Atlas, rules, and recording techniques for the scoring of cyclic alternating pattern (CAP) in human sleep.

By M. Terzano, L. Parrino, Adriano Sherieri, Ronald D. Chervin, S. Chokroverty, C. Guilleminault, M. Hirshkowitz, M. Mahowald, H. Moldofsky, A. Rosa and 2 more. Sleep Medicine, 2001

764 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Physiology

View Paper: https://www.semanticscholar.org/paper/86727718d827b459bd87f9138131f243fd24f143

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Citations in the corpus

Deep Learning for Time Series Anomaly Detection: A Survey (2022, neutral): “CalIt2 [55] Real MTS 10,080 2 2 Urban events management CAP [168] [66] Real MTS 921,700,000 108 21 Medical and health CICIDS2017 [154] Real MTS 2,830,540 15 83 Server machines monitoring Credit Card fraud detection [44] Real MTS 284,807 1 31 Fraud detectcion DMDS [179] Real MTS 725,402 1 32 Industri”
U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging (2019, neutral): “CAP A public database [Terzano et al., 2002] storing 108 PSG recordings of 16 healthy subjects and 92 pathological patients diagnosed with one of bruxism, insomnia, narcolepsy, nocturnal frontal lobe epilepsy, periodic leg movements, REM behavior disorder, or sleep-disordered breathing.”
Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals (2021, methodology): “As CAP dataset contains different sleep stages of subjects, some methods are limited by high complexity of data.”