0.20 ± 0.04logMAR), relative to native fluctuations or halving the amplitude (0.15 ± 0.03logMAR) (p < 0.01). Task precision, as adjudged from the slope of psychometric function, followed a similar pattern of loss as visual acuity. CONCLUSION: Combination of induced pseudomyopia and temporal fluctuations in refraction produces an additive loss of visual acuity and task precision, relative to baseline and each factor considered separately.

Real-time amplitude spectrum area estimation during chest compression from the ECG waveform using a 1D convolutional neural network.

Introduction: Amplitude spectrum area (AMSA) is a well-established measure than can predict defibrillation outcome and guiding individualized resuscitation of ventricular fibrillation (VF) patients. However, accurate AMSA can only be calculated during cardiopulmonary resuscitation (CPR) pause due to artifacts produced by chest compression (CC). In this study, we developed a real-time AMSA estimation algorithm using a convolutional neural network (CNN). Methods: Data were collected from 698 patients, and the AMSA calculated from the uncorrupted signals served as the true value for both uncorrupted and the adjacent corrupted signals. An architecture consisting of a 6-layer 1D CNN and 3 fully connected layers was developed for AMSA estimation. A 5-fold cross-validation procedure was used to train, validate and optimize the algorithm. An independent testing set comprised of simulated data, real-life CC corrupted data, and preshock data was used to evaluate the performance. Results: The mean absolute error, root mean square error, percentage root mean square difference and correlation coefficient were 2.182/1.951 mVHz, 2.957/2.574 mVHz, 22.887/28.649% and 0.804/0.888 for simulated and real-life testing data, respectively. The area under the receiver operating characteristic curve regarding predicting defibrillation success was 0.835, which was comparable to that of 0.849 using the true value of the AMSA. Conclusions: AMSA can be accurately estimated during uninterrupted CPR using the proposed method.

Strengthening transferability of adversarial examples by adaptive inertia and amplitude spectrum dropout.

Deep neural networks are sensitive to adversarial examples and would produce wrong results with high confidence. However, most existing attack methods exhibit weak transferability, especially for adversarially trained models and defense models. In this paper, two methods are proposed to generate highly transferable adversarial examples, namely Adaptive Inertia Iterative Fast Gradient Sign Method (AdaI2-FGSM) and Amplitude Spectrum Dropout Method (ASDM). Specifically, AdaI2-FGSM aims to integrate adaptive inertia into the gradient-based attack, and leverage the looking ahead property to search for a flatter maximum, which is essential to strengthen the transferability of adversarial examples. By introducing a loss-preserving transformation in the frequency domain, the proposed ASDM with the dropout invariance property can craft the copies of input images to overcome the poor generalization on the surrogate models. Furthermore, AdaI2-FGSM and ASDM can be naturally integrated as an efficient gradient-based attack method to yield more transferable adversarial examples. Extensive experimental results on the ImageNet-compatible dataset demonstrate that higher transferability is achieved by our method than some advanced gradient-based attacks.

Amplitude Spectrum Area of ventricular fibrillation to guide defibrillation: a small open-label, pseudo-randomized controlled multicenter trial.

BACKGROUND: Ventricular fibrillation (VF) waveform analysis has been proposed as a potential non-invasive guide to optimize timing of defibrillation. METHODS: The AMplitude Spectrum Area (AMSA) trial is an open-label, multicenter randomized controlled study reporting the first in-human use of AMSA analysis in out-of-hospital cardiac arrest (OHCA). The primary efficacy endpoint was the termination of VF for an AMSA ≥ 15.5 mV-Hz. Adult shockable OHCAs randomly received either an AMSA-guided cardiopulmonary resuscitation (CPR) or a standard-CPR. Randomization and allocation to trial group were carried out centrally. In the AMSA-guided CPR, an initial AMSA ≥ 15.5 mV-Hz prompted for immediate defibrillation, while lower values favored chest compression (CC). After completion of the first 2-min CPR cycle, an AMSA < 6.5 mV-Hz deferred defibrillation in favor of an additional 2-min CPR cycle. AMSA was measured and displayed in real-time during CC pauses for ventilation with a modified defibrillator. FINDINGS: The trial was early discontinued for low recruitment due to the COVID-19 pandemics. A total of 31 patients were recruited in 3 Italian cities, 19 in AMSA-CPR and 12 in standard-CPR, and included in the data analysis. No difference in primary outcome was observed between the two groups. Termination of VF occurred in 74% of patients in the AMSA-CPR compared to 75% in the standard CPR (OR 0.93 [95% CI 0.18-4.90]). No adverse events were reported. INTERPRETATION: AMSA was used prospectively in human patients during ongoing CPR. In this small trial, an AMSA-guided defibrillation provided no evidence of an improvement in termination of VF. TRIAL REGISTRATION: NCT03237910. FUNDING: European Commission - Horizon 2020; ZOLL Medical Corp., Chelmsford, USA (unrestricted grant); Italian Ministry of Health - Current research IRCCS.

