ECG Language processing (ELP): A new technique to analyze ECG signals.
ECG Language processing (ELP): A new technique to analyze ECG signals.
复制标题
DOI：
10.1016/j.cmpb.2021.105959
复制
发表时间：
2021-04
影响因子：
6.1
通讯作者：
Acharya UR
中科院分区：
文献类型：
--
作者：
Mousavi S;Afghah F;Khadem F;Acharya UR
语言是由单词组成的有限/无限句子集构成的。与自然语言类似，心电图 (ECG) 信号是研究心脏功能和诊断多种异常心律失常的最常见的无创工具，由三个或四个不同波的序列组成，包括 P 波、QRS 波群、T 波和 U 波。 ECG 信号可能包含每个波的几种不同变体（例如，QRS 复合波可以有各种外观）。因此，心电图信号是一系列心跳序列（类似于自然语言中的句子），并且每次心跳由一组不同形态的波（类似于句子中的单词）组成。类似于用于帮助计算机理解和解释人类自然语言的自然语言处理 (NLP)，可以开发受 NLP 启发的方法来帮助计算机更深入地理解心电图信号。在这项工作中，我们的目标是提出一种新颖的心电图分析技术，即心电图语言处理（ELP），重点是使计算机能够像医生那样理解心电图信号。我们在两项任务上评估了所提出的方法，包括心跳分类和心电图信号中心房颤动的检测。总体而言，与其他深度神经网络和现有算法相比，我们的技术使用较小的神经网络获得了更好的性能或可比的性能。在三个数据库（即 PhysioNet 的 MIT-BIH、MIT-BIH AFIB 和 PhysioNet Challenge 2017 AFIB 数据集数据库）上的实验结果表明，所提出的方法作为一个总体思想可以应用于各种生物医学应用，并且可以取得显着的性能。
A language is constructed of a finite/infinite set of sentences composing of words. Similar to natural languages, the 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, ECG language processing (ELP), focusing on empowering computers to understand ECG signals in a way physicians do. We evaluated the proposed approach on two tasks, including the classification of heartbeats and the detection of atrial fibrillation in the ECG signals. Overall, our technique resulted in better performance or comparable performance with smaller neural networks compared to other deep neural networks and existing algorithms. Experimental results on three databases (i.e., PhysioNet’s MIT-BIH, MIT-BIH AFIB, and PhysioNet Challenge 2017 AFIB Dataset databases) reveal that the proposed approach as a general idea can be applied to a variety of biomedical applications and can achieve remarkable performance.
登录
查看更多内容
DOI：
10.1109/bhi.2019.8834637
发表时间：
2019-05
期刊：
... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics
影响因子：
--
作者：
Mousavi SS;Afghah F;Razi A;Acharya UR
通讯作者：
Acharya UR
影响因子：
37.8
作者：
Hurst, JW
通讯作者：
Hurst, JW
影响因子：
4.6
作者：
PAN, J;TOMPKINS, WJ
通讯作者：
TOMPKINS, WJ
影响因子：
7.7
作者：
Acharya, U. Rajendra;Oh, Shu Lih;Tan, Ru San
通讯作者：
Tan, Ru San
DOI：
10.1109/icassp.2019.8683140
发表时间：
2019-05
期刊：
Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference)
影响因子：
--
作者：
Mousavi S;Afghah F
通讯作者：
Afghah F