论文标题
ECG语言处理（ELP）：一种分析ECG信号的新技术
ECG Language Processing (ELP): a New Technique to Analyze ECG Signals
论文作者
论文摘要
语言是由有限/无限的单词组成的句子组成的。与天然语言相似，心电图（ECG）信号是研究心脏功能并诊断出几种异常心律不齐的最常见的无创工具，由三到四个不同波浪的序列组成，包括P-Wave，QRS Complex，T-Wave，T波和U波。 ECG信号可能包含每个波的几种不同品种（例如，QRS复合物可以具有不同的外观）。因此，心电图是一系列与自然语言中句子相似的心跳的序列），每个心跳由不同形态学的一组波（类似于句子中的单词）组成。类似于自然语言处理（NLP），用于帮助计算机理解和解释人类的自然语言，可以开发受NLP启发的方法，以帮助计算机对心电图信号有了更深入的了解。在这项工作中，我们的目标是提出一种新型的心电图分析技术，即\ textit {ecg语言处理（ELP）}，重点是授权计算机以医生的方式理解ECG信号。我们在两个任务上评估了提出的方法，包括心跳分类和ECG信号中房颤的检测。在三个数据库（即PhysionNet的Mit-BIH，MIT-BIH AFIB和Physionet挑战2017 AFIB数据库数据库）上的实验结果表明，该提出的方法是一个一般思想，可以应用于各种生物医学应用，并能够实现出色的性能。
A language is constructed of a finite/infinite set of sentences composing of words. Similar to natural languages, Electrocardiogram (ECG) signal, the most common noninvasive tool to study the functionality of the heart and diagnose several abnormal arrhythmias, is made up of sequences of three or four distinct waves including the P-wave, QRS complex, T-wave and U-wave. An ECG signal may contain several different varieties of each wave (e.g., the QRS complex can have various appearances). For this reason, the ECG signal is a sequence of heartbeats similar to sentences in natural languages) and each heartbeat is composed of a set of waves (similar to words in a sentence) of different morphologies. Analogous to natural language processing (NLP) which is used to help computers understand and interpret the human's natural language, it is possible to develop methods inspired by NLP to aid computers to gain a deeper understanding of Electrocardiogram signals. In this work, our goal is to propose a novel ECG analysis technique, \textit{ECG language processing (ELP)}, focusing on empowering computers to understand ECG signals in a way physicians do. We evaluated the proposed method on two tasks including the classification of heartbeats and the detection of atrial fibrillation in the ECG signals. Experimental results on three databases (i.e., PhysionNet's MIT-BIH, MIT-BIH AFIB and PhysioNet Challenge 2017 AFIB Dataset databases) reveal that the proposed method is a general idea that can be applied to a variety of biomedical applications and is able to achieve remarkable performance.
