Explainable Sleep Stage Classification with Multimodal Electrophysiology Time-series

Ellis, Charles A.; Zhang, Rongen; Carbajal, Darwin A.; Miller, Robyn L.; Calhoun, Vince D.; Wang, May D.

doi:10.1109/embc46164.2021.9630506

Cited by 19 publications

(12 citation statements)

References 10 publications

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“…We applied a combination of LRP and spectral perturbations to identify (5) the relative importance of each canonical frequency band in each channel and (6) interactions between the representations learned by the model for the canonical frequency bands in each channel and every other channel. (7) We applied a combination of a novel prototyping approach and LRP to identify representative samples of each class and identify important waveforms that the model used to differentiate them. Our code is publicly available on GitHub and can be found at: https://github.com/cae67/MultichannelExplainabilityFramework.git.…”

Section: Methodsmentioning

confidence: 99%

“…As such, over time a growing number of studies have begun seeking to develop explainability methods uniquely adapted to the domain of deep learning-based raw EEG analysis. It should be noted, however, that methods like those developed for multimodal data explainability [6], [7], [61], [62] can be adapted to multichannel EEG data with minimal inconvenience.…”

Section: Introductionmentioning

confidence: 99%

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A Framework for Systematically Evaluating the Representations Learned by A Deep Learning Classifier from Raw Multi-Channel Electroencephalogram Data

Ellis

Sattiraju

Miller

et al. 2023

Preprint

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The application of deep learning methods to raw electroencephalogram (EEG) data is growing increasingly common. While these methods offer the possibility of improved performance relative to other approaches applied to manually engineered features, they also present the problem of reduced explainability. As such, a number of studies have sought to provide explainability methods uniquely adapted to the domain of deep learning-based raw EEG classification. In this study, we present a taxonomy of those methods, identifying existing approaches that provide insight into spatial, spectral, and temporal features. We then present a novel framework consisting of a series of explainability approaches for insight into classifiers trained on raw EEG data. Our framework provides spatial, spectral, and temporal explanations similar to existing approaches. However, it also, to the best of our knowledge, proposes the first explainability approaches for insight into spatial and spatio-spectral interactions in EEG. This is particularly important given the frequent use and well-characterized importance of EEG connectivity measures for neurological and neuropsychiatric disorder analysis. We demonstrate our proposed framework within the context of automated major depressive disorder (MDD) diagnosis, training a high performing one-dimensional convolutional neural network with a robust cross-validation approach on a publicly available dataset. We identify interactions between central electrodes and other electrodes and identify differences in frontal theta, beta, and gamma low between healthy controls and individuals with MDD. Our study represents a significant step forward for the field of deep learning-based raw EEG classification, providing new capabilities in interaction explainability and providing direction for future innovations through our proposed taxonomy.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Framework for Systematically Evaluating the Representations Learned by A Deep Learning Classifier from Raw Multi-Channel Electroencephalogram Data

Ellis

Sattiraju

Miller

et al. 2023

Preprint

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“…We applied two global ablation approaches to estimate classspecific modality importance. We presented a novel global ablation approach that is uniquely adapted to the electrophysiology domain (Ellis et al, 2021b) and compared our approach to a standard approach that has been used in previous studies (Lin et al, 2019;Pathak et al, 2021) CNN architecture. Layers (i) of the diagram repeat 3 times.…”

Section: Description Of Global Ablation Approachesmentioning

confidence: 99%

“…As a result, most studies have not used explainability ( Zhang et al, 2011 ; Kwon et al, 2018 ; Niroshana et al, 2019 ; Phan et al, 2019 ; Wang et al, 2020 ; Li et al, 2021 ), which is concerning because transparency is increasingly required to assist with model development and physician decision making ( Sullivan and Schweikart, 2019 ). As such, more multimodal explainability methods need to be developed ( Lin et al, 2019 ; Mellem et al, 2020 ; Ellis et al, 2021a , b , c , d ). In this study, we use automated sleep stage classification as a testbed for the development of multimodal explainability methods.…”

Section: Introductionmentioning

confidence: 99%

“…In the present study, we train a CNN for automated sleep stage classification using a publicly available dataset. We introduce a global ablation approach that is uniquely adapted for the electrophysiology domain ( Ellis et al, 2021b ). We then present a local ablation approach ( Ellis et al, 2021c ) and show how GBFA methods can be used for local insight into multimodal classifiers ( Ellis et al, 2021a ).…”

Section: Introductionmentioning

confidence: 99%

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Novel methods for elucidating modality importance in multimodal electrophysiology classifiers

Ellis

Sendi

Zhang

et al. 2023

Front. Neuroinform.

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IntroductionMultimodal 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.MethodsIn 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.ResultsWe 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.DiscussionOur 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.

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Identifying EEG Biomarkers of Depression with Novel Explainable Deep Learning Architectures

Ellis,

Sancho,

Miller

et al. 2024

Communications in Computer and Information Science

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Explainable Sleep Stage Classification with Multimodal Electrophysiology Time-series

Cited by 19 publications

References 10 publications

A Framework for Systematically Evaluating the Representations Learned by A Deep Learning Classifier from Raw Multi-Channel Electroencephalogram Data

A Framework for Systematically Evaluating the Representations Learned by A Deep Learning Classifier from Raw Multi-Channel Electroencephalogram Data

Novel methods for elucidating modality importance in multimodal electrophysiology classifiers

Identifying EEG Biomarkers of Depression with Novel Explainable Deep Learning Architectures

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