Heartbeat Classification by Random Forest With a Novel Context Feature: A Segment Label

FIGURE 1.. Illustration of the overall methodology.

FIGURE 2.. Difference between heartbeat classification and ECG classification. Left side is a single heartbeat with heartbeat label NOR(normal hearbeat). Right side is a period of ECG with ECG label NSR(normal sinus rhythm).

FIGURE 3.. Illustration of QRS width, QRS width in half level, and QRS width in quarter level of one heartbeat. The definition is originally from [8], here half level and quarter level is calculated between the highest signal strength and lowest signal strength of QRS complex.

FIGURE 4.. Illustration of ECG segmentation without overlap (left) and with half overlap (right). The upper one are the original ECG signals, and the rests are ECG segments got from the records.

FIGURE 5.. Model architecture of ecgclf_c, which is a ECG recording classifier in Fig. 1 serving as a segment label extractor.

FIGURE 6.. Hyperparameter tuning for number of decision trees based on leave-one-patient-out cross-validation on macro-averaged F1-scores of two proposed models, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$RFS_{b}$ \end{document} and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$RFS_{c}$ \end{document}, which are introduced in Section IV-C.