Aagesenlockhart4323

Objective.Automatic detection of premature beats on long electrocardiogram (ECG) recordings is of great significance for clinical diagnosis. In this paper, we propose a novel deep learning model, the ECGDet, to detect premature beats, including premature ventricular contractions (PVCs) and supraventricular premature beats (SPBs) on single-lead long-term ECGs.Approach.The ECGDet is proposed based on a convolutional neural network and squeeze-and-excitation network. It outputs the probabilities that the ECG samples belong to a premature contraction. Non-max suppression was used to select the most appropriate locations for the premature beats. The ECGDet was trained and tested on the MIT-BIH arrhythmia database (MITDB) using a five-fold cross-validation approach. A novel loss calculation method was introduced in the model training process. Then it was tuned and further tested on the China Physiological Signal Challenge (2020) database (CPSCDB).Main results.The results showed that the average F1 value of PVC detection was 92.6%, while that of SPB detection was 72.2% on MITDB. The ECGDet bagged the 2nd place for PVC detection and ranked 7th place of SPB detection in the China Physiological Signal Challenge (2020).Significance.The proposed ECGDet can automatically detect premature heartbeats without manually extracting the features. This technique can be used for long-term ECG signal analysis and has potential for clinical applications.Objective. A previous study has shown that a data-driven approach can significantly improve the discriminative power of transfer function analysis (TFA) used to differentiate between normal and impaired cerebral autoregulation (CA) in two groups of data. The data was collected from both healthy subjects (assumed to have normal CA) and symptomatic patients with severe stenosis (assumed to have impaired CA). However, the sample size of the labeled data was relatively small, owing to the difficulty in data collection. Therefore, in this proof-of-concept study, we investigate the feasibility of using an unsupervised learning model to differentiate between normal and impaired CA on TFA variables without requiring labeled data for learning.Approach. Continuous arterial blood pressure (ABP) and cerebral blood flow velocity (CBFV), which were recorded simultaneously for approximately 10 min, were included from 148 subjects (41 healthy subjects, 31 with mild stenosis, 13 with moderate stenosis, 22 asymptomatic patients with severe stenosis, and 41 symptomatic patients with severe stenosis). Tiecks' model was used to generate surrogate data with normal and impaired CA. A recently proposed unsupervised learning model was optimized and applied to separate the normal and impaired CA for both the surrogate data and real data.Main results. It achieved 98.9% and 74.1% accuracy for the surrogate and real data, respectively.Significance. To our knowledge, this is the first attempt to employ an unsupervised data-driven approach to assess CA using TFA. This method enables the development of a classifier to determine the status of CA, which is currently lacking.Objective. H-Cys(Trt)-OH price Sleep apnea hypopnea syndrome (SAHS) induces abnormalities in brain function. This study aims to find features that can characterize the impact of SAHS on the brain functional connectivity (FC) during sleep.Approach. Seventy-eight participants (39 SAHS patients and 39 age-matched healthy controls) were recruited and underwent a whole night of polysomnography. The improved weighted phase lag index algorithm was utilized to evaluate FC inδ,θ,α,β, andγbands of six EEG channels. The regional FC features were further constructed to characterize the asymmetries of FC between the left and right hemispheres, the imbalances of FC between the inter- and intra-hemispheres, and those between the anterior and posterior cortex, respectively. Then, support vector machines and feature evaluation were used to verify the discriminative ability for the abnormal FC in SAHS patients of the above-mentioned features.Main results.The study observed abnormal FC changes in SAHS patients during sleep in multiple frequency bands. Moreover, regional FC features performed better in SAHS screening, and important features were mainly distributed inβandγbands.Significance. Our research exhibited the abnormal regional FC in SAHS patients during sleep, which provided new insights and established indicators to investigate the changes of brain function in patients.Cardiac chemical exchange saturation transfer-magnetic resonance imaging (CEST-MRI) has been used to probe levels of various metabolites that provide insight into myocardial structure and function. However, imaging of the heart using CEST-MRI is prolonged by the need to repeatedly acquire multiple images for a full Z-spectrum and to perform saturation and acquisition around cardiac and respiratory cycles. Compressed sensing (CS) reconstruction of sparse data enables accelerated acquisition, but reconstruction artifacts may bias subsequently derived measures of CEST contrast. In this study, we examine the impact of CS reconstruction of increasingly under-sampled cardiac CEST-MRI data on subsequent CEST contrasts of amine-containing metabolites and amide-containing proteins. Cardiac CEST-MRI data sets were acquired in six mice using low and high RF saturation for single and dual contrast generation, respectively. CEST-weighted images were reconstructed using CS methods at 2-5× levels of under-sampling. CEST contrasts were derived from corresponding Z-spectra and the impact of accelerated imaging on accuracy was assessed via analysis of variance. CS reconstruction preserved myocardial signal to noise ratio as compared to conventional reconstruction. However, greater absolute error and distribution of derived contrasts was observed with increasing acceleration factors. The results from this study indicate that acquisition of radial cardiac CEST-MRI data can be modestly, but meaningfully, accelerated via CS reconstructions with little error in CEST contrast quantification.The inhalation administration method which has been applied to treat respiratory diseases has the characteristics of painlessness high efficiency and non-invasiveness, and the drug can also be targeted at the organ level first to reduce the loss of drug during circulation. Therefore, delivering medicine by inhalation administration has brought a new turnaround for lung cancer treatment. Herein from the perspective of combining traditional drug delivery design strategies with new drug delivery methods how to improve lung targeting efficiency and treatment efficacy is discussed. We also discuss the comparative advantages of inhaled drug delivery and traditional administration in the treatment of lung cancer such as intravenous injection. And the researches are divided into different forms of inhalation administration studied in the treatment of lung cancer in recent years, such as single-component loaded and multi-component loaded systems and their advantages. Finally, the obstacles of the application of carrier materials for inhalation administration and the prospects for improvement of lung cancer treatment methods are presented.