Joel Xue

Bio: Joel Xue is an academic researcher. The author has contributed to research in topics: Beat detection. The author has an hindex of 1, co-authored 1 publications receiving 1 citations.

Applications of Machine Learning in Ambulatory ECG

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.

Golden Standard or Obsolete Method? Review of ECG Applications in Clinical and Experimental Context

Abstract: Cardiovascular system and its functions under both physiological and pathophysiological conditions have been studied for centuries. One of the most important steps in the cardiovascular research was the possibility to record cardiac electrical activity. Since then, numerous modifications and improvements have been introduced; however, an electrocardiogram still represents a golden standard in this field. This paper overviews possibilities of ECG recordings in research and clinical practice, deals with advantages and disadvantages of various approaches, and summarizes possibilities of advanced data analysis. Special emphasis is given to state-of-the-art deep learning techniques intensely expanded in a wide range of clinical applications and offering promising prospects in experimental branches. Since, according to the World Health Organization, cardiovascular diseases are the main cause of death worldwide, studying electrical activity of the heart is still of high importance for both experimental and clinical cardiology.

Automated ECG Interpretation—A Brief History from High Expectations to Deepest Networks

Abstract: This article traces the development of automated electrocardiography from its beginnings in Washington, DC around 1960 through to its current widespread application worldwide. Changes in the methodology of recording ECGs in analogue form using sizeable equipment through to digital recording, even in wearables, are included. Methods of analysis are considered from single lead to three leads to twelve leads. Some of the influential figures are mentioned while work undertaken locally is used to outline the progress of the technique mirrored in other centres. Applications of artificial intelligence are also considered so that the reader can find out how the field has been constantly evolving over the past 50 years.

Differential Beat Accuracy for ECG Family Classification Using Machine Learning

Abstract: Holter systems record the electrocardiogram (ECG), which is used to identify beat families according to their origin and severity. Many systems have been proposed using signal conditioning and machine learning (ML) classification algorithms for beat family recognition. However, the design stage of these systems does not always consider the impact that tuning the intermediate blocks has on the beat family classification and the overall accuracy. We propose to use a new index based on the confusion matrices and bootstrap resampling to summarize the global performance for all family beats, so-called differential beat accuracy (DBA), which is obtained as the total number of beats correctly classified in each class minus the total number of beats incorrectly classified. We addressed the sensitivity of the different subblocks when creating a simple beat family classifier consisting of signal preprocessing blocks and a simple k-Nearest Neighbors classifier. The MIT-BIH Arrhythmia database was used for this purpose, following existing literature on the field. We benchmarked two implementations, one for biclass classification (supraventricular vs. non-supraventricular origin) and another for multiclass beat labeling. The usual preprocessing stages were scrutinized with the DBA to evaluate their impact on the quality of the complete ML system, such as signal detrending and filtering, beat balancing, or inter-beat distance. With the support of the DBA, our methodology was able to detect significant differences in terms of some of the options in the algorithm design. For instance, balancing the number of beats in each class for training significantly improved the classification accuracy of the minority classes at 3.22% for the multiclass dataset but not for the biclass dataset. Also, accuracy improved significantly by about 6% for the biclass regrouping without data normalization, whereas overall accuracy improved significantly by about 7% for the multiclass regrouping with data normalization. In addition, the analysis of the statistical dispersion of confusion matrices showed that this database should be considered with caution when training ML-based family classifiers. We can conclude that the proposed DBA can provide us with statistically principled criteria for designing ML-based classifiers and reducing their bias in strongly unbalanced beat family datasets.