Processing temporal sequences is central to a variety of applications in health care, and in particular multi-channel Electrocardiogram (ECG) is a highly prevalent diagnostic modality that relies on robust sequence modeling. While Recurrent Neural Networks (RNNs) have led to significant advances in automated diagnosis with time-series data, they perform poorly when models are trained using a limited set of channels. A crucial limitation of existing solutions is that they rely solely on discriminative models, which tend to generalize poorly in such scenarios. In order to combat this limitation, we develop a generative modeling approach to limited channel ECG classification. This approach first uses a Seq2Seq model to implicitly generate the missing channel information, and then uses the latent representation to perform the actual supervisory task. This decoupling enables the use of unsupervised data and also provides highly robust metric spaces for subsequent discriminative learning. Our experiments with the Physionet dataset clearly evidence the effectiveness of our approach over standard RNNs in disease prediction.
Advancing the State-of-the-Art for ECG Analysis through Structured State Space Models
Physikalisch-Technische Bundesanstalt（德国联邦物理技术研究院）；Fraunhofer Heinrich-Hertz-Institut（弗劳恩霍夫海因里希·赫兹研究所）；Carl von Ossietzky Universität Oldenburg（卡尔·冯·奥西茨基奥尔登堡大学）
The Effect of Data Augmentation on Classification of Atrial Fibrillation in Short Single-Lead ECG Signals Using Deep Neural Networks
Fraunhofer Institute for Integrated Circuits IIS（弗劳恩霍夫集成电路研究所）；Friedrich-Alexander University Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；University of Leeds（利兹大学）
Generative Example-Based Explanations: Bridging the Gap between Generative Modeling and Explainability
Center for Artificial Intelligence and Robotics（人工智能与机器人中心）；Technical University of Applied Sciences Würzburg-Schweinfurt（应用科学大学魏玛-施维林分校）；Bielefeld University（比勒菲尔德大学）
Generative modeling of conditional probability distributions on the level-sets of collective variables
ENPC, Institut Polytechnique de Paris（巴黎高等土木工程学校，巴黎理工学院）；Zuse Institute Berlin（柏林齐泽研究所）；CERMICS, ENPC, Institut Polytechnique de Paris（巴黎高等土木工程学校计算数学与应用研究中心，巴黎高等土木工程学校，巴黎理工学院）；Inria Paris（法国国家信息与自动化研究院巴黎分部）；Institute of Mathematics, Freie Universität Berlin（柏林自由大学数学研究所）
