Inverse design of a light nanorouter for a spatially multiplexed optical filter.

It is attractive to use an optical nanorouter by artificial nanostructures to substitute the traditional Bayer filter for an image array sensor, which, however, poses great challenges in balancing the design strategy and the ease of fabrication. Here, we implement and compare two inverse design schemes for rapid optimization of RGGB Bayer-type optical nanorouter. One is based on the multiple Mie scattering theory and the adjoint gradient that is applicable to arrays of nanospheres with varying sizes, and the other is based on the rigorous coupled wave analysis and the genetic algorithm. In both cases, we study layered nanostructures that can be efficiently modeled respectively which greatly accelerates the inverse design. It is shown that the color-dependent peak collection efficiencies of nanorouters designed in the two methods for red, green, and blue wavelengths reach 37%, 44%, and 45% and 52%, 50%, and 66%, respectively. We further demonstrate color nanorouters that provide light focusing to four quadrants working in both the visible and infrared bands, which promises multispectral imaging applications.

Identification of Patients with Potential Atrial Fibrillation during Sinus Rhythm Using Isolated P Wave Characteristics from 12-Lead ECGs.

Atrial fibrillation (AF) is largely underdiagnosed. Previous studies using deep neural networks with large datasets have shown that screening AF with a 12-lead electrocardiogram (ECG) during sinus rhythm (SR) is possible. However, the poor availability of these trained models and the small size of the retrievable datasets limit its reproducibility. This study proposes an approach to generate explainable features for detecting AF during SR with limited data. We collected 94,224 12-lead ECGs from 64,196 patients from Taipei Medical University Hospital. We selected ECGs during SR from 213 patients before AF diagnosis and randomly selected 247 age-matched participants without AF records as the controls. We developed a signal-processing technique, MA-UPEMD, to isolate P waves, and quantified the spatial and temporal features using principal component analysis and inter-lead relationships. By combining these features, the machine learning models yielded AUC of 0.64. We showed that, even with this limited dataset, the P wave, representing atrial electrical activity, is depicted by our proposed approach. The extracted features performed better than the bandpass filter-extracted P waves and deep neural network model. We provided a physiologically explainable and reproducible approach for classifying patients with AF during SR.