Detecting Cardiac Abnormalities from 12-lead ECG Signals Using Feature Extraction, Dimensionality Reduction, and Machine Learning Classification

Abstract

This work represents an entry to the 2020 PhysioNET Computing in Cardiology Challenge for the team named “Whitaker's Lab.” The algorithm we developed can be divided into three main components: feature extraction, dimensionality reduction, and classification. In the feature extraction stage, we process the provided 12-lead ECG signals to determine various features. We consider 12 time-domain statistical features per lead, as well as sparse coding features obtained from frequency information that is extracted from each ECG lead. After computing the features, we reduce the dimensionality of the statistical features using principal component analysis in an attempt to ease the computational requirements of the classifier. After feature extraction and dimensionality reduction, we classify each 12-lead ECG signal using a random forest classifier. The classifier is trained using a cross-validated grid search algorithm to help select hyperparameters. In an attempt to avoid overfitting, the classifier and unsupervised feature extraction algorithms are trained on disjoint subsets of the Challenge data. We were unable to rank and score in the test set, but using a holdout portion of the training set we achieved a validation score of − 0.744. This result is likely to be over-optimistic.