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ECG Language Processing (ELP) a New Technique to Analyze ECG Signals

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Document pages: 11 pages

Abstract: A language is constructed of a finite infinite set of sentences composing ofwords. Similar to natural languages, Electrocardiogram (ECG) signal, the mostcommon noninvasive tool to study the functionality of the heart and diagnoseseveral abnormal arrhythmias, is made up of sequences of three or four distinctwaves including the P-wave, QRS complex, T-wave and U-wave. An ECG signal maycontain several different varieties of each wave (e.g., the QRS complex canhave various appearances). For this reason, the ECG signal is a sequence ofheartbeats similar to sentences in natural languages) and each heartbeat iscomposed of a set of waves (similar to words in a sentence) of differentmorphologies. Analogous to natural language processing (NLP) which is used tohelp computers understand and interpret the human s natural language, it ispossible to develop methods inspired by NLP to aid computers to gain a deeperunderstanding of Electrocardiogram signals. In this work, our goal is topropose a novel ECG analysis technique, textit{ECG language processing (ELP)},focusing on empowering computers to understand ECG signals in a way physiciansdo. We evaluated the proposed method on two tasks including the classificationof heartbeats and the detection of atrial fibrillation in the ECG signals.Experimental results on three databases (i.e., PhysionNet s MIT-BIH, MIT-BIHAFIB and PhysioNet Challenge 2017 AFIB Dataset databases) reveal that theproposed method is a general idea that can be applied to a variety ofbiomedical applications and is able to achieve remarkable performance.

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