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）
Temesgen Mehari, Nils Strodthoff
备注：
Extended Abstract presented at Machine Learning for Health (ML4H) symposium 2022, November 28th, 2022, New Orleans, United States & Virtual, http://www.ml4h.cc, 6+5 pages
The field of deep-learning-based ECG analysis has been largely dominated by convolutional architectures. This work explores the prospects of applying the recently introduced structured state space models (SSMs) as a particularly promising approach due to its ability to capture long-term dependencies in time series. We demonstrate that this approach leads to significant improvements over the current state-of-the-art for ECG classification, which we trace back to individual pathologies. Furthermore, the model's ability to capture long-term dependencies allows to shed light on long-standing questions in the literature such as the optimal sampling rate or window size to train classification models. Interestingly, we find no evidence for using data sampled at 500Hz as opposed to 100Hz and no advantages from extending the model's input size beyond 3s. Based on this very promising first assessment, SSMs could develop into a new modeling paradigm for ECG analysis.
A Comprehensive Benchmark for Electrocardiogram Time-Series
Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences（中国科学院大学杭州先进研究所）；Computer Network Information Center, Chinese Academy of Sciences（中国科学院计算机网络信息中心）
Multi-Window Temporal Analysis for Enhanced Arrhythmia Classification: Leveraging Long-Range Dependencies in Electrocardiogram Signals
AI4Health Department, Oldenburg University（奥尔登堡大学AI4Health部门）；Department of Cardiology, Angiology and Intensive Care Medicine, Charité Campus Mitte, German Heart Center of the Charité-University Medicine Berlin（查理医院梅特校区心内科、血管科和重症医学科，查理-柏林大学医学中心心脏中心）
A Multimodal Deep Learning Framework for Predicting ICU Deterioration: Integrating ECG Waveforms with Clinical Data and Clinician Benchmarking
AI4Health Division, Carl von Ossietzky Universität Oldenburg（AI4Health部门，奥尔登堡大学）；CAPASITS, Secretariat of Health of the State of Colima（CAPASITS，科利马州卫生秘书处）；Department of Interventional Cardiology, Hospital Puerta de Hierro Colima（介入心脏病学部，科利马医院）；Institute for Medical Informatics and Statistics, Kiel University and University Hospital Schleswig-Holstein（医学信息学与统计研究所，基尔大学和石勒苏益格-荷尔斯泰因大学医院）；Department of Anesthesiology, Intensive Care Medicine, Emergency Medicine, and Pain Therapy, Carl von Ossietzky Universität & Klinikum Oldenburg（麻醉学、重症医学、急诊医学和疼痛治疗部，奥尔登堡大学及奥尔登堡诊所）；Department of Cardiology, Angiology and Intensive Care Medicine, Charité Campus Mitte, German Heart Center of the Charité-University Medicine Berlin（心内科、血管科和重症医学部，Charité梅尔特校区，Charité-大学医学中心柏林德国心脏中心）
CoRe-ECG: Advancing Self-Supervised Representation Learning for 12-Lead ECG via Contrastive and Reconstructive Synergy
Department of Electronic Engineering, Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院电子工程系）；Department of Electronic Engineering, Tsinghua University（清华大学电子工程系）；Shanghai AI Laboratory（上海人工智能实验室）；Department of Cardiology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua Medicine, Tsinghua University（清华大学临床医学院清华大学医学部北京清华长庚医院心内科）；Department of Anesthesiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College（中国医学科学院北京协和医学院国家癌症中心国家肿瘤临床医学研究中心肿瘤医院麻醉科）；Department of Electrocardiographic Diagnosis, Fuzhou University Affiliated Provincial Hospital（福州大学附属省立医院心电图诊断科）
TabKAN: Advancing Tabular Data Analysis using Kolmogorov-Arnold Network
Department of Computer Science, University of Kentucky（卡内基梅隆大学计算机科学系）；Independent Researcher（独立研究者）；Institute for Biomedical Informatics, University of Kentucky（生物医学信息学研究所）
HARDC : A novel ECG-based heartbeat classification method to detect arrhythmia using hierarchical attention based dual structured RNN with dilated CNN
Universiti Malaysia Pahang（马来西亚彭亨大学）；Queensland University of Technology（昆士兰科技大学）；Islamic University（伊斯兰大学）；The University of Sydney（悉尼大学）；University of Cambridge（剑桥大学）；The Garvan Institute of Medical Research（加文医学研究所）；The University of Queensland（昆士兰大学）
Structured Contrastive Learning for Interpretable Latent Representations
Department of Electrical and Electronic Engineering, Imperial College London（帝国理工学院伦敦电子与电气工程系）；Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford（牛津大学生物医学工程研究所）
GraphRank Pro+: Advancing Talent Analytics Through Knowledge Graphs and Sentiment-Enhanced Skill Profiling
UCEK, JNTU Kakinada（贾夫纳大学工程学院，贾夫纳印度理工学院卡基纳达分校）；Brane Enterprises（布兰恩企业）；School of EECS, College of Engineering and Mines, University of North Dakota（北达科他大学工程与矿业学院电子工程与计算机科学学院）
Advancing Graph Neural Networks with HL-HGAT: A Hodge-Laplacian and Attention Mechanism Approach for Heterogeneous Graph-Structured Data
National University of Singapore（新加坡国立大学）；the Hong Kong Polytechnic University（香港理工大学）；Fujian Agriculture and Forestry University（福建农林大学）；the University of Wisconsin-Madison（威斯康星大学麦迪逊分校）
SL-S4Wave: Self-Supervised Learning of Physiological Waveforms with Structured State Space Models
Massachusetts Institute of Technology（麻省理工学院）；OpenEvidence, USA（OpenEvidence（美国））；New York University（纽约大学）；Xi’an Jiaotong University（西安交通大学）；University of Toronto（多伦多大学）；Emory University（埃默里大学）
Initialization Is Critical: Advancing Federated Short-Term Load Forecasting under Load Heterogeneity via Model Initialization
School of Electrical Engineering and Computer Science, The Pennsylvania State University（宾夕法尼亚州立大学电气工程与计算机科学学院）；Department of Electrical and Computer Engineering, Bucknell University（巴克内尔大学电气与计算机工程系）
