Automatic Sleep Stage Classification Based on Two-channel EOG and One-channel EMG

Y, Li; Yang, Xu; Xu, Zeping; Zhang, Y; Cao, Zhigang

doi:10.21203/rs.3.rs-491468/v1

Cited by 2 publications

(3 citation statements)

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“…Most multimodal classification studies, regardless of whether they used extracted features (Phan et al, 2019;Li et al, 2021) or raw data (Niroshana et al, 2019;Wang et al, 2020), have not used explainability methods. Among the few studies involving explainability (Lajnef et al, 2015;Chambon et al, 2018;Pathak et al, 2021), some have used extracted features and forward feature selection (FFS) (Lajnef et al, 2015).…”

Section: Multimodal Explainability In Sleep Stage Classification and ...mentioning

confidence: 99%

“…Biomedical informatics studies (Lin et al, 2019;Mellem et al, 2020;Zhai et al, 2020), and electrophysiology studies (Niroshana et al, 2019;Phan et al, 2019;Wang et al, 2020;Li et al, 2021) in particular, have increasingly begun to incorporate multimodal data when training machine learning classifiers. Using complementary modalities can enable the extraction of better features and improve classification performance (Wang et al, 2020;Zhai et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…This is especially true for state-of-the-art deep learning models. 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).…”

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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