Tezuka et al. - 2021 - Real-time, automatic, open-source sleep stage classification system using single EEG for mice

[gsaurabhr]

Read this paper and discuss key takeaways for our project here.

[Ayush-Tibrewal]

Quick Summary of this paper

• The researchers developed a system to automatically and accurately classify sleep stages in mice using data from a single EEG (electroencephalogram) channel. This system can identify whether a mouse is awake, in non-REM (NREM) sleep, or in rapid eye movement (REM) sleep in real-time.
• The researchers used 214 recordings, each 6 hours long, with sleep stages annotated by human experts. The system processes data in 10-second epochs.
• The researchers created two systems: UTSN (Universal Time-Series Network) and UTSN-L (UTSN with Long Short-Term Memory).
• UTSN uses a convolutional neural network (CNN) to process raw EEG data, FFT (Fast Fourier Transformation) data, and zeitgeber time (ZT), which relates to the circadian rhythm.
• UTSN-L adds a Long Short-Term Memory (LSTM) network to consider past data, improving accuracy.
• The systems were evaluated against conventional methods like logistic regression, random forest, and AdaBoost. Metrics used for evaluation included sensitivity, accuracy, precision, F1 score, and Matthews Correlation Coefficient (MCC). UTSN-L performed better overall, especially for detecting REM sleep.
• UTSN-L (UTSN with Long Short-Term Memory) The overall accuracy was 90% For REM sleep detection, UTSN-L achieved 91% sensitivity and 98% specificity.