3260 papers • 126 benchmarks • 313 datasets
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These leaderboards are used to track progress in k-complex-detection
Use these libraries to find k-complex-detection models and implementations
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Critical validation and benchmarking of the proposed framework for joint spindle and K-complex detection based on a Tunable Q-factor Wavelet Transform (TQWT) and morphological component analysis (MCA) is provided by applying it to open-access EEG data from the Montreal Archive of Sleep Studies.
A novel deep learning architecture called Dreem One Shot Event Detector (DOSED) is proposed, inspired by object detectors developed for computer vision such as YOLO and SSD, which predicts locations, durations and types of events in EEG time series.
A deep learning approach based on convolutional and recurrent neural networks for sleep EEG event detection called Recurrent Event Detector (RED), which is event-agnostic and can be used directly to detect other types of sleep events.
Adding a benchmark result helps the community track progress.