Using Hilbert-Huang Transform to Assess EEG Slow Wave Activity During Anesthesia in Post-Cardiac Arrest Patients

Proceeding volume: 38Hypoxic ischemic encephalopathy (HIE) is a severe consequence of cardiac arrest (CA) representing a substantial diagnostic challenge. We have recently designed a novel method for the assessment of HIE after CA. The method is based on estimating the severity of the brain injury by analyzing changes in the electroencephalogram (EEG) slow wave activity while the patient is exposed to an anesthetic drug propofol in a controlled manner. In this paper, Hilbert-Huang Transform (HHT) was used to analyze EEG slow wave activity during anesthesia in ten post-CA patients. The recordings were made in the intensive care unit 36-48 hours after the CA in an experiment, during which the propofol infusion rate was incrementally decreased to determine the drug-induced changes in the EEG at different anesthetic levels. HHT was shown to successfully capture the changes in the slow wave activity to the behavior of intrinsic mode functions (IMFs). While, in patients with good neurological outcome defined after a six-month control period, propofol induced a significant increase in the amplitude of IMFs representing the slow wave activity, the patients with poor neurological outcome were unable to produce such a response. Consequently, the proposed method offer substantial prognostic potential by providing a novel approach for early estimation of HIE after CA.Peer reviewe

Composition and lipid spatial distribution of HDL particles in subjects with low and high HDL-cholesterol

Peer reviewe

Detection of Prostate Cancer Using Biparametric Prostate MRI, Radiomics, and Kallikreins : A Retrospective Multicenter Study of Men With a Clinical Suspicion of Prostate Cancer

Background Accurate detection of clinically significant prostate cancer (csPCa), Gleason Grade Group >= 2, remains a challenge. Prostate MRI radiomics and blood kallikreins have been proposed as tools to improve the performance of biparametric MRI (bpMRI). Purpose To develop and validate radiomics and kallikrein models for the detection of csPCa. Study Type Retrospective. Population A total of 543 men with a clinical suspicion of csPCa, 411 (76%, 411/543) had kallikreins available and 360 (88%, 360/411) did not take 5-alpha-reductase inhibitors. Two data splits into training, validation (split 1: single center, n = 72; split 2: random 50% of pooled datasets from all four centers), and testing (split 1: 4 centers, n = 288; split 2: remaining 50%) were evaluated. Field strength/Sequence A 3 T/1.5 T, TSE T2-weighted imaging, 3x SE DWI. Assessment In total, 20,363 radiomic features calculated from manually delineated whole gland (WG) and bpMRI suspicion lesion masks were evaluated in addition to clinical parameters, prostate-specific antigen, four kallikreins, MRI-based qualitative (PI-RADSv2.1/IMPROD bpMRI Likert) scores. Statistical Tests For the detection of csPCa, area under receiver operating curve (AUC) was calculated using the DeLong's method. A multivariate analysis was conducted to determine the predictive power of combining variables. The values of P-value < 0.05 were considered significant. Results The highest prediction performance was achieved by IMPROD bpMRI Likert and PI-RADSv2.1 score with AUC = 0.85 and 0.85 in split 1, 0.85 and 0.83 in split 2, respectively. bpMRI WG and/or kallikreins demonstrated AUCs ranging from 0.62 to 0.73 in split 1 and from 0.68 to 0.76 in split 2. AUC of bpMRI lesion-derived radiomics model was not statistically different to IMPROD bpMRI Likert score (split 1: AUC = 0.83, P-value = 0.306; split 2: AUC = 0.83, P-value = 0.488). Data Conclusion The use of radiomics and kallikreins failed to outperform PI-RADSv2.1/IMPROD bpMRI Likert and their combination did not lead to further performance gains. Level of Evidence 1 Technical Efficacy Stage 2Peer reviewe

Interaction of climate change with effects of conspecific and heterospecific density on reproduction

We studied the relationship between temperature and the coexistence of great titParus majorand blue titCyanistes caeruleus, breeding in 75 study plots across Europe and North Africa. We expected an advance in laying date and a reduction in clutch size during warmer springs as a general response to climate warming and a delay in laying date and a reduction in clutch size during warmer winters due to density-dependent effects. As expected, as spring temperature increases laying date advances and as winter temperature increases clutch size is reduced in both species. Density of great tit affected the relationship between winter temperature and laying date in great and blue tit. Specifically, as density of great tit increased and temperature in winter increased both species started to reproduce later. Density of blue tit affected the relationship between spring temperature and blue and great tit laying date. Thus, both species start to reproduce earlier with increasing spring temperature as density of blue tit increases, which was not an expected outcome, since we expected that increasing spring temperature should advance laying date, while increasing density should delay it cancelling each other out. Climate warming and its interaction with density affects clutch size of great tits but not of blue tits. As predicted, great tit clutch size is reduced more with density of blue tits as temperature in winter increases. The relationship between spring temperature and density on clutch size of great tits depends on whether the increase is in density of great tit or blue tit. Therefore, an increase in temperature negatively affected the coexistence of blue and great tits differently in both species. Thus, blue tit clutch size was unaffected by the interaction effect of density with temperature, while great tit clutch size was affected in multiple ways by these interactions terms.Peer reviewe

Digital Audio Watermarking Techniques and Technologies: Applications and Benchmarks

"Digital audio watermarking has been proposed as a new and alternative method to enforce intellectual property rights and protect digital audio from tampering. Digital Audio Watermarking Techniques and Technologies: Applications and Benchmarks is a comprehensive compilation of the major theoretical frameworks, research findings, and practical applications. With inclusive coverage of the most authoritative research in the area, Digital Audio Watermarking Techniques and Technologies: Applications and Benchmarks will serve as a vital reference to researchers and practitioners in a variety of disciplines, including engineering, information technology, and digital audio. With value to a diverse range of users, this Premier Reference Source suits libraries in numerous settings.

Explainability for medical image captioning

Abstract Medical image captioning is the process of generating clinically significant descriptions to medical images, which has many applications among which medical report generation is the most frequent one. In general, automatic captioning of medical images is of great interest for medical experts since it offers assistance in diagnosis, disease treatment and automating the workflow of the health practitioners. Recently, many efforts have been put forward to obtain accurate descriptions but medical image captioning still provides weak and incorrect descriptions. To alleviate this issue, it is important to explain why the model produced a particular caption based on some specific features. This is performed through Artificial Intelligence Explainability (XAI), which aims to unfold the ‘black-box’ feature of deep-learning based models. We present in this paper an explainable module for medical image captioning that provides a sound interpretation of our attention-based encoder-decoder model by explaining the correspondence between visual features and semantic features. We exploit for that, self-attention to compute word importance of semantic features and visual attention to compute relevant regions of the image that correspond to each generated word of the caption in addition to visualization of visual features extracted at each layer of the Convolutional Neural Network (CNN) encoder. We finally evaluate our model using the ImageCLEF medical captioning dataset

Enabling wireless backhauling for next generation mmWave networks

This paper presents some key findings w.r.t. the Radio Resource Management (RRM) in wireless Backhaul (BH) of mmWave networks. First, the envisioned design of mmWave backhaul architecture is outlined highlighting the most important newly needed functional blocks and in what they differ from a non-mmWave architectures. Next, the challenges and functionality of RRM techniques are discussed, focusing on Routing and Link scheduling algorithms in such BH architecture. Furthermore different possible interactions between RRM functions are explored. Finally, preliminary analytical and experimental study on the performance of different link scheduling and routing functions for mmWave backhauling are provided, highlighting in particular the impact of traffic load and dynamic route selection on BH End to End delay