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Objective: Rotors, regions of spiral wave reentry in cardiac tissues, are considered as the drivers of atrial fibrillation (AF), the most common arrhythmia. Whereas physics-based approaches have been widely deployed to detect the rotors, in-depth knowledge in cardiac physiology and electrogram interpretation skills are typically needed. The recent leap forward in smart sensing, data acquisition, and Artificial Intelligence (AI) has offered an unprecedented opportunity to transform diagnosis and treatment in cardiac ailment, including AF. This study aims to develop an image-decomposition-enhanced deep learning framework for automatic identification of rotor cores on both simulation and optical mapping data. Methods: We adopt the Ensemble Empirical Mode Decomposition algorithm (EEMD) to decompose the original image, and the most representative component is then fed into a You-Only-Look-Once (YOLO) object-detection architecture for rotor detection. Simulation data from a bi-domain simulation model and optical mapping acquired from isolated rabbit hearts are used for training and validation. Results: This integrated EEMD-YOLO model achieves high accuracy on both simulation and optical mapping data (precision: 97.2%, 96.8%, recall: 93.8%, 92.2%, and F1 score: 95.5%, 94.4%, respectively). Conclusion: The proposed EEMD-YOLO yields comparable accuracy in rotor detection with the gold standard in literature. 

The development of intrinsically stretchable electronics poses great challenges in synthesizing elastomeric conductors, semiconductors and dielectric materials. While a wide range of approaches, from special macrostructural engineering to molecular synthesis, have been employed to afford stretchable devices, this review surveys recent advancements in employing various morphological and nanostructural control methods to impart mechanical flexibility and/or to enhance electrical properties. The focus will be on (1) embedding percolation networks of one-dimensional conductive materials such as metallic nanowires and carbon nanotubes in an elastomer matrix to accommodate large external deformation without imposing a large strain along the one-dimensional materials, (2) design strategies to achieve intrinsically stretchable semiconductor materials that include direct blending of semiconductors with elastomers and synthesizing semiconductor polymers with appropriate side chains, backbones, cross-linking networks, and flexible blocks, and (3) employing interpenetrating polymer networks, bottlebrush structures and introducing inclusions in stretchable polymeric dielectric materials to improve electrical performance. Moreover, intrinsically stretchable electronic devices based on these materials, such as stretchable sensors, heaters, artificial muscles, optoelectronic devices, transistors and soft humanoid robots, will also be described. Limitations of these approaches and measures to overcome them will also be discussed.