Oct 30, 2018

A Human-Machine Coupled System for Efficient Sleep Spindle Detection by Iterative Revision

BioRxiv : the Preprint Server for Biology
Dasheng Bi

Abstract

Sleep spindles are characteristic events in EEG signals during non-REM sleep, and are known to be important biological markers. Manually labeling spindles by visual inspection, however, has proved to be a tedious task. Automatic detection algorithms generalize weakly for versatile spindle forms, and machine-learning methods require large datasets to train, which are unfeasible to acquire particularly for experimental animal groups. Here, a novel, integrated system based on a process of iterative “Selection-Revision” (iSR) is introduced to aid in the efficient detection of spindles. By coupling low-threshold automatic detection of spindle events based on selected parameters with manual “Revision,” the human task is effectively simplified from searching across signal traces to binary verification. Convergence was observed between resulting spindle sets through iSR, largely independent of their initial labeling, demonstrating the robustness of the method. Although possible breakdown of the revised spindle sets could be seen after multiple rounds of Revision, due to overfitting of the revised set to the initial human labeling, this could be compensated for by a Selection scheme tolerant to higher False-Negative rates of the machine...Continue Reading

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Mentioned in this Paper

Biological Markers
Study
Spindle
Patterns
Research
Electroencephalography
Metal Working Fluid
Sleep, Slow-Wave
Brain
Mental Disorders

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