Using switching multiple models for the automatic detection of spindles (original) (raw)

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Combining time-frequency and spatial information for the detection of sleep spindles

Frontiers in human neuroscience, 2015

EEG sleep spindles are short (0.5-2.0 s) bursts of activity in the 11-16 Hz band occurring during non-rapid eye movement (NREM) sleep. This sporadic activity is thought to play a role in memory consolidation, brain plasticity, and protection of sleep integrity. Many automatic detectors have been proposed to assist or replace experts for sleep spindle scoring. However, these algorithms usually detect too many events making it difficult to achieve a good tradeoff between sensitivity (Se) and false detection rate (FDr). In this work, we propose a semi-automatic detector comprising a sensitivity phase based on well-established criteria followed by a specificity phase using spatial and spectral criteria. In the sensitivity phase, selected events are those which amplitude in the 10-16 Hz band and spectral ratio characteristics both reject a null hypothesis (p < 0.1) stating that the considered event is not a spindle. This null hypothesis is constructed from events occurring during rapi...

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Identifying sleep spindles with multichannel EEG and classification optimization

A B S T R A C T 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 s for processing a 40-min EEG recording), making spindle detection faster and more objective.

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Using switching multiple models for the automatic detection of spindles (original) (raw)

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