Ventricular fibrillation waveform properties influenced by thoracic impedance guided chest compressions in a porcine model.

BACKGROUND AND OBJECTIVE: Quantitative measures extracted from ventricular fibrillation (VF) waveform reflect the metabolic state of the myocardium and are associated with survival outcome. The quality of delivered chest compressions during cardiopulmonary resuscitation are also linked with survival. The aim of this research is to explore the viability and effectiveness of a thoracic impedance (TI) based chest compression (CC) guidance system to control CC depth within individual subjects and influence VF waveform properties. METHODS: This porcine investigation includes an analysis of two protocols. CC were delivered in 2 min episodes at a constant rate of 110 CC min-1. Subject-specific CC depth was controlled using a TI-thresholding system where CC were performed according to the amplitude (ZRMS, 0.125 to 1.250 Ω) of a band-passed TI signal (ZCC). Protocol A was a retrospective analysis of a 12-porcine study to characterise the response of two VF waveform metrics: amplitude spectrum area (AMSA) and mean slope (MS), to varying CC quality. Protocol B was a prospective 12-porcine study to determine if changes in VF waveform metrics, due to CC quality, were associated with defibrillation outcome. RESULTS: Protocol A: A directly proportional relationship was observed between ZRMS and CC depth applied within each subject (r = 0.90; p <0.001). A positive relationship was observed between ZRMS and both AMSA (p <0.001) and MS (p <0.001), where greater TI thresholds were associated with greater waveform metrics. PROTOCOL B: MS was associated with return of circulation following defibrillation (odds ratio = 2.657; p = 0.043). CONCLUSION: TI-thresholding was an effective way to control CC depth within-subjects. Compressions applied according to higher TI thresholds evoked an increase in AMSA and MS. The response in MS due to deeper CC resulted in a greater incidence of ROSC compared to shallow chest compressions.

Inverse identification of geometric and acoustic parameters of inhomogeneous coatings through URCAS-based least-squares coupled cross-correlation algorithm.

Aiming to determine the geometric and acoustic parameters of inhomogeneous multi-layer coatings, an ultrasonic pressure reflection coefficient amplitude spectrum (URCAS) is derived using a material-oriented regularization scheme, when wave propagates perpendicularly to the coating surface. Based on the derived theoretical URCAS, a new objective function combining least-squares and cross-correlation algorithm is developed to simultaneously identify the thickness, sound velocity, density, attenuation coefficient amplitude, and attenuation coefficient power-law of the multi-layer coating. Genetic algorithm with the constraint of total multi-layer coating thickness being known is presented to optimize the nonlinear objective function for obtaining global optimal solution. Ultrasonic experiments were implemented on a dual-layer coating specimen with coating1/coating2/substrate structure using a flat immersion transducer with a central frequency of 15 MHz. The inversed thicknesses, sound velocities, and densities of the dual-layer coating were in good agreement with those measured through other methods, with less than 8.1% errors. The inversed attenuation coefficients of the coating 1 and coating 2 were α(f) = 1.02e-2 × f1.93 and α(f) = 4.62e-3 × f1.97, respectively. The upper bounds of the relative errors +r of inverted parameters were all less than 0.061. The proposed ultrasonic inversion method could be used to quantitatively characterize the surface integrity of inhomogeneous multi-layer coatings.

Sensitivity to the slope of the amplitude spectrum is dependent on the spectral slopes of recently viewed environments: A visual adaptation study in modified reality.

Scenes contain many statistical regularities that could benefit visual processing if accounted for by the visual system. One such statistic is the orientation-averaged slope (α) of the amplitude spectrum of natural scenes. Human observers show different discrimination sensitivity to α: sensitivity is highest for α values between 1.0 and 1.2 and decreases as α is steepened or shallowed. The range of α for peak discrimination sensitivity is concordant with the average α of natural scenes, which may indicate that visual mechanisms are optimized to process information at α values commonly encountered in the environment. Here we explore the association between peak discrimination sensitivity and the most viewed αs in natural environments. Specifically, we verified whether discrimination sensitivity depends on the recently viewed environments. Observers were immersed, using a Head-Mounted Display, in an environment that was either unaltered or had its average α steepened or shallowed by 0.4. Discrimination thresholds were affected by the average shift in α, but this effect was most prominent following adaptation to a shallowed environment. We modeled these data with a Bayesian observer and explored whether a change in the prior or a change in the likelihood best explained the psychophysical effects. Change in discrimination thresholds following adaptation could be explained by a shift in the central tendency of the prior concordant with the shift of the environment, in addition to a change in the likelihood. Our findings suggest that expectations on the occurrence of α that result from a lifetime of exposure remain plastic and able to accommodate for the statistical structure of recently viewed environments.

Electrocardiographic recording direction impacts ventricular fibrillation waveform measurements: A potential pitfall for VF-waveform guided defibrillation protocols.

