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Objective To discuss the feasibility of directly processing paper-based ECG by using deep learning method, and to propose a method for identifying atrial fibrillation ( AF) from paper-based ECG rapidly and accurately. Methods The 12-lead ECG signals of 1 040 AF and 1 344 non-AF patients were selected from CPSC 2018 Challenge data, and were plotted as ECGs. These ECGs were printed out on papers, and rescanned into images. The paper-based ECGs with white background were obtained by a series of preprocessing. These paper-based ECGs were used to construct AF recognition dataset; the artificial intelligence algorithm (AIA) was trained, validated and tested using the real labels provided by the Challenge as the gold standard. Results The sensitivity, specificity and F1 score of the AIA based on paper-based ECGs are 0. 957, 0. 978 and 0. 969, respectively, and the area under receiver operating characteristic curve is 0. 994. The Grad-CAM based feature visualization results show that in paper-based ECGs, the position of P wave and F wave at the onset of AF provide main references for the algorithm to identify AF, which is consistent with the ECG diagnostic criteria of AF in clinical practice. Conclusion The AF recognition algorithm based on paper-based ECGs shows good performance. The visualization results verify the high efficiency and feasibility of directly analyzing paper-based ECGs by using AIA, which could provide guidance for clinical practice. [ABSTRACT FROM AUTHOR]

In an order of the abstract structure of the human body from inside to outside, this paper lists the state-of-the-art research advances of artificial intelligence (AI) especially deep neural network in ECG analysis from aspects of electrical conduction system and the structure of the heart ( including heart valves, myocardium, coronary arteries, etc. ), other systems out of the heart, ect. This paper introduces the application of AI-enabled ECG analysis technology in identifying arhythmias, electrolyte disturbances, valvular heart disease, coronary heart disease, and diseases of the circulation system and digestive system. This paper also covers its innovative application in the field of personal identification, and how to use AI technologies such as transfer learning in practical research. This paper is aimed to promote more cross-field cooperations, explore deep values of ECG, and help to realize the screening, diagnosis and treatment of multi-system diseases. [ABSTRACT FROM AUTHOR]

Since the outbreak of corona virus disease 2019, deep learning has been widely used in predicting epidemic trends, screening high-risk groups and making early diagnosis. Novel coronavirus infection mainly causes pulmonary diseases, and also affects cardiovascular system, resulting in electrocardiogram (ECG) abnormalities. ECG tracking image technology is a hot spot in the research of deep learning. Compared with traditional ECG judgment methods, its accuracy and sensitivity are both higher. It can be used in the effective diagnosis, clinical decision-making and prognosis evaluation of patients with novel coronavirus infection. This paper reviews on the ECG changes after novel coronavirus infection, and the advances of ECG tracking image technology applied in the auxiliary diagnosis of novel coronavirus infection, so as to provide references for clinical healthcare workers. [ABSTRACT FROM AUTHOR]

As one of the most common arrhythmias in clinical practice, atrial fibrillation is characterized by being occult, paroxysmal, complex, and high mortality and disability. It is considered as a “silent killer”. This paper summarizes the progress of artificial intelligence applied in the diagnosis and prediction of atrial fibrillation, assistance for decision-making in ablation, plasma drug concentration monitoring, prognosis prediction, etc. from clinical perspectives. [ABSTRACT FROM AUTHOR]