2023
[Automated detection of sleep-arousal using multi-scale convolution and self-attention mechanism].
Li F, Xu Y, Zhang B, Cong F. [Automated detection of sleep-arousal using multi-scale convolution and self-attention mechanism]. Journal Of Biomedical Engineering 2023, 40: 27-34. PMID: 36854545, PMCID: PMC9989766, DOI: 10.7507/1001-5515.202204052.Peer-Reviewed Original ResearchConceptsMulti-scale convolutional layersArousal detectionSingle-channel EEG signalsSelf-attention mechanismTransfer learning methodConvolutional neural networkSleep staging taskEnd-to-endMulti-modal signalsSingle-channel electroencephalogramPrecision-recall curveConvolutional layersEvent detectionNeural networkAverage accuracyLearning methodsEEG signalsTask transferBaseline modelMulti-ModalStaging taskImprovement of model performancePortable sleep monitoringSingle modalityTime-consuming
2021
A Deep Learning Model for Automatic Sleep Scoring using Multimodality Time Series
Yan R, Li F, Zhou D, Ristaniemi T, Cong F. A Deep Learning Model for Automatic Sleep Scoring using Multimodality Time Series. 2021, 00: 1090-1094. DOI: 10.23919/eusipco47968.2020.9287518.Peer-Reviewed Original ResearchAutomatic sleep scoringRecalibrate channel-wise feature responsesEnd-to-end deep learning architectureChannel-wise feature responsesTwo-dimensional convolutional neural networkPublic sleep datasetsMultimodal time seriesAutomatically learn featuresConvolutional neural networkDeep learning architectureSleep-EDF datasetEnd-to-endDeep learning modelsMulti-modal signalsAutomated sleep scoringSoftmax classifierLearning architectureFeature responsesNeural networkSleep datasetsSHHS datasetLearning modelsComputational costDatasetInput channels
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