基于同伦等价框架的Čech复形生成用于心电图信号心肌梗死诊断
Cech Complex Generation with Homotopy Equivalence Framework for Myocardial Infarction Diagnosis using Electrocardiogram Signals
AI总结：
本文提出一种基于Čech复形生成与同伦等价检查的持久同调特征框架，用于ECG信号分析，以捕捉心搏周期连接性，并在MIT-BIH和PTB数据集上实现NSR、MCI与非MCI分类，AUC较现有方法平均提升2.8%。
摘要：
早期且最优地识别心脏异常，尤其是心肌梗死（MCI），有助于个体及时获得医疗救治以减轻病情严重程度。心电图（ECG）是一种简单、无创的生理信号模态，可用于检查心脏组织的电活动。现有的MCI检测方法主要依赖于ECG信号的时域、频域和空间域分析。这些传统技术在诊断过程中缺乏对心搏周期相互依赖关系的有效识别。因此，迫切需要纳入同一记录时段内各心搏周期之间的底层连接性，以改进异常检测。本文提出了一种新颖的ECG信号分析与分类框架，该框架通过带同伦等价检查的Čech复形生成，利用持久同调特征，并考虑了上述新兴需求。持久同调特征，如持久出生-死亡率、Betti曲线和持久熵，在与机器学习（ML）模型结合时，能够提供区域和心搏周期连接性的透明性。所提出的框架使用公开数据集（MIT-BIH和PTB）进行评估，机器学习模型的性能指标表明其在分类正常窦性心律（NSR）、MCI和非MCI受试者方面具有有效性，与现有方法相比，AUC（ROC曲线下面积）平均提高了2.8%。
英文摘要：
Early and optimal identification of cardiac anomalies, especially Myocardial infarction (MCI) can aid the individual in obtaining prompt medical attention to mitigate the severity. Electrocardiogram (ECG) is a simple non-invasive physiological signal modality, that can be used to examine the electrical activity of heart tissue. Existing methods for MCI detection mostly rely on the temporal, frequency, and spatial domain analysis of the ECG signals. These conventional techniques lack in effective identification of cardiac cycle inter-dependency during diagnosis. Hence, there is an emerging need for incorporating the underlying connectivity of the intra-sessional cardiac cycles for improved anomaly detection. This article proposes a novel framework for ECG signal analysis and classification using persistent homological features through Cech Complex generation with homotopy equivalence check, by taking the above-mentioned emerging needs into account. Homological features like persistent birth-death rates, betti curves, and persistent entropy provide transparency of the regional and cardiac cycle connectivity when combined with Machine Learning (ML) models. The proposed framework is assessed using publicly available datasets (MIT-BIH and PTB), and the performance metrics of machine learning models indicate its efficacy in classifying Normal Sinus Rhythm (NSR), MCI, and non-MCI subjects, achieving a 2.8% mean improvement in AUC (area under the ROC curve) over existing approaches.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
