Comprehensive survey of computational ECG analysis: Databases, methods and applications

Abstract

Electrocardiogram (ECG) recordings are indicative for the state of the human heart. Automatic analysis of these recordings can be performed using various computational methods from the areas of signal processing and machine learning. In addition to the 12-lead ECG devices and the Holter monitor, as currently the most widely used ECG screening methods in clinical practice, ECG recordings are recently often acquired with small novel wireless ECG body sensors. These novel types of body sensors allow for ECG monitoring and analysis to be used for a much broader array of applications than only diagnosing cardiovascular disorders. The new types of ECG measuring devices, as well as their broader and more frequent use, pose new challenges in the processing and analysis of ECG, and furthermore, raise the need for automatic, low-cost, real-time, and efficient ECG monitoring that can be used at home or under ambulatory settings alike. This paper provides a comprehensive survey on the variety of both ECG data and computational methods in various applications: morphological and rhythmic arrhythmia detection, signal quality assessment, biometric identification, respiration estimation, fetal ECG extraction, and physical and emotional monitoring. It includes an extensive overview of 45 diverse ECG public databases and their analysis with state-of-the-art computational ECG methods. We highlight the most notable achievements in each of these ECG application areas in the recent years, and, furthermore, identify future trends in computational ECG analysis, especially analysis of ECG from mobile devices. The general conclusion is that ECG for medical diagnosis is successfully analyzed with the existing methods, while different applications during daily ECG monitoring are still open fields. Given how deep learning has been able to successfully address a lot of the most significant computational ECG problems, like arrhythmia classification, in future, it is expected for deep learning methods to be comprehensively tested in areas where they have not been yet applied, such as respiration estimation and fetal ECG extraction.