Slow wave detection in sleeping mice: Comparison of traditional and machine learning methods

During slow-wave sleep the electroencephalographic (EEG) and local field potential (LFP) recordings reveal the presence of large amplitude slow waves. Systematic extraction of individual slow waves is not trivial.

In this study, we used the neural network pattern recognition to detect individual slow waves in LFP recorded from mice as well as other commonly used methods that are based on fast frequencies modulation, amplitude, or duration.

The number and quality of events detected as slow waves depended on the chosen method of detection, level of thresholds, or on combination of methods. Each individual method yields some false-positive and false-negative detections. Typically, the fast frequency-method has a higher false discovery rate, but almost no missing waves; amplitude-based method has relatively high false-positive and false-negative rates; duration-based method has low false-negative rates; neural network pattern recognition approach has the lowest false-positive rate among individual methods, often rejecting waves that were falsely detected by other approaches. Combining all 4 detection methods practically eliminated false-positive errors, but a large number of slow waves remained undetected.

The use of a particular method of slow wave detection needs to be adjusted to the objectives of a given study: to detect all slow waves, but also numerous false positives can be achieved using the fast frequency approach. Neural network pattern recognition method alone can detect slow waves with the lowest false-positive rate, that can be further minimized with the use of combination of other methods.