Cardiopulmonary coupling indices to assess weaning readiness from mechanical ventilation

The ideal moment to withdraw respiratory supply of patients under Mechanical Ventilation at Intensive Care Units (ICU), is not easy to be determined for clinicians. Although the Spontaneous Breathing Trial (SBT) provides a measure of the patients’ readiness, there is still around 15–20% of predictive failure rate. This work is a proof of concept focused on adding new value to the prediction of the weaning outcome. Heart Rate Variability (HRV) and Cardiopulmonary Coupling (CPC) methods are evaluated as new complementary estimates to assess weaning readiness. The CPC is related to how the mechanisms regulating respiration and cardiac pumping are working simultaneously, and it is defined from HRV in combination with respiratory information. Three different techniques are used to estimate the CPC, including Time-Frequency Coherence, Dynamic Mutual Information and Orthogonal Subspace Projections. The cohort study includes 22 patients in pressure support ventilation, ready to undergo the SBT, analysed in the 24 h previous to the SBT. Of these, 13 had a successful weaning and 9 failed the SBT or needed reintubation –being both considered as failed weaning. Results illustrate that traditional variables such as heart rate, respiratory frequency, and the parameters derived from HRV do not differ in patients with successful or failed weaning. Results revealed that HRV parameters can vary considerably depending on the time at which they are measured. This fact could be attributed to circadian rhythms, having a strong influence on HRV values. On the contrary, significant statistical differences are found in the proposed CPC parameters when comparing the values of the two groups, and throughout the whole recordings. In addition, differences are greater at night, probably because patients with failed weaning might be experiencing more respiratory episodes, e.g. apneas during the night, which is directly related to a reduced respiratory sinus arrhythmia. Therefore, results suggest that the traditional measures could be used in combination with the proposed CPC biomarkers to improve weaning readiness.

Electrical analogs of curved beams and application to piezoelectric network damping

In this paper, the method of electric analog synthesis is applied to design a piezo-electro-mechanical arch able to show the capacity of multimodal damping. An electric-analog circuit is designed by using a finite number of lumped elements representing the equivalent of a curved beam. Spatial and frequency coherence conditions are proven to be verified for the modes to be damped: in fact, lumped-element circuit can damp only a finite number of vibration modes. Analogous boundary conditions are ensured, so that natural frequencies and mode shapes of both the curved beam and the analog circuit are equal. The instance considered here is the vibration mitigation of a piezo-electro-mechanical arch. Having a view towards prototypical applications, all simulations consider values of physically feasible passive circuital elements. It is believed that the present results may represent a step towards the design of multi-physics metamaterials based on micro-structures exploiting the principle of multimodal damping.

Tracking Rhythms Coherence From Polysomnographic Records: A Time-Frequency Approach

The crosstalk between organs plays a crucial role in physiological processes. This coupling is a dynamical process, it must cope with a huge variety of rhythms with frequencies ranging from milliseconds to hours, days, seasons. The brain is a central hub for this crosstalk. During sleep, automatic rhythmic interrelations are enhanced and provide a direct insight into organ dysfunctions, however their origin remains a difficult issue, in particular in sleep disorders. In this study, we focus on EEG, ECG, and airflow recordings from polysomnography databases. Because these signals are non-stationary, non-linear, noisy, and span wide spectral ranges, a time-frequency analysis, based on wavelet transforms, is more appropriate to handle this complexity. We design a wavelet-based extraction method to identify the characteristic rhythms of these different signals, and their temporal variability. These new constructs are combined in pairs to compute their wavelet-based time-frequency complex coherence. These time-frequency coherence maps highlight the occurrence of a slowly modulated coherence pattern in the frequency range [0.01–0.06] Hz, which appears in both obstructive and central apnea. A preliminary exploration of a large database from the National Sleep Research Resource with respiration disorders, such as apnea provides some clues on its relation with autonomic cardio-respiratory coupling and brain rhythms. We also observe that during sleep apnea episodes (either obstructive or central), the cardiopulmonary coherence (in particular respiratory sinus-arrhythmia) in the frequency range [0.1–0.7] Hz strongly diminishes, suggesting a modification of this coupling. Finally, comparing time-averaged coherence with heart rate variability spectra in different apnea episodes, we discuss their common trait and their differences.

Weaning from Mechanical Ventilation: On the Improvement of the Prediction of Patients' Readiness with Cardiopulmonary Coupling Indices

The ideal moment to withdraw respiratory supply of patients under Mechanical Ventilation (MV) at Intensive Care Units (ICU), is not easy to be determined for clinicians. Although the Spontaneous Breathing Trial (SBT) provides a measure of the patients’ readiness, there is still around 15-20% of predictive failure rate. This work explores both Heart Rate Variability (HRV) and Cardiopulmonary Coupling (CPC) estimates as complementary information for readiness prediction. The CPC is related to how the mechanisms regulating respiration and cardiac pumping are working simultaneously, and it is defined from HRV in combination with respiratory information. Three different techniques are used to measure CPC, including Orthogonal Subspace Projections, Dynamic Mutual Information and Time-Frequency Coherence. 22 patients undergoing SBT in pressure support ventilation are analysed in the 24 hours previous to the SBT. 13 had a successful weaning and 9 failed the SBT or needed reintubation –being both considered as failed weaning. Results illustrate that traditional variables such as heart rate, respiratory frequency, and the parameters derived from HRV do not differ in patients with successful or failed weaning. However, significant statistical differences are found for the novel CPC parameters, throughout the whole recordings, comparing the values of the two groups. In addition, the night prior to SBT is the moment where differences are higher, probably because patients with failed weaning might be experiencing more respiratory episodes, e.g. apneas during the night, which is directly related to a reduced RSA. Therefore, results suggest that the traditional measures could be used in combination with these novel CPC biomarkers to help clinicians better predict if patients are ready to be weaned.

XFinger-Net: Pixel-Wise Segmentation Method for Partially Defective Fingerprint Based on Attention Gates and U-Net

Partially defective fingerprint image (PDFI) with poor performance poses challenges to the automated fingerprint identification system (AFIS). To improve the quality and the performance rate of PDFI, it is essential to use accurate segmentation. Currently, most fingerprint image segmentations use methods with ridge orientation, ridge frequency, coherence, variance, local gradient, etc. This paper proposes a method of XFinger-Net for segmenting PDFIs. Based on U-Net, XFinger-Net inherits its characteristics. The attention gate with fewer parameters is used to replace the cascaded network, which can suppress uncorrelated regions of PDFIs. Moreover, the XFinger-Net implements a pixel-level segmentation and takes non-blocking fingerprint images as an input to preserve the global characteristics of PDFIs. The XFinger-Net can achieve a very good segmentation effect as demonstrated in the self-made fingerprint segmentation test.

Dynamics of coherent activity between cortical areas defines a two-stage process of selective attention

ABSTRACTAnalysing a visual scene requires the brain to briefly keep in memory potentially relevant parts and then direct attention to their locations for detailed processing. To reveal the neuronal basis of the underlying working memory and top-down attention processes, we trained macaques to match two patterns presented with a delay between them. As the above processes are likely to require communication between brain regions, and the parietal cortex is involved in spatial attention, we simultaneously recorded neuronal activities from the interconnected parietal and middle temporal areas. We found that mnemonic information about the first pattern was retained in coherent oscillating activity between the areas in high-frequency bands, followed by coherent activity in low-frequency bands that mediate top-down attention on the relevant location.ONE SENTENCE SUMMARYGamma coherence allows retaining object features in a saliency map while lower frequency coherence facilitates attention.