MMA-RNN: A Multi-level Multi-task Attention-based Recurrent Neural Network for Discrimination and Localization of Atrial Fibrillation

The automatic detection of atrial fibrillation based on electrocardiograph (ECG) signals has received wide attention both clinically and practically. It is challenging to process ECG signals with cyclical pattern, varying length and unstable quality due to noise and distortion. Besides, there has been insufficient research on separating persistent atrial fibrillation from paroxysmal atrial fibrillation, and little discussion on locating the onsets and end points of AF episodes. It is even more arduous to perform well on these two distinct but interrelated tasks, while avoiding the mistakes inherent from stage-by-stage approaches. This paper proposes the Multi-level Multi-task Attention-based Recurrent Neural Network for three-class discrimination on patients and localization of the exact timing of AF episodes. Our model captures three-level sequential features based on a hierarchical architecture utilizing Bidirectional Long and Short-Term Memory Network (Bi-LSTM) and attention layers, and accomplishes the two tasks simultaneously with a multi-head classifier. The model is designed as an end-to-end framework to enhance information interaction and reduce error accumulation. Finally, we conduct experiments on CPSC 2021 dataset and the result demonstrates the superior performance of our method, indicating the potential application of MMA-RNN to wearable mobile devices for routine AF monitoring and early diagnosis.

SRCN3D: Sparse R-CNN 3D Surround-View Camera Object Detection and Tracking for Autonomous Driving

Detection And Tracking of Moving Objects (DATMO) is an essential component in environmental perception for autonomous driving. While 3D detectors using surround-view cameras are just flourishing, there is a growing tendency of using different transformer-based methods to learn queries in 3D space from 2D feature maps of perspective view. This paper proposes Sparse R-CNN 3D (SRCN3D), a novel two-stage fully-convolutional mapping pipeline for surround-view camera detection and tracking. SRCN3D adopts a cascade structure with twin-track update of both fixed number of proposal boxes and proposal latent features. Proposal boxes are projected to perspective view so as to aggregate Region of Interest (RoI) local features. Based on that, proposal features are refined via a dynamic instance interactive head, which then generates classification and the offsets applied to original bounding boxes. Compared to prior arts, our sparse feature sampling module only utilizes local 2D features for adjustment of each corresponding 3D proposal box, leading to a complete sparse paradigm. The proposal features and appearance features are both taken in data association process in a multi-hypotheses 3D multi-object tracking approach. Extensive experiments on nuScenes dataset demonstrate the effectiveness of our proposed SRCN3D detector and tracker. Code is available at https://github.com/synsin0/SRCN3D.