Comparing ECG Lead Subsets for Heart Arrhythmia/ECG Pattern Classification: Convolutional Neural Networks and Random Forest.

Background: Despite the growth in popularity of deep learning (DL), limited research has compared the performance of DL and conventional machine learning (CML) methods in heart arrhythmia/electrocardiography (ECG) pattern classification. In addition, the classification of heart arrhythmias/ECG patterns is often dependent on specific ECG leads for accurate classification, and it remains unknown how DL and CML methods perform on reduced subsets of ECG leads. In this study, we sought to assess the accuracy of convolutional neural network (CNN) and random forest (RF) models for classifying arrhythmias/ECG patterns using reduced ECG lead subsets representing DL and CML methods.

Optimization of Arrhythmia-based ECG-lead Selection for Computer-interpreted Heart Rhythm Classification.

The 12-lead ECG only has 8 independent ECG leads, which leads to diagnostic redundancy when using all 12 leads for heart arrhythmias classification. We have previously developed a deep learning (DL)-based computer-interpreted ECG (CIE) approach to identify an optimal 4-lead ECG subset for classifying heart arrhythmias. However, the clinical diagnostic criteria of cardiac arrhythmia types are often lead-specific, so this study is going to explore the selection of arrhythmia-based ECG-lead subsets rather than one general optimal ECG-lead subset, which could improve the classification performance for the CIE.