deepsleepnet and deepsleep2
About deepsleepnet
akaraspt/deepsleepnet
DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG
This project helps sleep researchers and clinicians automatically categorize sleep stages from raw, single-channel EEG data. It takes raw EEG recordings as input and outputs the corresponding sleep stages, enabling faster and more consistent sleep analysis. Researchers and clinicians studying sleep disorders or sleep patterns would use this.
About deepsleep2
rfonod/deepsleep2
😴 DeepSleep2 is a compact U-Net-inspired convolutional neural network with 740,551 parameters, designed to predict non-apnea sleep arousals from full-length multi-channel polysomnographic recordings at 5-millisecond resolution. Achieves similar performance to DeepSleep with lower computational cost.
This tool helps sleep researchers and clinicians automatically identify non-apnea sleep arousals from full-length polysomnographic recordings. It takes multi-channel physiological signals as input and outputs precise predictions of sleep arousal events at a 5-millisecond resolution. This is designed for professionals studying sleep patterns and disorders.
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