Application of Neural Networks to 12-Lead Electrocardiography　― Current Status and Future Directions ―

Abstract

AI is a broad term representing the replication of human "intelligence" with machines that include but are not limited to computers. In the current medical field, the term "AI" mainly refers to a complex machine-learning model that automatically extracts information from data and which is, in most cases, based on an NN. Therefore, in this article, the term "AI" will be used to refer to such statistical models and the methods to create them. The use of AI is in rapid development in many areas such as image recognition, 9 language recognition 10 and automatic driving. 11 In particular, in the image recognition field, AI has achieved results exceeding human ability in speed and accuracy. The technology has received attention from the medical field because it may extend the utilization of complex medical data beyond the limit of human brain function that is unable to handle high-dimension data. 12,13 As expected, the application of AI to medical still-images has been greatly successful, 7,14,15 but AI has also produced striking results in other tasks such as electronic digitalized health record analysis, 16,17 and prediction of clinical outcome from ECG. 1,18-33 Of note, AI applied to the interpretation

A new deep learning algorithm of 12-lead electrocardiogram for identifying atrial fibrillation during sinus rhythm

“…Changes in the normal ECG pattern occur in numerous heart abnormalities, including arrhythmia. We have chosen the RNN of deep neural networks, which have advantages in dealing with time-series data, such as ECG data 33 . The bi-directional connection is added so that time flow can be considered in forward and backward passes, and long short-term memory is used to maintain a series of information in the short and long terms.…”

An artificial intelligence-enabled ECG algorithm for identifying ventricular premature contraction during sinus rhythm

“…On the other hand, CNN extracts all the features of 12-lead ECG with kernels during two-dimensional data processing. The CNN kernels could be activated by specific wave patterns and recognized by the neural network analysis subsequently [ 24 ]. Therefore, two-dimensional analysis is taking the data as an image, more similar to the cardiologist’s way to interpret the 12-lead ECG.…”

“…From the previous study, the AI-driven algorithms had been applied in automatic diagnosis for various diseases [26], such as myocardial infarction needing urgent revascularization [24], systolic heart failure [25], subtle potassium change among the high-risk populations [26], and atrial fibrillation [25][26][27]. However, most of these studies were based on the single-lead ECG or one-dimensional (time-series) datasets.…”

Electrocardiography-Based Prediction of Sudden Cardiac Death in Heart Failure Patients: Application of Artificial Intelligence

“…22 RNN can theoretically learn the time-series voltage data more precisely than CNN, as it explicitly deals with data ordering. 25 Although some complex tasks may still require an RNN, the superiority of model performance using RNN over CNN is unclear; thus, consensus is lacking about the tasks that are suitable for RNN or CNN. In the present study, we attempted to establish an ECG-AI model using RNN combined with CNN to predict the incidence of SCD in patients with HF, and our results showed good performance beyond the conventional indication for ICD.…”