Novel methods for elucidating modality importance in multimodal electrophysiology classifiers.

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

SUBMITTER: Ellis CA 

PROVIDER: S-EPMC10050434 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature