ECG heartbeat classification plays a vital role in diagnosis of cardiac arrhythmia. The goal of the Physionet/CinC 2021 challenge was to accurately classify clinical diagnosis based on 12, 6, 4, 3 or 2-lead ECG recordings in order to aid doctors in the diagnoses of different heart conditions. Transformers have had great success in the field of natural language processing in the past years. Our team, CinCSEM, proposes to draw the parallel between text and periodic time series signals by viewing the repeated period as words and the whole signal as a sequence of such words. In this way, the attention mechanisms of the transformers can be applied to periodic time series signals. In our implementation, we follow the Transformer Encoder architecture, which combines several encoder layers followed by a dense layer with linear or sigmoid activation for generative pre-training or classification, respectively. The use case presented here is multi-label classification of heartbeat abnormalities of ECG recordings shared by the challenge. Our best entry, not exceeding the challenge's hardware limitations, achieved a score of 0.12, 0.07, 0.10, 0.10 and 0.07 on 12-lead, 6-lead, 4-lead, 3-lead and 2-lead test set respectively. Unfortunately, our team was unable to be ranked because of a missing pre-print.
Generative Deep Learning and Signal Processing for Data Augmentation of Cardiac Auscultation Signals: Improving Model Robustness Using Synthetic Audio
School of Electrical Engineering, Computing, and Mathematical Sciences (EECMS), Faculty of Science and Engineering, Curtin University（电气工程、计算与数学科学学院（EECMS），科学与工程学院， Curtin 大学）
On-device Computation of Single-lead ECG Parameters for Real-time Remote Cardiac Health Assessment: A Real-world Validation Study
College of Integrative Chinese and Western Medicine, Anhui University of Chinese Medicine（安徽中医药大学中西医结合学院）；HeartVoice Medical Technology（心音医疗科技）；The First Affiliated Hospital of Anhui Medical University（安徽医科大学第一附属医院）；National Institute of Health Data Science, Peking University（北京大学健康医疗大数据国家研究院）；The First Affiliated Hospital of University of Science and Technology of China（中国科学技术大学附属第一医院）；University of Science and Technology of China（中国科学技术大学）；Key Laboratory of Xinan Medicine of Ministry of Education, Anhui University of Chinese Medicine（安徽中医药大学教育部新安医学重点实验室）；Peking University Health Science Center（北京大学医学部）；Institute for Artificial Intelligence, Peking University（北京大学人工智能研究院）；Peking University First Hospital（北京大学第一医院）
Opportunistic Screening of Wolff-Parkinson-White Syndrome using Single-Lead AI-ECG Mobile System: A Real-World Study of over 3.5 million ECG Recordings in China
National Institute of Health Data Science, Peking University（北京大学健康数据科学国家研究院）；Heart Voice Medical Technology（心语医疗科技）；College of Integrative Chinese and Western Medicine, Anhui University of Chinese Medicine（安徽中医药大学中西医结合学院）；Institute of Medical Technology, Peking University Health Science Center（北京大学医学部医药技术研究所）；Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, the Second Hospital of Tianjin Medical University（天津医科大学第二医院心血管病研究所心脏内科（离子分子功能心血管疾病天津市重点实验室））；University of Chinese Academy of Sciences（中国科学院大学）；Division of Life Science and Medicine, University of Science and Technology of China（中国科学技术大学生命科学与医学部）
