Next Article in Journal
Unexplained Syncope: The Importance of the Electrophysiology Study
Previous Article in Journal / Special Issue
Computer Assisted Patient Monitoring: Associated Patient, Clinical and ECG Characteristics and Strategy to Minimize False Alarms
Review

Applications of Machine Learning in Ambulatory ECG

by 1,* and 2
1
Department of Biomedical Informatics, Emory University, Atlanta, GA 30322, USA
2
Diagnostic Cardiology, GE Healthcare, Wauwatosa, WI 53226, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Peter Macfarlane
Hearts 2021, 2(4), 472-494; https://doi.org/10.3390/hearts2040037
Received: 2 August 2021 / Revised: 3 October 2021 / Accepted: 8 October 2021 / Published: 13 October 2021
(This article belongs to the Special Issue The Application of Computer Techniques to ECG Interpretation)
The ambulatory ECG (AECG) is an important diagnostic tool for many heart electrophysiology-related cases. This review covers some key Ambulatory ECG applications of Machine Learning algorithms, which include both statistical learning and neural network-based deep learning algorithms.
The ambulatory ECG (AECG) is an important diagnostic tool for many heart electrophysiology-related cases. AECG covers a wide spectrum of devices and applications. At the core of these devices and applications are the algorithms responsible for signal conditioning, ECG beat detection and classification, and event detections. Over the years, there has been huge progress for algorithm development and implementation thanks to great efforts by researchers, engineers, and physicians, alongside the rapid development of electronics and signal processing, especially machine learning (ML). The current efforts and progress in machine learning fields are unprecedented, and many of these ML algorithms have also been successfully applied to AECG applications. This review covers some key AECG applications of ML algorithms. However, instead of doing a general review of ML algorithms, we are focusing on the central tasks of AECG and discussing what ML can bring to solve the key challenges AECG is facing. The center tasks of AECG signal processing listed in the review include signal preprocessing, beat detection and classification, event detection, and event prediction. Each AECG device/system might have different portions and forms of those signal components depending on its application and the target, but these are the topics most relevant and of greatest concern to the people working in this area. View Full-Text
Keywords: ambulatory ECG; machine learning; deep learning; pattern recognition; noise reduction; Holter ECG ambulatory ECG; machine learning; deep learning; pattern recognition; noise reduction; Holter ECG
Show Figures

Figure 1

MDPI and ACS Style

Xue, J.; Yu, L. Applications of Machine Learning in Ambulatory ECG. Hearts 2021, 2, 472-494. https://doi.org/10.3390/hearts2040037

AMA Style

Xue J, Yu L. Applications of Machine Learning in Ambulatory ECG. Hearts. 2021; 2(4):472-494. https://doi.org/10.3390/hearts2040037

Chicago/Turabian Style

Xue, Joel, and Long Yu. 2021. "Applications of Machine Learning in Ambulatory ECG" Hearts 2, no. 4: 472-494. https://doi.org/10.3390/hearts2040037

Find Other Styles

Article Access Map by Country/Region

1
Back to TopTop