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.