Karen K. Wurzel, D. Kaftan, M. Du Pont et al.
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Shyamal Y. Dharia, Stephen D. Smith, Camilo E. Valderrama
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Kelvin K. Nguyen, Arshia Ilaty, A. Smithwick et al.
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A complete prediction pipeline was developed that used a frozen SleepFM encoder for five-minute PSG segments, a bidirectional LSTM for temporal modelling, and annotation-guided attention based on algorithmic sleep stages and events for future cognitive-impairment prediction.
Xiao-Yun Wang, Wei-Ping Liu, Yi-Xuan Zhang et al.
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Joaquim Bauxell, A. Temko, Emanuel M. Popovici
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The results support the potential of routine polysomnography for opportunistic cognitive-risk assessment and fused the epoch-level features with whole-night aggregated features and demographics using a temporal convolutional network for binary prediction of cognitive impairment.
Somesh Saini, S. Thanaviratananich, M. Araujo
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