Multi-Label Reduced-Lead EEG Classification Using CNNs

Nima Wickramasinghe from the Department of Electronic and Telecommunication Engineering (ENTC), together with his mentor Mohamed Athif from the Department of Biomedical Engineering, Boston University (previously an undergraduate at ENTC) have written a research paper, titled “Multi-label Classification of Reduced-lead ECGs using an Interpretable Deep Convolutional Neural Network” which has been accepted to be published in the journal Physiological Measurement as a special issue paper.

In their work, they propose a novel method to identify the presence of 26 cardiac abnormalities in an ECG recording with reduced leads. Even though most of the previous work relies on 12-lead ECGs, classification using reduced leads remained unexplored. In their research, they trained a deep convolutional neural network to classify the ECG recordings and showed that the reduced-lead model performs comparably to the 12-lead model. In addition to accurately classifying the cardiac abnormalities, they have used SHAP (shapley additive explanations: a game-theoretic approach used to explain the output of any machine learning model) to interpret the deep learning model. The authors identified that the model learns almost the same diagnostic criteria used by cardiologists to classify cardiac abnormalities. By analyzing the model through SHAP, they were able to detect why the model underperforms in some of the classes, which was mainly due to the lack of discriminating features in reduced leads, labeling inconsistencies in the dataset, and low number of samples.

Physiological Measurement is a journal that covers the quantitative measurement and visualization of physiological structure and function in clinical research and practice, with an emphasis on the development of new methods of measurement and their validation. The Sustainable Education Foundation facilitated the collaboration between the 2 authors.

DOI Link to the paper: https://doi.org/10.1088/1361-6579/ac73d5