Identifying sleep spindles with multichannel EEG and classification optimization

Abstract

Researchers classify critical neural events during sleep called spindles that are related to memory consolidation using the method of scalp electroencephalography (EEG). Manual classification is time consuming and is susceptible to low inter-rater agreement. This could be improved using an automated approach. This study presents an optimized filter based and thresholding (FBT) model to set up a baseline for comparison to evaluate machine learning models using naïve features, such as raw signals, peak frequency, and dominant power. The FBT model allows us to formally define sleep spindles using signal processing but may miss examples most human scorers would agree are spindles. Machine learning methods in theory should be able to approach performance of human raters but they require a large quantity of scored data, proper feature representation, intensive feature engineering, and model selection. We evaluate both the FBT model and machine learning models with naïve features. We show that the machine learning models derived from the FBT model improve classification performance. An automated approach designed for the current data was applied to the DREAMS dataset [1]. With one of the expert’s annotation as a gold standard, our pipeline yields an excellent sensitivity that is close to a second expert’s scores and with the advantage that it can classify spindles based on multiple channels if more channels are available. More importantly, our pipeline could be modified as a guide to aid manual annotation of sleep spindles based on multiple channels quickly (6–10 seconds for processing a 40-minute EEG recording), making spindle detection faster and more objective.

A high-density scalp EEG dataset acquired during brief naps after a visual working memory task

“…In the python source code, the processed data can optionally be saved for later use with no further human input involved in the preprocessing. Additional distributed source code implements automated spindle detection using a filter based and thresholding pipeline with machine learning optimization [1] . An example 30 s epoch highlighting a posterior spindle in one subject is shown in Fig.…”

“…During each recording session participants underwent continuous recording from 64-channels, including 2 channels for electrooculography (EOG). The dataset includes raw Brainvision format .eeg, .vdhr, and .vmrk files, manually annotated sleep stage information, manually annotated spindles occurrences, and python source code for file input/output, minimal signal pre-processing including filtering and independent component analysis-based artifact correction, automatic identification of spindles using a filter based thresholding approach, and a framework for model validation and optimization using a supervised machine learning approach [1] .…”

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