Novel methods for elucidating modality importance in multimodal electrophysiology classifiers.

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.

Explainable Sleep Stage Classification with Multimodal Electrophysiology Time-series.

Many automated sleep staging studies have used deep learning approaches, and a growing number of them have used multimodal data to improve their classification performance. However, few studies using multimodal data have provided model explainability. Some have used traditional ablation approaches that "zero out" a modality.