ECG-CL: A Comprehensive Electrocardiogram Interpretation Method Based on Continual Learning

Electrocardiogram (ECG) monitoring is one of the most powerful technique of cardiovascular disease (CVD) early identification, and the introduction of intelligent wearable ECG devices has enabled daily monitoring. However, due to the need for professional expertise in the ECGs interpretation, general public access has once again been restricted, prompting the need for the development of advanced diagnostic algorithms. Classic rule-based algorithms are now completely outperformed by deep learning based methods. But the advancement of smart diagnostic algorithms is hampered by issues like small dataset, inconsistent data labeling, inefficient use of local and global ECG information, memory and inference time consuming deployment of multiple models, and lack of information transfer between tasks. We propose a multi-resolution model that can sustain high-resolution low-level semantic information throughout, with the help of the development of low-resolution high-level semantic information, by capitalizing on both local morphological information and global rhythm information. From the perspective of effective data leverage and inter-task knowledge transfer, we develop a parameter isolation based ECG continual learning (ECG-CL) approach. We evaluated our model's performance on four open-access datasets by designing segmentation-to-classification for cross-domain incremental learning, minority-to-majority class for category incremental learning, and small-to-large sample for task incremental learning. Our approach is shown to successfully extract informative morphological and rhythmic features from ECG segmentation, leading to higher quality classification results. From the perspective of intelligent wearable applications, the possibility of a comprehensive ECG interpretation algorithm based on single-lead ECGs is also confirmed.Comment: 10 page

Clinical applications of artificial intelligence in cardiology on the verge of the decade

Artificial intelligence (AI) has been hailed as the fourth industrial revolution and its influence on people’s lives is increasing. The research on AI applications in medicine is progressing rapidly. This revolution shows promise for more precise diagnoses, streamlined workflows, increased accessibility to healthcare services and new insights into ever-growing population-wide datasets. While some applications have already found their way into contemporary patient care, we are still in the early days of the AI-era in medicine. Despite the popularity of these new technologies, many practitioners lack an understanding of AI methods, their benefits, and pitfalls. This review aims to provide information about the general concepts of machine learning (ML) with special focus on the applications of such techniques in cardiovascular medicine. It also sets out the current trends in research related to medical applications of AI. Along with new possibilities, new threats arise — acknowledging and understanding them is as important as understanding the ML methodology itself. Therefore, attention is also paid to the current opinions and guidelines regarding the validation and safety of AI-powered tools

Characterization and modeling of the human left atrium using optical coherence tomography

With current needs to better understand the interaction between atrial tissue microstructure and atrial fibrillation dynamics, micrometer scale imaging with optical coherence tomography has significant potential to provide further insight on arrhythmia mechanisms and improve treatment guidance. However, optical coherence tomography imaging of cardiac tissue in humans is largely unexplored, and the ability of optical coherence tomography to identify the structural substrate of atrial fibrillation has not yet been investigated. Therefore, the objective of this thesis was to develop an optical coherence tomography imaging atlas of the human heart, study the utility of optical coherence tomography in providing useful features of human left atrial tissues, and develop a framework for optical coherence tomography-informed cardiac modeling that could be used to probe dynamics between electrophysiology and tissue structure. Human left atrial tissues were comprehensively imaged by optical coherence tomography for the first time, providing an imaging atlas that can guide identification of left atrial tissue features from optical coherence tomography imaging. Optical coherence tomography image features corresponding to myofiber and collagen fiber orientation, adipose tissue, endocardial thickness and composition, and venous media were established. Varying collagen fiber distributions in the myocardial sleeves were identified within the pulmonary veins. A scheme for mapping optical coherence tomography data of dissected left atrial tissues to a three-dimensional, anatomical model of the human left atrium was also developed, enabling the mapping of distributions of imaged adipose tissue and fiber orientation to the whole left atrial geometry. These results inform future applications of structural substrate mapping in the human left atrium using optical coherence tomography-integrated catheters, as well as potential directions of ex vivo optical coherence tomography atrial imaging studies. Additionally, we developed a workflow for creating optical mapping models of atrial tissue as informed by optical coherence tomography. Tissue geometry, fiber orientation, ablation lesion geometry, and heterogeneous tissue types were extracted from optical coherence tomography images and incorporated into tissue-specific meshes. Electrophysiological propagation was simulated and combined with photon scattering simulations to evaluate the influence of tissue-specific structure on electrical and optical mapping signals. Through tissue-specific modeling of myofiber orientation, ablation lesions, and heterogeneous tissue types, the influence of myofiber orientation on transmural activation, the relationship between fluorescent signals and lesion geometry, and the blurring of optical mapping signals in the presence of heterogeneous tissue types were investigated. By providing a comprehensive optical coherence tomography image database of the human left atrium and a workflow for developing optical coherence tomography-informed cardiac tissue models, this work establishes the foundation for utilizing optical coherence tomography to improve the structural substrate characterization of atrial fibrillation. Future developments include analysis of optical coherence tomography imaged tissue structure with respect to clinical presentation, development of automated processing to better leverage the large amount of imaging data, enhancements and validation of the modeling scheme, and in vivo evaluation of the left atrial structural substrate through optical coherence tomography-integrated catheter