  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.
