Abstract

Sleep spindle features show developmental changes during infancy and have the potential to provide an early biomarker for abnormal brain maturation. Manual identification of sleep spindles in the electroencephalogram (EEG) is time-consuming and typically requires highly-trained experts. Automated detection of sleep spindles would greatly facilitate this analysis. Research on the automatic detection of sleep spindles in infant EEG has been limited to-date. We present a random forest-based sleep spindle detection method (Spindle-AI) to estimate the number and duration of sleep spindles in EEG collected from 141 ex-term born infants, recorded at 4 months of age. The signal on channel F4-C4 was split into a training set (81 ex-term) and a validation set (30 ex-term). An additional 30 ex-term infant EEGs (channel F4-C4 and channel F3-C3) were used as an independent test set. Fourteen features were selected for input into a random forest algorithm to estimate the number and duration of spindles and the results were compared against sleep spindles annotated by an experienced clinical physiologist. The prediction of the number of sleep spindles in the independent test set demonstrated 93.3% to 93.9% sensitivity, 90.7% to 91.5% specificity, and 89.2% to 90.1% precision. The duration estimation of sleep spindle events in the independent test set showed a percent error of 5.7% to 7.4%. Spindle-AI has been implemented as a web server that has the potential to assist clinicians in the fast and accurate monitoring of sleep spindles in infant EEGs.

Highlights

  • Sleep spindles are an indicator of the development and integrity of the central nervous system in infants [1]

  • Spindle-AI has been implemented as a web server freely available for academic use at http://lisda.ucd.ie/Spindle-AI/

  • Spindle-AI has been implemented as a web server and is freely available for academic use at http://lisda.ucd.ie/Spindle-AI/

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Summary

Introduction

Sleep spindles are an indicator of the development and integrity of the central nervous system in infants [1]. They were first described by Loomis et al [2] as rhythmic 12-14 Hz oscillations which last. 0.5 to 3 seconds with a waxing and waning shape [3] They have been observed clearly in EEG during stages N2 and N3 [4] from the 4th week post-term and are present in the EEG of all infants by nine weeks post-term [5].

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