Abstract
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.
Highlights
Ambulatory electrocardiograms (AECG) have evolved greatly from the traditional24–48 h of Holter monitoring devices
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
Instead of a general overview of machine learning (ML)/DL techniques, which have been recently discussed in other reviews, this review focuses on how these new ML/DL algorithms can perform better in recognizing differences between physiologically meaningful signals and noise, and ways in which these new algorithms can be used together with traditional models to achieve even better performance and interpretability
Summary
For long Holter recordings, some learning algorithms can be applied for a period to accumulate initial templates, and the learning process can be updated with the longitudinal data to refine the Hearts 2021, 2, FOR PEER REVIEW template matching and beat detection. Some learning algorithms can be applied for a period to accumulate feature extraction of signals from noisy data is usually difficult and time-consuming, initial templates, and the learning process can be updated with the longitudinal data to refine the template matching and beat detection. Either automatic or semi-automatic learning models, which include template matching and clustering, were used during the analysis [14] These types of learning algorithms were mainly limited to current patient data, instead of using a wide group of patients’ data for training sets as the most current. We here cover how these points are relevant to each key step of AECG processing
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