Data Driven Sleep Stage Classification
Ami Beuret
Prof. Dr. Joachim Buhmann
Sleep phase classification is an important step to study the biology of sleep in both humans and animals. Previously, sleep phase identification has been done by tedious visual inspection of the recorded electroencephalogram (EEG) data. In addition to be a laborious task, this visual inspection is prone to disagreements between experts. Recently, machine learning approaches have automated the classification of sleep phases from recorded EEG and electromyogram (EMG) data, accelerating the speed of analysis while being a more objective assessment of the sleep states.
Previous state-of-the-art has employed convolutional neural networks (CNNs) on spectograms of EEG and EMG data as a first step. Then in order to impose physiologically plausible state transitions, either hidden Markov models (HHMs) [Miladinovi ́c et al., 2019] or long short-term memory (LSTM) modules ([Li et al., 2022]) are employed. Unfortunately there are still shortcomings with current methods. The achieved accuracy of these methods are still not high enough for many practical research purposes [Yamabe et al., 2019]. Additionally, these methods fail quickly in presence of minor distributional shifts [Barger et al., 2019]. Furthermore, In many practical cases, only one single EEG channel might be available. A more useful algorithm should achieve the same accuracy with only one EEG channel in contrast to many existing methods.
In this proposal we would like to explore alternative architectures such as vision transformers and multi-head attention modules in order to both extract features from spectograms and learn the temporal consistency between different sleep stages [Vaswani et al., 2017, Dosovitskiy et al., 2021]. Recent advances in attention modules have demonstrated equal or superior performance to the CNNs in various vision tasks.
**This master thesis will be the continuation of our previous work https://sleeplearning.ethz.ch. No special skills are required for the thesis beside the usual ptyhon skills and specially very high motivation.**
Proposal
Sleep phase classification is an important step to study the biology of sleep in both humans and animals. Previously, sleep phase identification has been done by tedious visual inspection of the recorded electroencephalogram (EEG) data. In addition to be a laborious task, this visual inspection is prone to disagreements between experts. Recently, machine learning approaches have automated the classification of sleep phases from recorded EEG and electromyogram (EMG) data, accelerating the speed of analysis while being a more objective assessment of the sleep states.
Previous state-of-the-art has employed convolutional neural networks (CNNs) on spectograms of EEG and EMG data as a first step. Then in order to impose physiologically plausible state transitions, either hidden Markov models (HHMs) [Miladinovi ́c et al., 2019] or long short-term memory (LSTM) modules ([Li et al., 2022]) are employed. Unfortunately there are still shortcomings with current methods. The achieved accuracy of these methods are still not high enough for many practical research purposes [Yamabe et al., 2019]. Additionally, these methods fail quickly in presence of minor distributional shifts [Barger et al., 2019]. Furthermore, In many practical cases, only one single EEG channel might be available. A more useful algorithm should achieve the same accuracy with only one EEG channel in contrast to many existing methods.
In this proposal we would like to explore alternative architectures such as vision transformers and multi-head attention modules in order to both extract features from spectograms and learn the temporal consistency between different sleep stages [Vaswani et al., 2017, Dosovitskiy et al., 2021]. Recent advances in attention modules have demonstrated equal or superior performance to the CNNs in various vision tasks.
**This master thesis will be the continuation of our previous work https://sleeplearning.ethz.ch. No special skills are required for the thesis beside the usual ptyhon skills and specially very high motivation.**
Proposal