AIM: In cardiac arrest, ventricular fibrillation (VF) waveform analysis has identified the amplitude spectrum area (AMSA) as a key predictor of defibrillation success and favorable neurologic survival. New resuscitation protocols are under investigation, where prompt defibrillation is restricted to cases with a high AMSA. Appreciating the variability of in-field pad placement, we aimed to assess the impact of recording direction on AMSA-values, and the inherent defibrillation advice. METHODS: Prospective VF-waveform study on 12-lead surface electrocardiograms (ECGs) obtained during defibrillation testing in ICD-recipients (2010-2017). AMSA-values (mVHz) of simultaneous VF-recordings were calculated and compared between all limb leads, with lead II as reference (proxy for in-field pad position). AMSA-differences between leads I and II were quantified using Bland-Altman analysis. Moreover, we investigated differences between these adjacent leads regarding classification into high (≥15.5), intermediate (6.5-15.5) or low (≤6.5) AMSA-values. RESULTS: In this cohort (n = 243), AMSA-values in lead II (10.2 ± 4.8) differed significantly from the other limb leads (I: 8.0 ± 3.4; III: 12.9 ± 5.6, both p < 0.001). The AMSA-value in lead I was, on average, 2.24 ± 4.3 lower than in lead II. Of the subjects with high AMSA-values in lead II, only 15% were classified as high if based on assessments of lead I. For intermediate and low AMSA-values, concordances were 66% and 72% respectively. CONCLUSIONS: ECG-recording direction markedly affects the result of VF-waveform analysis, with 20-30% lower AMSA-values in lead I than in lead II. Our data suggest that electrode positioning may significantly impact shock guidance by 'smart defibrillators', especially affecting the advice for prompt defibrillation.

Nice and slow: Measuring sensitivity and visual preference toward naturalistic stimuli varying in their amplitude spectra in space and time.

The 1/fα amplitude spectrum is a statistical property of natural scenes characterising a specific distribution of spatial and temporal frequencies and their associated luminance intensities. This property has been studied extensively in the spatial domain whereby sensitivity and visual preference overlap and peak for slopes within the natural range (α ≈ 1), but remains relatively less studied in the temporal domain. Here, we used a 4AFC task to measure sensitivity and a 2AFC task to measure visual preference and across a wide range of spatial (α = 0.25, 1.25, 2.25) and temporal (α = 0.25 to 2.50, step size: 0.25) slope conditions. Stimuli with a shallow temporal slope modulate rapidly (e.g. 0.25), whereas stimuli with a steep slope modulate slowly (e.g. 2.25). Interestingly, sensitivity and visual preference did not closely overlap. While the sensitivity of the visual system is highest for our stimulus with an intermediate modulation rate (1.25), which is most abundant in nature, the stimulus with the slowest modulation rate (2.25) was most preferred. It seems sensible for the visual system to be sensitive to spatiotemporal spectra that most commonly exist in nature (α ≈ 1). However, it is possible that preference might be related to what these properties signal in the natural world. Consider the cases of waves slowly vs. rapidly crashing on a beach or fast vs. slow animals. In both instances the slowest option is often the safest and preferential, suggesting that the temporal 1/fα amplitude spectrum provides additional information that may indicate preferred environmental conditions.

End-tidal carbon dioxide (ETCO2) and ventricular fibrillation amplitude spectral area (AMSA) for shock outcome prediction in out-of-hospital cardiac arrest. Are they two sides of the same coin?

AIM: Ventricular fibrillation amplitude spectral area (AMSA) and end-tidal carbon dioxide (ETCO2) are predictors of shock success, understood as restoration of an organized rhythm, and return of spontaneous circulation (ROSC). However, little is known about their combined use. We aimed to assess the prediction accuracy when combined, and to clarify if they are correlated in out of hospital cardiac arrest' victims. MATERIALS AND METHODS: Records acquired by external defibrillators in out-of-hospital cardiac arrest patients of the Lombardia Cardiac Arrest registry were processed. The 1-min pre-shock ETCO2 median value (METCO2) was computed from the capnogram and AMSA (2-48 mV.Hz range) computed applying the Fast Fourier Transform to a 2-second pre-shock filtered ECG interval (0.5-30 Hz). Support Vector Machine (SVM) predictive models based on METCO2, AMSA and their combination were fit; results were given as the area under the curve (AUC) of the receiver operating characteristic (ROC) curves. RESULTS: We considered 112 patients with 391 shocks delivered. METCO2 and AMSA were predictors of shock success [AUC (IQR) of the ROC curve: 0.59 (0.56-0.62); 0.68 (0.65-0.72), respectively] and of ROSC [0.56 (0.53-0.59); 0.74 (0.71-0.78),]. Their combination in a SVM model increased the accuracy for predicting shock success [AUC (IQR) of the ROC curve: 0.71 (0.68-0.75)] and ROSC [0.77 (0.73-0.8)]. AMSA and METCO2 were significantly correlated only in patients who achieved ROSC (rho = 0.33 p = 0.03). CONCLUSIONS: AMSA and ETCO2 predict shock success and ROSC after every shock, and their predictive power increases if combined. Notably, they were correlated only in patients who achieved ROSC.