Novel methods for elucidating modality importance in multimodal electrophysiology classifiers

• Type of Material: Article
• Authors: Charkes A Ellis, Emory University; Mohammad SE Sendi, Georgia State University; Rongen Zhang, Baylor University; Darwin A Carbajal, Georgia Institute of Technology; Dongmei Wang, Emory University; Robyn L Miller, Georgia State University; Vince Calhoun, Emory University
• Language: English
• Date: 2023-03-15
• Publisher: FRONTIERS MEDIA SA
• Publication Version: Final Published Version
• Copyright Statement: © 2023 Ellis, Sendi, Zhang, Carbajal, Wang, Miller and Calhoun.
• Final Published Version (URL): http://dx.doi.org/10.3389/fninf.2023.1123376
• Title of Journal or Parent Work: FRONTIERS IN NEUROINFORMATICS
• Volume: 17
• Start Page: 1123376
• End Page: 1123376
• Grant/Funding Information: This work was funded by the NIH grant R01EB006841.
• Keywords: INSOMNIA; SLEEP STAGE CLASSIFICATION; Mathematical & Computational Biology; YOUNG; Neurosciences & Neurology; electrooculography; NEURAL-NETWORK; multimodal classification; electrophysiology; Life Sciences & Biomedicine; Neurosciences; electromyography; Science & Technology; EEG; TIME; sleep stage classification; explainable deep learning; AGE; electroencephalography
• Research Categories: Engineering, Biomedical

Abstract

Introduction: Multimodal classification is increasingly common in electrophysiology studies. Many studies use deep learning classifiers with raw time-series data, which makes explainability difficult, and has resulted in relatively few studies applying explainability methods. This is concerning because explainability is vital to the development and implementation of clinical classifiers. As such, new multimodal explainability methods are needed. Methods: In this study, we train a convolutional neural network for automated sleep stage classification with electroencephalogram (EEG), electrooculogram, and electromyogram data. We then present a global explainability approach that is uniquely adapted for electrophysiology analysis and compare it to an existing approach. We present the first two local multimodal explainability approaches. We look for subject-level differences in the local explanations that are obscured by global methods and look for relationships between the explanations and clinical and demographic variables in a novel analysis. Results: We find a high level of agreement between methods. We find that EEG is globally the most important modality for most sleep stages and that subject-level differences in importance arise in local explanations that are not captured in global explanations. We further show that sex, followed by medication and age, had significant effects upon the patterns learned by the classifier. Discussion: Our novel methods enhance explainability for the growing field of multimodal electrophysiology classification, provide avenues for the advancement of personalized medicine, yield unique insights into the effects of demographic and clinical variables upon classifiers, and help pave the way for the implementation of multimodal electrophysiology clinical classifiers.