Build a bridge between ECG and EEG signals for atrial fibrillation diagnosis using AI methods.

Atrial fibrillation (AF) is a very common type of cardiac arrhythmia. The main characteristic of AF is an abnormally rapid and disordered atrial rhythm causing an atrial dysfunction, which can be visualized on an electrocardiograph (ECG) and distinguished by irregular fluctuations. Despite continuous and considerable efforts to analyze the pathophysiology of AF, it is challenging to determine the underlying pathogenesis of the disease in individual patients. This study aims to build a bridge between ECG and electroencephalogram (EEG) signals to probe the strong influence between human brain activity and AF by AI methods. We first found that the one-second data fragment shows the most excellent performance in our time window configuration. Secondly, in our proposed measurement, most cortical potentials were partly associated with AF. Thirdly, we found that only a few channels of data were sufficient for analysis. Finally, our experiment shows δ wave has the best performance compared to other wave bands. By AI methods, the paper contributes to concluding that δ wave band of EEG is the most associated brain wave type with AF. By EEG signals from typical regions, the central region, parietal and Occipital might be the most associated encephalic regions with AF. The clinical trial registration number for our study is ChiCTR2300068625.

Influence of Silver nanoparticles on nutrient removal and microbial communities in SBR process after long-term exposure.

The widespread utilization of silver nanoparticles (AgNPs) in industrial and commercial products inevitably raises the release into wastewater that might cause potential negative impacts on sewage treatment system. In this paper, long-term exposure experiments at four levels were conducted to determine whether AgNPs caused adverse impacts on nutrient removals in sequencing batch reactors (SBRs) and changes of microbial community structure. Compared with the control reactor (without AgNPs), carbon, nitrogen and phosphorus removal in presence of 0.1mg/L AgNPs was no difference. However, presence of 1.0 and 10mg/L AgNPs decreased the average removal efficiencies of COD from 95.4% to 85.2% and 68.3%, ammonia nitrogen from 98.8% to 71.2% and 49%, SOP from 97.6% to 75.5% and 54.1%, respectively. It was found that AgNPs could accumulate in sludge with the distribution coefficients of 39.2-114L/g, inhibit the protein and polysaccharide production in EPS, reduce the SOUR of sludge, and greatly increase LDH release from microbial cells. The illumina high-throughput sequencing results indicated that AgNPs concentration changed the structures of bacterial communities, associating with the effects of AgNPs on reactor performance. Sequence analyses showed that Proteobacteria, Bacteroidetes and Acidobacteria were the dominant phyla. It was notable that AgNPs addition reduced the contents of several nitrifying bacteria at genera level in sludge, leading to the lower removal of nitrogen.