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Shifting the paradigm in sleep classification algorithms
During sleep studies there is a need to influence sleep patterns in real time. Constant visual inspection of physiological signals by expert clinical staff is too costly and ineffective. To achieve unsupervised sleep detection, an automatic algorithm is needed.
Develop an algorithm suitable for real-time sleep classification, which automatically adapts to intra-night and inter-subject sleep pattern variations. The developed algorithm performance will be analyzed using offline EEG data and validated online in a lab environment using a wearable EEG device
Develop an algorithm suitable for real-time sleep classification, which automatically adapts to intra-night and inter-subject sleep pattern variations. The developed algorithm performance will be analyzed using offline EEG data and validated online in a lab environment using a wearable EEG device
The goal of this project is to develop a novel algorithm for real-time, automated classification of deep sleep phases based on wearable sensors and compare the performance to existing sleep classification algorithms
The goal of this project is to develop a novel algorithm for real-time, automated classification of deep sleep phases based on wearable sensors and compare the performance to existing sleep classification algorithms